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

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Fast Fourier Transform

Computer algebra systems are now ubiquitous in all areas of science and engineering. This highly successful textbook, widely regarded as the “bible of computer algebra”, gives a thorough introduction to the algorithmic basis of the mathematical engine in computer algebra systems. Designed to accompany oneor two-semester courses for advanced undergraduate or graduate students in computer science or mathematics, its comprehensiveness and reliability has also made it an essential reference for professionals in the area. Special features include: detailed study of algorithms including time analysis; implementation reports on several topics; complete proofs of the mathematical underpinnings; and a wide variety of applications (among others, in chemistry, coding theory, cryptography, computational logic, and the design of calendars and musical scales). A great deal of historical information and illustration enlivens the text. In this third edition, errors have been corrected and much of the Fast Euclidean Algorithm chapter has been renovated.

Modern computer algebra

The Artificial Intelligence field continues to be plagued by what can only be described as ‘bold promises for the future syndrome’, often perpetrated by researchers who should know better. While impartial assessment can point to concrete contributions over the past 50 years (such as automated theorem proving, games strategies, the LISP and Prolog high-level computer languages, Automatic Speech Recognition, Natural Language Processing, mobile robot path planning, unmanned vehicles, humanoid robots, data mining, and more), the more cynical argue that AI has witnessed more than its fair share of ‘unmitigated disasters’ during this time – see, for example [3,58,107,125,186]. The general public becomes rapidly jaded with such ‘bold predictions’ that fail to live up to their original hype, and which ultimately render the zealots’ promises as counter-productive.

Computational Intelligence: An Introduction

Many applications of Selforganizing Feature Maps (SOMs) need a high performance hardware system in order to be efficient. Because of the regular and modular structure of SOMs , a hardware realization is obvious. Based on the idea of a massively parallel system, several chips have been designed, manufactured and tested by the authors. In this paper a high performance system with the latest NBISOM_25 chips is presented. The NBISOM_25 integrated circuit contains 25 processing elements in a 5 by 5 array. Due to the scalability of the chips a VME-bus board was built with 16 ICs on it. The controller for the VME-bus and the SOM hardware are realized using FPGAs. The system runs SOM applications with up to 400 elements in parallel mode (20 by 20 map). Each weight vector can have up to 64 weights of 8 bit accuracy. The maximum performance of the board-system is 4.1 GCPS (recall) and 2.4 GCUPS (learning).

/pdf/som-hardware-accelerator-pa8p5xdqd2.pdf

SOM Hardware-Accelerator

Emulating the highly resource-efficient processing of visual motion information in the brain of flying insects, a bio-inspired controller for collision avoidance and navigation was implemented on a novel, integrated System-on-Chip-based hardware module. The hardware module is used to control visually-guided navigation behavior of the stick insect-like hexapod robot HECTOR. By leveraging highly parallelized bio-inspired algorithms to extract nearness information from visual motion in dynamically reconfigurable logic, HECTOR is able to navigate to predefined goal positions without colliding with obstacles. The system drastically outperforms CPU- and graphics card-based implementations in terms of speed and resource efficiency, making it suitable to be also placed on fast moving robots, such as flying drones.

https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0230620&type=printable

Resource-efficient bio-inspired visual processing on the hexapod walking robot HECTOR

A web service is a web-based application that allows client programs to access its functionality using open, standardized protocols. This approach ensures interconnectivity and interoperability so that the functionality of the web service can be accessed by virtually any client, regardless of location and programming language or platform. Autonomous mobile robots have the ability to perceive and physically interact with the real world. The ability of physical manipulation would fundamentally extend the service capability of web services. On the other hand, by equipping mobile autonomous robots with web service interfaces, a standardized, interoperable way for world-wide access to the robots could be provided. In this project we study the suitability of the web service standards and technologies for the teleoperation of mobile autonomous robots. As project result we provide access to a mobile autonomous robot via a web service interface.

Teleoperation of a Mobile Autonomous Robot using Web Services

The contents of this publication are intended for providing additional supplementary materials, i.e. proofs of formula derivations, presented in our paper entitled "An Analytical Study of Time of Flight Error Estimation in Two-Way Ranging Methods". The mentioned paper was submitted to ``Ninth International Conference on Indoor Positioning and Indoor Navigation (IPIN) 2018}'', which will be held in Nantes, France on September 24-27, 2018.

Supplementary Data for the Paper entitled ''An Analytical Study of Time of Flight Error Estimation in Two-Way Ranging Methods''

Many applications of Selforganizing Feature Maps (SOFMs) need a high performance hardware system in order to be efficient. Because of the regular and modular structure of SOFMs, a hardware realization is obvious. Based on the idea of a massively parallel system, several chips have been designed, manufactured and tested by the authors. In this paper a high performance system with the latest NBISOM_25 chips is presented. The NBISOM_25 integrated circuit contains 25 processing elements in a 5 by 5 array. Due to the scalability of the chips a VME-bus board was built with 16 ICs on it. The controller for the VME-bus and the SOFM hardware are realized using FPGAs. The system runs SOFM applications with up to 400 elements in parallel mode (20 by 20 map). Each weight vector can have up to 64 weights of 8 bit accuracy. The maximum performance of the board-system is 4.1 GCPS (recall) and 2.4 GCUPS (learning).

/pdf/a-high-performance-sofm-hardware-system-2s2nlreske.pdf

Ulrich Rückert

Papers

SOM Hardware-Accelerator

Resource-efficient bio-inspired visual processing on the hexapod walking robot HECTOR

Teleoperation of a Mobile Autonomous Robot using Web Services

Supplementary Data for the Paper entitled ''An Analytical Study of Time of Flight Error Estimation in Two-Way Ranging Methods''

A High Performance SOFM Hardware-System