A modern treatment focusing on learning and inference, with minimal prerequisites, real-world examples and implementable algorithms.

This book introduces neural mechanisms of biological vision and how artificial intelligence algorithms learn to interpret images.

A unique multidisciplinary perspective on the problem of visual object categorization.

This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras. You'll learn how to: Design ML architecture for computer vision tasks Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model Preprocess images for data augmentation and to support learnability Incorporate explainability and responsible AI best practices Deploy image models as web services or on edge devices Monitor and manage ML models

Computer Vision for Assistive Healthcare describes how advanced computer vision techniques provide tools to support common human needs, such as mental functioning, personal mobility, sensory functions, daily living activities, image processing, pattern recognition, machine learning and how language processing and computer graphics cooperate with robotics to provide such tools. Users will learn about the emerging computer vision techniques for supporting mental functioning, algorithms for analyzing human behavior, and how smart interfaces and virtual reality tools lead to the development of advanced rehabilitation systems able to perform human action and activity recognition. In addition, the book covers the technology behind intelligent wheelchairs, how computer vision technologies have the potential to assist blind people, and about the computer vision-based solutions recently employed for safety and health monitoring. - Gives the state-of-the-art computer vision techniques and tools for assistive healthcare - Includes a broad range of topic areas, ranging from image processing, pattern recognition, machine learning to robotics, natural language processing and computer graphics - Presents a wide range of application areas, ranging from mobility, sensory substitution, and safety and security, to mental and physical rehabilitation and training - Written by leading researchers in this growing field of research - Describes the outstanding research challenges that still need to be tackled, giving researchers good indicators of research opportunities

Available again, an influential book that offers a framework for understanding visual perception and considers fundamental questions about the brain and its functions. David Marr's posthumously published Vision (1982) influenced a generation of brain and cognitive scientists, inspiring many to enter the field. In Vision, Marr describes a general framework for understanding visual perception and touches on broader questions about how the brain and its functions can be studied and understood. Researchers from a range of brain and cognitive sciences have long valued Marr's creativity, intellectual power, and ability to integrate insights and data from neuroscience, psychology, and computation. This MIT Press edition makes Marr's influential work available to a new generation of students and scientists. In Marr's framework, the process of vision constructs a set of representations, starting from a description of the input image and culminating with a description of three-dimensional objects in the surrounding environment. A central theme, and one that has had far-reaching influence in both neuroscience and cognitive science, is the notion of different levels of analysis—in Marr's framework, the computational level, the algorithmic level, and the hardware implementation level. Now, thirty years later, the main problems that occupied Marr remain fundamental open problems in the study of perception. Vision provides inspiration for the continuing efforts to integrate knowledge from cognition and computation to understand vision and the brain.

This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning. Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: · Evaluation and Generalization in Interpretable Machine Learning · Explanation Methods in Deep Learning · Learning Functional Causal Models with Generative Neural Networks · Learning Interpreatable Rules for Multi-Label Classification · Structuring Neural Networks for More Explainable Predictions · Generating Post Hoc Rationales of Deep Visual Classification Decisions · Ensembling Visual Explanations · Explainable Deep Driving by Visualizing Causal Attention · Interdisciplinary Perspective on Algorithmic Job Candidate Search · Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions · Inherent Explainability Pattern Theory-based Video Event Interpretations

This text provides an introduction to computational aspects of early vision, in particular, color, stereo, and visual navigation. It integrates approaches from psychophysics and quantitative neurobiology, as well as theories and algorithms from machine vision and photogrammetry. When presenting mathematical material, it uses detailed verbal descriptions and illustrations to clarify complex points. The text is suitable for upper-level students in neuroscience, biology, and psychology who have basic mathematical skills and are interested in studying the mathematical modeling of perception.

Group and Crowd Behavior for Computer Vision provides a multidisciplinary perspective on how to solve the problem of group and crowd analysis and modeling, combining insights from the social sciences with technological ideas in computer vision and pattern recognition. The book answers many unresolved issues in group and crowd behavior, with Part One providing an introduction to the problems of analyzing groups and crowds that stresses that they should not be considered as completely diverse entities, but as an aggregation of people. Part Two focuses on features and representations with the aim of recognizing the presence of groups and crowds in image and video data. It discusses low level processing methods to individuate when and where a group or crowd is placed in the scene, spanning from the use of people detectors toward more ad-hoc strategies to individuate group and crowd formations. Part Three discusses methods for analyzing the behavior of groups and the crowd once they have been detected, showing how to extract semantic information, predicting/tracking the movement of a group, the formation or disaggregation of a group/crowd and the identification of different kinds of groups/crowds depending on their behavior. The final section focuses on identifying and promoting datasets for group/crowd analysis and modeling, presenting and discussing metrics for evaluating the pros and cons of the various models and methods. This book gives computer vision researcher techniques for segmentation and grouping, tracking and reasoning for solving group and crowd modeling and analysis, as well as more general problems in computer vision and machine learning. - Presents the first book to cover the topic of modeling and analysis of groups in computer vision - Discusses the topics of group and crowd modeling from a cross-disciplinary perspective, using social science anthropological theories translated into computer vision algorithms - Focuses on group and crowd analysis metrics - Discusses real industrial systems dealing with the problem of analyzing groups and crowds