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...sed for description. Shape context is similar to the SIFT descriptor, but is based on edges. Shape context is a 3D histogram of edge point locations and orientations. Edges are extracted by the Canny =-=[5]-=- detector. Location is quantized into nine bins of a log-polar coordinate system as displayed in Fig. 1e with the radius set to 6, 11, and 15 and orientation quantized into four bins (horizontal, vert...

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... regions are visible in Figures (c–e), e.g., the bookshelves, table and cans, and head statue, while three different disparity levels can be seen as horizontal lines in the (x, d) slice (Figure (f)). Note the dark bands in the various DSIs, which indicate regions that match at this disparity. (Smaller dark regions are often the result of textureless regions.) Other traditional matching costs include normalized cross-correlation [51, 93, 19], which behaves similar to sumof-squared-differences (SSD), and binary matching costs (i.e., match / no match) [73], based on binary features such as edges [4, 50, 27] or the sign of the Laplacian [82]. Binary matching costs are not commonly used in dense stereo methods, however. Some costs are insensitive to differences in camera gain or bias, for example gradient-based measures [100, 95] and non-parametric measures such as rank and census transforms [129]. Of course, it is also possible to correct for different camera characteristics by performing a preprocessing step for bias-gain or histogram equalization [48, 32]. Other matching criteria include phase and filter-bank responses [74, 63, 56, 57]. Finally, Birchfield and Tomasi have proposed a matching co...

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...ottom. These 10 images demonstrate that even for a simple Lambertian surface, image variation can be significant. Below this we show the output of a Canny edge detector run on the 10 different images =-=[10]-=-. The variation between the different edge images of the same object is quite striking. Our model consists of 7500 three dimensional points that are uniformly distributed on the surface of the 3 bumps...

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...ative of the smoothing function. Hence, smoothing and differentiation can be combined into a single convolution template. An extremely popular convolution kernel is the derivative of a Gaussian (DOG) =-=[75]-=-. In one dimension, the DOG is defined as g(x) = \Gammax exp(\Gammax 2 =oe 2 ) where oe is a design parameter governing the amount of smoothing that takes place. Although the DOG has been demonstrated...

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...uping algorithms useless. 2.2.1 Facial Features Sirohey proposed a localization method to segment a face from a cluttered background for face identification [145]. It uses an edge map (Canny detector =-=[15]-=-) and heuristics to remove and group edges so that only the ones on the face Fig. 3. (a) and (b) n = 8. (c) n = 4. Horizontal and vertical profiles. It is feasible to detect a single face by searching...

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...roblem of early vision and it has been intensively studied. Approaches to image segmentation can be roughly classified into four groups: (i) Local filtering approaches such as the Canny edge detector =-=[4]-=-, (ii) Snake [19] and Balloon methods [6], [7], [37], [32](iii) Region growing and merging techniques [2], [1],[26], and (iv) Global optimization approaches based on energy functions [28] or Bayesian ...

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... [114] or elongated anatomic structures [202] in medical imaging. Line correspondence is usually expressed by pairs of line ends or middle points. Standard edge detection methods, like Canny detector =-=[28]-=- or a detector based on the Laplacian of Gaussian [126], are employed for the line feature detection. The survey of existing edge detection method together with their evaluation can be found in [222]....