From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

Wireless sensor networks (WSNs) monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002–2013 of machine learning methods that were used to address common issues in WSNs. The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=3963&context=sis_research

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks (WSNs) are networks of distributed autonomous devices that can sense or monitor physical or environmental conditions cooperatively. WSNs face many challenges, mainly caused by communication failures, storage and computational constraints and limited power supply. Paradigms of computational intelligence (CI) have been successfully used in recent years to address various challenges such as data aggregation and fusion, energy aware routing, task scheduling, security, optimal deployment and localization. CI provides adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs. CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures and scenario changes. However, WSN developers are usually not or not completely aware of the potential CI algorithms offer. On the other side, CI researchers are not familiar with all real problems and subtle requirements of WSNs. This mismatch makes collaboration and development difficult. This paper intends to close this gap and foster collaboration by offering a detailed introduction to WSNs and their properties. An extensive survey of CI applications to various problems in WSNs from various research areas and publication venues is presented in the paper. Besides, a discussion on advantages and disadvantages of CI algorithms over traditional WSN solutions is offered. In addition, a general evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for WSNs.

Computational Intelligence in Wireless Sensor Networks: A Survey

Currently, the network traffic control systems are mainly composed of the Internet core and wired/wireless heterogeneous backbone networks. Recently, these packet-switched systems are experiencing an explosive network traffic growth due to the rapid development of communication technologies. The existing network policies are not sophisticated enough to cope with the continually varying network conditions arising from the tremendous traffic growth. Deep learning, with the recent breakthrough in the machine learning/intelligence area, appears to be a viable approach for the network operators to configure and manage their networks in a more intelligent and autonomous fashion. While deep learning has received a significant research attention in a number of other domains such as computer vision, speech recognition, robotics, and so forth, its applications in network traffic control systems are relatively recent and garnered rather little attention. In this paper, we address this point and indicate the necessity of surveying the scattered works on deep learning applications for various network traffic control aspects. In this vein, we provide an overview of the state-of-the-art deep learning architectures and algorithms relevant to the network traffic control systems. Also, we discuss the deep learning enablers for network systems. In addition, we discuss, in detail, a new use case, i.e., deep learning based intelligent routing. We demonstrate the effectiveness of the deep learning-based routing approach in contrast with the conventional routing strategy. Furthermore, we discuss a number of open research issues, which researchers may find useful in the future.

State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems

The recent technological advances in micro electro-mechanical systems have promoted the development of a powerful class of sensor-based distributed intelligent systems capable of ubiquitously retrieving multimedia information, namely Wireless Multimedia Sensor Networks (WMSNs). WMSNs are gaining more popularity day by day as they are envisioned to support a large number of both non-real time and real-time multimedia applications. However, satisfying the stringent quality of service (QoS) requirements of multimedia transmission in a resource-constrained sensor network environment places new challenges to routing. As an outcome, optimal energy and application-specific QoS aware routing for WMSNs has gained considerable research attention recently. In this paper, current state-of-the-art in energy-efficient routing techniques for WMSNs is surveyed together with the highlights of the performance issues of each strategy. We outline the design challenges of routing protocols for WMSNs followed by the limitations of current techniques designed for non-multimedia data transmission. Further, a classification of recent routing protocols for WMSNs and a discussion of possible future research trends are presented.

A Survey on Energy-Efficient Routing Techniques with QoS Assurances for Wireless Multimedia Sensor Networks

In recent years, various fragile watermarking schemes have been proposed for image authentication and integrity verification. In this paper, a novel block-based fragile watermarking scheme for image authentication and tamper proofing is proposed. Realizing that block-wise dependency is a basic requirement for fragile watermarking schemes to withstand counterfeiting attacks, the proposed scheme uses the fuzzy c-means (FCM) clustering technique to create the relationship between image blocks. The effectiveness of the proposed scheme is demonstrated through a series of attack simulations. Experimental results show that the proposed scheme not only provides satisfactory imperceptibility, but also achieves superior tamper detection and localization accuracy under different attacks, such as the cut-and-paste attack and the vector quantization (VQ) attack.

A fuzzy c-means clustering-based fragile watermarking scheme for image authentication

This paper proposes the Top-View Transformation Model for image coordinate transformation, which involves transforming a perspective projection image into its corresponding bird's eye vision A fitting parameters searching algorithm estimates the parameters that are used to transform the coordinates from the source image Using this approach, it is not necessary to provide any interior and exterior orientation parameters of the camera The designed car parking assistant system can be installed at the rear end of the car, providing the driver with a clearer image of the area behind the car The processing time can be reduced by storing and using the transformation matrix estimated from the first image frame for a sequence of video images The transformation matrix can be stored as the Matrix Mapping Table, and loaded into the embedded platform to perform the transformation Experimental results show that the proposed approaches can provide a clearer and more accurate bird's eye view to the vehicle driver

A Vision Based Top-View Transformation Model for a Vehicle Parking Assistant

A majority-voting based watermarking scheme for color image tamper detection and recovery

A hybrid DWT-SVD copyright protection scheme based on k-means clustering and visual cryptography

In this paper we describe fuzzy Hopfield neural network (FHNN) technique to solve the TDMA (time division multiple access) broadcast scheduling problem in wireless sensor networks (WSN). We formulate it as discrete energy minimization problem and map it into Hopfield neural network with the fuzzy c-means strategy to find the TDMA schedule for nodes in a communication network. The broadcast scheduling problem for wireless sensor networks is an NP-complete problem. Each time slot is regarded as a data sample and every node is taken as a cluster. Time slots are adequately distributed to the dedicated node while satisfying the constraints. The aim is to minimize the TDMA cycle length and maximize the node transmissions avoiding both primary and secondary conflicts. The FHNN reduces the processing time and increases the convergence rate for Broadcast Scheduling Problem. Simulation results show that the FHNN improves performance substantially through solving well-known benchmark problems.

Ming-Shi Wang

Papers

A fuzzy c-means clustering-based fragile watermarking scheme for image authentication

A Vision Based Top-View Transformation Model for a Vehicle Parking Assistant

A majority-voting based watermarking scheme for color image tamper detection and recovery

A hybrid DWT-SVD copyright protection scheme based on k-means clustering and visual cryptography

Broadcast scheduling in wireless sensor networks using fuzzy Hopfield neural network