This paper presents a method for reconstructing the visual appearance of a scene from any viewpoint, given a collection of stereo pairs. A representation is suggested which is flexible and efficient, and algorithms to recover three dimensional structure and grey-level detail by combining a number

All visual sensors, biological and artificial, are finite in resolution by necessity. This causes the effective reflectance of surfaces in a scene to vary with magnification. A reflectance model for matte surfaces is described that captures the effect of macroscopic surface undulations on image

when the object crosses the y-axis; thus ˜ψ = ψ − π/2, where ψ is the angle of observation used in Taylor’s calculations. A relativistic ray-tracing argument now shows that the observed object will appear to us as rotated counterclockwise through an angle φ, satisfying the equation [Taylor X-12 p. 356

Abstract not found

Abstract not found

A system to retrieve images using a description of visual appearance is presented. A multi-scale invariant vector representation is obtained by first filtering images in the database with Gaussian derivative filters at several scales and then computing low order differential invariants. The multi

A texture characterization method has been explored to model the visual appearance of paintings, before and after a surface treatment. A preliminary experiment has been conducted to test the texture feature vectors before and after varnishing with Grumbacher’s Picture Varnish liquid. Numerical

motion and appearance information to detect a walking person. Past approaches have built detectors based on motion information or detectors based on appearance information, but ours is the first to combine both sources of information in a single detector. The implementation described runs at about 4

Active Appearance Models (AAMs) and the closely related concepts of Morphable Models and Active Blobs are generative models of a certain visual phenomenon. Although linear in both shape and appearance, overall, AAMs are nonlinear parametric models in terms of the pixel intensities. Fitting an AAM

Abstract. We present a novel method for generic visual categorization: the problem of identifying the object content of natural images while generalizing across variations inherent to the object class. This bag of keypoints method is based on vector quantization of affine invariant descriptors