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STOCHASTIC ASPECTS OF MODEL BASED VISION
MODELBASED VISION SYSTEMS AND THEIR APPLICATIONS
THREEDIMENSIONAL OBJECT MODELING AND RECOGNITION
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algorithm analysis angle application approach aspect assume attributes camera cell complete Computer condition considered consists constraints convex coordinates corresponding database defined described detection determine developed directional distributed edge EE event error estimate example extracted face Figure filter function geometric given graph histograms hypothesis IEEE implementation interception intersection known linking Machine matching measure method microtubule model points model-based multiple networks node object object recognition observed obtained occluded operations orientation pair parallel parameters path Pattern performed pixel plane points position possible predicted present probability problem projection random recognition reduce reference region relations represent representation respectively rotation scene segmentation selected shown shows single space step structure surface technique textures transformation vector verification viewing viewpoint visible Vision