Song-Chun Zhu – författare

As the first book of a three-part series, this book is offered as a tribute to pioneers in vision, such as Béla Julesz, David Marr, King-Sun Fu, Ulf Grenander, and David Mumford. The authors hope to provide foundation and, perhaps more importantly, further inspiration for continued research in vision. This book covers David Marr's paradigm and various underlying statistical models for vision. The mathematical framework herein integrates three regimes of models (low-, mid-, and high-entropy regimes) and provides foundation for research in visual coding, recognition, and cognition. Concepts are first explained for understanding and then supported by findings in psychology and neuroscience, after which they are established by statistical models and associated learning and inference algorithms. A reader will gain a unified, cross-disciplinary view of research in vision and will accrue knowledge spanning from psychology to neuroscience to statistics.

As the first book of a three-part series, this book is offered as a tribute to pioneers in vision, such as Béla Julesz, David Marr, King-Sun Fu, Ulf Grenander, and David Mumford. The authors hope to provide foundation and, perhaps more importantly, further inspiration for continued research in vision. This book covers David Marr''s paradigm and various underlying statistical models for vision. The mathematical framework herein integrates three regimes of models (low-, mid-, and high-entropy regimes) and provides foundation for research in visual coding, recognition, and cognition. Concepts are first explained for understanding and then supported by findings in psychology and neuroscience, after which they are established by statistical models and associated learning and inference algorithms. A reader will gain a unified, cross-disciplinary view of research in vision and will accrue knowledge spanning from psychology to neuroscience to statistics. 

As the first book of a three-part series, this book is offered as a tribute to pioneers in vision, such as Béla Julesz, David Marr, King-Sun Fu, Ulf Grenander, and David Mumford. The authors hope to provide foundation and, perhaps more importantly, further inspiration for continued research in vision. This book covers David Marr's paradigm and various underlying statistical models for vision. The mathematical framework herein integrates three regimes of models (low-, mid-, and high-entropy regimes) and provides foundation for research in visual coding, recognition, and cognition. Concepts are first explained for understanding and then supported by findings in psychology and neuroscience, after which they are established by statistical models and associated learning and inference algorithms. A reader will gain a unified, cross-disciplinary view of research in vision and will accrue knowledge spanning from psychology to neuroscience to statistics.

This volume on visual commonsense reasoning, part of a comprehensive three-volume series, presents a computational framework for bridging the gap between modern computer vision capabilities and human-like visual understanding. While current AI systems excel at pattern recognition tasks, they often lack the sophisticated reasoning capabilities that humans demonstrate effortlessly in understanding and interacting with their environment. This work addresses this limitation by integrating physical, social, and abstract reasoning within a unified computational framework.The volume is organized into three parts. The first part establishes the theoretical foundations of visual commonsense through a systematic examination of physical understanding, including affordances, intuitive physics, causality, and tool use. These components form the basis for understanding how objects and environments behave and interact. The second part delves into social reasoning aspects, exploring intent, theory of mind, and nonverbal communication - crucial capabilities for AI systems to interpret and predict human behavior. The third part investigates abstract visual reasoning, examining higher-level cognitive capabilities.Drawing from cognitive science, computer vision, and artificial intelligence, this work:Provides a systematic treatment of visual commonsense ranging from foundational theories to practical implementationsIntroduces computational frameworks integrating multiple forms of reasoningDemonstrates applications through extensive examples and case studiesHighlights current challenges and future directions in developing human-like visual AIThis carefully crafted volume serves as an invaluable resource for researchers, graduate students, and practitioners in computer vision, artificial intelligence, cognitive science, and related fields. It offers both theoretical insights and practical guidance for developing AI systems with more sophisticated visual understanding capabilities, moving closer to human-like visual intelligence.

This volume on visual commonsense reasoning, part of a comprehensive three-volume series, presents a computational framework for bridging the gap between modern computer vision capabilities and human-like visual understanding. While current AI systems excel at pattern recognition tasks, they often lack the sophisticated reasoning capabilities that humans demonstrate effortlessly in understanding and interacting with their environment. This work addresses this limitation by integrating physical, social, and abstract reasoning within a unified computational framework.The volume is organized into three parts. The first part establishes the theoretical foundations of visual commonsense through a systematic examination of physical understanding, including affordances, intuitive physics, causality, and tool use. These components form the basis for understanding how objects and environments behave and interact. The second part delves into social reasoning aspects, exploring intent, theory of mind, and nonverbal communication - crucial capabilities for AI systems to interpret and predict human behavior. The third part investigates abstract visual reasoning, examining higher-level cognitive capabilities.Drawing from cognitive science, computer vision, and artificial intelligence, this work:Provides a systematic treatment of visual commonsense ranging from foundational theories to practical implementationsIntroduces computational frameworks integrating multiple forms of reasoningDemonstrates applications through extensive examples and case studiesHighlights current challenges and future directions in developing human-like visual AIThis carefully crafted volume serves as an invaluable resource for researchers, graduate students, and practitioners in computer vision, artificial intelligence, cognitive science, and related fields. It offers both theoretical insights and practical guidance for developing AI systems with more sophisticated visual understanding capabilities, moving closer to human-like visual intelligence.

This volume contains the papers presented at the Sixth International Conference on Energy Minimization Methods on Computer Vision and Pattern Recognition (EMMCVPR 2007), held at the Lotus Hill Institute, Ezhou, Hubei, China, August 27–29, 2007. The motivation for this conference is the realization that many problems in computer vision and pattern recognition can be formulated in terms of probabilistic inference or optimization of energy functions. EMMCVPR 2007 addressed the critical issues of representation, learning, and inference. Important new themes include pr- abilistic grammars, image parsing, and the use of datasets with ground-truth to act as benchmarks for evaluating algorithms and as a way to train learning algorithms. Other themes include the development of efficient inference algorithms using advanced techniques from statistics, computer science, and applied mathematics. We received 140 submissions for this workshop. Each paper was reviewed by three committee members. Based on these reviews we selected 22 papers for oral presen- tion and 15 papers for poster presentation. This book makes no distinction between oral and poster papers. We have organized these papers in seven sections on al- rithms, applications, image parsing, image processing, motion, shape, and thr- dimensional processing. Finally, we thank those people who helped make this workshop happen. We - knowledge the Program Committee for their help in reviewing the papers.

This volume contains the papers presented at the Sixth International Conference on Energy Minimization Methods on Computer Vision and Pattern Recognition (EMMCVPR 2007), held at the Lotus Hill Institute, Ezhou, Hubei, China, August 27–29, 2007. The motivation for this conference is the realization that many problems in computer vision and pattern recognition can be formulated in terms of probabilistic inference or optimization of energy functions. EMMCVPR 2007 addressed the critical issues of representation, learning, and inference. Important new themes include pr- abilistic grammars, image parsing, and the use of datasets with ground-truth to act as benchmarks for evaluating algorithms and as a way to train learning algorithms. Other themes include the development of efficient inference algorithms using advanced techniques from statistics, computer science, and applied mathematics. We received 140 submissions for this workshop. Each paper was reviewed by three committee members. Based on these reviews we selected 22 papers for oral presen- tion and 15 papers for poster presentation. This book makes no distinction between oral and poster papers. We have organized these papers in seven sections on al- rithms, applications, image parsing, image processing, motion, shape, and thr- dimensional processing. Finally, we thank those people who helped make this workshop happen. We - knowledge the Program Committee for their help in reviewing the papers.

This book seeks to bridge the gap between statistics and computer science. It provides an overview of Monte Carlo methods, including Sequential Monte Carlo, Markov Chain Monte Carlo, Metropolis-Hastings, Gibbs Sampler, Cluster Sampling, Data Driven MCMC, Stochastic Gradient descent, Langevin Monte Carlo, Hamiltonian Monte Carlo, and energy landscape mapping. Due to its comprehensive nature, the book is suitable for developing and teaching graduate courses on Monte Carlo methods. To facilitate learning, each chapter includes several representative application examples from various fields. The book pursues two main goals: (1) It introduces researchers to applying Monte Carlo methods to broader problems in areas such as Computer Vision, Computer Graphics, Machine Learning, Robotics, Artificial Intelligence, etc.; and (2) it makes it easier for scientists and engineers working in these areas to employ Monte Carlo methods to enhance their research.