The Soft Computing techniques, which are based on the information processing of biological systems are now massively used in the area of pattern recognition, making prediction & planning, as well as acting on the environment. Ideally speaking, soft computing is not a subject of homogeneous concepts and techniques; rather, it is an amalgamation of distinct methods that confirms to its guiding principle. At present, the main aim of soft computing is to exploit the tolerance for imprecision and uncertainty to achieve tractability, robustness and low solutions cost. The principal constituents of soft computing techniques are probabilistic reasoning, fuzzy logic, neuro-computing, genetic algorithms, belief networks, chaotic systems, as well as learning theory. This book covers contributions from various authors to demonstrate the use of soft computing techniques in various applications of engineering.

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Machine Vision Solutions in Automotive Industry Kinect Quality Enhancement for Triangular Mesh Reconstruction with a Medical Image Application.- Matlab GUI Package for Comparing Data Clustering Algorithms.- Multi Objective Line Symmetry Based Evolutionary Clustering Approach.- An Efficient Method for Contrast Enhancement of Digital Mammographic Images.- Simulation of Obstacle Detection and Speed Control for Autonomous Robotic Vehicle.- A Review of Global Path Planning Algorithms for Planar Navigation of Autonomous Underwater Robots.- Pseudo-Fractional Tap-Length Learning Based Applied Soft Computing for Structure Adaptation of LMS in High Noise Environment Medical Image Analysis Using Soft Computing Techniques.- Selection of Robotic Grippers Under MCDM Environment - An Optimized Trade Off Technique.- Modeling and Simulation of Viscous Flow in the Hydraulic System of Electro Optical Tracking System.- Comparison of Edge Detection Algorithm for Part Identification in a Vision Guided Robotic Assembly System.