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Chessa M., Solari F., Sabatini S.P. (eds.) Human-Centric Machine Vision

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Chessa M., Solari F., Sabatini S.P. (eds.) Human-Centric Machine Vision
Издательство InTech, 2012, -188 pp.
In the last decade, the algorithms for the processing of the visual information have greatly evolved, providing efficient and effective solutions to cope with the variability and the complexity of real-world environments. These achievements yield to the development of Machine Vision systems that overcome the typical industrial applications, where the environments are controlled and the tasks are very specific, towards the use of innovative solutions to face with everyday needs of people. In particular, the Human-Centric Machine Vision can help to solve the problems raised by the needs of our society, e.g. security and safety, health care, medical imaging, human machine interface, and assistance in vehicle guidance. In such applications it is necessary to handle changing, unpredictable and complex situations, and to take care of the presence of humans.
This book focuses both on human-centric applications and on bio-inspired Machine Vision algorithms. Chapter 1 describes a method to detect the 3D orientation of human eyes for possible use in biometry, human-machine interaction, and psychophysics experiments. Features’ extraction based on wavelet moments and moment invariants are applied in different fields, such as face and facial expression recognition, and hand posture detection in Chapter
2. Innovative tools for assisting medical imaging are described in Chapters 3 and 4, where a texture classification method for the detection of calcification clusters in mammography and a technique for the screening of the retinopathy of the prematurity are presented. A real-time mice scratching detection and quantification system is described in Chapter 5, and a tool that reliably determines the presence of micro-organisms in water samples is presented in Chapter
6. Bioinspired algorithms are used in order to solve complex tasks, such as the robotic cognitive autonomous navigation in Chapter 7, and the transformation of image filters by using complex-value neural networks in Chapter
8. Finally, the potential of Machine Vision and of the related technologies in various application domains of critical importance for economic growth is reviewed in Chapter 9.
The Perspective Geometry of the Eye: Toward Image-Based Eye-Tracking
Feature Extraction Based on Wavelet Moments and Moment Invariants inMachine Vision Systems
A Design for Stochastic Texture Classification Methods in Mammography Calcification Detection
Optimized Imaging Techniques to Detect and Screen the Stages of Retinopathy of Prematurity
Automatic Scratching Analyzing System for Laboratory Mice: SCLABA-Real
Machine Vision Application to Automatic Detection of Living Cells/Objects
Reading Mobile Robots and 3D Cognitive Mapping
Transformations of Image Filters for Machine Vision Using Complex-Valued Neural Networks
Boosting Economic Growth Through Advanced Machine Vision
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