14, other methods, even though the elevation data is used, are less effective for labeling confusing manmade objects and fine-structured objects simultaneously. In this paper, super-pixels with similar features are combined using the . 448456. Here, we take RefineNet based on 101-layer ResNet for comparison. Segmentation: Grouping the pixels in a localized image by creating a segmentation mask. The class labels in these data sets include objects such as sofa, bookshelf, refrigerator, and bed. Semantic labeling of large volumes of image and video archives is difficult, if not impossible, with the traditional methods due to the huge amount of human effort required for manual labeling . Technically, For online test, we use all the 24 images as training set. Based on this observation, we propose to reutilize the low-level features with a coarse-to-fine refinement strategy, as shown in the rightmost part of Fig. Moreover, as Fig. Pinheiro, P.O., Lin, T.-Y., Collobert, R., Dollr, P., 2016. This is because it may need different hyper-parameter values (such as learning rate) to make them converge when training different deep models. Hu, F., Xia, G.-S., Hu, J., Zhang, L., 2015. (Long etal., 2015) propose FCN for semantic segmentation, which achieves the state-of-the-art performance on three benchmarks (Everingham etal., 2015; Silberman etal., 2012; Liu etal., 2008). pp. Large region (high-level context) contains more semantics and wider visual cues, while small region (low-level context) otherwise. Compared with VGG ScasNet, ResNet ScasNet has better performance while suffering higher complexity. Accordingly, a tough problem locates on how to perform accurate labeling with the coarse output of FCNs-based methods, especially for fine-structured objects in VHR images. 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS). For offline validation, we randomly split the 16 images with ground truth available into a training set of 8 images, and a validation set of 8 images. depending upon our learning of On the contrary, VGG ScasNet can converge well even though the BN layer is not used since it is relatively easy to train. Selected Topics in Applied Earth Observations and Remote Sensing. Abstract: Recently many multi-label image recognition (MLR) works have made significant progress by introducing pre-trained object detection models to generate lots of proposals or utilizing statistical label co-occurrence enhance the correlation among different categories. github - ashishgupta023/semantic-labeling-of-images: the supervised learning method described in this project extracts low level features such as edges, textures, rgb values, hsv values, location , number of line pixels per superpixel etc. 47(4), pp. The pascal visual object classes challenge: A Six residual correction modules are employed for multi-feature fusion. So, in this post, we are only considering labelme (lowercase). With the acquired contextual information, a coarse-to-fine refinement strategy is performed to refine the fine-structured objects. As it shows, Ours-VGG achieves almost the same performance with Deeplab-ResNet, while Ours-ResNet achieves more decent score. It progressively reutilizes the low-level features learned by CNNs shallow layers with long-span connections. P.M., 2017. As it shows, compared with the baseline, the overall performance of fusing multi-scale contexts in the parallel stack (see Fig. In the following, each basic layer used in the proposed network will be introduced, and their specific configurations will be presented in Section 3.4. 1. and Remote Sensing. IEEE Geoscience Remote Sensing readily able to classify every part of it as either a person, Inspired by the image-level discrepancy dominated in object detection, we introduce a Multi-Adversarial Faster-RCNN (MAF). Abstract Information on where rubber plantations are located and when they were established is essential for understanding changes in the regional carbon cycle, biodiversity, hydrology and ecosystem. 28742883. For example, the size of the last feature maps in VGG-Net (Simonyan and Zisserman, 2015) is 1/32 of input size. Ioffe, S., Szegedy, C., 2015. 25282535. By contrast, there is an improvement of near 3% on mean IoU when our approach of self-cascaded fusion is adopted. is applied to the output layer in Image labeling is . We only choose three shallow layers for refinement as shown in Fig. As it shows, the performance of VGG ScasNet improves slightly, while ResNet ScasNet improves significantly. Meanwhile, for This paper presents a novel semantic segmentation method of very high resolution remotely sensed images based on fully convolutional networks (FCNs) and feedforward neural networks (FFNNs). Although the labeling results of our models have a few flaws, they can achieve relatively more coherent labeling and more precise boundaries. Technically, multi-scale contexts are first captured by different convolutional operations, and then they are successively aggregated in a self-cascaded manner. As shown in Fig. Work fast with our official CLI. CNNs consist of multiple trainable layers which can extract expressive features of different levels (Lecun etal., 1998). Ours-VGG and Ours-ResNet show better robustness to the cast shadows. (He etal., 2015a). ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences. In addition, to denotes the fusion operation. In this paper, we propose a novel self-cascaded convolutional neural network (ScasNet), as illustrated in Fig. Field by setting edge relations between neighborhoods As can be seen, all the categories on Vaihingen dataset achieve a considerable improvement except for the car. The boundary responses of cars and trees can be clearly seen. Example: Benchmarks Add a Result These leaderboards are used to track progress in Semantic Role Labeling Datasets FrameNet CoNLL-2012 OntoNotes 5.0 global scales using multi-temporal dmsp/ols nighttime light data. 886893. We expect the stacked layers to fit another mapping, which we call inverse residual mapping as: Actually, the aim of H[] is to compensate for the lack of information caused by the latent fitting residual, thus to achieve the desired underlying fusion f=f+H[]. Deeplab-ResNet: Chen et al. Matikainen, L., Karila, K., 2011. pp. Secondly, all the models are trained based on the widely used transfer learning (Yosinski etal., 2014; Penatti etal., 2015; Hu etal., 2015; Xie etal., 2015) in the field of deep learning. For the training sets, we use a two-stage method to perform data augmentation. Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for adjusting confidence scores of image labels for images. In our method, only raw image data is used for training. A weight sharing technique that the parameters (i.e., weights and bias) are shared among each kernel across an entire feature map, is adopted to reduce parameters in great deal (Rumelhart etal., 1986). 54(5), of urban trees using very high resolution satellite imagery. 50(3), 879893. Bell, S., LawrenceZitnick, C., Bala, K., Girshick, R., 2016. common feature value and maximizing the same, this is a The models are build based on three levels of features: 1) pixel level, 2) region level, and 3) scene level features. arXiv preprint arXiv:1612.01337. We need to know the scene information around them, which could provide much wider visual cues to better distinguish the confusing objects. Penatti, O. On combining multiple features Convolutional Sensing. Rectified linear units improve restricted A possible reason is that, our refinement strategy is effective enough for labeling the car with the resolution of 9cm. It usually requires extra boundary supervision and leads to extra model complexity despite boosting the accuracy of object localization. Scene recognition by manifold regularized deep IEEE Transactions on Pattern Analysis and Machine Intelligence. Geoscience and Remote Sensing Symposium (IGARSS). In this work, we describe a new, general, and efficient method for unstructured point cloud labeling. Actually, the final feature maps outputted by the FCN-based methods is quite coarse due to multiple sub-samplings. with deep convolutional neural networks. greatly prevent the fitting residual from accumulating. Abstract In: IEEE Conference on Computer Vision and Pattern very difficult to obtain both coherent and accurate labeling results. SegNet: Badrinarayanan et al. The output of each convolutional operation is computed by dot product between the weights of the kernel and the corresponding local area (local receptive field). Learning multiscale and deep representations for This paper extends a semantic ontology method to extract label terms of the annotated image. The well-known semantic segmentation technique is used in medical image analysis to identify and label regions of images. The basic understanding of an image from a human IEEE Transactions on Geoscience and Remote Sensing. Semantic segmentation with 15 to 500 segments Superannotate is a Silicon Valley startup with a large engineering presence in Armenia. This feature space generated for the entire dataset is Transferring deep convolutional Gong, M., Yang, H., Zhang, P., 2017. hyperspectral data via morphological component analysis-based image Badrinarayanan, V., Kendall, A., Cipolla, R., 2015. Change detection based on In: Neural Information Processing Systems. 13(h) shows, there is much information lost when two feature maps with semantics of different levels are fused. L() is the ReLU activation function. In: International Conference on Learning Representations Moreover, we do not use the elevation data (DSM and NDSM), additional hand-crafted features, model ensemble strategy or any postprocessing. on Geoscience and Remote Sensing. Potsdam Challenge: On benchmark test of Potsdamhttp://www2.isprs.org/potsdam-2d-semantic-labeling.html, qualitative and quantitative comparison with different methods are exhibited in Fig. 100 new scans are now part of the . pp. deep feature representation and mapping transformation for Learn how to label with Segments.ai's image labeling technology for segmentation.Label for free at https://segments.ai !It's the fastest and most accurate la. For 53(8), 44834495. pp. Specifically, the shallow layers with fine resolution are progressively reintroduced into the decoder stream by long-span connections. extraction of roads and buildings in remote sensing imagery with Segmentation, Direction-aware Residual Network for Road Extraction in VHR Remote Meanwhile, as can be seen in Table 5, the quantitative performances of our method also outperform other methods by a considerable margin on all the categories. Specifically, except for our models, all the other models are trained by finetuning their corresponding best models pre-trained on PASCAL VOC 2012 (Everingham etal., 2015) on semantic segmentation task. They use multi-scale images (Farabet etal., 2013; Mostajabi etal., 2015; Cheng etal., 2016; Liu etal., 2016b; Chen etal., 2016a; Zhao and Du, 2016) or multi-region images (Gidaris and Komodakis, 2015; Luus etal., 2015) as input to CNNs. Dense semantic labeling of subdecimeter resolution The evaluation results are listed in Table 6. As a result, the adverse influence of latent fitting residual in multi-feature fusion can be well counteracted, i.e, the residual is well corrected. Taking as input a 3D mesh model reconstructed from the image based 3D modeling system, coupled . grouped and unified basic unit for image understanding on specific classes. Transfer learning classification based on random-scale stretched convolutional neural network Ronneberger, O., Fischer, P., Brox, T., 2015. IEEE Transactions on Geoscience and In this study, a strategy is proposed to effectively address this issue. Sensing. Sensing Images, http://www2.isprs.org/commissions/comm3/wg4/results.html, http://www2.isprs.org/vaihingen-2d-semantic-labeling-contest.html, http://www2.isprs.org/potsdam-2d-semantic-labeling.html, http://www2.isprs.org/commissions/comm3/wg4/semantic-labeling.html. Semantic segmentation associates every pixel of an image with a class label such as a person, flower, car and so on. Specifically, for confusing manmade objects, ScasNet improves the labeling It consists of 4-band IRRGB (Infrared, Red, Green, Blue) image data, and corresponding DSM and NDSM data. vision library (v2.5). arXiv preprint arXiv:1606.02585. intensiveness widely, to implement this we use a VGG ScasNet: In VGG ScasNet, the encoder is based on a VGG-Net variant (Chen etal., 2015), which is to obtain finer feature maps (about 1/8 of input size rather than 1/32). In this paper, we propose a semantic segmentation method based on superpixel region merging and convolutional neural network (CNN), referred to as regional merging neural network (RMNN). We use the best performance model FCN-8s as comparison. RiFCN: Recurrent Network in Fully Convolutional Network for Semantic CNN + DSM + NDSM + RF + CRF (ADL_3): The method proposed by (Paisitkriangkrai etal., 2016). Completion, High-Resolution Semantic Labeling with Convolutional Neural Networks, Cascade Image Matting with Deformable Graph Refinement, RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic 33203328. recognition. significant importance in a wide range of remote sensing applications. He, K., Zhang, X., Ren, S., Sun, J., 2016. The labels may say things like "dog," "vehicle," "sky," etc. The main purpose of using semantic image segmentation is build a computer-vision based application that requires high accuracy. In our network, we use bilinear interpolation. A., Plaza, A., 2015b. Meanwhile, the obtained feature maps with multi-scale contexts can be aligned automatically due to their equal resolution. Here are some examples of the operations associated with annotating a single image: Annotation A tag already exists with the provided branch name. In addition, to correct the latent fitting residual caused by semantic gaps in multi-feature fusion, several residual correction schemes are employed throughout the network. You would then merge all of the layers together to make a final image that you would use for your purposes. In addition to the label, children were taught two arbitrary semantic features for each item. pp. Very deep convolutional networks for In: International Conference on Learning been extracted from image data provided in the input To further verify the validity of each aspect of our ScasNet, features of some key layers in VGG ScasNet are visualized in Fig. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. As shown in Fig. As can be seen in Fig. Cheng, G., Zhu, F., Xiang, S., Wang, Y., Pan, C., 2016. In the first. A FCN is designed which takes as input intensity and range data and, with the help of aggressive deconvolution and recycling of early network layers, converts them into a pixelwise classification at full resolution. These factors always lead to inaccurate labeling results. pp. They usually perform operations of multi-scale dilated convolution (Chen etal., 2015), multi-scale pooling (He etal., 2015b; Liu etal., 2016a; Bell etal., 2016) or multi-kernel convolution (Audebert etal., 2016), and then fuse the acquired multi-scale contexts in a direct stack manner. There are three kinds of elementwise operations: product, sum, max. In the following, we will describe five important aspects of ScasNet, including 1) Multi-scale contexts Aggregation, 2) Fine-structured Objects Refinement, 3) Residual Correction, 4) ScasNet Configuration, 5) Learning and Inference Algorithm. It progressively refines the target objects If you don't like sloth, you can use any image editing software, like GIMP where you would make one layer per label and use polygons and flood fill of different hues to create your data. where Pgt is the set of ground truth pixels and Pm is the set of prediction pixels, and denote intersection and union operations, respectively. Mas, J.F., Flores, J.J., 2008. Lin, G., Milan, A., Shen, C., Reid, I.D., 2016. 13(c) and (d) indicate, the layers of the first two stages tend to contain a lot of noise (e.g., too much littery texture), which could weaken the robustness of ScasNet. Target Imagenet classification We evaluate the proposed ScasNet on three challenging public datasets for semantic labeling. Yes No Provide feedback Edit this page on GitHub Next topic: Bounding Box Previous topic: Step 5: Monitoring Your Labeling Job Need help? A residual correction scheme is proposed to correct the latent fitting residual caused by semantic gaps in multi-feature fusion. 675678. IEEE Transactions on 53(3), 15921606. Meanwhile, in CNNs, the feature extraction module and the classifier module are integrated into one framework, thus the extracted features are more suitable for specific task than hand-crafted features, such as HOG. mentioned earlier the feature space parameters that fit More details about dilated convolution can be referred in (Yu and Koltun, 2016). Based on thorough reviews conducted by three reviewers per manuscript, seven high-quality . As Fig. Scene semantic 11341142. applied to document recognition. Furthermore, it poses additional challenge to simultaneously label all these size-varied objects well. net: Detecting objects in context with skip pooling and recurrent neural Specifically, we first crop a resized image (i.e., x) into a series of patches without overlap. Semantic image segmentation is the technique that involves detecting objects within an image and grouping them based on defined categories. BIO notation is typically used for semantic role labeling. He, K., Zhang, X., Ren, S., Sun, J., 2015b. 55(6), 33223337. They fuse the output of two multi-scale SegNets, which are trained with IRRG images and synthetic data (NDVI, DSM and NDSM) respectively. Zeiler, M.D., Krishnan, D., Taylor, G.W., Fergus, R., 2010. ISPRS Vaihingen Challenge Dataset: This is a benchmark dataset for ISPRS 2D Semantic labeling challenge in Vaihingen (ISPRS, 2016). images with convolutional neural networks. Nevertheless, as shown in Fig. However, when residual correction scheme is elaborately applied to correct the latent fitting residual in multi-level feature fusion, the performance improves once more, especially for the car. Srivastava, N., Hinton, G.E., Krizhevsky, A., Sutskever, I., Salakhutdinov, The pseudo-code of learning procedure of ScasNet is shown in Algorithm 1, . 10 exhibit that, our best model performs better on all the given categories. Maggiori, E., Tarabalka, Y., Charpiat, G., Alliez, P., 2017. Max-pooling samples the maximum in the region to be pooled, while ave-pooling computes the mean value. The State University of New York, University at Buffalo. on Machine Learning. Furthermore, these results are obtained using only image data with a single model, without using the elevation data like the Digital Surface Model (DSM), model ensemble strategy or any postprocessing. In order to decompose features at a higher and more All codes of the two specific ScasNet are released on the github***https://github.com/Yochengliu/ScasNet. For this To solve this problem, some researches try to reuse the low-level features learned by CNNs shallow layers (Zeiler and Fergus, 2014). Meanwhile, ScasNet is quite robust to the occlusions and cast shadows, and it can perform coherent labeling even for very uneven regions. Remote superpixel generation and then realizing a feature space, as 53(1), Vaihingen Challenge Validation Set: As shown in Fig. Moreover, recently, CNNs with deep learning, have demonstrated remarkable learning ability in computer vision field, such as scene recognition, Based on CNNs, many patch-classification methods are proposed to perform semantic labeling (Mnih, 2013; Mostajabi etal., 2015; Paisitkriangkrai etal., 2016; Nogueira etal., 2016; Alshehhi etal., 2017; Zhang etal., 2017), . If nothing happens, download Xcode and try again. It greatly corrects the latent fitting residual caused by the semantic gaps in features of different levels, thus further improves the performance of ScasNet. Feature learning and change feature It should be noted that due to the complicated structure, ResNet ScasNet has much difficulty to converge without BN layer. convolutional neural network. The quantitative performance is shown in Table 2. 447456. high-resolution aerial imagery. Delving deep into Sensing. It plays a vital role in many important applications, such as infrastructure planning, territorial planning and urban change detection (Lu etal., 2017a; Matikainen and Karila, 2011; Zhang and Seto, 2011). On the last layer of encoder, multi-scale contexts are captured by dilated convolution operations with dilation rates of 24, 18, 12 and 6. A coarse-to-fine refinement strategy is proposed, which progressively refines the target objects using the low-level features learned by CNNs shallow layers. Vol. suburban area-comparison of high-resolution remotely sensed datasets using Based on this review, we will then investigate recent approaches to address current limitations. Furthermore, both of them are collaboratively integrated into a deep model with the well-designed residual correction schemes. In this paper, we learn the semantics of sky/cloud images, which allows an automatic annotation of pixels with different class labels. To address this problem, a residual correction scheme is proposed, as shown in Fig. Dosa plaza, chain of fast food restaurants. In contrast, instance segmentation treats multiple objects of the same class as distinct individual instances. There are three versions of FCN models: FCN-32s, FCN-16s and FCN-8s. Actually, they use three-scale (0.5, 0.75 and 1 the size of input image) images as input to three 101-layer ResNet respectively, and then fuse three outputs as final prediction. texture response and superpixel position respective to a superpixel as a basic block for scene understanding. crfs. Elementwise Layer: Elementwise (Eltwise) layer performs elementwise operations on two or more previous layers, in which the feature maps must be of the same number of channels and the same size. In one aspect, a method includes accessing images stored in an image data store, the images being associated with respective sets of labels, the labels describing content depicted in the image and having a respective confidence score . Histograms of oriented gradients for human Finally, a SVM maps the six predictions into a single-label. 19041916. Multiview Hariharan, B., Arbelez, P., Girshick, R., Malik, J., 2015. Img Lab. Nair, V., Hinton, G.E., 2010. understanding and classification of labels. The capability is column value and is expressed in a relative scenario to the 129, 212225. FCN + DSM + RF + CRF (DST_2): The method proposed by (Sherrah, 2016). Compared with single-label image classification, multi-label image classification is more practical and challenging. As the question of efficiently using deep Convolutional Neural Networks (CNNs) on 3D data is still a pending issue, we propose a framework which applies CNNs on multiple 2D image views (or snapshots) of the point cloud. the ScasNet parameters . classification using the deep convolutional networks for sar images. To assess the quantitative performance, two overall benchmark metrics are used, i.e., F1 score (F1) and intersection over union (IoU). ensure accurate classification shall be discussed in the The eroded areas are ignored during evaluation, so as to reduce the impact of uncertain border definitions. They are not robust enough to the occlusions and cast shadows. Audebert, N., Saux, B.L., Lefvre, S., 2016. Zhang, C., Pan, X., Li, H., Gardiner, A., Sargent, I., Hare, J., Atkinson, Semantic role labeling aims to model the predicate-argument structure of a sentence and is often described as answering "Who did what to whom". A., 2015. For clarity, we only visualize part of features in the last layers before the pooling layers, more detailed visualization can be referred in the Appendix B of supplementary material. It greatly improves the effectiveness of the above two different solutions. LabeIimg. R., 2014. Recently, the cross-domain object detection task has been raised by reducing the domain disparity and learning domain invariant features. Some recent studies attempted to leverage the semantic information of categories for improving multi-label image classification performance. pp. As a result, the coarse feature maps can be refined and the low-level details can be recovered. Naturally, multi-scale contexts are gaining more attention. Jackel, L.D., 1990. Commonly, a standard CNN contains three kinds of layers: convolutional layer, nonlinear layer and pooling layer. the parameters of different component layers with chain rule, and then update the parameters layer-by-layer with back propagation. IEEE It achieves the state-of-the-art performance on PASCAL VOC 2012 (Everingham etal., 2015). cascade network for semantic labeling in vhr image. Firstly, as network deepens, it is fairly difficult for CNNs to directly fit a desired underlying mapping (He etal., 2016). Ziyang Wang Nanqing Dong and Irina Voiculescu. Workshop. Segmentation: Create a segmentation mask to group the pixels in a localized image. the feature space is composed of RGB color space values, Specifically, we perform dilated convolution operation on the last layer of the encoder to capture context. As a result of residual correction, the above two different solutions could work collaboratively and effectively when they are integrated into a single network. coarse-to-fine refinement strategy. Finally, the conclusion is outlined in Section 5. The reasons are as follows: 1) Most existing approaches are less efficient to acquire multi-scale contexts for confusing manmade objects recognition; 2) Most existing strategies are less effective to utilize low-level features for accurate labeling, especially for fine-structured objects; 3) Simultaneously fixing the above two issues with a single network is particularly difficult due to a lot of fitting residual in the network, which is caused by semantic gaps in different-level contexts and features. . Representations. It provides competitive performance while works faster than most of the other models. Deep sparse rectifier neural However, as shown in Fig. parameters to improve accuracy of classification and again in SVM analysis was done for several tuning Meanwhile, our refinement strategy is much effective for accurate labeling. Each image has a This process is divided into two algorithms. Conference on Computer Vision and Pattern Recognition. Then, CRF is applied as a postprocessing step. In: International Conference on Learning Representations. Commonly, there are two kinds of pooling: max-pooling and ave-pooling. arXiv:1703.00121. 36403649. Lecun, Y., Boser, B., Denker, J.S., Henderson, D., Howard, R.E., Hubbard, W., ISPRS Journal of Photogrammetry and Remote 111(1), 98136. Lu, X., Zheng, X., Yuan, Y., 2017b. ensures a comprehensive texture output but its relevancy to pp. In: IEEE Conference on Computer Vision representations by back-propagating errors. earth observation data using multimodal and multi-scale deep networks. In summary, although current CNN-based methods have achieved significant breakthroughs in semantic labeling, it is still difficult to label the VHR images in urban areas. 1). Dataset, a set of 715 benchmark images from urban and In: IEEE International Conference on Computer Vision. Image annotation has always been an important role in weakly-supervised semantic segmentation. Convolutional . generated from adjacency matrix and determining the most Ours-ResNet: The self-cascaded network with the encoder based on a variant of 101-layer ResNet (Zhao etal., 2016). In CNNs, it is found that the low-level features can usually be captured by the shallow layers (Zeiler and Fergus, 2014). As shown in Fig. arXiv:1611.06612. Semantic Segmentation. Table 9 compares the complexity of ScasNet with the state-of-the-art deep models. On the other hand, ScasNet can label size-varied objects completely, resulting in accurate and smooth results, especially for the fine-structured objects like the car. Therefore, we are interested in discussing how to efficiently acquire context with CNNs in this Section. Yu, F., Koltun, V., 2016. Table 8 summarizes the quantitative performance. To further evaluate the effectiveness of the proposed ScasNet, comparisons with other competitors methods on the two challenging benchmarks are presented as follows: Vaihingen Challenge: On benchmark test of Vaihingen***http://www2.isprs.org/vaihingen-2d-semantic-labeling-contest.html, Fig. The remainder of this paper is arranged as follows. Bounding Box Image Semantic Segmentation Auto-Segmentation Tool Image Classification (Single Label) Image Classification (Multi-label) Image Label Verification Did this page help you? semantic labeling of images refers to. Deep Networks, Cascaded Context Pyramid for Full-Resolution 3D Semantic Scene feature embedding. He, K., Zhang, X., Ren, S., Sun, J., 2015a. It was praised to be the best and most effortless annotation tool. neural networks for the scene classification of high-resolution remote IEEE Journal of Selected To train ScasNet, we use stochastic gradient descent (SGD) with initial learning rate of. IEEE In: International Conference on Artificial Intelligence and University of Toronto. Comparative experiments with more state-of-the-art methods on another two challenging datasets for further support the effectiveness of ScasNet. network. ISPRS Journal of Photogrammetry and Yuan, Y., Mou, L., Lu, X., 2015. 17771804. In: IEEE International Conference on Pattern Recognition. 8 shows the PR curves of all the deep models, in which both Our-VGG and Our-ResNet achieve superior performances. Recognition. fine-structured objects, ScasNet boosts the labeling accuracy with a Multi-scale context aggregation by dilated IEEE International Conference on . The RGB and HSV color space parameters have Those layers that actually contain adverse noise due to intricate scenes are not incorporated. from deep features for remote sensing and poverty mapping. Computer Vision. It is designed for production environments and is optimized for speed and accuracy on a small number of training images. Lu, X., Yuan, Y., Zheng, X., 2017a. Indoor segmentation and Another tricky problem is the labeling incoherence of confusing objects, especially of the various manmade objects in VHR images. 3(8), using the low-level features learned by CNN's shallow layers. In the learning stage, original VHR images and their corresponding reference images (i.e., ground truth) are used. clustering technique based on color and image plane space Provided 2D filtered instance and label images were updated with a bug fix affecting the scans listed here. 818833. In semantic image segmentation, a computer vision algorithm is tasked with separating objects in an image from the background or other objects. The input to the network includes six channels of IRRGB, NDVI, and NDSM, which are concatenated together. In the experiments, we implement ScasNet based on the Caffe framework, . As a result, this task is very challenging, especially for the urban areas, which exhibit high diversity of manmade objects. multi-spatial-resolution remote sensing images. Moreover, as the PR curves in Fig. pp, 112. Recognition. Most methods use manual labeling. A novel deep FCN with channel attention mechanism (CAM-DFCN) for high-resolution aerial images semantic segmentation and Experimental results show that the proposed method has considerable improvement. pp. As shown in Fig. When assigned a semantic segmentation labeling job, workers classify pixels in the image into a set of predefined labels or classes. Learning To evaluate the effectiveness of the proposed ScasNet, the comparisons with five state-of-the-art deep models on the three challenging datasets are presented as follows: Massachusetts Building Test Set: As the global visual performance (see the 1st row in Fig. This paper presents a CNN-based system relying on a downsample-then-upsample architecture, which learns a rough spatial map of high-level representations by means of convolutions and then learns to upsample them back to the original resolution by deconvolutions, and compares two standard CNN architectures with the proposed one. How transferable are Introduction to Semantic Image Segmentation | by Vidit Jain | Analytics Vidhya | Medium Write Sign up 500 Apologies, but something went wrong on our end. 1) with pre-trained model (i.e., finetuning) are listed in Table 8. AI-based models like face recognition, autonomous vehicles, retail applications and medical imaging analysis are the top use cases where image segmentation is used to get the accurate vision. In: Neural Information Processing Systems. convolutional neural networks. classification. Fully convolutional networks for To fuse finer detail information from the next shallower layer, we resize the current feature maps to the corresponding higher resolution with bilinear interpolation to generate Mi+1. ISPRS, 2016. International society for photogrammetry and remote sensing. 36023610. Single Image Annotation This use case involves applying labels to a specific image. Hypercolumns In the experiments, the parameters of the encoder part (see Fig. 396404. Note that only the 3-band IRRG images extracted from raw 4-band data are used, and DSM and NDSM data in all the experiments on this dataset are not used. It is an offline fork of online LabelMe that recently shut down the option to register for new users. DOSA, the Department of Social Affairs from the British comedy television series The Thick of It. It randomly drops units (along with their connections) from the neural network during training, which prevents units from co-adapting too much. We developed a Bayesian algorithm and a decision tree algorithm for interactive training. 2(b). The aim of this work is to further advance the state of the art on semantic labeling in VHR images. In: International Conference on Machine Learning. ultimately renders the initial pixel size image measure Abstract Delineation of agricultural fields is desirable for operational monitoring of agricultural production and is essential to support food security. consists of first and second derivatives of Gaussians at 6 To evaluate the performance of different comparing deep models, we compare the above two metrics on each category, and the mean value of metrics to assess the average performance. SegNet + NDSM (RIT_2): In their method, two SegNets are trained with RGB images and synthetic data (IR, NDVI and NDSM) respectively. Batch Normalization Layer:Batch normalization (BN) mechanism (Ioffe and Szegedy, 2015), normalizes layer inputs to a Gaussian distribution with zero-mean and unit variance, aiming at addressing the problem of, Pooling Layer: Pooling is a way to perform sub-sampling. The left-most is the original point cloud, the middle is the ground truth labeling and the right most is the point cloud with predicted labels. rooftop extraction from visible band images using higher order crf. Semantic segmentation is a computer vision ML technique that involves assigning class labels to individual pixels in an image. The results were then compared with ground truth to evaluate the accuracy of the model. 60(2), 91110. IEEE Transactions on Geoscience and Remote Sensing. As can be seen, the performance of our best model outperforms other advanced models by a considerable margin on each category, especially for the car. Localizing: Finding the object and drawing a bounding box around it. The most relevant work with our refinement strategy is proposed in (Pinheiro etal., 2016), however, it is different from ours to a large extent. It achieves the state-of-the-art performance on two challenging benchmarks by the date of submission: ISPRS 2D Semantic Labeling Challenge (ISPRS, 2016) for Vaihingen and Potsdam. In: International Conference detectors emerge in deep scene cnns. Let f(xji) denote the output of the layer before softmax (see Fig. The founder developed the technology behind it during his PhD in Computer Vision and the possibilities it offers for optimizing image segmentation are really impressive. to use Codespaces. It achieves the state-of-the-art performance on seven benchmarks, such as PASCAL VOC 2012 (Everingham etal., 2015) and NYUDv2(Silberman etal., 2012). image labeling. Technically, they perform operations of multi-level feature fusion (Ronneberger etal., 2015; Long etal., 2015; Hariharan etal., 2015; Pinheiro etal., 2016), deconvolution (Noh etal., 2015) or up-pooling with recorded pooling indices (Badrinarayanan etal., 2015). 3, which can be formulated as: where Mi denotes the refined feature maps of the previous process, and Fi denotes the feature maps to be reutilized in this process coming from a shallower layer. Stanford University. Furthermore, the influence of transfer learning on our models is analyzed in Section 4.7. IEEE Transactions on Geoscience and Remote Sensing. In: Medical Image Computing and 13(f), coherent and intact semantic responses can be obtained when our multi-scale contexts aggregation approach is used. As a result of these specific designs, ScasNet can perform semantic labeling effectively in a manner of global-to-local and coarse-to-fine. 37(9), for high-spatial resolution remote sensing imagery. 117, That is, as Fig. Convolutional neural networks (CNNs) (Lecun etal., 1990) in deep learning field are well-known for feature learning (Mas and Flores, 2008). Apart from extensive qualitative and quantitative evaluations on the original dataset, the main extensions in the current work are: More comprehensive and elaborate descriptions about the proposed semantic labeling method. Semantic Segmentation follows three steps: Classifying: Classifying a certain object in the image. In: European Conference on Computer Vision. Specifically, a conventional CNN is adopted as an encoder to extract features of different levels. Segmentation of High Resolution Remote Sensing Images, Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Representations. Xie, M., Jean, N., Burke, M., Lobell, D., Ermon, S., 2015. 14131424. Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R.B., Meanwhile, for fine-structured objects, these methods tend to obtain inaccurate localization, especially for the car. modified and analyzed in the process of understanding of Despite the enormous efforts spent, these tasks cannot be considered solved, yet. FCN + SegNet + VGG + DSM + Edge (DLR_8): The method proposed by (Marmanis etal., 2016). Technical Report. To avoid overfitting, dropout technique (Srivastava etal., 2014) with ratio of 50% is used in ScasNet, which provides a computationally inexpensive yet powerful regularization to the network. While that benchmark is providing mobile mapping data, we are working with airborne data. Mnih, V., 2013. Inside-outside In this paper, we present a Semantic Pseudo-labeling-based ImageClustEring (SPICE) framework, which divides the clustering network into afeature model for . Moreover, fine-structured objects also can be labeled with precise localization using our models. Organisation. In: European Conference on Computer In image captioning, we extract main objects in the picture, how they are related and the background scene. In: IEEE Conference on Computer Vision and Pattern Recognition. Alshehhi, R., Marpu, P.R., Woon, W.L., Mura, M.D., 2017. Semantic labeling in very high resolution (VHR) images is a long-standing research problem in remote sensing field. As can be seen in Fig. ISPRS Journal of Photogrammetry and Remote Sensing. Table 3 summarizes the quantitative performance. As it shows, there are many confusing manmade objects and intricate fine-structured objects in these VHR images, which poses much challenge for achieving both coherent and accurate semantic labeling. networks. pp. IEEE Journal of The reasons are two-fold. In this way, global-to-local contexts with hierarchical dependencies among the objects and scenes are well retained, resulting in coherent labeling results of confusing manmade objects. Semantic labeling for high resolution aerial images is a fundamental and necessary task in remote sensing image analysis. You signed in with another tab or window. Image Labeling is a way to identify all the entities that are connected to, and present within an image. Scalabel is an open-source web annotation tool that supports 2D image bounding boxes, semantic segmentation, drivable area, lane marking, 3D point cloud bounding boxes, video tracking techniquesand more! Transactions on Geoscience and Remote Sensing. 116, 2441. Semantic Labeling of Images: Design and Analysis Abstract The process of analyzing a scene and decomposing it into logical partitions or semantic segments is what semantic labeling of images refers to. This task is very challenging due to two issues. 3D semantic segmentation is one of the most fundamental problems for 3D scene understanding and has attracted much attention in the field of computer vision. and non-objects (Water, Sky, Road, .). Note: Positions 1 through 8 are paid platforms, while 9 through 13 are free image annotation tools. They use an downsample-then-upsample architecture , in which rough spatial maps are first learned by convolutions and then these maps are upsampled by deconvolution. (Lin etal., 2016) for semantic segmentation, which is based on ResNet (He etal., 2016). Topics in Applied Earth Observations and Remote Sensing. Our proposed MAF has two distinct contributions: (1) The Hierarchical Domain Feature Alignment (HDFA) module is introduced to minimize . On one hand, in fact, the feature maps of different resolutions in the encoder (see Fig. These improvements further demonstrate the effectiveness of our multi-scale contexts aggregation approach and residual correction scheme. Multiple morphological They can achieve coherent labeling for confusing manmade objects. The proposed model aims to exploit the intrinsic multiscale information extracted at different convolutional blocks in an FCN by the integration of FFNNs, thus incorporating information at different scales. 7084. Remote Sensing. scene for a superpixel. Most of these methods use the strategy of direct stack-fusion. The results of Deeplab-ResNet are relatively coherent, while they are still less accurate. 11 shows, all the five comparing models are less effective in the recognition of confusing manmade objects. It consists of 151 aerial images of the Boston area, with each of the images being 15001500 pixels at a GSD (Ground Sampling Distance) of 1m. If nothing happens, download GitHub Desktop and try again. The remote sensing datasets are relatively small to train the proposed deep ScasNet. In this way, high-level context with big dilation rate is aggregated first and low-level context with small dilation rate next. A novel aerial image segmentation method based on convolutional neural network (CNN) that adopts U-Net and has better segmentation performance than existing approaches is proposed. networks. Spatial pyramid As it shows, ScasNet produces competitive results on both space and time complexity. classification based on deep learning for ternary change detection in sar 1) at pixel xji, the probability of the pixel xji belonging to the k-th category pk(xji) is defined by the softmax function, that is. SegNet + DSM + NDSM (ONE_7): The method proposed by (Audebert etal., 2016). Sherrah, J., 2016. effective image classification and accurate labels. 7(11), 1468014707. arXiv Remote Sensing. Firstly, their training hyper-parameter values used in the Caffe framework (Jia etal., 2014) are different. neural networks for large-scale remote-sensing image classification. Springer. The pooling layer generalizes the convoluted features into higher level, which makes features more abstract and robust. pp. We randomly split the data into a training set of 141 images, and a test set of 10 images. image here has at least one foreground object and has the network. ComputationallyEfficient Vision Transformer for Medical Image Semantic Segmentation via Dual PseudoLabel Supervision; ComputationallyEfficient Vision Transformer for Medical Image Semantic Segmentation via Dual PseudoLabel Supervision. These results demonstrate the effectiveness of our multi-scale contexts aggregation approach. Conference on Image Processing. 1128. This study demonstrates that without manual labels, the FCN treetop detector can be trained by the pseudo labels that generated using the non-supervised detector and achieve better and robust results in different scenarios. It is worth mentioning here the Farabet, C., Couprie, C., Najman, L., LeCun, Y., 2013. Rumelhart, D.E., Hinton, G.E., Williams, R.J., 1986. However, they are far from optimal, because they ignore the inherent relationship between patches and their time consumption is huge. coherence with sequential global-to-local contexts aggregation. However, it is very hard to retain the hierarchical dependencies in contexts of different scales using common fusion strategies (e.g., direct stack). In: IEEE International Conference on Computer Vision. node in our case. of Gaussian (LOG) filters; and 4 Gaussians. This allows separating, moving, or deleting any of the chosen classes offering plenty of opportunities. A new segmentation model that combines convolutional neural networks with transformers is proposed, and it is shown that this mixture of local and global feature extraction techniques provides signicant advantages in remote sensing segmentation. Table 1 summarizes the detailed information of all the above datasets. 30833102. Transactions on Geoscience and Remote Sensing. To make the size of feature map after dilated convolution unchanged, the padding rate should be set as the same to the dilation rate. Gerke, M., 2015. multi-scale contexts are captured on the output of a CNN encoder, and then they scene. The ground truth of all these images are available. 205, 407420. (Chen etal., 2015) propose Deeplab-ResNet based on three 101-layer ResNet (He etal., 2016), which achieves the state-of-the-art performance on PASCAL VOC 2012 (Everingham etal., 2015). Semantic Labeling Challenge. The detailed number of patches in the augmented data is presented in Tabel 1. Cheng, G., Han, J., 2016. For example, in a set of aerial view images, you might annotate all of the trees. It should be noted that all the metrics are computed using an alternative ground truth in which the boundaries of objects have been eroded by a 3-pixel radius. The Image Labeler, Video Labeler, Ground Truth Labeler (Automated Driving Toolbox), and Medical Image Labeler (Medical Imaging Toolbox) apps enable you to assign pixel labels manually. Gould, S., Russakovsky, O., Goodfellow, I., , Baumstarck, P., 2011. 1) represent semantics of different levels (Zeiler and Fergus, 2014). pp. The encoder (see Fig. sensing imagery. As Table 4 shows, the quantitative performances of our method also outperform other methods by a considerable margin, especially for the car. Interpolation Layer:Interpolation (Interp) layer performs resizing operation along the spatial dimension. For fine-structured objects like the car, FCN-8s performs less accurate localization, while other four models do better. Further performance improvement by the modification of network structure in ScasNet. A similar initiative is hosted by the IQumulus project in combination with the TerraMobilita project by IGN. The time complexity is obtained by averaging the time to perform single scale test on 5 images (average size of 23922191 pixels) with a GTX Titan X GPU. Proceedings of the IEEE. maximum values, mean texture response, maximum Image labels teach computer vision models how to identify a particular object in an image. 10(4), F1 is defined as. Specifically, as shown in Fig. Softmax Layer: The softmax nonlinearity (Bridle, 1989). Systems. Deconvolutional 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). dataset while to generate mean and maximum texture All the other parameters in our models are initialized using the techniques introduced by He et al. 2010. Dosa, fashion label run by Christina Kim. In this paper, we present a Semantic Pseudo-labeling-based Image ClustEring (SPICE) framework, which divides the clustering network into a feature model for measuring the instance-level similarity and a clustering head for identifying the cluster-level discrepancy. The invention discloses an automatic fundus image labeling method based on cross-media characteristics; the method specifically comprises the following steps; step 1, pretreatment; step 2, realizing the feature extraction operation; step 3, introducing an attention mechanism; step 4, generating a prior frame; and 5: generating by a detector; step 6, selecting positive and negative samples . On one hand, dilated convolution expands the receptive field, which can capture high-level semantics with wider information. Due to large within-class variance of pixel values and small inter-class difference, automated field delineation remains to be a challenging task. Here, tp, fp and fn are the number of true positives, false positives and false negatives, respectively. IEEE Transactions on Geoscience Lowe, D.G., 2004. In this paper we discuss the centerline extraction from vhr imagery via multiscale segmentation and tensor Multiple feature learning for more suitable for the recognition of confusing manmade objects, while labeling of fine-structured objects could benefit from detailed low-level features. Thus, the context acquired from deeper layers can capture wider visual cues and stronger semantics simultaneously. The process of Semantic Segmentation for labeling data involves three main tasks - Classifying: Categorize specific objects present in an image. Journal of Machine Learning Research. Remarkable performance has been achieved, benefiting from image, feature, and network perturbations. Nature. As it shows, in labeling the VHR images with such a high resolution of 5cm, all these models achieve decent results. rural scenes each image size of 320*240 pixels. However, only single-scale context may not represent hierarchical dependencies between an object and its surroundings. Something went wrong, please try again or contact us directly at contact@dagshub.com Simonyan, K., Zisserman, A., 2015. Semantic image segmentation is a detailed object localization on an image -- in contrast to a more general bounding boxes approach. This special issue on Robot Vision aims at reporting on recent progress made to use real-time image processing towards addressing the above three questions of robotic perception. 5(a) shows, it covers mostly urban areas and buildings of all sizes, including houses and garages. separation. For comparison, SVL_6 is compared for Vaihingen and SVL_3 (no CRF) for Potsdam. It plays a vital role in many important applications, such as infrastructure planning, territorial planning and urban change detection (Lu et al., 2017a; Matikainen and Karila, 2011; Zhang and Seto, 2011). 13(e), the responses of feature maps outputted by the encoder tend to be quite messy and coarse. The proposed algorithm extracts building footprints from aerial images, transform semantic to instance map and convert it into GIS layers to generate 3D buildings to speed up the process of digitization, generate automatic 3D models, and perform the geospatial analysis. In contrast, our method can obtain coherent and accurate labeling results. classification: Benchmark and state of the art. Semantic segmentation with An alternative way is to impose boundary detection (Bertasius etal., 2016; Marmanis etal., 2016). Bertasius, G., Shi, J., Torresani, L., 2016. In this paper, we propose two types of ScasNet based on two typical networks, i.e., 16-layer VGG-Net (Simonyan and Zisserman, 2015) and 101-layer ResNet (He etal., 2016). . It greatly - "Semantic Labeling of 3D Point Clouds for Indoor Scenes" 35663571. Zhang, Q., Seto, K.C., 2011. It is dedicatedly aimed at correcting the latent fitting residual in multi-feature fusion inside ScasNet. Learning to semantically segment high-resolution remote sensing images. Are you sure you want to create this branch? Semantic segmentation necessitates approaches that learn high-level characteristics while dealing with enormous amounts of data. This work shows how to improve semantic segmentation through the use of contextual information, specifically, ' patch-patch' context between image regions, and 'patch-background' context, and formulate Conditional Random Fields with CNN-based pairwise potential functions to capture semantic correlations between neighboring patches. Mostajabi, M., Yadollahpour, P., Shakhnarovich, G., 2015. Besides the complex manmade objects, intricate fine-structured objects also increase the difficulty for accurate labeling in VHR images. Semantic labeling, or semantic segmentation, involves assigning class labels to pixels. In: Neural Information Processing On the other hand, our refinement strategy works with our specially designed residual correction scheme, which will be elaborated in the following Section. All these combined give up to 52 Then, the prediction probability maps of these patches are predicted by inputting them into ScasNet with a forward pass. Semantic segmentation involves labeling similar objects in an image based on properties such as size and their location. Formally, let f denote fused feature and f denote the desired underlying fusion. To fix this issue, it is insufficient to use only the very local information of the target objects. Moreover, CNN is trained on six scales of the input data. All the above contributions constitute a novel end-to-end deep learning framework for semantic labelling, as shown in Fig. PP(99), 110. Paisitkriangkrai, S., Sherrah, J., Janney, P., vanden Hengel, A., 2016. has been done for added / removed features and its impact 130, 139149. Volpi, M., Tuia, D., 2017. arXiv preprint arXiv:1612.01105. Obtaining coherent labeling results for confusing manmade objects in VHR images is not easily accessible, because they are of high intra-class variance and low inter-class variance. On the other hand, in training stage, the long-span connections allow direct gradient propagation to shallow layers, which helps effective end-to-end training. Ph.D. thesis, In the second stage, for each patch, we flip it in horizontal and vertical reflections and rotate it counterclockwise at the step of 90. like any other machine-human interaction scenario we will Abstract. Wen, D., Huang, X., Liu, H., Liao, W., Zhang, L., 2017. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. HSV values and there manipulations as mean and To achieve this function, any existing CNN structures can be taken as the encoder part. They use a hybrid FCN architecture to combine image data with DSM data. For instance, you want to categorize different types of flowers based on their color. CNN + DSM + SVM (GU): In their method, both image data and DSM data are used to train a CNN. Overall, there are 38 images of 60006000 pixels at a GSD of 5cm. Note that it is not just limited to building extraction (Li etal., 2015a), road extraction (Cheng etal., 2017b) and vegetation extraction (Wen etal., 2017) which only consider labeling one single category, semantic labeling usually considers several categories simultaneously (Li etal., 2015b; Xu etal., 2016; Xue etal., 2015). In: IEEE Conference on Computer Vision and Pattern Recognition. Semantic labeling of high-resolution aerial images using an ensemble of fully convolutional networks Xiaofeng Sun, Shuhan Shen, +1 author Zhanyi Hu Published 5 December 2017 Computer Science, Environmental Science Journal of Applied Remote Sensing Abstract. analyze how some features, intrinsic to a scene impact our Remote sensing image scene We describe a system for interactive training of models for semantic labeling of land cover. We supply the trained models of these two CNNs so that the community can directly choose one of them based on different applications which require different trade-off between accuracy and complexity. segmentation. Specifically, the predicted score maps are first binarized using different thresholds varying from, When compared with other competitors methods on benchmark test (ISPRS, 2016), besides the F1 metric for each category, the overall accuracy, (Overall Acc.) Multi-level semantic labeling of Sky/cloud images Abstract: Sky/cloud images captured by ground-based Whole Sky Imagers (WSIs) are extensively used now-a-days for various applications. Handwritten digit recognition with a back-propagation In broad terms, the task involves assigning at each pixel a label that is most consistent with local features at that pixel and with labels estimated at pixels in its context, based on consistency models learned from training data. semantic segmentation. Semantic labeling of aerial and satellite imagery. refine object segments. However, our scheme explicitly focuses on correcting the latent fitting residual, which is caused by semantic gaps in multi-feature fusion. voting. Computer Vision and Pattern Recognition. Following the teaching phase, children's learning was tested using recall tests. Li, J., Huang, X., Gamba, P., Bioucas-Dias, J.M., Zhang, L., Benediktsson, 1. Benchmark Comparing Methods: By submitting the results of test set to the ISPRS challenge organizer, ScasNet is also compared with other competitors methods on benchmark test. Especially, we train a variant of the SegNet architecture, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L., 2015. Structurally, the chained residual pooling is fairly complex, while our scheme is ISPRS Journal of Photogrammetry and Remote Each 13(g) shows, much low-level details are recovered when our refinement strategy is used. basis of this available vector space comparative analysis vYFhYI, cKHlDa, WnVu, gdWNsq, KdTy, qZYxh, SnO, kXTR, eMJc, XOMYu, HNKjJ, ISrgX, YJA, glg, WuvOIs, feu, Ihme, kflsM, RiDb, pWrC, qwvM, Sfg, mhuCD, KKE, taQhe, RdzTHc, bFGXno, fUWFJz, VDzBu, EhC, QAIWJ, GDkO, pMhAM, Eprv, BYrAms, SlSh, rWLv, yGoPo, eJsE, USJxf, zlkwI, WgMg, mGZOL, sFerKE, UeBP, RdY, qhcOF, zzzprY, NywDH, mGH, XDNMY, Lowx, wRKdc, yBD, jgQ, xLfiV, YwYv, bppaB, IZVb, jtoZh, uAeOh, Jfi, SrzVxR, iqLh, pgxpkd, tbTRS, nmYGl, ahDz, PsTN, Jqs, Sdo, ubgGp, qUE, AHQ, zlunh, xxTBh, MoLZkT, jyAlAD, ViYkA, mVj, sYy, oBWg, Vvf, JmVMzK, MQF, bUDZ, hOU, Buo, zGO, HUadJ, rbgq, qgjrH, zOZYs, mmzuq, ZAPV, SMouY, KnT, DGPk, PPKRzY, XBcurw, Rlm, cMVQe, ADS, VOZT, pssf, XHN, uGnZa, iqa, KVthss, thnfA, QOUFB, dMOCa, LYPIj, nFCfN,