30 research outputs found
Improved line/edge detection and visual reconstruction
Lines and edges provide important information for object categorization and recognition. In addition, one
brightness model is based on a symbolic interpretation of the cortical multi-scale line/edge representation. In
this paper we present an improved scheme for line/edge extraction from simple and complex cells and we illustrate
the multi-scale representation. This representation can be used for visual reconstruction, but also for nonphotorealistic
rendering. Together with keypoints and a new model of disparity estimation, a 3D wireframe representation
of e.g. faces can be obtained in the future
Face segregation and recognition by cortical multi-scale line and edge coding
Models of visual perception are based on image representations in
cortical area V1 and higher areas which contain many cell layers for feature
extraction. Basic simple, complex and end-stopped cells provide input for line,
edge and keypoint detection. In this paper we present an improved method for
multi-scale line/edge detection based on simple and complex cells. We illustrate
the line/edge representation for object reconstruction, and we present models for
multi-scale face (object) segregation and recognition that can be embedded into
feedforward dorsal and ventral data streams (the βwhatβ and βwhereβ subsystems)
with feedback streams from higher areas for obtaining translation, rotation
and scale invariance
Painterly rendering using human vision
Painterly rendering has been linked to computer vision, but we propose to link it to human vision because perception
and painting are two processes that are interwoven. Recent progress in developing computational models allows
to establish this link. We show that completely automatic rendering can be obtained by applying four image
representations in the visual system: (1) colour constancy can be used to correct colours, (2) coarse background
brightness in combination with colour coding in cytochrome-oxidase blobs can be used to create a background
with a big brush, (3) the multi-scale line and edge representation provides a very natural way to render
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brush strokes, and (4) the multi-scale keypoint representation serves to create saliency maps for Focus-of-Attention,
and FoA can be used to render important structures. Basic processes are described, renderings are shown, and
important ideas for future research are discussed
Spatial Stereoresolution for Depth Corrugations May Be Set in Primary Visual Cortex
Stereo β3Dβ depth perception requires the visual system to extract binocular disparities between the two eyes' images. Several current models of this process, based on the known physiology of primary visual cortex (V1), do this by computing a piecewise-frontoparallel local cross-correlation between the left and right eye's images. The size of the βwindowβ within which detectors examine the local cross-correlation corresponds to the receptive field size of V1 neurons. This basic model has successfully captured many aspects of human depth perception. In particular, it accounts for the low human stereoresolution for sinusoidal depth corrugations, suggesting that the limit on stereoresolution may be set in primary visual cortex. An important feature of the model, reflecting a key property of V1 neurons, is that the initial disparity encoding is performed by detectors tuned to locally uniform patches of disparity. Such detectors respond better to square-wave depth corrugations, since these are locally flat, than to sinusoidal corrugations which are slanted almost everywhere. Consequently, for any given window size, current models predict better performance for square-wave disparity corrugations than for sine-wave corrugations at high amplitudes. We have recently shown that this prediction is not borne out: humans perform no better with square-wave than with sine-wave corrugations, even at high amplitudes. The failure of this prediction raised the question of whether stereoresolution may actually be set at later stages of cortical processing, perhaps involving neurons tuned to disparity slant or curvature. Here we extend the local cross-correlation model to include existing physiological and psychophysical evidence indicating that larger disparities are detected by neurons with larger receptive fields (a size/disparity correlation). We show that this simple modification succeeds in reconciling the model with human results, confirming that stereoresolution for disparity gratings may indeed be limited by the size of receptive fields in primary visual cortex