However , image compositing in natural scenes with complex backgrounds remains a tedious task , requiring experienced artists to hand-segment . In order to achieve automatic compositing in natural scenes , we propose a fully automated method that integrates instance segmentation and image matting processes to generate high-quality semantic mattes that can be used for image editing task . Our approach can be seen both as a refinement of existing instance segmentation algorithms and as a fully automated semantic image matting method . We introduce the first approach to solve the challenging problem of unsupervised 4D visual scene understanding for complex dynamic scenes with multiple interacting people from multi-view video . Our approach simultaneously estimates a detailed model that includes a per-pixel semantically and temporally coherent reconstruction , together with instance-level segmentation exploiting photo-consistency , semantic and motion information . We further leverage recent advances in 3D pose estimation to constrain the joint semantic instance segmentation and 4D temporally coherent reconstruction . We build on the observation that the scene is typically composed of a static background , as well as a relatively small number of traffic participants which move rigidly in 3D . We propose to estimate the traffic participants using instance-level segmentation . For each traffic participant , we use the epipolar constraints that govern each independent motion for faster and more accurate estimation . However , most existing methods still perform poorly for curved text detection , mainly due to the limitation of their text representations ( e.g. , horizontal boxes , rotated rectangles or quadrangles ) . To solve this problem , we propose a novel method to detect irregular scene texts based on instance-aware segmentation . The key idea is to design an attention guided semantic segmentation model to precisely label the weighted borders of text regions . This paper presents a novel instance segmentation scheme for lane mark and drivable area detection simultaneously , which combines an evolved fully convolutional networks ( FCNs ) and inverse perspective mapping ( IPM ) technology . Numerous existing methods make an attempt to find out road surface using lane mark , thereby deriving to the drivable area . Automated nuclei segmentation and classification exist but are challenging to overcome issues like nuclear intra-class variability and clustered nuclei separation . To address such challenges , we put forward an application of instance segmentation and classification framework built on an Unet architecture by adding residual blocks , densely connected blocks and a fully convolutional layer as a bottleneck between encoder-decoder blocks for Pap smear images . The number of convolutional layers in the standard Unet has been replaced by densely connected blocks to ensure feature reuse-ability property while the introduction of residual blocks in the same attempts to converge the network more rapidly . To ease this problem , we propose a novel text-context-aware scene text detection CNN structure , which appropriately encodes channel and spatial attention information to construct context-aware and discriminative feature map for multi-oriented and multi-language text detection tasks . With high representation ability of textcontext-aware feature map , the proposed instance segmentation based method can not only robustly detect multi-oriented and multi-language text from natural scene images , but also produce better text detection results by greatly reducing false positives . Experiments on ICDAR2015 and ICDAR2017-MLT datasets show that the proposed method has achieved superior performances in precision , recall and F-measure than most of the existing studies . In this paper , a robotic semantic grasping method is proposed to estimate six-degree-of-freedom ( 6DOF ) grasping pose for the robotic manipulator , thereby a grip perpendicularly to the surface of the object can be achieved . Firstly , the graspable location is detected by an instance segmentation detection model and ellipse fitting method . Secondly , by processing the pixel mask and point clouds of the target object , the 6DOF grasping pose is estimated . Current instance segmentation approaches consist of ensembles of modules that are trained independently of each other , thus missing opportunities for joint learning . Here we propose a new instance segmentation paradigm consisting in an end-to-end method that learns how to segment instances sequentially . The model is based on a recurrent neural network that sequentially finds objects and their segmentations one at a time . In order to accelerate the process , the SSD-like detection structure and two-path architecture which can generate more accurate segmentation prediction without heavy calculation burden are adopted . Our experiments on the PASCAL VOC and the MSCOCO datasets demonstrate the benefits of our approach , which accelerate the instance segmentation process with competitive result compared to MaskRCNN . Code is public available ( https://github.com/lzx1413/FSSI ) . Our framework , while conceptually simple and theoretically abundant , is also practically effective and computationally efficient . We demonstrate substantial improvements over state - of - the - art instance segmentation for object proposal generation , as well as demonstrating the benefits of grouping loss for classification tasks such as boundary detection and semantic segmentation . section : Introduction We compare our performance with some of these works and two other recent approaches . We report two metrics defined in : Symmetric Best Dice ( ) , which denotes the accuracy of the instance segmentation and Absolute Difference in Count ( ) which is the absolute value of the mean of the difference between the predicted number of leaves and the ground truth over all images . The large - scale Cityscapes dataset focuses on semantic understanding of urban street scenes . Another failure mode is incorrect semantic segmentation : in the lower right example , the semantic segmentation network accidentally mistakes an empty bicycle storage for actual bikes . The instance segmentation network is left no choice but to give it a shot , and tries to split up the imaginary bikes into individual objects . Nevertheless , we achieve competitive results on the Cityscapes leaderboard , outperforming all but one unpublished work . Our approach is based on a deep Convolutional Neural Network ( CNN ) , which consists of an initial category - level part segmentation module . Using the output of a human detector , we are then able to associate segmented parts with detected humans in the image using a differentiable Conditional Random Field ( CRF ) , producing a part - level instance segmentation of the image . Our formulation is robust to false - positive detections as well as imperfect bounding boxes which do not cover the entire human , in contrast to other instance segmentation methods based on object detectors In fact , a lot of work on human parsing has focussed on datasets such as Fashionista [ reference ] , ATR [ reference ] and Deep Fashion [ reference ] where images typically contain only one , centred person . The notion of instance - level segmentation only matters when more than one person is present in an image , motivating us to evaluate our method on the Pascal Person - Parts dataset [ reference ] where multiple people can appear in unconstrained environments . Recent human parsing approaches have typically been similar to semantic segmentation works using fully convolutional networks ( FCNs ) [ reference ] , but trained to label parts [ reference ][ reference ][ reference ] instead of object classes . P}. Further details of this module are included in the appendix . section : Instance - level segmentation module This module creates an instance - level segmentation of the image by associating each pixel in the input category - level segmentation , Q , with one of the D input human - detections or the background label . section : Obtaining segmentations at other granularities Given the part instance prediction produced by our proposed network , we are able to easily obtain human instance segmentation and semantic part segmentation . In order to achieve human instance segmentation , we map the predicted part instance labels ( i , j ) , i.e. part j of person i , to i. We describe our dataset and experimental set - up in Sec . 4.1 , before presenting results on instance - level part segmentation ( Fig . 1c ) , instance - level human segmentation ( Fig . 1d ) and semantic part segmentation We also use the publicly available code for MNC [ reference ] , which won the MS - COCO 2016 instance segmentation challenge , and finetune their public model trained on VOC 2011 [ reference ] on Person - Part instances as a baseline . section : Results on Instance - level Part Segmentation We first train our model in a piecewise manner , by first optimising the parameters of the category - level segmentation module , and then " freezing " the weights of this module and only training the instance network . Fig . 6 displays the success cases of our method , while Fig . 7 shows examples of failure cases . Furthermore , we illustrate the strengths and weaknesses of our part instance segmentation method in comparison to MNC [ reference ] in Fig . 8 , and compare our instance - level human segmentation results , which we obtain by the simple mapping described in Sec . 3.4 of our main paper , to MNC in Fig . ( c ) ) . To address these problems , in this paper , we propose a novel instance segmentation network , namely , Progressive Scale Expansion Network ( PSENet ) . There are two advantages of the proposed PSENet . The challenge can be considered as two independent problems : object detection and semantic segmentation . For the detection problem , we use Cascade R-CNN with FPN and Hyper Task Cascade . For the segmentation problem , we use DeepLab v3 + model with bce+dice loss . The Pansharpening method was used for image correction to inject spatial details from panchromatic image to multidimensional image . In order to increase the operational efficiency and accuracy of automotive vehicles detection at aerial images received by the unmanned aerial complexes for more efficient use of received information in the system of decision making support it was selected the Cascade Mask R-CNN model . This model is more suitable for task solution of multiclass classification and small-sized object detection at the image . Overall , this framework can learn more discriminative features progressively while integrating complementary features together in each stage . Without bells and whistles , a single HTC obtains 38.4 % and 1.5 % improvement over a strong Cascade Mask R-CNN baseline on MSCOCO dataset . Moreover , our overall system achieves 48.6 mask AP on the test-challenge split , ranking 1st in the COCO 2018 Challenge Object Detection Task . In this paper we focus on detection of ridge , the important landmark in ROP diagnosis , using Convolutional Neural Network(CNN ) . Our contribution is to use a CNN-based model Mask R-CNN for demarcation line/ridge detection allowing clinicians to detect ROP stage 2 better . The proposed system applies a pre-processing step of image enhancement to overcome poor image quality . However , the detection performance of such methods deteriorates when samples are reduced . To address this , the authors propose an improved Mask R-CNN-based method : the ResNet Group Cascade ( RGC ) Mask R-CNN . First , they compared ResNet with different layers , finding that ResNeXt-101 - 64 × 4d is superior to other backbone networks . A good detector should predict high quality bounding boxes that cover objects tightly . To understand the quality of the bounding boxes predicted by CornerNet , we evaluate the performance of CornerNet at multiple IoU thresholds , and compare the results with other state - of - the - art detectors , including RetinaNet , Cascade R - CNN [ reference ] and IoU - Net [ reference ] . Tab . 5 shows that CornerNet achieves a much higher AP at 0.9 IoU than other detectors , outperforming Cascade R - CNN + We propose a novel approach for finger-vein recognition , focused on direct extraction of actual finger-vein patterns from NIR finger images without any specific pre- or post-processing , using semantic segmentation convolutional neural networks ( CNNs ) . We utilize three network architectures and besides identifying efficient training and configuration settings for these networks , using manually annotated training data , we present a training model based on automatically generated labels to improve the networks ’ performance . We exploit knowledge of class hierarchies to aid the training of semantic segmentation convolutional neural networks . We do not modify the architecture of the network itself , but rather propose to compute a loss that is a summation of classification losses at different levels of class abstraction . These texture regions are then reconstructed using a motion model at the decoder instead of inter-frame prediction . A Convolutional Neural Networks based semantic segmentation combined with post-processing generates pixel-level texture masks that are more accurate compared to block-based texture masks in our previous work . We show that for many standard test sets , the proposed method achieves significant data rate reductions with improved visual quality . In order to correct the labels of pixels that were incorrectly classified with high confidence level by the semantic segmentation algorithm , we generate multiple region growing steps through a Monte Carlo sampling of the seeds of the regions . Our method improves the accuracy of a state-of-the-art fully convolutional semantic segmentation approach on the publicly available COCO and PASCAL datasets , and it shows significantly better results on selected sequences of the finely-annotated DAVIS dataset . Moreover , local processing and processing at a large scale prove to be important in music classification , and a combination of large-scale and small-scale feature extraction strategies can be complementary for ensembling . In 1-D , our convolution-based segmentation method is comparable to a state-of-the-art Gestalt-based segmentation approach in classification experiments . In the last three Music Information Retrieval Evaluation eXchange campaigns , our proposed method for the discovery of repeated themes and sections applied to monophonic symbolic music has been shown to be a competitive approach over all measures in that evaluation . In this paper , we focus on the problem of image segmentation from natural language referring expressions . Existing works tackle this problem by augmenting the convolutional semantic segmentation networks with an LSTM sentence encoder , which is optimized by a pixel-wise classification loss . We argue that the distribution similarity between the inference and ground truth plays an important role in referring image segmentation . In this paper , we reduce the data preparation cost by leveraging weak supervision in the form of object bounding boxes . To accomplish this , we propose a principled framework that trains a deep convolutional segmentation model that combines a large set of weakly supervised images ( having only object bounding box labels ) with a small set of fully supervised images ( having semantic segmentation labels and box labels ) . Our framework trains the primary segmentation model with the aid of an ancillary model that generates initial segmentation labels for the weakly supervised instances and a self-correction module that improves the generated labels during training using the increasingly accurate primary model . The improvement of false negatives rate of tiny nodules , the reduction of false alarms and the optimization of time consumption are some of them that need to be solved as soon as possible . In view of the above problems , in this paper , we first propose a novel full convolution segmentation framework for lung cavity extraction in preprocessing stage to solve the time consumption problem of the existing pulmonary nodule detection systems . Furthermore , a 2D-NestedUNet segmentation network and a 3D-RPN detection network is stacked to get the high recall and low false positive rate on nodule candidate extraction , especially the recall of tiny nodules . Instance segmentation is one of the fundamental vision tasks . Recently , fully convolutional instance segmentation methods have drawn much attention as they are often simpler and more efficient than two-stage approaches like Mask R-CNN . To date , almost all such approaches fall behind the two-stage Mask R-CNN method in mask precision when models have similar computation complexity , leaving great room for improvement . Accurate localisation of mandibular canals in lower jaws is important in dental implantology , in which the implant position and dimensions are currently determined manually from 3D CT images by medical experts to avoid damaging the mandibular nerve inside the canal . Here we present a deep learning system for automatic localisation of the mandibular canals by applying a fully convolutional neural network segmentation on clinically diverse dataset of 637 cone beam CT volumes , with mandibular canals being coarsely annotated by radiologists , and using a dataset of 15 volumes with accurate voxel-level mandibular canal annotations for model evaluation . We show that our deep learning model , trained on the coarsely annotated volumes , localises mandibular canals of the voxel-level annotated set , highly accurately with the mean curve distance and average symmetric surface distance being 0.56 mm and 0.45 mm , respectively . Accurate and efficient first-arrival picking in a real noisy environment is a challenge for most of the automatic first-arrival picking methods . We have developed a novel workflow to automatically pick the first arrival of microseismics by using a state-of-the art pixel-wise convolutional image segmentation method . We first form the training data by randomly selecting part of the microseismic traces and manually pick the time index of the first arrivals . Hinton et al . have also proposed to train networks to compute explicit instantiation parameters of features as part of a recognition process . Other authors have proposed to perform object localization via ConvNet - based segmentation . The simplest approach consists in training the ConvNet to classify the central pixel ( or voxel for volumetric images ) of its viewing window as a boundary between regions or not . We introduce a novel architecture , the Synchronized Spectral CNN ( SyncSpecCNN ) to address these issues . The basic architecture of our neural network is similar to the fully convolutional segmentation network of , namely , we repeat the operation of convolving a vertex function by kernels and applying a non - linear transformation . However , our network combines processing in the primal and dual ( spectral ) domains . We propose a simple yet effective instance segmentation framework , termed CondInst ( conditional convolutions for instance segmentation ) . Top-performing instance segmentation methods such as Mask R-CNN rely on ROI operations ( typically ROIPool or ROIAlign ) to obtain the final instance masks . In contrast , we propose to solve instance segmentation from a new perspective . Instead of using instance-wise ROIs as inputs to a network of fixed weights , we employ dynamic instance-aware networks , conditioned on instances . CondInst enjoys two advantages : 1 ) Instance segmentation is solved by a fully convolutional network , eliminating the need for ROI cropping and feature alignment . 2 ) Due to the much improved capacity of dynamically-generated conditional convolutions , the mask head can be very compact ( e.g. , 3 conv .
However , image compositing in natural scenes with complex backgrounds remains a tedious task , requiring experienced artists to hand-segment . In order to achieve automatic compositing in natural scenes , we propose a fully automated method that integrates instance segmentation and image matting processes to generate high-quality semantic mattes that can be used for image editing task . Our approach can be seen both as a refinement of existing instance segmentation algorithms and as a fully automated semantic image matting method .
We introduce the first approach to solve the challenging problem of unsupervised 4D visual scene understanding for complex dynamic scenes with multiple interacting people from multi-view video . Our approach simultaneously estimates a detailed model that includes a per-pixel semantically and temporally coherent reconstruction , together with instance-level segmentation exploiting photo-consistency , semantic and motion information . We further leverage recent advances in 3D pose estimation to constrain the joint semantic instance segmentation and 4D temporally coherent reconstruction .
We build on the observation that the scene is typically composed of a static background , as well as a relatively small number of traffic participants which move rigidly in 3D . We propose to estimate the traffic participants using instance-level segmentation . For each traffic participant , we use the epipolar constraints that govern each independent motion for faster and more accurate estimation .
However , most existing methods still perform poorly for curved text detection , mainly due to the limitation of their text representations ( e.g. , horizontal boxes , rotated rectangles or quadrangles ) . To solve this problem , we propose a novel method to detect irregular scene texts based on instance-aware segmentation . The key idea is to design an attention guided semantic segmentation model to precisely label the weighted borders of text regions .
This paper presents a novel instance segmentation scheme for lane mark and drivable area detection simultaneously , which combines an evolved fully convolutional networks ( FCNs ) and inverse perspective mapping ( IPM ) technology . Numerous existing methods make an attempt to find out road surface using lane mark , thereby deriving to the drivable area .
Automated nuclei segmentation and classification exist but are challenging to overcome issues like nuclear intra-class variability and clustered nuclei separation . To address such challenges , we put forward an application of instance segmentation and classification framework built on an Unet architecture by adding residual blocks , densely connected blocks and a fully convolutional layer as a bottleneck between encoder-decoder blocks for Pap smear images . The number of convolutional layers in the standard Unet has been replaced by densely connected blocks to ensure feature reuse-ability property while the introduction of residual blocks in the same attempts to converge the network more rapidly .
To ease this problem , we propose a novel text-context-aware scene text detection CNN structure , which appropriately encodes channel and spatial attention information to construct context-aware and discriminative feature map for multi-oriented and multi-language text detection tasks . With high representation ability of textcontext-aware feature map , the proposed instance segmentation based method can not only robustly detect multi-oriented and multi-language text from natural scene images , but also produce better text detection results by greatly reducing false positives . Experiments on ICDAR2015 and ICDAR2017-MLT datasets show that the proposed method has achieved superior performances in precision , recall and F-measure than most of the existing studies .
In this paper , a robotic semantic grasping method is proposed to estimate six-degree-of-freedom ( 6DOF ) grasping pose for the robotic manipulator , thereby a grip perpendicularly to the surface of the object can be achieved . Firstly , the graspable location is detected by an instance segmentation detection model and ellipse fitting method . Secondly , by processing the pixel mask and point clouds of the target object , the 6DOF grasping pose is estimated .
Current instance segmentation approaches consist of ensembles of modules that are trained independently of each other , thus missing opportunities for joint learning . Here we propose a new instance segmentation paradigm consisting in an end-to-end method that learns how to segment instances sequentially . The model is based on a recurrent neural network that sequentially finds objects and their segmentations one at a time .
In order to accelerate the process , the SSD-like detection structure and two-path architecture which can generate more accurate segmentation prediction without heavy calculation burden are adopted . Our experiments on the PASCAL VOC and the MSCOCO datasets demonstrate the benefits of our approach , which accelerate the instance segmentation process with competitive result compared to MaskRCNN . Code is public available ( https://github.com/lzx1413/FSSI ) .
Our framework , while conceptually simple and theoretically abundant , is also practically effective and computationally efficient . We demonstrate substantial improvements over state - of - the - art instance segmentation for object proposal generation , as well as demonstrating the benefits of grouping loss for classification tasks such as boundary detection and semantic segmentation . section : Introduction
We compare our performance with some of these works and two other recent approaches . We report two metrics defined in : Symmetric Best Dice ( ) , which denotes the accuracy of the instance segmentation and Absolute Difference in Count ( ) which is the absolute value of the mean of the difference between the predicted number of leaves and the ground truth over all images . The large - scale Cityscapes dataset focuses on semantic understanding of urban street scenes .
Another failure mode is incorrect semantic segmentation : in the lower right example , the semantic segmentation network accidentally mistakes an empty bicycle storage for actual bikes . The instance segmentation network is left no choice but to give it a shot , and tries to split up the imaginary bikes into individual objects . Nevertheless , we achieve competitive results on the Cityscapes leaderboard , outperforming all but one unpublished work .
Our approach is based on a deep Convolutional Neural Network ( CNN ) , which consists of an initial category - level part segmentation module . Using the output of a human detector , we are then able to associate segmented parts with detected humans in the image using a differentiable Conditional Random Field ( CRF ) , producing a part - level instance segmentation of the image . Our formulation is robust to false - positive detections as well as imperfect bounding boxes which do not cover the entire human , in contrast to other instance segmentation methods based on object detectors
In fact , a lot of work on human parsing has focussed on datasets such as Fashionista [ reference ] , ATR [ reference ] and Deep Fashion [ reference ] where images typically contain only one , centred person . The notion of instance - level segmentation only matters when more than one person is present in an image , motivating us to evaluate our method on the Pascal Person - Parts dataset [ reference ] where multiple people can appear in unconstrained environments . Recent human parsing approaches have typically been similar to semantic segmentation works using fully convolutional networks ( FCNs ) [ reference ] , but trained to label parts [ reference ][ reference ][ reference ] instead of object classes .
P}. Further details of this module are included in the appendix . section : Instance - level segmentation module This module creates an instance - level segmentation of the image by associating each pixel in the input category - level segmentation , Q , with one of the D input human - detections or the background label .
section : Obtaining segmentations at other granularities Given the part instance prediction produced by our proposed network , we are able to easily obtain human instance segmentation and semantic part segmentation . In order to achieve human instance segmentation , we map the predicted part instance labels ( i , j ) , i.e. part j of person i , to i.
We describe our dataset and experimental set - up in Sec . 4.1 , before presenting results on instance - level part segmentation ( Fig . 1c ) , instance - level human segmentation ( Fig . 1d ) and semantic part segmentation
We also use the publicly available code for MNC [ reference ] , which won the MS - COCO 2016 instance segmentation challenge , and finetune their public model trained on VOC 2011 [ reference ] on Person - Part instances as a baseline . section : Results on Instance - level Part Segmentation We first train our model in a piecewise manner , by first optimising the parameters of the category - level segmentation module , and then " freezing " the weights of this module and only training the instance network .
Fig . 6 displays the success cases of our method , while Fig . 7 shows examples of failure cases . Furthermore , we illustrate the strengths and weaknesses of our part instance segmentation method in comparison to MNC [ reference ] in Fig . 8 , and compare our instance - level human segmentation results , which we obtain by the simple mapping described in Sec . 3.4 of our main paper , to MNC in Fig .
( c ) ) . To address these problems , in this paper , we propose a novel instance segmentation network , namely , Progressive Scale Expansion Network ( PSENet ) . There are two advantages of the proposed PSENet .
The challenge can be considered as two independent problems : object detection and semantic segmentation . For the detection problem , we use Cascade R-CNN with FPN and Hyper Task Cascade . For the segmentation problem , we use DeepLab v3 + model with bce+dice loss .
The Pansharpening method was used for image correction to inject spatial details from panchromatic image to multidimensional image . In order to increase the operational efficiency and accuracy of automotive vehicles detection at aerial images received by the unmanned aerial complexes for more efficient use of received information in the system of decision making support it was selected the Cascade Mask R-CNN model . This model is more suitable for task solution of multiclass classification and small-sized object detection at the image .
Overall , this framework can learn more discriminative features progressively while integrating complementary features together in each stage . Without bells and whistles , a single HTC obtains 38.4 % and 1.5 % improvement over a strong Cascade Mask R-CNN baseline on MSCOCO dataset . Moreover , our overall system achieves 48.6 mask AP on the test-challenge split , ranking 1st in the COCO 2018 Challenge Object Detection Task .
In this paper we focus on detection of ridge , the important landmark in ROP diagnosis , using Convolutional Neural Network(CNN ) . Our contribution is to use a CNN-based model Mask R-CNN for demarcation line/ridge detection allowing clinicians to detect ROP stage 2 better . The proposed system applies a pre-processing step of image enhancement to overcome poor image quality .
However , the detection performance of such methods deteriorates when samples are reduced . To address this , the authors propose an improved Mask R-CNN-based method : the ResNet Group Cascade ( RGC ) Mask R-CNN . First , they compared ResNet with different layers , finding that ResNeXt-101 - 64 × 4d is superior to other backbone networks .
A good detector should predict high quality bounding boxes that cover objects tightly . To understand the quality of the bounding boxes predicted by CornerNet , we evaluate the performance of CornerNet at multiple IoU thresholds , and compare the results with other state - of - the - art detectors , including RetinaNet , Cascade R - CNN [ reference ] and IoU - Net [ reference ] . Tab . 5 shows that CornerNet achieves a much higher AP at 0.9 IoU than other detectors , outperforming Cascade R - CNN +
We propose a novel approach for finger-vein recognition , focused on direct extraction of actual finger-vein patterns from NIR finger images without any specific pre- or post-processing , using semantic segmentation convolutional neural networks ( CNNs ) . We utilize three network architectures and besides identifying efficient training and configuration settings for these networks , using manually annotated training data , we present a training model based on automatically generated labels to improve the networks ’ performance .
We exploit knowledge of class hierarchies to aid the training of semantic segmentation convolutional neural networks . We do not modify the architecture of the network itself , but rather propose to compute a loss that is a summation of classification losses at different levels of class abstraction .
These texture regions are then reconstructed using a motion model at the decoder instead of inter-frame prediction . A Convolutional Neural Networks based semantic segmentation combined with post-processing generates pixel-level texture masks that are more accurate compared to block-based texture masks in our previous work . We show that for many standard test sets , the proposed method achieves significant data rate reductions with improved visual quality .
In order to correct the labels of pixels that were incorrectly classified with high confidence level by the semantic segmentation algorithm , we generate multiple region growing steps through a Monte Carlo sampling of the seeds of the regions . Our method improves the accuracy of a state-of-the-art fully convolutional semantic segmentation approach on the publicly available COCO and PASCAL datasets , and it shows significantly better results on selected sequences of the finely-annotated DAVIS dataset .
Moreover , local processing and processing at a large scale prove to be important in music classification , and a combination of large-scale and small-scale feature extraction strategies can be complementary for ensembling . In 1-D , our convolution-based segmentation method is comparable to a state-of-the-art Gestalt-based segmentation approach in classification experiments . In the last three Music Information Retrieval Evaluation eXchange campaigns , our proposed method for the discovery of repeated themes and sections applied to monophonic symbolic music has been shown to be a competitive approach over all measures in that evaluation .
In this paper , we focus on the problem of image segmentation from natural language referring expressions . Existing works tackle this problem by augmenting the convolutional semantic segmentation networks with an LSTM sentence encoder , which is optimized by a pixel-wise classification loss . We argue that the distribution similarity between the inference and ground truth plays an important role in referring image segmentation .
In this paper , we reduce the data preparation cost by leveraging weak supervision in the form of object bounding boxes . To accomplish this , we propose a principled framework that trains a deep convolutional segmentation model that combines a large set of weakly supervised images ( having only object bounding box labels ) with a small set of fully supervised images ( having semantic segmentation labels and box labels ) . Our framework trains the primary segmentation model with the aid of an ancillary model that generates initial segmentation labels for the weakly supervised instances and a self-correction module that improves the generated labels during training using the increasingly accurate primary model .
The improvement of false negatives rate of tiny nodules , the reduction of false alarms and the optimization of time consumption are some of them that need to be solved as soon as possible . In view of the above problems , in this paper , we first propose a novel full convolution segmentation framework for lung cavity extraction in preprocessing stage to solve the time consumption problem of the existing pulmonary nodule detection systems . Furthermore , a 2D-NestedUNet segmentation network and a 3D-RPN detection network is stacked to get the high recall and low false positive rate on nodule candidate extraction , especially the recall of tiny nodules .
Instance segmentation is one of the fundamental vision tasks . Recently , fully convolutional instance segmentation methods have drawn much attention as they are often simpler and more efficient than two-stage approaches like Mask R-CNN . To date , almost all such approaches fall behind the two-stage Mask R-CNN method in mask precision when models have similar computation complexity , leaving great room for improvement .
Accurate localisation of mandibular canals in lower jaws is important in dental implantology , in which the implant position and dimensions are currently determined manually from 3D CT images by medical experts to avoid damaging the mandibular nerve inside the canal . Here we present a deep learning system for automatic localisation of the mandibular canals by applying a fully convolutional neural network segmentation on clinically diverse dataset of 637 cone beam CT volumes , with mandibular canals being coarsely annotated by radiologists , and using a dataset of 15 volumes with accurate voxel-level mandibular canal annotations for model evaluation . We show that our deep learning model , trained on the coarsely annotated volumes , localises mandibular canals of the voxel-level annotated set , highly accurately with the mean curve distance and average symmetric surface distance being 0.56 mm and 0.45 mm , respectively .
Accurate and efficient first-arrival picking in a real noisy environment is a challenge for most of the automatic first-arrival picking methods . We have developed a novel workflow to automatically pick the first arrival of microseismics by using a state-of-the art pixel-wise convolutional image segmentation method . We first form the training data by randomly selecting part of the microseismic traces and manually pick the time index of the first arrivals .
Hinton et al . have also proposed to train networks to compute explicit instantiation parameters of features as part of a recognition process . Other authors have proposed to perform object localization via ConvNet - based segmentation . The simplest approach consists in training the ConvNet to classify the central pixel ( or voxel for volumetric images ) of its viewing window as a boundary between regions or not .
We introduce a novel architecture , the Synchronized Spectral CNN ( SyncSpecCNN ) to address these issues . The basic architecture of our neural network is similar to the fully convolutional segmentation network of , namely , we repeat the operation of convolving a vertex function by kernels and applying a non - linear transformation . However , our network combines processing in the primal and dual ( spectral ) domains .
We propose a simple yet effective instance segmentation framework , termed CondInst ( conditional convolutions for instance segmentation ) . Top-performing instance segmentation methods such as Mask R-CNN rely on ROI operations ( typically ROIPool or ROIAlign ) to obtain the final instance masks . In contrast , we propose to solve instance segmentation from a new perspective . Instead of using instance-wise ROIs as inputs to a network of fixed weights , we employ dynamic instance-aware networks , conditioned on instances . CondInst enjoys two advantages : 1 ) Instance segmentation is solved by a fully convolutional network , eliminating the need for ROI cropping and feature alignment . 2 ) Due to the much improved capacity of dynamically-generated conditional convolutions , the mask head can be very compact ( e.g. , 3 conv .
Instance Segmentation Based Semantic Matting for Compositing Applications U4D: Unsupervised 4D Dynamic Scene Understanding Exploiting Semantic Information and Deep Matching for Optical Flow Irregular scene text detection via attention guided border labeling Lane Mark and Drivable Area Detection Using a Novel Instance Segmentation Scheme A shape context fully convolutional neural network for segmentation and classification of cervical nuclei in Pap smear images A Text-Context-Aware CNN Network for Multi-oriented and Multi-language Scene Text Detection A Robotic Semantic Grasping Method For Pick-and-place Tasks Recurrent Instance Segmentation Fast Single Shot Instance Segmentation Recurrent Pixel Embedding for Instance Grouping Semantic Instance Segmentation with a Discriminative Loss Function Holistic, Instance-Level Human Parsing Shape Robust Text Detection With Progressive Scale Expansion Network Endoscopy Artefact Detection and Segmentation using Deep Convolutional Neural Network Object Detection Method Based on Aerial Image Instance Segmentation in Poor Optical Conditions for Integration of Data into an Infocommunication System Hybrid Task Cascade for Instance Segmentation Early detection of retinopathy of prematurity stage using deep learning approach Object detection based on RGC mask R-CNN CornerNet: Detecting Objects as Paired Keypoints Finger-vein Recognition using Deep Fully Convolutional Neural Semantic Segmentation Networks: The Impact of Training Data A Hierarchical Loss for Semantic Segmentation Pixel-level Texture Segmentation Based AV1 Video Compression Semantic Segmentation Refinement by Monte Carlo Region Growing of High Confidence Detections Convolutional Methods for Music Analysis Referring Image Segmentation by Generative Adversarial Learning Weakly Supervised Semantic Image Segmentation with Self-correcting Networks An automatic and efficient pulmonary nodule detection system based on multi-model ensemble BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation Deep Learning Method for Mandibular Canal Segmentation in Dental Cone Beam Computed Tomography Volumes Semiautomatic first-arrival picking of microseismic events by using the pixel-wise convolutional image segmentation method OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation Conditional Convolutions for Instance Segmentation
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ECCV (6) SII ICDAR ACCV (4) 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ArXiv BMVC 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) EndoCV@ISBI Medical Imaging: Computer-Aided Diagnosis ECCV WIFS VISIGRAPP (4: VISAPP) ICASSP ICLR 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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