We present in this paper a one-shot solution to characterize polyps in colonoscopy images . Our method uses a fully convolutional neural network model for semantic segmentation . Next , we apply transfer learning to provide detection and localization . Dense prediction models learned using eConv and ePool can transfer the baseline CNN 's parameters as a starting point , and can inverse transfer the learned parameters in a dense model back to the original one , which has both fast testing speed and high accuracy . The proposed eConv and ePool layers have achieved higher accuracy than baseline CNN in various tasks , including semantic segmentation , object localization , image categorization and apparent age estimation , not only in those tasks requiring dense pixel labeling . Abstract This paper formulates cloud and cloud shadow detection as a semantic segmentation problem and proposes a deep convolutional neural network ( CNN ) based method to detect them in Landsat imagery . Different from traditional machine learning methods , deep CNN-based methods convolve the entire input image to extract multi-level spatial and spectral features , and then deconvolve these features to produce the detailed segmentation . Phase unwrapping is an important problem in several applications that attempts to restore original phase from wrapped phase . In this paper , we propose a novel phase unwrapping model based on the deep convolutional neural network by formulating the phase unwrapping as a semantic segmentation problem . The proposed architecture consists of a convolutional encoder network and corresponding decoder network followed by a pixel-wise classification layer . However , the features learned by these methods are usually not discriminative to differentiate 1 ) the patches which share the same semantic label but different appearances , named intra - class inconsistency as shown in the first row of Figure [ reference ] ; 2 ) the two adjacent patches which have different semantic labels but with similar appearances , named inter - class indistinction as shown in the second row of Figure [ reference ] . To address these two challenges , we rethink the semantic segmentation task from a more macroscopic point of view . In this way , we regard the semantic segmentation as a task to assign a consistent semantic label to a category of things , rather than to each single pixel . document : Learning to Adapt Structured Output Space for Semantic Segmentation Convolutional neural network - based approaches for semantic segmentation rely on supervision with pixel - level ground truth , but may not generalize well to unseen image domains . document : ParseNet : Looking Wider to See Better We present a technique for adding global context to fully convolutional networks for semantic segmentation . The approach is simple , using the average feature for a layer to augment the features at each location . section : Introduction Semantic image segmentation is a fundamental task in computer vision . It predicts dense labels for all pixels in the image , and is regarded as a very important task that can help deep understanding of scene , objects , and human . Most networks operating on more practically interesting image sizes tend to have the order of one , to two , hundred layers , the 200 - layer ResNet and 96 - layer Inception - ResNet . The progression to deeper networks continues , however , with Zhao et al . having trained a 269 - layer network for semantic image segmentation . These networks were trained using the ImageNet classification dataset , where the images are of much higher resolution . This new methodology for 3D information extraction is based on a processing chain structured of the following modules : 1 ) 3D filtering : the purpose is to preserve the contours of the structures and to smooth the homogeneous areas ; several filters were tested and finally an anisotropic diffusion filter was used . 2 ) 3D Segmentation . This module compares three different methods : Region growing Algorithm , Cubic spline hand assisted , and Level Set Method . Large values of K created artifacts on segmented images , and relatively much larger value of c than the value of λ influenced the balance between the boundary term and the data term in the energy function , leading to unacceptable segmentation results . For a range of parameter settings , we performed 3D image segmentation , and in each case compared the results with the expert-delineated lung boundaries . We used simple 6-neighborhood systems for n-link in 3D . A novel approach to 3D part segmentation is presented . Beginning with range data of a 3D object , we simulate the charge density distribution over an object 's surface which has been tessellated by a triangular mesh . The three networks are jointly trained ( i.e. cross-modality ) by verifying whether two sampled data of different modalities belong to the same object , meanwhile , the 2D convolutional neural network is additionally optimized through minimizing intra-object distance while maximizing inter-object distance of rendered images in different views ( i.e. cross-view ) . The effectiveness of the learned 2D and 3D features is evaluated by transferring them on five different tasks including multi-view 2D shape recognition , 3D shape recognition , multi-view 2D shape retrieval , 3D shape retrieval , and 3D part-segmentation . Extensive evaluations on all the five different tasks across different datasets demonstrate strong generalization and effectiveness of the learned 2D and 3D features by the proposed self-supervised method . However , for fine-grained tasks like part segmentation , volumetric data has not been widely adopted compared to other representations . Aiming at delivering an effective volumetric method for 3D shape part segmentation , this paper proposes a novel volumetric convolutional neural network . Our method can extract discriminative features encoding detailed information from voxelized 3D data under limited resolution . Towards these goals , we introduce a spectral parametrization of dilated convolutional kernels and a spectral transformer network . Experimentally we tested SyncSpecCNN on various tasks , including 3D shape part segmentation and keypoint prediction . State-of-the-art performance has been achieved on all benchmark datasets . The principle , realization and clinical application of 3D ultrasound imaging system are introduced in this paper . A Freehand 3D ultrasound imaging system is discussed in detail , including the techniques of the speckle noise suppression,3D reconstruction from spatial irregularly distributed 2D images , 3D automatic segmentation and 3D visualization . The results in vivo and vitro are provided . In this paper , an alternative approach using a hybrid of the graph cut technique and the geodesic active contour shape prior level set method is presented . Despite being relatively accurate , level set methods are not commonly used for 3D segmentation purposes because they are computationally expensive . This paper shows that , with 3D graph cut results as initialization for level sets , the processing time for such level set based methods can be substantially reduced while preserving the accuracy of the segmentation . Additive manufacturing was not chosen as easiest option , it lead to a collection of research opportunities . The process includes scanning , 3D segmenting , printing strategy and printing , assembling and testing . The complete process was done in less then 2,5 months . In this paper we focus on the problem of 3D segmention in volumetric computed tomography imagery to identify organs in the abdomen . We propose and evaluate different models and modeling strategies for 3D segmentation based on traditionalMarkov Random Fields ( MRFs ) and their discriminative counterparts known as Conditional Random Fields ( CRFs ) . Unlike many of the existing approaches , the inference-learning blending allows us to learn efficiently high-order graphical models , over regions of any size , and very large number of parameters . We demonstrate the effectiveness of our approach , while presenting state-of-the-art results in stereo estimation , semantic segmentation , shape reconstruction , and indoor scene understanding . Indoor scene analysis is important for some applications , such as augmented reality . Specifically , we confine the indoor scene analysis problem to two aspects : reconstructing the geometry and understanding the observed element . The invention discloses an annotation method based on a monolithic InSAR orthophoto . The method comprises the following steps : 1 , acquiring the monolithic InSAR orthophoto ; 2 , annotating based on the monolithic InSAR orthophoto according to the following annotation processes : 201 , establishing a ground object characteristic database of the InSAR orthophoto , 202 , carrying out indoor interpretation : determining and positioning ground objects according to the ground object characteristic database of the InSAR orthophoto , 203 , carrying out outdoor check , 204 , carrying out annotation and supplement measure on a shadow region and a layover region , 205 , carrying out annotation and supplement measure on new added ground objects and concealed ground objects , and 206 , carrying out indoor data processing ; and 3 , carrying out junction processing . The annotation method which has the advantages of reasonable design , simple step , convenient realization , good usage effect , and high annotation precision allows practical problems that present fully digital aerophotogrammetry systems and annotation methods can not satisfy annotation needs of the InSAR orthophoto , the annotation effect of the monolithic InSAR orthophoto is bad , technical advantages of an InSAR can not fully performed and the like to be solved . New advances in remote sensing technology and their impacts on medicinal plant resources survey was considered in those schemes . Remote sensing monitoring solutions for 8 typical Xinjiang Chinese medicinal plants resources were determined after presenting work goals , work principles , and work roadmap , which contains three components:1 ) applicability oriented image data differentiate customization ; 2 ) working mechanism of indoor interpretation and field work combined closely ; 3 ) computer automatic interpretation priority . In the rst part , we present the system architecture and the models of objects , scene and sensors and the diierent interpretation and detection strategies . After , we present current experiments on indoor scene interpretation and an interpretation running example using constrained low-level feature extraction mechanism to improve low-level processing results . The architecture of MESSIE , the objects model and the different interpretation and detection strategies are included . Current experiments on indoor scene interpretation and an interpretation running example using constrained low-level feature extraction mechanism to improve low-level processing results are described.<<ETX > > With the availability of low-cost depth-visual sensing devices , such as Microsoft Kinect , we are experiencing a growing interest in indoor environment understanding , at the core of which is semantic segmentation in RGB-D image . The latest research shows that the convolutional neural network ( CNN ) still dominates the image semantic segmentation field . Especially , indoor scene recognition is an important part of computer vision and feature recognition as the first step of automatic uninterrupted robotic movement or computer vision applications like automatic interior designing algorithms . However , the indoor scene detection is not straightforward because it has lots of variations of image appearance , such as light effect , occlusion , image orientation , illuminating condition and object variety . Many novel methods have been proposed to resolve each variation listed above . Considering indoor scenes are designed for human use and are utilised everyday , attaining this understanding is crucial for applications such as robotic mapping and navigation , smart home and security systems , and home healthcare and assisted living . However , although we as humans utilise such spaces in our day-to-day lives , analysis of human activity is not commonly applied towards enhancing indoor scene-level understanding . As such , the work presented in this thesis investigates the benefits of including human activity information in indoor scene understanding challenges , aiming to demonstrate its potential contributions , applications , and versatility . The first contribution of this thesis utilises human activity to reveal scene regions occluded behind objects and clutter . Depth images have always been playing critical roles for indoor scene understanding problems , and are particularly important for tasks in which 3D inferences are involved . However , since depth images are not universally available , abandoning them from the testing stage can significantly improve the generality of a method . To realize a multi-resolution architecture , we borrow well-established mesh simplification methods from the geometry processing domain and adapt them to define mesh-preserving pooling and unpooling operations . We experimentally show that combining both types of convolutions in our architecture leads to significant performance gains for 3D semantic segmentation , and we report competitive results on three scene segmentation benchmarks . Models and code will be made publicly available . We introduce 3D-SIS , a novel neural network architecture for 3D semantic instance segmentation in commodity RGB-D scans . The core idea of our method to jointly learn from both geometric and color signal , thus enabling accurate instance predictions . Towards these goals , we introduce a spectral parameterization of dilated convolutional kernels and a spectral transformer network . Experimentally we tested our SyncSpecCNN on various tasks , including 3D shape part segmentation and 3D keypoint prediction . State - of - the - art performance has been achieved on all benchmark datasets .
We present in this paper a one-shot solution to characterize polyps in colonoscopy images . Our method uses a fully convolutional neural network model for semantic segmentation . Next , we apply transfer learning to provide detection and localization .
Dense prediction models learned using eConv and ePool can transfer the baseline CNN 's parameters as a starting point , and can inverse transfer the learned parameters in a dense model back to the original one , which has both fast testing speed and high accuracy . The proposed eConv and ePool layers have achieved higher accuracy than baseline CNN in various tasks , including semantic segmentation , object localization , image categorization and apparent age estimation , not only in those tasks requiring dense pixel labeling .
Abstract This paper formulates cloud and cloud shadow detection as a semantic segmentation problem and proposes a deep convolutional neural network ( CNN ) based method to detect them in Landsat imagery . Different from traditional machine learning methods , deep CNN-based methods convolve the entire input image to extract multi-level spatial and spectral features , and then deconvolve these features to produce the detailed segmentation .
Phase unwrapping is an important problem in several applications that attempts to restore original phase from wrapped phase . In this paper , we propose a novel phase unwrapping model based on the deep convolutional neural network by formulating the phase unwrapping as a semantic segmentation problem . The proposed architecture consists of a convolutional encoder network and corresponding decoder network followed by a pixel-wise classification layer .
However , the features learned by these methods are usually not discriminative to differentiate 1 ) the patches which share the same semantic label but different appearances , named intra - class inconsistency as shown in the first row of Figure [ reference ] ; 2 ) the two adjacent patches which have different semantic labels but with similar appearances , named inter - class indistinction as shown in the second row of Figure [ reference ] . To address these two challenges , we rethink the semantic segmentation task from a more macroscopic point of view . In this way , we regard the semantic segmentation as a task to assign a consistent semantic label to a category of things , rather than to each single pixel .
document : Learning to Adapt Structured Output Space for Semantic Segmentation Convolutional neural network - based approaches for semantic segmentation rely on supervision with pixel - level ground truth , but may not generalize well to unseen image domains .
document : ParseNet : Looking Wider to See Better We present a technique for adding global context to fully convolutional networks for semantic segmentation . The approach is simple , using the average feature for a layer to augment the features at each location .
section : Introduction Semantic image segmentation is a fundamental task in computer vision . It predicts dense labels for all pixels in the image , and is regarded as a very important task that can help deep understanding of scene , objects , and human .
Most networks operating on more practically interesting image sizes tend to have the order of one , to two , hundred layers , the 200 - layer ResNet and 96 - layer Inception - ResNet . The progression to deeper networks continues , however , with Zhao et al . having trained a 269 - layer network for semantic image segmentation . These networks were trained using the ImageNet classification dataset , where the images are of much higher resolution .
This new methodology for 3D information extraction is based on a processing chain structured of the following modules : 1 ) 3D filtering : the purpose is to preserve the contours of the structures and to smooth the homogeneous areas ; several filters were tested and finally an anisotropic diffusion filter was used . 2 ) 3D Segmentation . This module compares three different methods : Region growing Algorithm , Cubic spline hand assisted , and Level Set Method .
Large values of K created artifacts on segmented images , and relatively much larger value of c than the value of λ influenced the balance between the boundary term and the data term in the energy function , leading to unacceptable segmentation results . For a range of parameter settings , we performed 3D image segmentation , and in each case compared the results with the expert-delineated lung boundaries . We used simple 6-neighborhood systems for n-link in 3D .
A novel approach to 3D part segmentation is presented . Beginning with range data of a 3D object , we simulate the charge density distribution over an object 's surface which has been tessellated by a triangular mesh .
The three networks are jointly trained ( i.e. cross-modality ) by verifying whether two sampled data of different modalities belong to the same object , meanwhile , the 2D convolutional neural network is additionally optimized through minimizing intra-object distance while maximizing inter-object distance of rendered images in different views ( i.e. cross-view ) . The effectiveness of the learned 2D and 3D features is evaluated by transferring them on five different tasks including multi-view 2D shape recognition , 3D shape recognition , multi-view 2D shape retrieval , 3D shape retrieval , and 3D part-segmentation . Extensive evaluations on all the five different tasks across different datasets demonstrate strong generalization and effectiveness of the learned 2D and 3D features by the proposed self-supervised method .
However , for fine-grained tasks like part segmentation , volumetric data has not been widely adopted compared to other representations . Aiming at delivering an effective volumetric method for 3D shape part segmentation , this paper proposes a novel volumetric convolutional neural network . Our method can extract discriminative features encoding detailed information from voxelized 3D data under limited resolution .
Towards these goals , we introduce a spectral parametrization of dilated convolutional kernels and a spectral transformer network . Experimentally we tested SyncSpecCNN on various tasks , including 3D shape part segmentation and keypoint prediction . State-of-the-art performance has been achieved on all benchmark datasets .
The principle , realization and clinical application of 3D ultrasound imaging system are introduced in this paper . A Freehand 3D ultrasound imaging system is discussed in detail , including the techniques of the speckle noise suppression,3D reconstruction from spatial irregularly distributed 2D images , 3D automatic segmentation and 3D visualization . The results in vivo and vitro are provided .
In this paper , an alternative approach using a hybrid of the graph cut technique and the geodesic active contour shape prior level set method is presented . Despite being relatively accurate , level set methods are not commonly used for 3D segmentation purposes because they are computationally expensive . This paper shows that , with 3D graph cut results as initialization for level sets , the processing time for such level set based methods can be substantially reduced while preserving the accuracy of the segmentation .
Additive manufacturing was not chosen as easiest option , it lead to a collection of research opportunities . The process includes scanning , 3D segmenting , printing strategy and printing , assembling and testing . The complete process was done in less then 2,5 months .
In this paper we focus on the problem of 3D segmention in volumetric computed tomography imagery to identify organs in the abdomen . We propose and evaluate different models and modeling strategies for 3D segmentation based on traditionalMarkov Random Fields ( MRFs ) and their discriminative counterparts known as Conditional Random Fields ( CRFs ) .
Unlike many of the existing approaches , the inference-learning blending allows us to learn efficiently high-order graphical models , over regions of any size , and very large number of parameters . We demonstrate the effectiveness of our approach , while presenting state-of-the-art results in stereo estimation , semantic segmentation , shape reconstruction , and indoor scene understanding .
Indoor scene analysis is important for some applications , such as augmented reality . Specifically , we confine the indoor scene analysis problem to two aspects : reconstructing the geometry and understanding the observed element .
The invention discloses an annotation method based on a monolithic InSAR orthophoto . The method comprises the following steps : 1 , acquiring the monolithic InSAR orthophoto ; 2 , annotating based on the monolithic InSAR orthophoto according to the following annotation processes : 201 , establishing a ground object characteristic database of the InSAR orthophoto , 202 , carrying out indoor interpretation : determining and positioning ground objects according to the ground object characteristic database of the InSAR orthophoto , 203 , carrying out outdoor check , 204 , carrying out annotation and supplement measure on a shadow region and a layover region , 205 , carrying out annotation and supplement measure on new added ground objects and concealed ground objects , and 206 , carrying out indoor data processing ; and 3 , carrying out junction processing . The annotation method which has the advantages of reasonable design , simple step , convenient realization , good usage effect , and high annotation precision allows practical problems that present fully digital aerophotogrammetry systems and annotation methods can not satisfy annotation needs of the InSAR orthophoto , the annotation effect of the monolithic InSAR orthophoto is bad , technical advantages of an InSAR can not fully performed and the like to be solved .
New advances in remote sensing technology and their impacts on medicinal plant resources survey was considered in those schemes . Remote sensing monitoring solutions for 8 typical Xinjiang Chinese medicinal plants resources were determined after presenting work goals , work principles , and work roadmap , which contains three components:1 ) applicability oriented image data differentiate customization ; 2 ) working mechanism of indoor interpretation and field work combined closely ; 3 ) computer automatic interpretation priority .
In the rst part , we present the system architecture and the models of objects , scene and sensors and the diierent interpretation and detection strategies . After , we present current experiments on indoor scene interpretation and an interpretation running example using constrained low-level feature extraction mechanism to improve low-level processing results .
The architecture of MESSIE , the objects model and the different interpretation and detection strategies are included . Current experiments on indoor scene interpretation and an interpretation running example using constrained low-level feature extraction mechanism to improve low-level processing results are described.<<ETX > >
With the availability of low-cost depth-visual sensing devices , such as Microsoft Kinect , we are experiencing a growing interest in indoor environment understanding , at the core of which is semantic segmentation in RGB-D image . The latest research shows that the convolutional neural network ( CNN ) still dominates the image semantic segmentation field .
Especially , indoor scene recognition is an important part of computer vision and feature recognition as the first step of automatic uninterrupted robotic movement or computer vision applications like automatic interior designing algorithms . However , the indoor scene detection is not straightforward because it has lots of variations of image appearance , such as light effect , occlusion , image orientation , illuminating condition and object variety . Many novel methods have been proposed to resolve each variation listed above .
Considering indoor scenes are designed for human use and are utilised everyday , attaining this understanding is crucial for applications such as robotic mapping and navigation , smart home and security systems , and home healthcare and assisted living . However , although we as humans utilise such spaces in our day-to-day lives , analysis of human activity is not commonly applied towards enhancing indoor scene-level understanding . As such , the work presented in this thesis investigates the benefits of including human activity information in indoor scene understanding challenges , aiming to demonstrate its potential contributions , applications , and versatility . The first contribution of this thesis utilises human activity to reveal scene regions occluded behind objects and clutter .
Depth images have always been playing critical roles for indoor scene understanding problems , and are particularly important for tasks in which 3D inferences are involved . However , since depth images are not universally available , abandoning them from the testing stage can significantly improve the generality of a method .
To realize a multi-resolution architecture , we borrow well-established mesh simplification methods from the geometry processing domain and adapt them to define mesh-preserving pooling and unpooling operations . We experimentally show that combining both types of convolutions in our architecture leads to significant performance gains for 3D semantic segmentation , and we report competitive results on three scene segmentation benchmarks . Models and code will be made publicly available .
We introduce 3D-SIS , a novel neural network architecture for 3D semantic instance segmentation in commodity RGB-D scans . The core idea of our method to jointly learn from both geometric and color signal , thus enabling accurate instance predictions .
Towards these goals , we introduce a spectral parameterization of dilated convolutional kernels and a spectral transformer network . Experimentally we tested our SyncSpecCNN on various tasks , including 3D shape part segmentation and 3D keypoint prediction . State - of - the - art performance has been achieved on all benchmark datasets .
Towards a Single Solution for Polyp Detection, Localization and Segmentation in Colonoscopy Images Dense CNN Learning with Equivalent Mappings Cloud and cloud shadow detection in Landsat imagery based on deep convolutional neural networks A Deep Learning-based Model for Phase Unwrapping Learning a Discriminative Feature Network for Semantic Segmentation Learning to Adapt Structured Output Space for Semantic Segmentation ParseNet: Looking Wider to See Better ICNet for Real-Time Semantic Segmentation on High-Resolution Images Wider or Deeper: Revisiting the ResNet Model for Visual Recognition 3D Segmentation with an Application of Level Set-Method using MRI Volumes for Image Guided Surgery 3D segmentation of lung CT data with graph-cuts: analysis of parameter sensitivities 3D part segmentation using simulated electrical charge distributions Self-supervised Feature Learning by Cross-modality and Cross-view Correspondences VoxSegNet: Volumetric CNNs for Semantic Part Segmentation of 3D Shapes SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation Study on Three Dimensional Ultrasound Imaging Automatic 3D Prostate MR Image Segmentation Using Graph Cuts and Level Sets with Shape Prior Suited for performance: fast full-scale replication of athlete with FDM 3D Segmentation in CT Imagery with Conditional Random Fields and Histograms of Oriented Gradients Blending Learning and Inference in Structured Prediction A Real-Time Indoor Scene Analysis Method Based on RGBD Stream Annotation method based on monolithic InSAR (interferometric synthetic aperture radar) orthophoto Remote Sensing Survey Scheme of Typical Chinese Medicine Resources in Xinjiang Multispecialist System for 3D Scene Analysis Scene analysis system Indoor scene understanding via RGB-D image segmentation employing depth-based CNN and CRFs Adaptive Indoor Scene Classification with Multi-SVM Classification to solve Multi-Class Problem Indoor scene understanding from visual analysis of human activity Learning to Synthesize 3D Indoor Scenes from Monocular Images DualConvMesh-Net: Joint Geodesic and Euclidean Convolutions on 3D Meshes 3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation
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VISIGRAPP (4: VISAPP) ICVGIP 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ArXiv ECCV Pattern Recognit. Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging ICPR PCM SCF MLMI ECAI ICIP (3) ACM Multimedia CVPR 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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