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Author: Baiyang Liu Publisher: ISBN: Category : Computer vision Languages : en Pages : 79
Book Description
This dissertation describes a novel selection-based dictionary learning method with a sparse representation to tackle the object tracking problem in computer vision. The sparse representa- tion has been widely used in many applications including visual tracking, compressive sensing, image de-noising and image classification, and learning a good dictionary for the sparse rep- resentation is critical for obtaining high performance. The most popular existing dictionary learning algorithms are generalized from K-means, which compute the dictionary columns to minimize the overall target reconstruction error iteratively. For better discriminative capability to differentiate target-object (positive) from background (negative) data, a class of dictionary algorithms has been developed to learn the dictionary from both the positive and the negative data. However, these methods do not work well for visual tracking in a dynamic environment in which the background can change considerably between frames in a non-linear way. The background cannot be modeled statically with the usual linear models. In this tdissertation, I report on the development of a selection-based dictionary learning algorithm (K-Selection) that constructs the dictionary by choosing its columns from the training data. Each column is the most representative basis for the whole dataset, which also has a clear physical meaning. With locality-constraints, the subspace represented by the learned dictionary is not restricted to the training data alone, and is also less sensitive to outliers. The sparse representation based on this dictionary learning method supports a more robust tracker trained on the target-object data alone. This is because the learned dictionary has more discriminative power and can better distinguish the object from the background clutter. By extending the dictionary with encoded spatial information, I present a new tracking algorithm which is robust to dynamic appearance changes and occlusions. The performance of the proposed algorithms have been validated for several challenging visual tracking applications through a series of comparative experiments.
Author: Baiyang Liu Publisher: ISBN: Category : Computer vision Languages : en Pages : 79
Book Description
This dissertation describes a novel selection-based dictionary learning method with a sparse representation to tackle the object tracking problem in computer vision. The sparse representa- tion has been widely used in many applications including visual tracking, compressive sensing, image de-noising and image classification, and learning a good dictionary for the sparse rep- resentation is critical for obtaining high performance. The most popular existing dictionary learning algorithms are generalized from K-means, which compute the dictionary columns to minimize the overall target reconstruction error iteratively. For better discriminative capability to differentiate target-object (positive) from background (negative) data, a class of dictionary algorithms has been developed to learn the dictionary from both the positive and the negative data. However, these methods do not work well for visual tracking in a dynamic environment in which the background can change considerably between frames in a non-linear way. The background cannot be modeled statically with the usual linear models. In this tdissertation, I report on the development of a selection-based dictionary learning algorithm (K-Selection) that constructs the dictionary by choosing its columns from the training data. Each column is the most representative basis for the whole dataset, which also has a clear physical meaning. With locality-constraints, the subspace represented by the learned dictionary is not restricted to the training data alone, and is also less sensitive to outliers. The sparse representation based on this dictionary learning method supports a more robust tracker trained on the target-object data alone. This is because the learned dictionary has more discriminative power and can better distinguish the object from the background clutter. By extending the dictionary with encoded spatial information, I present a new tracking algorithm which is robust to dynamic appearance changes and occlusions. The performance of the proposed algorithms have been validated for several challenging visual tracking applications through a series of comparative experiments.
Author: Qiang Zhang Publisher: Springer Nature ISBN: 303102253X Category : Technology & Engineering Languages : en Pages : 133
Book Description
The last few years have witnessed fast development on dictionary learning approaches for a set of visual computing tasks, largely due to their utilization in developing new techniques based on sparse representation. Compared with conventional techniques employing manually defined dictionaries, such as Fourier Transform and Wavelet Transform, dictionary learning aims at obtaining a dictionary adaptively from the data so as to support optimal sparse representation of the data. In contrast to conventional clustering algorithms like K-means, where a data point is associated with only one cluster center, in a dictionary-based representation, a data point can be associated with a small set of dictionary atoms. Thus, dictionary learning provides a more flexible representation of data and may have the potential to capture more relevant features from the original feature space of the data. One of the early algorithms for dictionary learning is K-SVD. In recent years, many variations/extensions of K-SVD and other new algorithms have been proposed, with some aiming at adding discriminative capability to the dictionary, and some attempting to model the relationship of multiple dictionaries. One prominent application of dictionary learning is in the general field of visual computing, where long-standing challenges have seen promising new solutions based on sparse representation with learned dictionaries. With a timely review of recent advances of dictionary learning in visual computing, covering the most recent literature with an emphasis on papers after 2008, this book provides a systematic presentation of the general methodologies, specific algorithms, and examples of applications for those who wish to have a quick start on this subject.
Author: Andrew Fitzgibbon Publisher: Springer ISBN: 3642337090 Category : Computers Languages : en Pages : 909
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 shapes, 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: Huchuan Lu Publisher: Springer ISBN: 9811304696 Category : Computers Languages : en Pages : 128
Book Description
This book presents the state of the art in online visual tracking, including the motivations, practical algorithms, and experimental evaluations. Visual tracking remains a highly active area of research in Computer Vision and the performance under complex scenarios has substantially improved, driven by the high demand in connection with real-world applications and the recent advances in machine learning. A large variety of new algorithms have been proposed in the literature over the last two decades, with mixed success. Chapters 1 to 6 introduce readers to tracking methods based on online learning algorithms, including sparse representation, dictionary learning, hashing codes, local model, and model fusion. In Chapter 7, visual tracking is formulated as a foreground/background segmentation problem, and tracking methods based on superpixels and end-to-end deep networks are presented. In turn, Chapters 8 and 9 introduce the cutting-edge tracking methods based on correlation filter and deep learning. Chapter 10 summarizes the book and points out potential future research directions for visual tracking. The book is self-contained and suited for all researchers, professionals and postgraduate students working in the fields of computer vision, pattern recognition, and machine learning. It will help these readers grasp the insights provided by cutting-edge research, and benefit from the practical techniques available for designing effective visual tracking algorithms. Further, the source codes or results of most algorithms in the book are provided at an accompanying website.
Author: Bojana Begovic Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Modern era of signal processing has developed many technical tools for recording and processing large and growing amount of data together with algorithms specialised for data analysis. This gives rise to new challenges in terms of data processing and modelling data representation. Fields ranging from experimental sciences, astronomy, computer vision,neuroscience mobile networks etc., are all in constant search for scalable and efficient data processing tools which would enable more effective analysis of continuous video streams containing millions of pixels. Therefore, the question of digital signal representation is still of high importance, despite the fact that it has been the topic of a significant amount of work in the past. Moreover, developing new data processing methods also affects the quality of everyday life, where devices such as CCD sensors from digital cameras or cell phones are intensively used for entertainment purposes. Specifically, one of the novel processing tools is signal sparse coding which represents signals as linear combinations of a few representational basis vectors i.e., atoms given an overcomplete dictionary. Applications that employ sparse representation are many such as denoising, compression, and regularisation in inverse problems, feature extraction, and more. In this thesis we introduce and study a particular signal representation denoted as the scalable sparse coding. It is based on a novel design for the dictionary learning algorithm, which has proven to be effective for scalable sparse representation of many modalities such as high motion video sequences, natural and solar images. The proposed algorithm is built upon the foundation of the K-SVD framework originally designed to learn non-scalable dictionaries for natural images. The scalable dictionary learning design is mainly motivated by the main perception characteristics of the Human Visual System (HVS) mechanism. Specifically, its core structure relies on the exploitation of the spatial high-frequency image components and contrast variations in order to achieve visual scene objects identification at all scalable levels. The implementation of HVS properties is carried out by introducing a semi-random Morphological Component Analysis (MCA) based initialisation of the scalable dictionary and the regularisation of its atom's update mechanism. Subsequently, this enables scalable sparse image reconstruction. In general, dictionary learning for sparse representations leads to state-of-the-art image restoration results for several different problems in the field of image processing. Experiments in this thesis show that these are equally achievable by accommodating all dictionary elements to tailor the scalable data representation and reconstruction, hence modelling data that admit sparse representation in a novel manner. Furthermore, achieved results demonstrateand validate the practicality of the proposed scheme making it a promising candidate for many practical applications involving both time scalable display, denoising and scalable compressive sensing (CS). Performed simulations include scalable sparse recovery for representation of static and dynamic data changing over time such as video sequences and natural images. Lastly, we contribute novel approaches for scalable denoising and contrast enhancement (CE), applied on solar images corrupted with pixel-dependent Poisson and zero-mean additive white Gaussian noise. Given that solar data contain noise introduced by charge-coupled devices within the on-board acquisition system these artefacts, prior to image analysis, have to be removed. Thus, novel image denoising and contrast enhancement methods are necessary for solar preprocessing.
Author: Gang Hua Publisher: Springer ISBN: 3319488813 Category : Computers Languages : en Pages : 932
Book Description
The three-volume set LNCS 9913, LNCS 9914, and LNCS 9915 comprises the refereed proceedings of the Workshops that took place in conjunction with the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. The three-volume set LNCS 9913, LNCS 9914, and LNCS 9915 comprises the refereed proceedings of the Workshops that took place in conjunction with the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. 27 workshops from 44 workshops proposals were selected for inclusion in the proceedings. These address the following themes: Datasets and Performance Analysis in Early Vision; Visual Analysis of Sketches; Biological and Artificial Vision; Brave New Ideas for Motion Representations; Joint ImageNet and MS COCO Visual Recognition Challenge; Geometry Meets Deep Learning; Action and Anticipation for Visual Learning; Computer Vision for Road Scene Understanding and Autonomous Driving; Challenge on Automatic Personality Analysis; BioImage Computing; Benchmarking Multi-Target Tracking: MOTChallenge; Assistive Computer Vision and Robotics; Transferring and Adapting Source Knowledge in Computer Vision; Recovering 6D Object Pose; Robust Reading; 3D Face Alignment in the Wild and Challenge; Egocentric Perception, Interaction and Computing; Local Features: State of the Art, Open Problems and Performance Evaluation; Crowd Understanding; Video Segmentation; The Visual Object Tracking Challenge Workshop; Web-scale Vision and Social Media; Computer Vision for Audio-visual Media; Computer VISion for ART Analysis; Virtual/Augmented Reality for Visual Artificial Intelligence; Joint Workshop on Storytelling with Images and Videos and Large Scale Movie Description and Understanding Challenge.
Author: Julien Mairal Publisher: Now Publishers ISBN: 9781680830088 Category : Computers Languages : en Pages : 216
Book Description
Sparse Modeling for Image and Vision Processing offers a self-contained view of sparse modeling for visual recognition and image processing. More specifically, it focuses on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.
Author: Vishal M. Patel Publisher: Springer Science & Business Media ISBN: 1461463815 Category : Technology & Engineering Languages : en Pages : 111
Book Description
Compressed sensing or compressive sensing is a new concept in signal processing where one measures a small number of non-adaptive linear combinations of the signal. These measurements are usually much smaller than the number of samples that define the signal. From these small numbers of measurements, the signal is then reconstructed by non-linear procedure. Compressed sensing has recently emerged as a powerful tool for efficiently processing data in non-traditional ways. In this book, we highlight some of the key mathematical insights underlying sparse representation and compressed sensing and illustrate the role of these theories in classical vision, imaging and biometrics problems.
Author: Kyoung Mu Lee Publisher: Springer ISBN: 364237431X Category : Computers Languages : en Pages : 764
Book Description
The four-volume set LNCS 7724--7727 constitutes the thoroughly refereed post-conference proceedings of the 11th Asian Conference on Computer Vision, ACCV 2012, held in Daejeon, Korea, in November 2012. The total of 226 contributions presented in these volumes was carefully reviewed and selected from 869 submissions. The papers are organized in topical sections on object detection, learning and matching; object recognition; feature, representation, and recognition; segmentation, grouping, and classification; image representation; image and video retrieval and medical image analysis; face and gesture analysis and recognition; optical flow and tracking; motion, tracking, and computational photography; video analysis and action recognition; shape reconstruction and optimization; shape from X and photometry; applications of computer vision; low-level vision and applications of computer vision.
Author: Baiyang Liu
Author: Baiyang Liu
Author: Qiang Zhang
Author: Andrew Fitzgibbon
Author: Huchuan Lu
Author: Bojana Begovic
Author: 
Author: Gang Hua
Author: Julien Mairal
Author: Vishal M. Patel
Author: Kyoung Mu Lee