Download or read book Unsupervised Learning for Object Representations by Watching and Moving written by Yanchao Yang and published by . This book was released on 2018 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: The power of deep neural networks comes mainly from huge labeled datasets. Even though it shines on many computer vision tasks, supervised learning bears little hope to hack into the core of intelligent visual systems. On the other side, unsupervised learning is believed to be the future of AI; however, its performance is always inferior compared to the supervised counterpart. The goal of our research is to develop unsupervised learning algorithms for computer vision tasks while matching or even outperforming the supervised ones. Our key is a representation that is as informative as the supervisory labels, which can be constructed from an unlimited amount of unlabeled data. In theory, this representation contains richer information than the processed supervisory signal. Moreover, we develop algorithms that can utilize existing labeled datasets to expedite the information extraction from the unlimited unlabeled data. Our research is lined up in an order similar to the visual development in early infancy, such that we can also investigate the interplay between different visual functionalities. The final goal is to develop a robotic visual system akin to a human's, that can automatically acquire semantics from concepts of objects fostered by basic perceptions of motion and depth with the minimum amount of human supervision.

Download or read book Investigations of Factors that Affect Unsupervised Learning of 3D Object Representations written by Moqian Tian and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Humans have an amazing ability to learn to recognize objects across transformations that present very different retinal stimuli, such as changes in size, illumination, and rotations in space. Such identity-preserving image transformations (DiCarlo, Zoccolan, & Rust, 2012) put extraordinary pressure on our visual system because the computations needed to assign vastly different 2D images of an object to the same identity are non-trivial. However, both behavioral (Biederman & Cooper, 1991a, 1991b; Fiser & Biederman, 1995; Potter, 1976; Thorpe, Fize, & Marlot, 1996) and neural (Hung, Kreiman, Poggio, & DiCarlo, 2005) evidence suggest that the visual system solves this problem accurately and rapidly. While rotations in the image plane preserve the visible features, rotations in-depth may reveal new features of an opaque object and thus present the most difficult transformation for the visual system to resolve, because the resulting 2D image from an in-depth rotation may be unrecoverable from the original image. Thus, understanding how people achieve viewpoint invariance, or the ability to recognize objects from different views and rotations, is key to understanding the visual object recognition system. There is a general consensus that learning is an important component for developing viewpoint invariant object recognition (Logothetis and Pauls, 1992; Tarr and Pinker, 1989). Many studies show that learning can occur in an unsupervised way just from viewing example images of new objects (Edelman and Bulthoff, 1992; Tarr and Pinker, 1989). Two major theories regarding how the visual system achieves viewpoint invariance -- 3D-based theories (Biederman, 1987) and view-based theories (Ullman and Basri, 1989) -- recognize the importance of learning in achieving viewpoint invariant object recognition. However, they differ in what information is used during learning and what representation is consequently built. For example, view-based theories consider spatial and temporal continuities as necessary glue for linking multiple views of an object during unsupervised learning, but 3D-based theories consider feature information to be more important. They also differ on whether the object representation that is built after learning is 3D based or view based. To address these gaps in the published literature, I examined two core questions: What kind of spatial and temporal information in the visual input during unsupervised learning is critical for achieving viewpoint invariant recognition? And what kind of object representation is generated during the learning process? In Chapter 1, I will present a theoretical overview of the issues. Section 1 reviews theories and computational models of viewpoint invariant recognition, with a focus on the debate between 3D-based theories and view-based theories; Section 2 reviews psychophysical and neural evidence supporting each theory; and Section 3 discusses the predictions of the learning mechanisms of each of the competing theories. Chapter 2 presents results from a series of experiments that investigated the spatio-temporal information in the visual input during unsupervised learning that is key for learning the 3D structure of novel objects. Chapter 3 presents data from a series of experiments that examine how the format of the visual information during unsupervised learning affects learning the 3D structure of novel objects. Finally, in Chapter 4, I will discuss the theoretical implications of the findings presented in Chapters 2 & 3, and propose a new framework based on these results.

Download or read book Moving Objects Detection Using Machine Learning written by Navneet Ghedia and published by Springer Nature. This book was released on 2022-01-01 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book shows how machine learning can detect moving objects in a digital video stream. The authors present different background subtraction approaches, foreground segmentation, and object tracking approaches to accomplish this. They also propose an algorithm that considers a multimodal background subtraction approach that can handle a dynamic background and different constraints. The authors show how the proposed algorithm is able to detect and track 2D & 3D objects in monocular sequences for both indoor and outdoor surveillance environments and at the same time, also able to work satisfactorily in a dynamic background and with challenging constraints. In addition, the shows how the proposed algorithm makes use of parameter optimization and adaptive threshold techniques as intrinsic improvements of the Gaussian Mixture Model. The presented system in the book is also able to handle partial occlusion during object detection and tracking. All the presented work and evaluations were carried out in offline processing with the computation done by a single laptop computer with MATLAB serving as software environment.

Download or read book Computer Vision ECCV 2024 written by Aleš Leonardis and published by Springer Nature. This book was released on with total page 587 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Unsupervised Learning in Space and Time written by Marius Leordeanu and published by Springer Nature. This book was released on 2020-04-17 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses one of the most important unsolved problems in artificial intelligence: the task of learning, in an unsupervised manner, from massive quantities of spatiotemporal visual data that are available at low cost. The book covers important scientific discoveries and findings, with a focus on the latest advances in the field. Presenting a coherent structure, the book logically connects novel mathematical formulations and efficient computational solutions for a range of unsupervised learning tasks, including visual feature matching, learning and classification, object discovery, and semantic segmentation in video. The final part of the book proposes a general strategy for visual learning over several generations of student-teacher neural networks, along with a unique view on the future of unsupervised learning in real-world contexts. Offering a fresh approach to this difficult problem, several efficient, state-of-the-art unsupervised learning algorithms are reviewed in detail, complete with an analysis of their performance on various tasks, datasets, and experimental setups. By highlighting the interconnections between these methods, many seemingly diverse problems are elegantly brought together in a unified way. Serving as an invaluable guide to the computational tools and algorithms required to tackle the exciting challenges in the field, this book is a must-read for graduate students seeking a greater understanding of unsupervised learning, as well as researchers in computer vision, machine learning, robotics, and related disciplines.

Download or read book Computer Vision ECCV 2018 written by Vittorio Ferrari and published by Springer. This book was released on 2018-10-05 with total page 875 pages. Available in PDF, EPUB and Kindle. Book excerpt: The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.

Download or read book Unsupervised Learning of Event and Object Classes from Video written by Muralikrishna Sridhar and published by . This book was released on 2010 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: We present a method for unsupervised learning of event classes from videos in which multiple activities may occur simultaneously. Unsupervised discovery of event classes avoids the need to hand-crafted event classes and thereby makes it possible in principle to scale-up to the huge number of event classes that occur in the real world. Research into an unsupervised approach has important consequences for tasks such as video understanding and summarization, modelling usual and unusual behaviour and video indexing for retrieval. These tasks are becoming increasingly important for scenarios such as surveillance, video search, robotic vision and sports highlights extraction as a consequence of the increasing proliferation of videos. The proposed approach is underpinned by a generative probabilistic model for events and a graphical representation for the qualitative spatial relationships between objects and their temporal evolution. Given a set of tracks for the objects within a scene, a set of event classes is derived from the most likely decomposition of the 'activity graph' of spatio-temporal relationships between all pairs of objects into a set of labelled events involving subsets of these objects. The posterior probability of candidate solutions favours decompositions in which events of the same class have a similar relational structure, together with three other measures of well-formedness. A Markov Chain Monte Carlo (MCMC) procedure is used to efficiently search for the MAP solution. This search moves between possible decompositions of the activity graph into sets of unlabelled events and at each move adds a close to optimal labellings (for this decomposition) using spectral clustering. Experiments on simulated and real data show that the discovered event classes are often semantically meaningful and correspond well with ground-truth event classes assigned by hand. Event Learning is followed by learning of functional object categories. Equivalence classes of objects are discovered on the basis of their similar functional role in multiple event instantiations. Objects are represented in a multidimensional space that captures their functional role in all the events. Unsupervised learning in this space results in functional object-categories. Experiments in the domain of aircraft handling suggests that our spatio-temporal representation together with the learning techniques are a promising framework for learning functional object-categories from video.

Download or read book Deep Learning for Video Understanding written by Zuxuan Wu and published by Springer Nature. This book was released on with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Video Based Machine Learning for Traffic Intersections written by Tania Banerjee and published by CRC Press. This book was released on 2023-10-17 with total page 213 pages. Available in PDF, EPUB and Kindle. Book excerpt: Video Based Machine Learning for Traffic Intersections describes the development of computer vision and machine learning-based applications for Intelligent Transportation Systems (ITS) and the challenges encountered during their deployment. This book presents several novel approaches, including a two-stream convolutional network architecture for vehicle detection, tracking, and near-miss detection; an unsupervised approach to detect near-misses in fisheye intersection videos using a deep learning model combined with a camera calibration and spline-based mapping method; and algorithms that utilize video analysis and signal timing data to accurately detect and categorize events based on the phase and type of conflict in pedestrian-vehicle and vehicle-vehicle interactions. The book makes use of a real-time trajectory prediction approach, combined with aligned Google Maps information, to estimate vehicle travel time across multiple intersections. Novel visualization software, designed by the authors to serve traffic practitioners, is used to analyze the efficiency and safety of intersections. The software offers two modes: a streaming mode and a historical mode, both of which are useful to traffic engineers who need to quickly analyze trajectories to better understand traffic behavior at an intersection. Overall, this book presents a comprehensive overview of the application of computer vision and machine learning to solve transportation-related problems. Video Based Machine Learning for Traffic Intersections demonstrates how these techniques can be used to improve safety, efficiency, and traffic flow, as well as identify potential conflicts and issues before they occur. The range of novel approaches and techniques presented offers a glimpse of the exciting possibilities that lie ahead for ITS research and development. Key Features: Describes the development and challenges associated with Intelligent Transportation Systems (ITS) Provides novel visualization software designed to serve traffic practitioners in analyzing the efficiency and safety of an intersection Has the potential to proactively identify potential conflict situations and develop an early warning system for real-time vehicle-vehicle and pedestrian-vehicle conflicts

Download or read book Computer Vision ACCV 2018 written by C.V. Jawahar and published by Springer. This book was released on 2019-05-25 with total page 727 pages. Available in PDF, EPUB and Kindle. Book excerpt: The six volume set LNCS 11361-11366 constitutes the proceedings of the 14th Asian Conference on Computer Vision, ACCV 2018, held in Perth, Australia, in December 2018. The total of 274 contributions was carefully reviewed and selected from 979 submissions during two rounds of reviewing and improvement. The papers focus on motion and tracking, segmentation and grouping, image-based modeling, dep learning, object recognition object recognition, object detection and categorization, vision and language, video analysis and event recognition, face and gesture analysis, statistical methods and learning, performance evaluation, medical image analysis, document analysis, optimization methods, RGBD and depth camera processing, robotic vision, applications of computer vision.

Download or read book Computer Vision ECCV 2020 written by Andrea Vedaldi and published by Springer Nature. This book was released on 2020-11-12 with total page 830 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.

Download or read book Field and Service Robotics written by Peter Corke and published by Springer Science & Business Media. This book was released on 2006-08-03 with total page 620 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 5th International Conference on Field and Service Robotics (FSR05) was held in Port Douglas, Australia, on 29th - 31st July 2005, and brought together the worlds' leading experts in field and service automation. The goal of the conference was to report and encourage the latest research and practical results towards the use of field and service robotics in the community with particular focus on proven technology. The conference provided a forum for researchers, professionals and robot manufacturers to exchange up-to-date technical knowledge and experience. Field robots are robots which operate in outdoor, complex, and dynamic environments. Service robots are those that work closely with humans, with particular applications involving indoor and structured environments. There are a wide range of topics presented in this issue on field and service robots including: Agricultural and Forestry Robotics, Mining and Exploration Robots, Robots for Construction, Security & Defence Robots, Cleaning Robots, Autonomous Underwater Vehicles and Autonomous Flying Robots. This meeting was the fifth in the series and brings FSR back to Australia where it was first held. FSR has been held every 2 years, starting with Canberra 1997, followed by Pittsburgh 1999, Helsinki 2001 and Lake Yamanaka 2003.

Download or read book Hands On Unsupervised Learning Using Python written by Ankur A. Patel and published by "O'Reilly Media, Inc.". This book was released on 2019-02-21 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence. Since the majority of the world's data is unlabeled, conventional supervised learning cannot be applied. Unsupervised learning, on the other hand, can be applied to unlabeled datasets to discover meaningful patterns buried deep in the data, patterns that may be near impossible for humans to uncover. Author Ankur Patel shows you how to apply unsupervised learning using two simple, production-ready Python frameworks: Scikit-learn and TensorFlow using Keras. With code and hands-on examples, data scientists will identify difficult-to-find patterns in data and gain deeper business insight, detect anomalies, perform automatic feature engineering and selection, and generate synthetic datasets. All you need is programming and some machine learning experience to get started. Compare the strengths and weaknesses of the different machine learning approaches: supervised, unsupervised, and reinforcement learning Set up and manage machine learning projects end-to-end Build an anomaly detection system to catch credit card fraud Clusters users into distinct and homogeneous groups Perform semisupervised learning Develop movie recommender systems using restricted Boltzmann machines Generate synthetic images using generative adversarial networks

Download or read book Computer Vision ECCV 2022 written by Shai Avidan and published by Springer Nature. This book was released on 2022-11-10 with total page 811 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.

Download or read book Understanding Machine Learning written by Shai Shalev-Shwartz and published by Cambridge University Press. This book was released on 2014-05-19 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Download or read book Representations and Techniques for 3D Object Recognition and Scene Interpretation written by Derek Hoiem and published by Morgan & Claypool Publishers. This book was released on 2011 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the grand challenges of artificial intelligence is to enable computers to interpret 3D scenes and objects from imagery. This book organizes and introduces major concepts in 3D scene and object representation and inference from still images, with a focus on recent efforts to fuse models of geometry and perspective with statistical machine learning. The book is organized into three sections: (1) Interpretation of Physical Space; (2) Recognition of 3D Objects; and (3) Integrated 3D Scene Interpretation. The first discusses representations of spatial layout and techniques to interpret physical scenes from images. The second section introduces representations for 3D object categories that account for the intrinsically 3D nature of objects and provide robustness to change in viewpoints. The third section discusses strategies to unite inference of scene geometry and object pose and identity into a coherent scene interpretation. Each section broadly surveys important ideas from cognitive science and artificial intelligence research, organizes and discusses key concepts and techniques from recent work in computer vision, and describes a few sample approaches in detail. Newcomers to computer vision will benefit from introductions to basic concepts, such as single-view geometry and image classification, while experts and novices alike may find inspiration from the book's organization and discussion of the most recent ideas in 3D scene understanding and 3D object recognition. Specific topics include: mathematics of perspective geometry; visual elements of the physical scene, structural 3D scene representations; techniques and features for image and region categorization; historical perspective, computational models, and datasets and machine learning techniques for 3D object recognition; inferences of geometrical attributes of objects, such as size and pose; and probabilistic and feature-passing approaches for contextual reasoning about 3D objects and scenes. Table of Contents: Background on 3D Scene Models / Single-view Geometry / Modeling the Physical Scene / Categorizing Images and Regions / Examples of 3D Scene Interpretation / Background on 3D Recognition / Modeling 3D Objects / Recognizing and Understanding 3D Objects / Examples of 2D 1/2 Layout Models / Reasoning about Objects and Scenes / Cascades of Classifiers / Conclusion and Future Directions

Download or read book How Humans Recognize Objects Segmentation Categorization and Individual Identification written by Chris Fields and published by Frontiers Media SA. This book was released on 2016-08-18 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: Human beings experience a world of objects: bounded entities that occupy space and persist through time. Our actions are directed toward objects, and our language describes objects. We categorize objects into kinds that have different typical properties and behaviors. We regard some kinds of objects – each other, for example – as animate agents capable of independent experience and action, while we regard other kinds of objects as inert. We re-identify objects, immediately and without conscious deliberation, after days or even years of non-observation, and often following changes in the features, locations, or contexts of the objects being re-identified. Comparative, developmental and adult observations using a variety of approaches and methods have yielded a detailed understanding of object detection and recognition by the visual system and an advancing understanding of haptic and auditory information processing. Many fundamental questions, however, remain unanswered. What, for example, physically constitutes an “object”? How do specific, classically-characterizable object boundaries emerge from the physical dynamics described by quantum theory, and can this emergence process be described independently of any assumptions regarding the perceptual capabilities of observers? How are visual motion and feature information combined to create object information? How are the object trajectories that indicate persistence to human observers implemented, and how are these trajectory representations bound to feature representations? How, for example, are point-light walkers recognized as single objects? How are conflicts between trajectory-driven and feature-driven identifications of objects resolved, for example in multiple-object tracking situations? Are there separate “what” and “where” processing streams for haptic and auditory perception? Are there haptic and/or auditory equivalents of the visual object file? Are there equivalents of the visual object token? How are object-identification conflicts between different perceptual systems resolved? Is the common assumption that “persistent object” is a fundamental innate category justified? How does the ability to identify and categorize objects relate to the ability to name and describe them using language? How are features that an individual object had in the past but does not have currently represented? How are categorical constraints on how objects move or act represented, and how do such constraints influence categorization and the re-identification of individuals? How do human beings re-identify objects, including each other, as persistent individuals across changes in location, context and features, even after gaps in observation lasting months or years? How do human capabilities for object categorization and re-identification over time relate to those of other species, and how do human infants develop these capabilities? What can modeling approaches such as cognitive robotics tell us about the answers to these questions? Primary research reports, reviews, and hypothesis and theory papers addressing questions relevant to the understanding of perceptual object segmentation, categorization and individual identification at any scale and from any experimental or modeling perspective are solicited for this Research Topic. Papers that review particular sets of issues from multiple disciplinary perspectives or that advance integrative hypotheses or models that take data from multiple experimental approaches into account are especially encouraged.