Thanks to the widespread use of digital pathology and the development of artificial intelligence methods , automatic histopathological image analysis methods help pathologists in their decision‐making process . In this process , rather than producing labels for whole‐slide image patches , semantic segmentation is very useful , which facilitates the pathologists ’ interpretation . In this study , automatic semantic segmentation based on cell type is proposed for the first time in the literature using novel deep convolutional networks structure ( DCNN ) . This paper presents a semantic segmentation based method for automatically synthesizing two-tone cartoon portraits in black-and-white style . Synthesizing two-tone portraits from photographs can be considered as a heterogeneous image transformation problem , of which the result should be vivid portraits with distinct freehand-like features , such as clean backgrounds and continuous lines . The system solves different tasks ( semantic segmentation and object detections ) in an opportunistic and distributed fashion but still allows communication between modules to improve their respective performances . We propose the use of the semantic space to improve specific out-of-the-box object detectors and an update model to take the evidence from different detection into account in the semantic segmentation process . Our proposal is evaluated with the KITTI dataset , on the object detection benchmark and on five different sequences manually annotated for the semantic segmentation task , demonstrating the efficacy of our approach . Degraded-Manuscript Binarization in Diverse Document Textures and Layouts using Deep Encoder-Decoder Networks . Handwritten document-image binarization is a semantic segmentation process to differentiate ink pixels from background pixels . It is one of the essential steps towards character recognition , writer identification , and script-style evolution analysis . While we would like to train models on large amounts of synthetic data such as data collected from graphics game engines , such models fail to generalize to real - world imagery . For example , a state - of - the - art semantic segmentation model trained on synthetic dashcam data fails to segment the road in real images , and its overall per - pixel label accuracy drops from 93 % ( if trained on real imagery ) to 54 % ( if trained only on synthetic data , see Table [ reference ] ) . Feature - level unsupervised domain adaptation methods address this problem by aligning the features extracted from the network across the source ( e.g. synthetic ) and target ( e.g. real ) domains , without any labeled target samples . The original ablation study for starts from and fixes . Additionally , there is an extreme case when and , which means we only use the traditional semantic segmentation method to deal with this task . Here we conduct the experiment by setting and to serve as a baseline with only one segmentation mask result for predictions . Manual delineation practices require anatomical knowledge , are expensive , time consuming and can be inaccurate due to human error . Here , we describe a semantic segmentation network for tumor subregion segmentation from 3D MRIs based on encoder - decoder architecture . Due to a limited training dataset size , a variational auto - encoder branch is added to reconstruct the input image itself in order to regularize the shared decoder and impose additional constraints on its layers . The calculated outcomes for datasets from each of the above-mentioned models were analyzed based on various known statistical indexes like root relative squared error ( RRSE ) , root mean squared error ( RMSE ) , mean absolute error ( MAE ) , correlation coefficient ( R2 ) , and relative absolute error ( RAE ) . It was found that between the discussed machine learning-based solutions of MLPr , LLWL , AMT , RF , ENet , and RBFr , the RF was nominated as the most appropriate predictive network . The RF network outcomes determined the R2 , MAE , RMSE , RAE , and RRSE for the training dataset to be 0.9997 , 0.19 , 0.2399 , 2.078 , and 2.3795 , respectively . In the present study , we analyzed the accuracy and performance of UNet and ENet architectures for the problem of semantic image segmentation . In addition , we investigated the ENet architecture by replacing of some convolution layers with box-convolution layers . The analysis performed on the original dataset consisted of histology slices with mast cells . One of these fields is the Semantic Segmentation with many applications like autonomous driving . In that line , this paper carried out a study using an ENet architecture for semantic segmentation on road images for intelligent vehicles and multiple experiments of deep learning training with a wealth of combinations of training procedures , including multiple loss functions and datasets for out-of-domain performance assessment . Besides some expected outcomes , the results allow identifying a set of combinations with performances that stand out and are advisable for researchers to tackle more efficiently problems of a similar nature . The approach is based on semantic segmentation of images that come from a camera installed in the combine cab . Semantic segmentation is performed using the ENet neural network , which is designed to work in real time . As a result of segmentation , 5 classes are recognized : cut crop , uncut crop , obstacles , harvester part , and background . Motivated by this observation and inspired by recent works that tackle multiple tasks with a single integrated architecture , in this paper we present a real-time efficient implementation based on ENet that solves three autonomous driving related tasks at once : semantic scene segmentation , instance segmentation and monocular depth estimation . Our approach builds upon a branched ENet architecture with a shared encoder but different decoder branches for each of the three tasks . The presented method can run at 21 fps at a resolution of 1024x512 on the Cityscapes dataset without sacrificing accuracy compared to running each task separately . CNNs designed to take advantage of features extracted from multiple sensor modalities have the potential to increase the perception capability of autonomous systems . The work in this thesis extends the real-time CNN segmentation model ENet [ 39 ] to learn using a multimodal representation of the environment . Namely we investigate learning from disparity images generated by SGM [ 20 ] in conjunction with RGB color images from the Cityscapes dataset [ 10 ] . We evaluate all models using Intersection-over-Union ( IoU ) as a primary performance metric and the instance-level IoU ( iIoU ) as a secondary metric . Compared to the baseline ENet model , we achieve comparable segmentation performance and are also able to take advantage of features These language-specific dictionaries are then concatenated to form a composite discriminative dictionary . For sparse coding , the orthogonal matching pursuit ( OMP ) , least absolute shrinkage and selection operator ( LASSO ) , and elastic net ( ENet ) algorithms are explored . The regularized multiclass logistic regression is employed for score calibration . We propose the use of deep neural networks ( DNN ) for solving the problem of inferring the position and relevant properties of lanes of urban roads with poor or absent horizontal signalization , in order to allow the operation of autonomous cars in such situations . We take a segmentation approach to the problem and use the Efficient Neural Network ( ENet ) DNN for segmenting LiDAR remission grid maps into road maps . We represent road maps using what we called road grid maps . In this paper , we propose a reinforcement learning-based label denoising method for distantly supervised relation extraction . The model consists of two modules : extraction network ( ENet ) and policy network ( PNet ) . The core of our label denoising is designing a policy in the PNet to obtain latent labels , where we can select the actions of using the distantly supervised labels or the predicted labels from the ENet . [ reference ] ( a ) with high accuracy and low speed . Recent fast semantic segmentation methods of ENet and SQ , contrarily , take quite different positions in the plot . The speed is much accelerated ; but accuracy drops , where the final mIoUs are lower than 60 % . In this work , we propose to combine the advantages from both methods . Specifically , our proposed model , DeepLabv3 + , extends DeepLabv3 by adding a simple yet effective decoder module to refine the segmentation results especially along object boundaries . We further explore the Xception model and apply the depthwise separable convolution to both Atrous Spatial Pyramid Pooling and decoder modules , resulting in a faster and stronger encoder-decoder network . In this paper proposes the network architecture for text localization , describes the procedure for the formation of the training set , and considers the algorithm for pre-processing images , reducing the amount of processed data and simplifying the segmentation of the object “ background ” . The network architecture is a modification of well-known DeepLabv3 network and takes into account the specifics of images of text characters . The proposed method is able to determine the location of text characters in the images with acceptable accuracy . In order to make the attention information of low-stage channel better transmit to high-stage features , we add the attention expansion mechanism of low-stage channel . On the other hand , noting the influence of channel number on multi-scale features fusion of Atrous Spatial Pyramid Pooling ( ASPP ) model in DeepLabv3 network , we allocate different channels to different convolution or pooling operations without increasing the total number of channels . Finally , experimental results on MIT Scene Parsing Benchmark dataset ( SceneParse150 ) are included to demonstrate the effectiveness of the proposed model . We also describe efficient ways of applying these mobile models to object detection in a novel framework we call SSDLite . Additionally , we demonstrate how to build mobile semantic segmentation models through a reduced form of DeepLabv3 which we call Mobile DeepLabv3 . is based on an inverted residual structure where the shortcut connections are between the thin bottleneck layers . The intermediate expansion layer uses lightweight depthwise convolutions to filter features as a source of non-linearity . We also describe efficient ways of applying these mobile models to object detection in a novel framework we call SSDLite . Additionally , we demonstrate how to build mobile semantic segmentation models through a reduced form of DeepLabv3 which we call Mobile DeepLabv3 . The MobileNetV2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input an MobileNetV2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer . After the training phase , the success rates of the trained models were compared . The most successful DeepLabv3 model has achieved a success rate of 78.83 % on Cityscapes test set . Afterwards , an ensemble of two different DeepLabv3 models and the Extended DeepLabv3 model is tested . Although a number of solutions have been proposed , both segmentation tasks remain challenging . In this paper , we propose an ensemble of deep segmentation models ( enDeepSeg ) that combines the U-Net and DeepLabv3 + to address this challenge . We first extract image patches to train the U-Net and DeepLabv3 + model , respectively , and then use the weighted sum of the segmentation probability maps produced by both models to determine the label of each pixel . The resulting softmax outputs are then post processed such that we can investigate mean and variance over all image crops as well as mean and variance of uncertainty heat maps obtained from pixel-wise uncertainty measures , like the entropy , applied to each crop 's softmax output . In our tests , we use the publicly available DeepLabv3 + MobilenetV2 network ( trained on the Cityscapes dataset ) and demonstrate that the incorporation of crops improves the quality of the prediction and that we obtain more reliable uncertainty measures . These are then aggregated over predicted segments for either classifying between IoU=0 and IoU>0 ( meta classification ) or predicting the IoU via linear regression ( meta regression ) . The application of deep learning networks allows for two analytical purposes . Application of the DeepLabv3 pre-trained model extracts phenotypic parameters describing the shape of cells constituting Bacillaria colonies . Application of a semantic model trained on nematode embryogenesis data ( OpenDevoCell ) provides a means to analyze masked images of potential intracellular features . In this paper , we propose a practical method to implement autonomous indoor navigation based on semantic image segmentation using state-of-the-art performance model on mobile devices , especially Android devices . We apply a system called ‘ Mobile DeepLabv3 ’ , which uses atrous convolution when applying semantic image segmentation by using MobileNetV2 as a network backbone . The ADE20 K dataset is used to train our models specific to indoor environments . We also elaborate on implementation details and share our experience on training our system . The proposed ` DeepLabv3 ' system significantly improves over our previous DeepLab versions without DenseCRF post-processing and attains comparable performance with other state-of-art models on the PASCAL VOC 2012 semantic image segmentation benchmark . When designing a semantic segmentation module for a practical application , such as autonomous driving , it is crucial to understand the robustness of the module with respect to a wide range of image corruptions . While there are recent robustness studies for full-image classification , we are the first to present an exhaustive study for semantic segmentation , based on the state-of-the-art model DeepLabv3 + . To increase the realism of our study , we utilize almost 400,000 images generated from Cityscapes , PASCAL VOC 2012 , and ADE20 K . Most of the former approaches move towards enhancing the performance in terms of accuracy with a little awareness of computational efficiency . In this paper , we introduce LiteSeg , a lightweight architecture for semantic image segmentation . In this work , we explore a new deeper version of Atrous Spatial Pyramid Pooling module ( ASPP ) and apply short and long residual connections , and depthwise separable convolution , resulting in a faster and efficient model . LiteSeg architecture is introduced and tested with multiple backbone networks as Darknet19 , MobileNet , and ShuffleNet to provide multiple trade-offs between accuracy and computational cost . The proposed model LiteSeg , with MobileNetV2 as a backbone network , achieves an accuracy of 67.81 % mean intersection over union at 161 frames per second with 640 × 360 resolution on the Cityscapes dataset .
Thanks to the widespread use of digital pathology and the development of artificial intelligence methods , automatic histopathological image analysis methods help pathologists in their decision‐making process . In this process , rather than producing labels for whole‐slide image patches , semantic segmentation is very useful , which facilitates the pathologists ’ interpretation . In this study , automatic semantic segmentation based on cell type is proposed for the first time in the literature using novel deep convolutional networks structure ( DCNN ) .
This paper presents a semantic segmentation based method for automatically synthesizing two-tone cartoon portraits in black-and-white style . Synthesizing two-tone portraits from photographs can be considered as a heterogeneous image transformation problem , of which the result should be vivid portraits with distinct freehand-like features , such as clean backgrounds and continuous lines .
The system solves different tasks ( semantic segmentation and object detections ) in an opportunistic and distributed fashion but still allows communication between modules to improve their respective performances . We propose the use of the semantic space to improve specific out-of-the-box object detectors and an update model to take the evidence from different detection into account in the semantic segmentation process . Our proposal is evaluated with the KITTI dataset , on the object detection benchmark and on five different sequences manually annotated for the semantic segmentation task , demonstrating the efficacy of our approach .
Degraded-Manuscript Binarization in Diverse Document Textures and Layouts using Deep Encoder-Decoder Networks . Handwritten document-image binarization is a semantic segmentation process to differentiate ink pixels from background pixels . It is one of the essential steps towards character recognition , writer identification , and script-style evolution analysis .
While we would like to train models on large amounts of synthetic data such as data collected from graphics game engines , such models fail to generalize to real - world imagery . For example , a state - of - the - art semantic segmentation model trained on synthetic dashcam data fails to segment the road in real images , and its overall per - pixel label accuracy drops from 93 % ( if trained on real imagery ) to 54 % ( if trained only on synthetic data , see Table [ reference ] ) . Feature - level unsupervised domain adaptation methods address this problem by aligning the features extracted from the network across the source ( e.g. synthetic ) and target ( e.g. real ) domains , without any labeled target samples .
The original ablation study for starts from and fixes . Additionally , there is an extreme case when and , which means we only use the traditional semantic segmentation method to deal with this task . Here we conduct the experiment by setting and to serve as a baseline with only one segmentation mask result for predictions .
Manual delineation practices require anatomical knowledge , are expensive , time consuming and can be inaccurate due to human error . Here , we describe a semantic segmentation network for tumor subregion segmentation from 3D MRIs based on encoder - decoder architecture . Due to a limited training dataset size , a variational auto - encoder branch is added to reconstruct the input image itself in order to regularize the shared decoder and impose additional constraints on its layers .
The calculated outcomes for datasets from each of the above-mentioned models were analyzed based on various known statistical indexes like root relative squared error ( RRSE ) , root mean squared error ( RMSE ) , mean absolute error ( MAE ) , correlation coefficient ( R2 ) , and relative absolute error ( RAE ) . It was found that between the discussed machine learning-based solutions of MLPr , LLWL , AMT , RF , ENet , and RBFr , the RF was nominated as the most appropriate predictive network . The RF network outcomes determined the R2 , MAE , RMSE , RAE , and RRSE for the training dataset to be 0.9997 , 0.19 , 0.2399 , 2.078 , and 2.3795 , respectively .
In the present study , we analyzed the accuracy and performance of UNet and ENet architectures for the problem of semantic image segmentation . In addition , we investigated the ENet architecture by replacing of some convolution layers with box-convolution layers . The analysis performed on the original dataset consisted of histology slices with mast cells .
One of these fields is the Semantic Segmentation with many applications like autonomous driving . In that line , this paper carried out a study using an ENet architecture for semantic segmentation on road images for intelligent vehicles and multiple experiments of deep learning training with a wealth of combinations of training procedures , including multiple loss functions and datasets for out-of-domain performance assessment . Besides some expected outcomes , the results allow identifying a set of combinations with performances that stand out and are advisable for researchers to tackle more efficiently problems of a similar nature .
The approach is based on semantic segmentation of images that come from a camera installed in the combine cab . Semantic segmentation is performed using the ENet neural network , which is designed to work in real time . As a result of segmentation , 5 classes are recognized : cut crop , uncut crop , obstacles , harvester part , and background .
Motivated by this observation and inspired by recent works that tackle multiple tasks with a single integrated architecture , in this paper we present a real-time efficient implementation based on ENet that solves three autonomous driving related tasks at once : semantic scene segmentation , instance segmentation and monocular depth estimation . Our approach builds upon a branched ENet architecture with a shared encoder but different decoder branches for each of the three tasks . The presented method can run at 21 fps at a resolution of 1024x512 on the Cityscapes dataset without sacrificing accuracy compared to running each task separately .
CNNs designed to take advantage of features extracted from multiple sensor modalities have the potential to increase the perception capability of autonomous systems . The work in this thesis extends the real-time CNN segmentation model ENet [ 39 ] to learn using a multimodal representation of the environment . Namely we investigate learning from disparity images generated by SGM [ 20 ] in conjunction with RGB color images from the Cityscapes dataset [ 10 ] .
We evaluate all models using Intersection-over-Union ( IoU ) as a primary performance metric and the instance-level IoU ( iIoU ) as a secondary metric . Compared to the baseline ENet model , we achieve comparable segmentation performance and are also able to take advantage of features
These language-specific dictionaries are then concatenated to form a composite discriminative dictionary . For sparse coding , the orthogonal matching pursuit ( OMP ) , least absolute shrinkage and selection operator ( LASSO ) , and elastic net ( ENet ) algorithms are explored . The regularized multiclass logistic regression is employed for score calibration .
We propose the use of deep neural networks ( DNN ) for solving the problem of inferring the position and relevant properties of lanes of urban roads with poor or absent horizontal signalization , in order to allow the operation of autonomous cars in such situations . We take a segmentation approach to the problem and use the Efficient Neural Network ( ENet ) DNN for segmenting LiDAR remission grid maps into road maps . We represent road maps using what we called road grid maps .
In this paper , we propose a reinforcement learning-based label denoising method for distantly supervised relation extraction . The model consists of two modules : extraction network ( ENet ) and policy network ( PNet ) . The core of our label denoising is designing a policy in the PNet to obtain latent labels , where we can select the actions of using the distantly supervised labels or the predicted labels from the ENet .
[ reference ] ( a ) with high accuracy and low speed . Recent fast semantic segmentation methods of ENet and SQ , contrarily , take quite different positions in the plot . The speed is much accelerated ; but accuracy drops , where the final mIoUs are lower than 60 % .
In this work , we propose to combine the advantages from both methods . Specifically , our proposed model , DeepLabv3 + , extends DeepLabv3 by adding a simple yet effective decoder module to refine the segmentation results especially along object boundaries . We further explore the Xception model and apply the depthwise separable convolution to both Atrous Spatial Pyramid Pooling and decoder modules , resulting in a faster and stronger encoder-decoder network .
In this paper proposes the network architecture for text localization , describes the procedure for the formation of the training set , and considers the algorithm for pre-processing images , reducing the amount of processed data and simplifying the segmentation of the object “ background ” . The network architecture is a modification of well-known DeepLabv3 network and takes into account the specifics of images of text characters . The proposed method is able to determine the location of text characters in the images with acceptable accuracy .
In order to make the attention information of low-stage channel better transmit to high-stage features , we add the attention expansion mechanism of low-stage channel . On the other hand , noting the influence of channel number on multi-scale features fusion of Atrous Spatial Pyramid Pooling ( ASPP ) model in DeepLabv3 network , we allocate different channels to different convolution or pooling operations without increasing the total number of channels . Finally , experimental results on MIT Scene Parsing Benchmark dataset ( SceneParse150 ) are included to demonstrate the effectiveness of the proposed model .
We also describe efficient ways of applying these mobile models to object detection in a novel framework we call SSDLite . Additionally , we demonstrate how to build mobile semantic segmentation models through a reduced form of DeepLabv3 which we call Mobile DeepLabv3 . is based on an inverted residual structure where the shortcut connections are between the thin bottleneck layers . The intermediate expansion layer uses lightweight depthwise convolutions to filter features as a source of non-linearity .
We also describe efficient ways of applying these mobile models to object detection in a novel framework we call SSDLite . Additionally , we demonstrate how to build mobile semantic segmentation models through a reduced form of DeepLabv3 which we call Mobile DeepLabv3 . The MobileNetV2 architecture is based on an inverted residual structure where the input and output of the residual block are thin bottleneck layers opposite to traditional residual models which use expanded representations in the input an MobileNetV2 uses lightweight depthwise convolutions to filter features in the intermediate expansion layer .
After the training phase , the success rates of the trained models were compared . The most successful DeepLabv3 model has achieved a success rate of 78.83 % on Cityscapes test set . Afterwards , an ensemble of two different DeepLabv3 models and the Extended DeepLabv3 model is tested .
Although a number of solutions have been proposed , both segmentation tasks remain challenging . In this paper , we propose an ensemble of deep segmentation models ( enDeepSeg ) that combines the U-Net and DeepLabv3 + to address this challenge . We first extract image patches to train the U-Net and DeepLabv3 + model , respectively , and then use the weighted sum of the segmentation probability maps produced by both models to determine the label of each pixel .
The resulting softmax outputs are then post processed such that we can investigate mean and variance over all image crops as well as mean and variance of uncertainty heat maps obtained from pixel-wise uncertainty measures , like the entropy , applied to each crop 's softmax output . In our tests , we use the publicly available DeepLabv3 + MobilenetV2 network ( trained on the Cityscapes dataset ) and demonstrate that the incorporation of crops improves the quality of the prediction and that we obtain more reliable uncertainty measures . These are then aggregated over predicted segments for either classifying between IoU=0 and IoU>0 ( meta classification ) or predicting the IoU via linear regression ( meta regression ) .
The application of deep learning networks allows for two analytical purposes . Application of the DeepLabv3 pre-trained model extracts phenotypic parameters describing the shape of cells constituting Bacillaria colonies . Application of a semantic model trained on nematode embryogenesis data ( OpenDevoCell ) provides a means to analyze masked images of potential intracellular features .
In this paper , we propose a practical method to implement autonomous indoor navigation based on semantic image segmentation using state-of-the-art performance model on mobile devices , especially Android devices . We apply a system called ‘ Mobile DeepLabv3 ’ , which uses atrous convolution when applying semantic image segmentation by using MobileNetV2 as a network backbone . The ADE20 K dataset is used to train our models specific to indoor environments .
We also elaborate on implementation details and share our experience on training our system . The proposed ` DeepLabv3 ' system significantly improves over our previous DeepLab versions without DenseCRF post-processing and attains comparable performance with other state-of-art models on the PASCAL VOC 2012 semantic image segmentation benchmark .
When designing a semantic segmentation module for a practical application , such as autonomous driving , it is crucial to understand the robustness of the module with respect to a wide range of image corruptions . While there are recent robustness studies for full-image classification , we are the first to present an exhaustive study for semantic segmentation , based on the state-of-the-art model DeepLabv3 + . To increase the realism of our study , we utilize almost 400,000 images generated from Cityscapes , PASCAL VOC 2012 , and ADE20 K .
Most of the former approaches move towards enhancing the performance in terms of accuracy with a little awareness of computational efficiency . In this paper , we introduce LiteSeg , a lightweight architecture for semantic image segmentation . In this work , we explore a new deeper version of Atrous Spatial Pyramid Pooling module ( ASPP ) and apply short and long residual connections , and depthwise separable convolution , resulting in a faster and efficient model . LiteSeg architecture is introduced and tested with multiple backbone networks as Darknet19 , MobileNet , and ShuffleNet to provide multiple trade-offs between accuracy and computational cost . The proposed model LiteSeg , with MobileNetV2 as a backbone network , achieves an accuracy of 67.81 % mean intersection over union at 161 frames per second with 640 × 360 resolution on the Cityscapes dataset .
Cell‐type based semantic segmentation of histopathological images using deep convolutional neural networks Semantic Segmentation Based Automatic Two-Tone Portrait Synthesis A fast, modular scene understanding system using context-aware object detection BiNet: Degraded-Manuscript Binarization in Diverse Document Textures and Layouts using Deep Encoder-Decoder Networks CyCADA: Cycle-Consistent Adversarial Domain Adaptation Shape Robust Text Detection With Progressive Scale Expansion Network 3D MRI brain tumor segmentation using autoencoder regularization Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques Comparison of UNet, ENet, and BoxENet for Segmentation of Mast Cells in Scans of Histological Slices Performance Analysis on Deep Learning Semantic Segmentation with multivariate Training Procedures Detecting uncut crop edge with convolutional neural networks Fast Scene Understanding for Autonomous Driving Deep Multimodal Fusion Networks for Semantic Segmentation Sparse representation classification over discriminatively learned dictionary for language recognition Mapping Road Lanes Using Laser Remission and Deep Neural Networks Reinforcement Learning for Distantly Supervised Relation Extraction ICNet for Real-Time Semantic Segmentation on High-Resolution Images Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation Localization of Text in Photorealistic Images LANet: A Ladder Attention Network for Image Semantic Segmentation MobileNetV2: Inverted Residuals and Linear Bottlenecks Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation Semantic Segmentation with Extended DeepLabv3 Architecture An Ensemble of Deep Neural Networks for Segmentation of Lung and Clavicle on Chest Radiographs Uncertainty Measures and Prediction Quality Rating for the Semantic Segmentation of Nested Multi Resolution Street Scene Images Towards a digital diatom: image processing and deep learning analysis of Bacillaria paradoxa dynamic morphology Indoor Semantic Segmentation for Robot Navigating on Mobile Rethinking Atrous Convolution for Semantic Image Segmentation Benchmarking the Robustness of Semantic Segmentation Models LiteSeg: A Novel Lightweight ConvNet for Semantic Segmentation
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ICIG (3) ICRA ICML 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) BrainLes@MICCAI ICARSC ECCV ICCSA (4) IECON SIU MIUA ICUFN DICTA
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