Computer Vision

Models, Learning, and Inference

Author: Simon J. D. Prince

Publisher: Cambridge University Press

ISBN: 1107011795

Category: Computers

Page: 580

View: 8529


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

Feature Extraction and Image Processing for Computer Vision

Author: Mark Nixon,Alberto Aguado

Publisher: Academic Press

ISBN: 0128149779

Category: Computers

Page: 650

View: 6735


Feature Extraction for Image Processing and Computer Vision is an essential guide to the implementation of image processing and computer vision techniques, with tutorial introductions and sample code in MATLAB and Python. Algorithms are presented and fully explained to enable complete understanding of the methods and techniques demonstrated. As one reviewer noted, "The main strength of the proposed book is the link between theory and exemplar code of the algorithms." Essential background theory is carefully explained. This text gives students and researchers in image processing and computer vision a complete introduction to classic and state-of-the art methods in feature extraction together with practical guidance on their implementation. The only text to concentrate on feature extraction with working implementation and worked through mathematical derivations and algorithmic methods A thorough overview of available feature extraction methods including essential background theory, shape methods, texture and deep learning Up to date coverage of interest point detection, feature extraction and description and image representation (including frequency domain and colour) Good balance between providing a mathematical background and practical implementation Detailed and explanatory of algorithms in MATLAB and Python

Image Processing, Analysis, and Machine Vision

Author: Milan Sonka,Vaclav Hlavac,Roger Boyle

Publisher: Cengage Learning

ISBN: 1133593607

Category: Technology & Engineering

Page: 920

View: 3658


The brand new edition of IMAGE PROCESSING, ANALYSIS, AND MACHINE VISION is a robust text providing deep and wide coverage of the full range of topics encountered in the field of image processing and machine vision. As a result, it can serve undergraduates, graduates, researchers, and professionals looking for a readable reference. The book's encyclopedic coverage of topics is wide, and it can be used in more than one course (both image processing and machine vision classes). In addition, while advanced mathematics is not needed to understand basic concepts (making this a good choice for undergraduates), rigorous mathematical coverage is included for more advanced readers. It is also distinguished by its easy-to-understand algorithm descriptions of difficult concepts, and a wealth of carefully selected problems and examples. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

Deep Learning for Computer Vision

Image Classification, Object Detection, and Face Recognition in Python

Author: Jason Brownlee

Publisher: Machine Learning Mastery


Category: Computers

Page: 563

View: 2461


Step-by-step tutorials on deep learning neural networks for computer vision in python with Keras.

Biologically Inspired Computer Vision

Fundamentals and Applications

Author: Gabriel Cristobal,Laurent Perrinet,Matthias S. Keil

Publisher: John Wiley & Sons

ISBN: 3527412646

Category: Science

Page: 480

View: 2250


As the state-of-the-art imaging technologies became more and more advanced, yielding scientific data at unprecedented detail and volume, the need to process and interpret all the data has made image processing and computer vision increasingly important. Sources of data that have to be routinely dealt with today's applications include video transmission, wireless communication, automatic fingerprint processing, massive databanks, non-weary and accurate automatic airport screening, robust night vision, just to name a few. Multidisciplinary inputs from other disciplines such as physics, computational neuroscience, cognitive science, mathematics, and biology will have a fundamental impact in the progress of imaging and vision sciences. One of the advantages of the study of biological organisms is to devise very different type of computational paradigms by implementing a neural network with a high degree of local connectivity. This is a comprehensive and rigorous reference in the area of biologically motivated vision sensors. The study of biologically visual systems can be considered as a two way avenue. On the one hand, biological organisms can provide a source of inspiration for new computational efficient and robust vision models and on the other hand machine vision approaches can provide new insights for understanding biological visual systems. Along the different chapters, this book covers a wide range of topics from fundamental to more specialized topics, including visual analysis based on a computational level, hardware implementation, and the design of new more advanced vision sensors. The last two sections of the book provide an overview of a few representative applications and current state of the art of the research in this area. This makes it a valuable book for graduate, Master, PhD students and also researchers in the field.

Structured Learning and Prediction in Computer Vision

Author: Sebastian Nowozin,Christoph H. Lampert

Publisher: Now Publishers Inc

ISBN: 1601984561

Category: Computers

Page: 196

View: 1996


Structured Learning and Prediction in Computer Vision introduces the reader to the most popular classes of structured models in computer vision.

Machine Learning

A Probabilistic Perspective

Author: Kevin P. Murphy

Publisher: MIT Press

ISBN: 0262018020

Category: Computers

Page: 1067

View: 2160


A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

Applied Graph Theory in Computer Vision and Pattern Recognition

Author: Abraham Kandel,Horst Bunke,Mark Last

Publisher: Springer Science & Business Media

ISBN: 3540680195

Category: Computers

Page: 265

View: 3315


This book will serve as a foundation for a variety of useful applications of graph theory to computer vision, pattern recognition, and related areas. It covers a representative set of novel graph-theoretic methods for complex computer vision and pattern recognition tasks. The first part of the book presents the application of graph theory to low-level processing of digital images such as a new method for partitioning a given image into a hierarchy of homogeneous areas using graph pyramids, or a study of the relationship between graph theory and digital topology. Part II presents graph-theoretic learning algorithms for high-level computer vision and pattern recognition applications, including a survey of graph based methodologies for pattern recognition and computer vision, a presentation of a series of computationally efficient algorithms for testing graph isomorphism and related graph matching tasks in pattern recognition and a new graph distance measure to be used for solving graph matching problems. Finally, Part III provides detailed descriptions of several applications of graph-based methods to real-world pattern recognition tasks. It includes a critical review of the main graph-based and structural methods for fingerprint classification, a new method to visualize time series of graphs, and potential applications in computer network monitoring and abnormal event detection.

Computer Vision -- ECCV 2006

9th European Conference on Computer Vision, Graz, Austria, May 7-13, 2006, Proceedings

Author: Aleš Leonardis,Horst Bischof

Publisher: Springer Science & Business Media

ISBN: 3540338365

Category: Computers

Page: 654

View: 8010


The four-volume set comprising LNCS volumes 3951/3952/3953/3954 constitutes the refereed proceedings of the 9th European Conference on Computer Vision, ECCV 2006, held in Graz, Austria, in May 2006. The 192 revised papers presented were carefully reviewed and selected from a total of 811 papers submitted. The four books cover the entire range of current issues in computer vision. The papers are organized in topical sections on recognition, statistical models and visual learning, 3D reconstruction and multi-view geometry, energy minimization, tracking and motion, segmentation, shape from X, visual tracking, face detection and recognition, illumination and reflectance modeling, and low-level vision, segmentation and grouping.

Handbook of Statistics

Machine Learning: Theory and Applications

Author: N.A

Publisher: Newnes

ISBN: 0444538666

Category: Mathematics

Page: 552

View: 3623


Statistical learning and analysis techniques have become extremely important today, given the tremendous growth in the size of heterogeneous data collections and the ability to process it even from physically distant locations. Recent advances made in the field of machine learning provide a strong framework for robust learning from the diverse corpora and continue to impact a variety of research problems across multiple scientific disciplines. The aim of this handbook is to familiarize beginners as well as experts with some of the recent techniques in this field. The Handbook is divided in two sections: Theory and Applications, covering machine learning, data analytics, biometrics, document recognition and security. very relevant to current research challenges faced in various fields self-contained reference to machine learning emphasis on applications-oriented techniques