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

We have implemented a 32bit ALU operating at voltages from 115mV to 1V on a die area of 0.021mm² in 90nm bulk CMOS. The energy minimum of
0.45pJ/instruction is achieved at a supply voltage of 210mV with the ALUs operating at a clock frequency of 3MHz. A yield of 88.5% can be reported for a supply voltage of 200mV, and 75% for a supply voltage of 120mV
without any body biasing applied. The ALUs have been implemented with an automated design flow and a custom standard cell library, optimized for sub-threshold
operation.

A Sub-200mV 32bit ALU with 0.45pJ/instruction in 90nm CMOS

The paper proposes a novel architecture for a highly customisable FPGA-SoC-based Computer-on-Module (CoM) targeting embedded vision applications. Apart from a Xilinx Zynq SoC, the module integrates an Adapteva Epiphany floating point accelerator in a Toradex Apalis compliant form factor. The CoM has been successfully integrated into two robot platforms to enhance their vision processing capabilities. For evaluation, visually-guided collision avoidance and navigation has been implemented, mimicking the behaviour of insects. The hardware/software partitioning is presented together with a comparison to an HLS-based solution for the given application. The proposed stream-based FPGA implementation achieves a speedup of 721 and an increase in energy efficiency by a factor of 800 compared to an OpenCV-based implementation on one of the embedded ARM processors of the Zynq SoC.

Resource-efficient Reconfigurable Computer-on-Module for Embedded Vision Applications

The Impact of Communication on Hardware Accelerators for Neural Networks

The diversity of today's mobile applications requires embedded processor cores with a high resource efficiency, that means, the devices should provide a high performance at low area requirements and power consumption. The fine-grained parallelism supported by multiple functional units of VLIW architectures offers a high throughput at reasonable low clock frequencies compared to single-core RISC processors. To efficiently utilize the processor pipeline, common system architectures have to cope with data hazards due to data dependencies between consecutive operations. On the one hand, such hazards can be resolved by complex forwarding circuits (i.e., a pipeline bypass) which forward intermediate results to a subsequent instruction. On the other hand, the pipeline bypass can strongly affect or even dominate the total resource requirements and degrade the maximum clock frequency. In this work the CoreVA VLIW architecture is used for the development and the analysis of application-specific bypass configurations. It is shown that many paths of a comprehensive bypass system are rarely used and may not be required for certain applications. For this reason, several strategies have been implemented to enhance the efficiency of the total system by introducing application-specific bypass configurations. The configuration can be carried out statically by only implementing required paths or at runtime by dynamically reconfiguring the hardware. An algorithm is proposed which derives an optimized configuration by iteratively disabling single bypass paths. The adaptation of these application-specific bypass configurations allows for a reduction of the critical path by 26p. As a result, the execution time and energy requirements could be reduced by up to 21.5p. Using Dynamic Frequency Scaling (DFS) and dynamic deactivation/reactivation of bypass paths allows for a runtime reconfiguration of the bypass system. This ensures the highest efficiency while processing varying applications.

A systematic approach for optimized bypass configurations for application-specific embedded processors

A huge number of techniques for detecting and mapping obstacles based on LIDAR and SONAR exist, though

not taking approximative sensors with high levels of uncertainty into consideration. The proposed mapping

method in this article is undertaken by detecting surfaces and approximating objects by distance using sensors

with high localization ambiguity. Detection is based on an Inverse Particle Filter, which uses readings from

single or multiple sensors as well as a robotâs motion. This contribution describes the extension of the Sequential

Importance Resampling filter to detect objects based on an analytical sensor model and embedding into

Occupancy Grid Maps. The approach has been applied to the autonomous mini robot AMiRo in a distributed

way. There were promising results for its low-power, low-cost proximity sensors in various real life mapping

scenarios, which outperform the standard Inverse Sensor Model approach.

Ulrich Rückert

Papers

A Sub-200mV 32bit ALU with 0.45pJ/instruction in 90nm CMOS

Resource-efficient Reconfigurable Computer-on-Module for Embedded Vision Applications

The Impact of Communication on Hardware Accelerators for Neural Networks

A systematic approach for optimized bypass configurations for application-specific embedded processors

Occupancy Grid Mapping with Highly Uncertain Range Sensors based on Inverse Particle Filters