Spatio-temporal Segmentation and Object Tracking PDF Download
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Author: Fabrice Moscheni
Publisher:
ISBN:
Category :
Languages : en
Pages : 161
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Book Description
Author: Fabrice Moscheni
Publisher:
ISBN:
Category :
Languages : en
Pages : 161
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Book Description
Author: Vladimir Mashtalir
Publisher: Springer Nature
ISBN: 3030354806
Category : Technology & Engineering
Languages : en
Pages : 279
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Book Description
This book proposes a number of promising models and methods for adaptive segmentation, swarm partition, permissible segmentation, and transform properties, as well as techniques for spatio-temporal video segmentation and interpretation, online fuzzy clustering of data streams, and fuzzy systems for information retrieval. The main focus is on the spatio-temporal segmentation of visual information. Sets of meaningful and manageable image or video parts, defined by visual interest or attention to higher-level semantic issues, are often vital to the efficient and effective processing and interpretation of viewable information. Developing robust methods for spatial and temporal partition represents a key challenge in computer vision and computational intelligence as a whole. This book is intended for students and researchers in the fields of machine learning and artificial intelligence, especially those whose work involves image processing and recognition, video parsing, and content-based image/video retrieval.
Author:
Publisher:
ISBN: 9783030354817
Category : Image segmentation
Languages : en
Pages :
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Book Description
This book proposes a number of promising models and methods for adaptive segmentation, swarm partition, permissible segmentation, and transform properties, as well as techniques for spatio-temporal video segmentation and interpretation, online fuzzy clustering of data streams, and fuzzy systems for information retrieval. The main focus is on the spatio-temporal segmentation of visual information. Sets of meaningful and manageable image or video parts, defined by visual interest or attention to higher-level semantic issues, are often vital to the efficient and effective processing and interpretation of viewable information. Developing robust methods for spatial and temporal partition represents a key challenge in computer vision and computational intelligence as a whole. This book is intended for students and researchers in the fields of machine learning and artificial intelligence, especially those whose work involves image processing and recognition, video parsing, and content-based image/video retrieval.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 240
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Book Description
Visual analysis is concerned with problems to identify object status or scene layout in images or videos. There are numerous concepts that are of great interest for visual analysis and understanding in the computer vision and machine learning communities. For instance, researchers have been working on developing algorithms to recognize, detect and segment objects/scenes in images. To understand such content, numerous challenges make these problems significantly challenging in the real world scenario, since objects or scenes usually appear in different conditions such as viewpoints, scales, and background noise, and even may deform with different shapes, parts or poses. In addition to images, video understanding has drawn much attention in various research areas due to the ease of obtaining video data and the importance of video applications, such as virtual reality, autonomous driving and video surveillance. Different from images, videos contain richer information in the temporal domain, thereby it also produces difficulties and requires larger computational powers to fully exploit video content. In this thesis, we propose to construct optimization frameworks for video object tracking and segmentation tasks. First, we utilize a spatial-temporal model to jointly optimize video object segmentation and optical flow estimation, and show that both results can be improved in the proposed framework. Second, we introduce a co-segmentation algorithm to further understand object semantics by considering relations between objects among a collection of videos. As a result, our proposed algorithms achieve state-of-the-art performance in video object segmentation. With such visual understanding in images and videos, the following question would be how to use them in real world applications. In this thesis, we focus on the visual synthesis problem, where it is a task for people to create or edit contents in the original data. For instance, numerous image editing problems have been studied widely, such as inpainting, harmonization and colorization. For these tasks, as the human can easily discover unrealistic artifacts after the original data is edited, one important challenge is to create realistic contents. To tackle this challenge, we propose to extract semantics by utilizing visual analysis as the guidance to improve the realism of synthesized outputs. With such guidance, we show that our visual synthesis systems produce visually pleasing and realistic results on sky replacement and object/scene composition tasks.
Author: Bai, Luyi
Publisher: IGI Global
ISBN: 1668491095
Category : Computers
Languages : en
Pages : 527
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Book Description
In the world of data management, one of the most formidable challenges faced by academic scholars is the effective handling of spatiotemporal data within the semantic web. As our world continues to change dynamically with time, nearly every aspect of our lives, from environmental monitoring to urban planning and beyond, is intrinsically linked to time and space. This synergy has given rise to an avalanche of spatiotemporal data, and the pressing question is how to manage, model, and query this voluminous information effectively. The existing approaches often fall short in addressing the intricacies and uncertainties that come with spatiotemporal data, leaving scholars struggling to unlock its full potential. Uncertain Spatiotemporal Data Management for the Semantic Web is the definitive solution to the challenges faced by academic scholars in the realm of spatiotemporal data. This book offers a visionary approach to an all-encompassing guide in modeling and querying spatiotemporal data using innovative technologies like XML and RDF. Through a meticulously crafted set of chapters, this book sheds light on the nuances of spatiotemporal data and also provides practical solutions that empower scholars to navigate the complexities of this domain effectively.
Author: Jürgen Beyerer
Publisher: KIT Scientific Publishing
ISBN: 3866449887
Category : Computers
Languages : en
Pages : 160
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Book Description
This book is a collection of 11 review technical reports summarizing the presentations at the 2012 Joint Workshop of Fraunhofer IOSB and Vision and Fusion Laboratory at KIT Karlsruhe, made by the students of the both institutions. The topics include image processing, visual inspection, pattern recognition and classification, human-machine interaction, world modeling, and optical signal processing.
Author: Carl Edward Rasmussen
Publisher: Springer Science & Business Media
ISBN: 3540229450
Category : Computers
Languages : en
Pages : 596
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Book Description
This book constitutes the refereed proceedings of the 26th Symposium of the German Association for Pattern Recognition, DAGM 2004, held in Tübingen, Germany in August/September 2004. The 22 revised papers and 48 revised poster papers presented were carefully reviewed and selected from 146 submissions. The papers are organized in topical sections on learning, Bayesian approaches, vision and faces, vision and motion, biologically motivated approaches, segmentation, object recognition, and object recognition and synthesis.
Author: Sankar Kumar Pal
Publisher: World Scientific
ISBN: 9813144564
Category : Computers
Languages : en
Pages : 875
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Book Description
Containing twenty six contributions by experts from all over the world, this book presents both research and review material describing the evolution and recent developments of various pattern recognition methodologies, ranging from statistical, linguistic, fuzzy-set-theoretic, neural, evolutionary computing and rough-set-theoretic to hybrid soft computing, with significant real-life applications.Pattern Recognition and Big Data provides state-of-the-art classical and modern approaches to pattern recognition and mining, with extensive real life applications. The book describes efficient soft and robust machine learning algorithms and granular computing techniques for data mining and knowledge discovery; and the issues associated with handling Big Data. Application domains considered include bioinformatics, cognitive machines (or machine mind developments), biometrics, computer vision, the e-nose, remote sensing and social network analysis.
Author: Amritpal Singh Saini
Publisher:
ISBN:
Category : Cloud computing
Languages : en
Pages : 69
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Book Description
There is considerable research work going on segmentation of RGB-D clouds due its applications in tasks like scene understanding, robotics etc. The availability of inexpensive and easy to use RGB-D cameras and computational capabilities of GPUs has lead to development of numerous applications in this area. Recently proposed cloud segmentation methods are either slow in operation or do not operate in an online fashion making them unsuitable for applications in robotics. In this work we deal with the aforementioned problem. We propose a method to perform online segmentation of RGB-D scene. Our framework is built on dense scene mapping methods like Kinect fusion. It allows us to generate accurate and dense depth maps and provide camera pose information. Instead of directly operating on a large 3D point cloud we process individual RGB and depth frames which are assembled in a dense cloud in an incremental fashion. Pose information is used to integrate the segmentation maps into the global label cloud using GPU. We perform multi-view integration of segments as the camera is moved around in the scene by formulating the problem as weighted graph. We will discuss applications of our segmentation framework to perform real time and scalable object discovery and object detection.
Author: Vipin Kumar
Publisher: Springer
ISBN: 354044839X
Category : Computers
Languages : en
Pages : 1093
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Book Description
The three-volume set, LNCS 2667, LNCS 2668, and LNCS 2669, constitutes the refereed proceedings of the International Conference on Computational Science and Its Applications, ICCSA 2003, held in Montreal, Canada, in May 2003.The three volumes present more than 300 papers and span the whole range of computational science from foundational issues in computer science and mathematics to advanced applications in virtually all sciences making use of computational techniques. The proceedings give a unique account of recent results in computational science.
