Applications of AI, Machine Vision and Robotics
World Scientific, 1995 - Technology & Engineering - 259 pages
This text features a broad array of research efforts in computer vision including low level processing, perceptual organization, object recognition and active vision. The volume's nine papers specifically report on topics such as sensor confidence, low level feature extraction schemes, non-parametric multi-scale curve smoothing, integration of geometric and non-geometric attributes for object recognition, design criteria for a four degree-of-freedom robot head, a real-time vision system based on control of visual attention and a behavior-based active eye vision system. The scope of the book provides an excellent sample of current concepts, examples and applications from multiple areas of computer vision.
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ACTIVITY_SPACE adaptive filter assume blur estimation blur measurements blur model blur spread estimates camera blur camera power spectrum camera's frequency response camera's point spread compiled sensor confidences Computer Vision constrained algorithm conventional algorithm corroboration process cost function cylindrical sinc function data structures defocus determine differing focal gradients discrete Fourier transform estimated activity ESTIMATED_STATE estimation algorithm estimation process estimation_data evidence evidence_space evidential decay process expected activity extraction feature detector forgetting factor frequency domain fusion Gaussian constraint Gaussian function geometric Hessian matrix image data implementation L. F. HOLEVA large focal gradient linear Machine Vision Newton's method object depth optical partial confidence partial sensor confidence Pattern Recognition point spread function problem pseudocode range measurements reconstruction recovery Robot Vision robotic system sampling rate sensor fusion sensor performance shift-invariant signal situation_space small focal gradient spatial domain squared error stable stationary subset sum of squared surface tion ultrasonic sensors update values weight vector