Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Learning Rule to Model the Development of Orientation Selectivity in Visual Cortex
Neural Processing Letters
Robust Handwritten Character Recognition with Features Inspired by Visual Ventral Stream
Neural Processing Letters
Segmentation and Edge Detection Based on Spiking Neural Network Model
Neural Processing Letters
Review article: Edge and line oriented contour detection: State of the art
Image and Vision Computing
A novel method to look for the hysteresis thresholds for the Canny edge detector
Pattern Recognition
Contour Detection and Hierarchical Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A collaborative decision-making model for orientation detection
Applied Soft Computing
An orientation detection model based on fitting from multiple local hypotheses
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
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Contrast stimuli are the essential components that constitute visual scenes. This paper studies how retinal ganglion cells respond to a contrast stimulus covering their concentric receptive fields with center-surround antagonism, and thereby proposes a mathematical model that describes the response by the multiplication of the contrast of the stimulus and a normalized response function with respect to the coverage ratio and the center/surround ratio. The obtained response curves turn out to be consistent with physiological data. This model partially accounts for the contrast sensitivities of ganglion cells and the invariance of visual perception to the contrast. A computational approach based on this neural model is developed for orientation detection, and is further applied to image representation. The experiments achieve convincing results on challenging image datasets. Moreover, it is revealed that the produced orientation maps remarkably enhance the efficiencies and the effectiveness of segmentation algorithms.