Author: Joel Gibson
Publisher:
ISBN:
Category : Approximation theory
Languages : en
Pages : 76
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Book Description
In this dissertation we apply sparse constraints to improve optical flow and trajectories. We apply sparsity in two ways. First, with 2-frame optical flow, we enforce a sparse representation of flow patches using a learned overcomplete dictionary. Second, we apply a low rank constraint to trajectories via robust coupling. We begin with a review of optical flow fundamentals. We discuss the commonly used flow estimation strategies and the advantages and shortcomings of each. We introduce the concepts associated with sparsity including dictionaries and low rank matrices. Next we study the related fields of multi-frame optical flow and trajectories. Since the beginning of modern optical flow estimation methods, multiple frames have been used in an effort to improve the computation of motion. We look at why most of these efforts have failed. More recently, researchers have stitched together sequences of optical flow fields to create trajectories. These trajectories are temporally coherent, a necessary property for virtually every real-world application of optical flow. New methods compute these trajectories directly using variational methods and low-rank constraints. We also identify the need for appropriate data sets and evaluation methods for this nascent field. Optical flow is an ill-posed underconstrained inverse problem. Many recent approaches use total variation (TV) to constrain the flow solution to satisfy color constancy. In our first results presented, we find that learning a 2D overcomplete dictionary from the total variation result and then enforcing a sparse constraint on the flow improves the result. A new technique using partially overlapping patches accelerates the calculation. This approach is implemented in a coarse-to-fine strategy. Our results show that combining total variation and a sparse constraint from a learned dictionary is more effective than total variation alone. In our second results we compute optical flow and trajectories from an image sequence. Sparsity in trajectories is measured by matrix rank. We introduce a low rank constraint of linear complexity using random subsampling of the data. We demonstrate that, by using a robust coupling with the low rank constraint, our approach outperforms baseline methods on general image sequences.
Author: Joel Gibson
Publisher: Springer
ISBN: 3319449419
Category : Computers
Languages : en
Pages : 57
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Book Description
This brief focuses on two main problems in the domain of optical flow and trajectory estimation: (i) The problem of finding convex optimization methods to apply sparsity to optical flow; and (ii) The problem of how to extend sparsity to improve trajectories in a computationally tractable way. Beginning with a review of optical flow fundamentals, it discusses the commonly used flow estimation strategies and the advantages or shortcomings of each. The brief also introduces the concepts associated with sparsity including dictionaries and low rank matrices. Next, it provides context for optical flow and trajectory methods including algorithms, data sets, and performance measurement. The second half of the brief covers sparse regularization of total variation optical flow and robust low rank trajectories. The authors describe a new approach that uses partially-overlapping patches to accelerate the calculation and is implemented in a coarse-to-fine strategy. Experimental results show that combining total variation and a sparse constraint from a learned dictionary is more effective than employing total variation alone. The brief is targeted at researchers and practitioners in the fields of engineering and computer science. It caters particularly to new researchers looking for cutting edge topics in optical flow as well as veterans of optical flow wishing to learn of the latest advances in multi-frame methods. /div
Author: Troy McDaniel
Publisher: Springer Nature
ISBN: 3030544079
Category : Computers
Languages : en
Pages : 532
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Book Description
This book constitutes the proceedings of the Second International Conference on Smart Multimedia, ICSM 2019, which was held in San Diego, CA, USA, in December 2019. The 45 papers presented were selected from about 100 submissions and are grouped in sections on 3D mesh and depth image processing; image understanding; miscellaneous; smart multimedia for citizen-centered smart living; 3D perception and applications; video applications; multimedia in medicine; haptics and applications; smart multimedia beyond the visible spectrum; machine learning for multimedia; image segmentation and processing; biometrics; 3D and image processing; and smart social and connected household products.
Author: Yuri Boykov
Publisher: Springer Science & Business Media
ISBN: 3642230938
Category : Computers
Languages : en
Pages : 437
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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: Thierry Bouwmans
Publisher: CRC Press
ISBN: 1482205386
Category : Computers
Languages : en
Pages : 633
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Book Description
Background modeling and foreground detection are important steps in video processing used to detect robustly moving objects in challenging environments. This requires effective methods for dealing with dynamic backgrounds and illumination changes as well as algorithms that must meet real-time and low memory requirements.Incorporating both establish
Author: Andrew Fitzgibbon
Publisher: Springer
ISBN: 364233718X
Category : Computers
Languages : en
Pages : 902
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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: Omar Oreifej
Publisher: Springer Science & Business Media
ISBN: 3319041843
Category : Computers
Languages : en
Pages : 116
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Book Description
Various fundamental applications in computer vision and machine learning require finding the basis of a certain subspace. Examples of such applications include face detection, motion estimation, and activity recognition. An increasing interest has been recently placed on this area as a result of significant advances in the mathematics of matrix rank optimization. Interestingly, robust subspace estimation can be posed as a low-rank optimization problem, which can be solved efficiently using techniques such as the method of Augmented Lagrange Multiplier. In this book, the authors discuss fundamental formulations and extensions for low-rank optimization-based subspace estimation and representation. By minimizing the rank of the matrix containing observations drawn from images, the authors demonstrate how to solve four fundamental computer vision problems, including video denosing, background subtraction, motion estimation, and activity recognition.
Author: Ning Xu
Publisher: Springer Nature
ISBN: 3031446569
Category :
Languages : en
Pages : 194
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Book Description
Author: David Fleet
Publisher: Springer
ISBN: 331910599X
Category : Computers
Languages : en
Pages : 855
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Book Description
The seven-volume set comprising LNCS volumes 8689-8695 constitutes the refereed proceedings of the 13th European Conference on Computer Vision, ECCV 2014, held in Zurich, Switzerland, in September 2014. The 363 revised papers presented were carefully reviewed and selected from 1444 submissions. The papers are organized in topical sections on tracking and activity recognition; recognition; learning and inference; structure from motion and feature matching; computational photography and low-level vision; vision; segmentation and saliency; context and 3D scenes; motion and 3D scene analysis; and poster sessions.
Author:
Publisher: Institute of Electrical & Electronics Engineers(IEEE)
ISBN: 9780769519500
Category : Computers
Languages : en
Pages : 748
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Book Description
ICCV 2003 includes 43 full papers covering the latest research and progress in all areas of vision. The proceedings tackles necessary topics such as image representation, compression and coding, image segmentation, object recognition, active vision, 2D and 3D vision, sensing, and texture, color, and motion analysis.
