| Feature detection |
|---|
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Output of a typical corner detection algorithm
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| Edge detection |
| Canny · Canny-Deriche · Differential · Sobel · Prewitt · Roberts Cross |
| Interest point detection |
| Corner detection |
| Harris operator · Shi and Tomasi · Level curve curvature · SUSAN · FAST |
| Blob detection |
| Laplacian of Gaussian (LoG) · Difference of Gaussians (DoG) · Determinant of Hessian (DoH) · Maximally stable extremal regions · PCBR |
| Ridge detection |
| Hough transform |
| Structure tensor |
| Affine invariant feature detection |
| Affine shape adaptation · Harris affine · Hessian affine |
| Feature description |
| SIFT · SURF · GLOH · HOG · LESH |
| Scale-space |
| Scale-space axioms · Implementation details · Pyramids |
LESH (Local Energy based Shape Histogram) is a recently proposed image descriptor in computer vision. It can be used to get a description of the underlying shape. The LESH feature descriptor is built on local energy model of feature perception, see e.g. phase congruency for more details. It encodes the underlying shape by accumulating local energy of the underlying signal along several filter orientations, several local histograms from different parts of the image/patch are generated and concatenated together into a 128-dimensional compact spatial histogram. It is designed to be scale invariant. The LESH features can be used in applications like shape-based image retrieval, object detection, and pose estimation.