Edge Detection for Pattern Recognition: A Survey <s> Edge features <s> To make vision possible, the visual nervous system must represent the most informative features in the light pattern captured by the eye. Here we use Gaussian scale-space theory to derive a multiscale model for edge analysis and we test it in perceptual experiments. At all scales there are two stages of spatial filtering. An odd-symmetric, Gaussian first derivative filter provides the input to a Gaussian second derivative filter. Crucially, the output at each stage is half-wave rectified before feeding forward to the next. This creates nonlinear channels selectively responsive to one edge polarity while suppressing spurious or "phantom" edges. The two stages have properties analogous to simple and complex cells in the visual cortex. Edges are found as peaks in a scale-space response map that is the output of the second stage. The position and scale of the peak response identify the location and blur of the edge. The model predicts remarkably accurately our results on human perception of edge location and blur for a wide range of luminance profiles, including the surprising finding that blurred edges look sharper when their length is made shorter. The model enhances our understanding of early vision by integrating computational, physiological, and psychophysical approaches. © ARVO. <s> BIB001 </s> Edge Detection for Pattern Recognition: A Survey <s> Edge features <s> Automatic recognition of face sketches is a challenging problem. It has application in forensics. An artist drawn sketch based on the descriptions from the witnesses can be used as the test image to recognize a person from the photo database of suspects. In this paper, we propose a novel method for face sketch recognition. We use the edge features of a face sketch and face photo image to create a feature string called ’edge-string’. The edge-strings of the face photo and face sketch are then compared using the Smith-Waterman algorithm for local alignments. The results on CUHK (Chinese University of Hong Kong) student dataset show the effectiveness of the proposed approach in face sketch recognition. <s> BIB002
The inference of the edges from being a descriptor to identify the structure to being a feasible feature for pattern matching requires the understanding of human vision BIB001 . The edge features of the objects has moved from being a digital only representation to a discrete edge representation in digital images, and often is highly sensitive to variations in natural variability within the images. The most common natural variability that have a significant impact on the quality of the edge features are illumination changes and sensor noises. Whereas, that would affect the recognition of the objects using the feature also depend on the scale and shift sensitivity of the edges, the pose of the objects, the quality and quantity of training image set (Chartier & Lepage, n.d.; Dollar et al., n.d.; Dongcheng Shi et al., n.d.; Gruen et al., 1993; Krammer & Schweinzer, 2006; Lin & Kung, 2000; Mae & Shirai, n.d.; Robinson, n.d.; Shirai et al., 1996 . Computationally efficient implementation of the edges is also a serious issue to consider in the realtime detection and extraction of object edges (Alex, Asari, & Alex, 2013; Dollár & Zitnick, 2015; King, 1985; Latecki, 1998; Mathew & James, 2015 . The ability for the edges to become robust to noise is an important issues when the edge features is used to perform pattern recognition tasks such a face and face sketch recognition ( Lee & Yunwoo, 2000; Nezhadarya & Ward, 2011; O'Gorman, Sammon, & Seul, 2008; Ren, Lei, Dai, & Li, 2015; Sinha & Dougherty, 1998 . The methods such as BIB002 , 2013; R. Hu et al., 2013; Mathew & James, 2015; Van De Ville & Unser, 2008 ) making use of the psychological mechanism of human mind and brain tends to outperform the gradient only approaches indicating the benefit of drawing inspiration from the neuroscience studies to the benefit of image processing and pattern recognition studies. Another frequent and useful approach to generate useful features from the gradients is to combine the different filters to achieve fused features having several aspects of the edge information useful for pattern matching (George & Unnikrishnan, 2016; Ghantous, Milad, Soumik, & Magdy, 2008; Ghassemian, n 