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Author: Jia He
Publisher: Springer Science & Business Media
ISBN: 9814451606
Category : Technology & Engineering
Languages : en
Pages : 82
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Book Description
This book focuses on interactive segmentation techniques, which have been extensively studied in recent decades. Interactive segmentation emphasizes clear extraction of objects of interest, whose locations are roughly indicated by human interactions based on high level perception. This book will first introduce classic graph-cut segmentation algorithms and then discuss state-of-the-art techniques, including graph matching methods, region merging and label propagation, clustering methods, and segmentation methods based on edge detection. A comparative analysis of these methods will be provided with quantitative and qualitative performance evaluation, which will be illustrated using natural and synthetic images. Also, extensive statistical performance comparisons will be made. Pros and cons of these interactive segmentation methods will be pointed out, and their applications will be discussed. There have been only a few surveys on interactive segmentation techniques, and those surveys do not cover recent state-of-the art techniques. By providing comprehensive up-to-date survey on the fast developing topic and the performance evaluation, this book can help readers learn interactive segmentation techniques quickly and thoroughly.
Author: Jia He
Publisher: Springer Science & Business Media
ISBN: 9814451606
Category : Technology & Engineering
Languages : en
Pages : 82
Get Book Here
Book Description
This book focuses on interactive segmentation techniques, which have been extensively studied in recent decades. Interactive segmentation emphasizes clear extraction of objects of interest, whose locations are roughly indicated by human interactions based on high level perception. This book will first introduce classic graph-cut segmentation algorithms and then discuss state-of-the-art techniques, including graph matching methods, region merging and label propagation, clustering methods, and segmentation methods based on edge detection. A comparative analysis of these methods will be provided with quantitative and qualitative performance evaluation, which will be illustrated using natural and synthetic images. Also, extensive statistical performance comparisons will be made. Pros and cons of these interactive segmentation methods will be pointed out, and their applications will be discussed. There have been only a few surveys on interactive segmentation techniques, and those surveys do not cover recent state-of-the art techniques. By providing comprehensive up-to-date survey on the fast developing topic and the performance evaluation, this book can help readers learn interactive segmentation techniques quickly and thoroughly.
Author: Dhruv Batra
Publisher: Springer Science & Business Media
ISBN: 1461419158
Category : Computers
Languages : en
Pages : 56
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Book Description
The authors survey a recent technique in computer vision called Interactive Co-segmentation, which is the task of simultaneously extracting common foreground objects from multiple related images. They survey several of the algorithms, present underlying common ideas, and give an overview of applications of object co-segmentation.
Author: Sachin Meena
Publisher:
ISBN:
Category :
Languages : en
Pages : 121
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Book Description
Over the past two decades interactive methods for clinical and biomedical image segmentation have been investigated since the pioneering work of Live-Wire, Live-Lane [17] and Intelligent Scissors [1]. Fully automatic image segmentation is essential for quantitative analysis but remains an unsolved problem, so user driven interactive methods continue to be a powerful alternative when extremely precise segmentation is required. However, manual methods although routinely used are tedious, time-consuming, expensive, inconsistent between experts and error prone. In semi-supervised interactive segmentation the goal is for the user to provide a small amount of partial information or hints for an automatic algorithm to use in order to produce accurate boundaries suitable for the user. The coupled interaction between the user provided input and the semi-supervised segmentation algorithm should be minimal and robust. Commonly used drawing tools for interactive segmentation interfaces include active contour or boundary drawing, scribbles to identify foreground and background regions, and rectangles to outline the object of interest. But interactive segmentation using a sparse set of seed-points has not been widely investigated. In this work we investigate the use of sparse seed point-based for interactive image segmentation task. We have also proposed a new regression based framework, making use of Elastic Body Splines (EBS) to perform interactive image segmentation. Elastic Body Splines belonging to the family of 3D splines were recently introduced to capture tissue deformations within a physical model-based approach for non-rigid biomedical image registration [18]. ElasticMap model the displacement of points in a 3D homogeneous isotropic elastic body subject to forces. We propose a novel extension of using elastic body splines for interactive learning-based figure-ground segmentation. The task of interactive image segmentation, with user provided foreground-background labeled seeds or samples, is formulated as learning a spatially dependent interpolating pixel classification function that is then used to assign labels for all unlabeled pixels in the image. The spline function we chose to model the semisupervised pixel classifier is the ElasticMap which can use sparse point-scribble input from the user and has a closed form solution. Experimental results demonstrate the applicability of the EBS approach for image segmentation. The ElasticMap method for interactive foreground segmentation uses on an average just four to six labeled pixels as input from the user. Using such sparsely labeled information the proposed EBS method produces very accurate results with an average accuracy consistently exceeding 95 percent on three different benchmark datasets and outperforms eleven other popular interactive image segmentation methods.
Author: Varun Gulshan
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
This thesis investigates two well defined problems in image segmentation, viz. in- teractive and semantic image segmentation. Interactive segmentation involves power assisting a user in cutting out objects from an image, whereas semantic segmenta- tion involves partitioning pixels in an image into object categories. Vve investigate various models and energy formulations for both these problems in this thesis. In order to improve the performance of interactive systems, low level texture features are introduced as a replacement for the more commonly used RGB fea- tures. To quantify the improvement obtained by using these texture features, two annotated datasets of images are introduced (one consisting of natural images, and the other consisting of camouflaged objects). A significant improvement in perfor- mance is observed when using texture features for the case of monochrome images and images containing camouflaged objects. We also explore adding mid-level cues such as shape constraints into interactive segmentation by introducing the idea of geodesic star convexity, which extends the existing notion of a star convexity prior in two important ways: (i) It allows for multiple star centres as opposed to single stars in the original prior and (ii) It generalises the shape constraint by allowing for Geodesic paths as opposed to Euclidean rays. Global minima of our energy func- tion can be obtained subject to these new constraints. We also introduce Geodesic Forests, which exploit the structure of shortest paths in implementing the extended constraints. These extensions to star convexity allow us to use such constraints in a practical segmentation system. This system is evaluated by means of a "robot user" to measure the amount of interaction required in a precise way, and it is shown that having shape constraints reduces user effort significantly compared to existing interactive systems. We also introduce a new and harder dataset which augments the existing GrabCut dataset with more realistic images and ground truth taken from the PASCAL VOC segmentation challenge. In the latter part of the thesis, we bring in object category level information in order to make the interactive segmentation tasks easier, and move towards fully automated semantic segmentation. An algorithm to automatically segment humans from cluttered images given their bounding boxes is presented. A top down seg- mentation of the human is obtained using classifiers trained to predict segmentation masks from local HOG descriptors. These masks are then combined with bottom up image information in a local GrabCut like procedure. This algorithm is later completely automated to segment humans without requiring a bounding box, and is quantitatively compared with other semantic segmentation methods. We also introduce a novel way to acquire large quantities of segmented training data rel- atively effortlessly using the Kinect. In the final part of this work, we explore various semantic segmentation methods based on learning using bottom up super- pixelisations. Different methods of combining multiple super-pixelisations are dis- cussed and quantitatively evaluated on two segmentation datasets. We observe that simple combinations of independently trained classifiers on single super-pixelisations perform almost as good as complex methods based on jointly learning across multiple super-pixelisations. We also explore CRF based formulations for semantic segmen- tation, and introduce novel visual words based object boundary description in the energy formulation. The object appearance and boundary parameters are trained jointly using structured output learning methods, and the benefit of adding pairwise terms is quantified on two different datasets.
Author: Mustafa Gokhan Uzunbas
Publisher:
ISBN:
Category : Electron microscopy
Languages : en
Pages : 94
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Book Description
Image segmentation i.e. dividing an image into regions and categories is a classic yet still challenging problem. The key to success is to use/develop the right method for the right appli- cation. In this dissertation, we aim to develop automatic and interactive segmentation methods for different types of tissues that are acquired at different scales and resolutions from different medical imaging modalities such as Magnetic Resonance (MR), Computed Tomography (CT) and Electron Microscopy (EM) imaging. First, we developed an automated segmentation method for segmenting multiple organs simultaneously from MR and CT images. We propose a hybrid method that takes advantage of two well known energy-minimization-based approaches combined in a unified framework. We validate this proposed method on cardiac four-chamber segmentation from CT and knee joint bones segmentation from MR images. We compare our method with other existing techniques and show certain improvements and advantages. Second, we developed a graph partitioning algorithm for characterizing neuronal tissue structurally and contextually from EM images. We propose a multistage decision mechanism that utilizes differential geometric properties of objects in a cellular processing context. Our results indicate that this proposed approach can successfully partition images into structured segments with minimal expert supervision and can potentially form a basis for a larger scale volumetric data interpretation. We compare our method with other proposed methods in a workshop challenge and show promising results. Third, we developed an efficient learning-based method for segmentation of neuron struc- tures from 2D and 3D EM images. We propose a graphical-model-based framework to do inference on hierarchical merge-tree of image regions. In particular, we extract the hierarchy of regions in the low level, design 2D and 3D discriminative features to extract higher level information and utilize a Conditional Random Field based parameter learning on top of it. The effectiveness of the proposed method in 2D is demonstrated by comparing our method with other methods in a workshop challenge. Our method outperforms all participant methods ex- cept one. In 3D, we compare our method to existing methods and show that the accuracy of our results are comparable to state-of-the-art while being much more efficient. Finally, we extended our inference algorithm to a proofreading framework for manual cor- rections of automatic segmentation results. We propose a very efficient and easy-to-use inter- face for high resolution 3D EM images. In particular, we utilize the probabilistic confidence level of the graphical model to guide the user during interaction. We validate the effective- ness of this framework by robot simulations and demonstrate certain advantages compared to baseline methods.
Author: Meng Tang
Publisher:
ISBN:
Category : Image processing
Languages : en
Pages : 169
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Book Description
Image segmentation, i.e., assigning each pixel a discrete label, is an essential task in computer vision with lots of applications. Major techniques for segmentation include for example Markov Random Field (MRF), Kernel Clustering (KC), and nowadays popular Convolutional Neural Networks (CNN). In this work, we focus on optimization for image segmentation. Techniques like MRF, KC, and CNN optimize MRF energies, KC criteria, or CNN losses respectively, and their corresponding optimization is very different. We are interested in the synergy and the complementary benefits of MRF, KC, and CNN for interactive segmentation and semantic segmentation. Our first contribution is pseudo-bound optimization for binary MRF energies that are high-order or non-submodular. Secondly, we propose Kernel Cut, a novel formulation for segmentation, which combines MRF regularization with Kernel Clustering. We show why to combine KC with MRF and how to optimize the joint objective. In the third part, we discuss how deep CNN segmentation can benefit from non-deep (i.e., shallow) methods like MRF and KC. In particular, we propose regularized losses for weakly-supervised CNN segmentation, in which we can integrate MRF energy or KC criteria as part of the losses. Minimization of regularized losses is a principled approach to semi-supervised learning, in general. Our regularized loss method is very simple and allows different kinds of regularization losses for CNN segmentation. We also study the optimization of regularized losses beyond gradient descent. Our regularized losses approach achieves state-of-the-art accuracy in semantic segmentation with near full supervision quality.
Author: Yusuf Oguzhan Artan
Publisher:
ISBN:
Category :
Languages : en
Pages : 248
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Book Description
Author: Houssem-Eddine Gueziri
Publisher:
ISBN:
Category :
Languages : en
Pages : 125
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Book Description
Author: Lilly Spirkovska
Publisher:
ISBN:
Category :
Languages : en
Pages : 18
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Book Description
Author: Diana L. Delibaltov
Publisher:
ISBN: 9781321567656
Category :
Languages : en
Pages : 189
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Book Description
First, we briefly explore the approach of correcting an over-segmented volume by using a trained model. The proposed method automatically initializes with seeds according to the local density of cells in the volume. Next, this algorithm merges pairs of super-pixels based on a learned model using a feature representation which effectively discriminates between spurious and correct boundaries.
