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Author: Dhruv Batra Publisher: Springer Science & Business Media ISBN: 1461419158 Category : Computers Languages : en Pages : 56
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: Dhruv Batra Publisher: Springer Science & Business Media ISBN: 1461419158 Category : Computers Languages : en Pages : 56
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: Avik Hati Publisher: Springer Nature ISBN: 9811985707 Category : Technology & Engineering Languages : en Pages : 231
Book Description
This book presents and analyzes methods to perform image co-segmentation. In this book, the authors describe efficient solutions to this problem ensuring robustness and accuracy, and provide theoretical analysis for the same. Six different methods for image co-segmentation are presented. These methods use concepts from statistical mode detection, subgraph matching, latent class graph, region growing, graph CNN, conditional encoder–decoder network, meta-learning, conditional variational encoder–decoder, and attention mechanisms. The authors have included several block diagrams and illustrative examples for the ease of readers. This book is a highly useful resource to researchers and academicians not only in the specific area of image co-segmentation but also in related areas of image processing, graph neural networks, statistical learning, and few-shot learning.
Author: Ferran Albert Cabezas Castellvi Publisher: ISBN: Category : Languages : en Pages :
Book Description
[ANGLÈS] For so many years the problem of object segmentation have been present in image processing field. Click'n'Cut, an already existing web tool for interactive object segmentation, helps us to obtain segmentations of the objects by clicking in green (foreground clicks) inside the object to segment, and in red(background clicks) outside the object to segment. However, the behavior of all human in front of this web tool is not equal. And so, it can be possible that these human interactions can not help us to obtain a good object segmentation, so that we would have a bad human interaction. The main aim of this project is to implement some techniques that allow us to treat with these bad human interactions in order to obtain the best object segmentation.
Author: Varun Gulshan Publisher: ISBN: Category : Languages : en Pages :
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: Yuri Boykov Publisher: Springer Science & Business Media ISBN: 3642230938 Category : Computers Languages : en Pages : 437
Book Description
This book constitutes the refereed proceedings of the 8th International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, EMMCVPR 2011, held in St. Petersburg, Russia in July , 2011. The book presents 30 revised full papers selected from a total of 52 submissions. The book is divided in sections on discrete and continuous optimization, segmentation, motion and video, learning and shape analysis.
Author: Kiriakos N. Kutulakos Publisher: Springer ISBN: 3642357407 Category : Computers Languages : en Pages : 494
Book Description
The two volumes LNCS 6553 and 6554 constitute the refereed post-proceedings of 7 workshops held in conjunction with the 11th European Conference on Computer Vision, held in Heraklion, Crete, Greece in September 2010. The 62 revised papers presented together with 2 invited talks were carefully reviewed and selected from numerous submissions. The second volume contains 34 revised papers selected from the following workshops: Workshop on color and Reflectance in Imaging and Computer Vision (CRICV 2010); Workshop on Media Retargeting (MRW 2010); Workshop on Reconstruction and Modeling of Large-Scale 3D Virtual Environments (RMLE 2010); and Workshop on Computer Vision on GPUs (CVGPU 2010).
Author: Andrew Fitzgibbon Publisher: Springer ISBN: 3642337651 Category : Computers Languages : en Pages : 905
Book Description
The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed proceedings of the 12th European Conference on Computer Vision, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers presented were carefully reviewed and selected from 1437 submissions. The papers are organized in topical sections on geometry, 2D and 3D shape, 3D reconstruction, visual recognition and classification, visual features and image matching, visual monitoring: action and activities, models, optimisation, learning, visual tracking and image registration, photometry: lighting and colour, and image segmentation.
Author: Dhruv Batra
Author: Dhruv Batra
Author: Avik Hati
Author: Ferran Albert Cabezas Castellvi
Author: Andrew Mui
Author: 張芸菱
Author: 李冠億
Author: Varun Gulshan
Author: Yuri Boykov
Author: Kiriakos N. Kutulakos
Author: Andrew Fitzgibbon