Image Edge And Corner Detection Based On Anisotropic Gaussian Kernels

Edge and corner detection are essential front-end process in computer vision, suchas object recognition, motion tracking and image registration etc. It has been proved thatthe anisotropic Gaussian kernels are noise robust and overcome the defect that theisotropic Gaussian kernel does not have enough capability to extract finemulti-directional intensity variation of a gray-scale image. The anisotropic Gaussianfilters can be utilized to extract image feature effectively, for example edge or corners.This dissertation makes the study on the application of the isotropic Gaussiankernels on the image pre-processing, such as the edge and corner detection of an imagevia the anisotropic Gaussian kernels. The main researches are as the four parts below:1. Edge detection via the combition of the isotropic and anisotropic Gaussiankernels. A noise-robustness edge detection algorithm is presented, which fusessmall-scale isotropic Gaussian kernel and large-scale anisotropic Gaussian kernels(ANGKs) to attain the edge map strength (EMS). The main merit of the algorithm isthat it has high edge resolution while it is robust to noise. From the ANGks, anisotropicdirectional derivatives (ANDDs) are derived to capture the locally directional intensityvariation of an image, and then the EMS is obtained using the ANGKs. The noiserobustness is highly depended on the scale while the edge resolution is depended on theratio of the scale to the anisotropic factor. Moreover, the image smoothing results by theANGKs reveal edge stretch effect. The fusion of large-scale ANGKs and small-scaleisotropic Gaussian kernel generates the fused ESM, which is noise robust, high edgeresolution and little edge stretch. Then the fused edge map is embedded into theframework of the Canny edge detector, thus a new noise-robustness edge detectionalgorithm is achieved, which included two modifications: contrast equalization andnoise-dependent lower thresholds. At last, the proposed edge detector is examined usingthe empirical Receiver Operating Characteristic (ROC) Curves of the tested images andthe Pratt s Figure of Merit (FOM).2. A new contour-based corner detection using multi-chord curvature polynomialalgorithm is conducted. Multi-chord curvature polynomial algorithm for cornerdetection is proposed based upon chord-to-point distance accumulation (CPDA)technique and curvature product. Firstly, the edge map is extracted by Canny edgedetector. Then, at each chord, a multi-chord curvature polynomial is used as the sum ormultiplication of the contour curvature. The new method can not only effectivelyenhance curvature extreme peaks, but also prevent smoothing some corners. Lastly to reduce false or missing detection made by experiment threshold, local adaptivelythreshold is used to detect corners.3. Several techniques of the contoured-based corner detection via ANGKs arestudied. Three effective corner detection algorithms based upon the edge contours areproposed. Firstly, on the idea that the gradient directional angle variation of thesmoothing edge pixel and the around pixels is regular while that of the corners isanisotropic, the edge and around pixels are smoothed by the ANGKs and then themaximal magnitude direction counts as the principal direction of the tested pixel. Theprobability distributions and the information entropy of the tested edge pixel and aroundpixels are calculated. And the corners are decided by the information entropy. Secondly,the directional magnitudes are calculated after smoothing the edge pixels and theirsurrounding regular pixels using ANGKs. The autocorrelation matrix of the directionalmagnitudes at each pixel and their surrounding pixels is then contracted. The localmaxima of the sum of the normalized eigenvalues on the contour are labeled as corners.Thirdly, the principle direction of an edge pixel is calculated after smoothing the edgepixels using ANGKs. Then the angle difference of the principal direction of the testedge pixel s two surrounding edge pixels is used as the corner measure to extractcorners. The experimental results show that the three detection algorithms are effective.4. A new corner detector and corner classifier using the anisotropic Gaussiankernels is introduced. The detection algorithm mainly utilizes the anisotropic directionalderivative (ANDD) representations derived from the anisotropic Gaussian kernels todetect and classify corners. The corner detection fuses the ideas of the contour-baseddetection and intensity-based detection and consists of three cascaded parts. First, theedge map of an image is obtained by the one edge detector and from the edge map thecontours are extracted. Next, the ANDD representations on contours are calculated afterthe contours are smoothed by the ANGKs and the ANDD representation at each pixel isnormalized by the maximal magnitude. The area surrounded by the normalized ANDDrepresentation forms a new corner measure. Finally, the non-maximum suppression andthresholding are operated on each contour to pick out corners in terms of the cornermeasure. Moreover, based upon the number of the peaks of the ANDD representation, asimple corner classifier is given. Experiments show that the proposed corner detector is reasonable and the proposed corner classifier is effective.