Computer Vision for Multimedia Applications: Methods and Solutions

Computer Vision for Multimedia Applications: Methods and Solutions

Jinjun Wang, Jian Cheng, Shuqiang Jiang

Copyright: © 2011 |Pages: 354

ISBN13: 9781609600242|ISBN10: 160960024X|EISBN13: 9781609600266

DOI: 10.4018/978-1-60960-024-2

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Wang, Jinjun, et al., editors. Computer Vision for Multimedia Applications: Methods and Solutions. IGI Global, 2011. https://doi.org/10.4018/978-1-60960-024-2

APA

Wang, J., Cheng, J., & Jiang, S. (Eds.). (2011). Computer Vision for Multimedia Applications: Methods and Solutions. IGI Global. https://doi.org/10.4018/978-1-60960-024-2

Chicago

Wang, Jinjun, Jian Cheng, and Shuqiang Jiang, eds. Computer Vision for Multimedia Applications: Methods and Solutions. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60960-024-2

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Although a number of methods for solving computer vision tasks exist, these methods are often task-specific and can seldom be generalized over a wide range of applications. In addition, many computer vision algorithms have not been thoroughly studied.

Computer Vision for Multimedia Applications: Methods and Solutions includes the latest developments in computer vision methods applicable to various problems in multimedia computing. This publication presents discussions on new ideas, as well as problems in computer vision and multimedia computing. It will serve as an important reference in multimedia and computer vision for academicians, researchers, and academic libraries.

Table of Contents

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Front Materials

Title Page

Copyright Page

Editorial Advisory Board

Preface

Acknowledgment

Chapters

Computer Vision for Human Computer Interaction

Chapter 1

Intelligent Vision Systems for Landmark-Based Vehicle Navigation (pages 1-19)

Wen Wu, Jie Yang, Xilin Chen

Human drivers often use landmarks for navigation. For example, we tell people to turn left after the second traffic light and to make a right at Starbucks. In our daily life, a landmark can be anything that is easily recognizable and...

Chapter 2

Spontaneous Facial Expression Analysis and Synthesis for Interactive Facial Animation (pages 20-37)

Yongmian Zhang, Jixu Chen, Yan Tong, Qiang Ji

This chapter describes a probabilistic framework for faithful reproduction of spontaneous facial expressions on a synthetic face model in a real time interactive application. The framework consists of a coupled Bayesian network (BN)...

Chapter 3

A Modular Framework for Vision-Based Human Computer Interaction (pages 38-59)

Giancarlo Iannizzotto, Francesco La Rosa

This chapter introduces the VirtualBoard framework for building vision-based Perceptual User Interfaces (PUI). While most vision-based Human Computer Interaction applications developed over the last decade focus on the technological...

Chapter 4

Robust Human Face Tracking in Eigenspace for Perceptual Human-Robot Interaction (pages 60-72)

Richard Jiang, Abdul Sadka

This chapter introduces a robust human face tracking scheme for vision-based human-robot interaction, where the detected face-like regions in the video sequence are tracked using unscented Kalman filter (UKF), and face occlusion are...

Chapter 5

3D Face Modeling for Multi-Feature Extraction for Intelligent Systems (pages 73-89)

Zahid Riaz, Suat Gedikli, Michael Beetz, Bernd Radig

In this chapter, we focus on the human robot joint interaction application where robots can extract the useful multiple features from human faces. The idea follows daily life scenarios where humans rely mostly on face to face...

Computer Vision for Multimedia Content Summary and Retrieval

Chapter 6

Video Summarization by Redundancy Removing and Content Ranking (pages 91-101)

Tao Wang, Yue Gao, Patricia Wang, Wei Hu, Jianguo Li, Yangzhou Du, Yimin Zhang

Video summary is very important for users to grasp a whole video’s content quickly for efficient browsing and editing. In this chapter, we propose a novel video summarization approach based on redundancy removing and content ranking....

Chapter 7

Multi-Sensored Vision for Autonomous Production of Personalized Video Summary (pages 102-120)

Fan Chen, Damien Delannay, Christophe De Vleeschouwer, Pascaline Parisot

This chapter provides a survey of the major research efforts that have exploited computer vision tools to extend the content production industry towards automated infrastructures allowing contents to be produced, stored, and accessed...

Chapter 8

A Perceptual Approach for Image Representation and Retrieval: The Case of Textures (pages 121-141)

Noureddine Abbadeni

This chapter describes an approach based on human perception to content-based image representation and retrieval. We consider textured images and propose to model the textural content of images by a set of features having a...

Computer Vision for Multimedia Content Analysis

Chapter 9

Event Detection in Sports Video Based on Generative-Discriminative Models (pages 143-165)

Guoliang Fan, Yi Ding

Semantic event detection is an active and interesting research topic in the field of video mining. The major challenge is the semantic gap between low-level features and high-level semantics. In this chapter, we will advance a new...

Chapter 10

Content-Based Video Scene Clustering and Segmentation (pages 166-179)

Hong Lu, Xiangyang Xue

With the amount of video data increasing rapidly, automatic methods are needed to deal with large-scale video data sets in various applications. In content-based video analysis, a common and fundamental preprocess for these...

Chapter 11

Vision Based Hand Posture Recognition (pages 180-195)

Kongqiao Wang, Yikai Fang, Xiujuan Chai

Vision based gesture recognition is a hot research topic in recent years. Many researchers focus on how to differentiate various hand shapes, e.g. the static hand gesture recognition or hand posture recognition. It is one of the...

Multimedia Authentication

Chapter 12

Detecting Image Forgeries using Geometric Cues (pages 197-217)

Lin Wu, Yang Wang

This chapter presents a framework for detecting fake regions by using various methods including watermarking technique and blind approaches. In particular, we describe current categories on blind approaches which can be divided into...

Chapter 13

Salient Region Detection for Biometric Watermarking (pages 218-236)

Ma Bin, Li Chun-lei, Wang Yun-hong, Bai Xiao

Visual saliency, namely the perceptual significance to human vision system (HVS), is a quality that differentiates an object from its neighbors. Detection of salient regions which contain prominent features and represent main...

Biologically Inspired Multimedia Computing

Chapter 14

Bio-Inspired Scheme for Classification of Visual Information (pages 238-262)

Le Dong, Ebroul Izquierdo, Shuzhi Ge

In this chapter, research on visual information classification based on biologically inspired visually selective attention with knowledge structuring is presented. The research objective is to develop visual models and corresponding...

Chapter 15

Ant-Inspired Visual Saliency Detection in Image (pages 263-272)

Jing Tian, Weiyu Yu

Visual saliency detection aims to produce saliency map of images via simulating the behavior of the human visual system (HVS). An ant-inspired approach is proposed in this chapter. The proposed approach is inspired by the ant’s...

Chapter 16

Modeling Visual Saliency in Images and Videos (pages 273-293)

Yiqun Hu, Viswanath Gopalakrishnan, Deepu Rajan

Visual saliency, which distinguishes “interesting” visual content from others, plays an important role in multimedia and computer vision applications. This chapter starts with a brief overview of visual saliency as well as the...

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Compilation of References

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