Showing papers on "Corner detection published in 1993"

Velocity adaptive handoff algorithms for microcellular systems

Abstract: Fast handoff algorithms for urban microcells using velocity adaptation and corner detection are presented. Three velocity estimators are compared with respect to their accuracy, sensitivity to non-isotropic scattering, capability of detecting when a mobile turns a corner, and response time in typical microcell environments. Simulation results show that velocity adaptive corner detecting handoff algorithms yield faster and more consistent handoff performance than traditional methods.

Early jump-out corner detectors

Abstract: The authors present two new corner detectors. One works by using dissimilarity, along the contour direction to detect curves in the image contour, and the other estimates image curvature along the contour direction. These operators are fast, robust to noise, and require no subjective thresholding. >

Corner and curve detection along a boundary using line segment triangles

Abstract: The work presented is part of a project to locate objects from colour stereo images using hue (or colour). The first step is to convert the images from the RGB camera and frame-grabber format to the HSI (hue, saturation and intensity) format. It is then possible to apply window thresholding to the hue component to extract the silhouette of any target object(s) in the image, knowing the range of hue values which span those of the object(s) to be located. Small areas of the selected hue are removed from the threshold image, leaving the larger areas to be filtered by an edge-detecting filter and then eroded to a 1-pixel wide boundary, defining the outline of the silhouette. A boundary-following algorithm extracts an ordered list of (x,y) coordinate pairs defining the boundary of each sufficiently large area of the selected hue value. The processing of this list of coordinates is the main focus of this paper. Some early results are presented which show promise. >

Electrostatic field-based detection of corners of planar curves

Abstract: A corner is often defined as the junction point between two or more straight line edges, or a point on the object's boundary curve having a curvature extremum. Our definition of a corner is a point having an electrostatic field extremum along an equipotential contour. Based on this definition, a novel approach for corner detection using electrostatic field theory is proposed. The approach is motivated by the simple observation that the electrostatic field is known to be concentrated around pointed conductor edges. It has the flavour of the multiscale-based approach of corner detection. In this paper, the relevant background on electrostatic field theory is reviewed. Corner detection using the proposed approach is then presented. Experimental results are then given to demonstrate the potential and merits of the electrostatic field-based approach. >

Model based corner detection

Abstract: A method is presented to correct an approximate polygonal sketch of an image object boundary by adjusting the location of each corner point in the sketch. The method is suitable for the segmentation of unknown images using interactive techniques, and for locating object boundaries in model-based segmentation. For each point, p, at which two lines meet in an open or closed polygonal sketch, a corner segmentation model is derived based on the angle, orientation and scale of the corner defined, and the image function f(x, y) in the immediate region about p. A corner template is then constructed and matched in a small neighborhood about p, thereby providing a corrected polygonal sketch. The segmentation model is correct in 95% of the cases, and the corner is accurately located. >

Boundary-based corner detection using wavelet transform

Abstract: A novel corner detection algorithm based on the wavelet transform is presented. The algorithm detects corners by applying the shape information of the orientation profile of the corner. The shape information is extracted by using the wavelet transform. The conducted experiments have shown that our algorithm is more effective than the conventional corner detection algorithms. Compared with the traditional multiscale corner detection algorithms, our algorithm is computationally simpler because we have to compute the wavelet transform for only one or two scales. >

Bayesian Corner Detection

Abstract: Corners play important roles in high level image understanding. They are the main features in many 2D or 3D image models associated with image understanding algorithms. The Bayesian corner detection method inputs a sequence of row-column pairs along an arc and outputs the corner positions and the corner included angles that maximize the a posteriori probability. Experiments on artificially generated sequences permit the measurement of errors of the estimated corner positions and included angles versus different noise perturbations, angles and line lengths respectively.

Corner detection for chain-coded curves

Abstract: A corner detection scheme for chain coded curves is proposed that significantly improves on the performance of current algorithms. The proposed scheme measures the number of links to either side of a point that can produce the largest digital straight line. That value is used as an indication of curvature at that point with very high curvature being indicative of a corner. A modification of the proposed algorithm can further reduce the false detection rate with virtually no affect on the number of corners missed by omitting certain patterns that can arise from contours with arbitrarily small curvature.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Automatic wall motion detection in Bi-dimensional echocardiography

Abstract: AhfructCsrdiuc wull motion unulysis using echocnrdiogruphic image sequences provides an efficient method to diegnose und quantify different curdiuc puthnloxies. I n this study we have unulyzed the crtrdiuc motion tsing vcltlcity field techniques to obtuin twenty four vectors representing curdiuc mci.wle regional motion. Thirty nine images of u forty frume wquence were processed, showing the glol)ul movement of cnrdiuc wull. An excrlent reduction in the procerrsing lime is aciiievcd through the previous sepnentution of the mycurdium, detecting tire epicurdium with a model-lmed method guided hy U genetic algorithm.

Vertices and corners: normalized average detection

Abstract: Most of the information regarding the shape of polyhedral objects is preserved in the edges and the vertices of these objects. Gray level images of scenes containing such objects are often processed to extract edge and vertex information to produce equivalent line sketches. An accurate line sketch of a scene serves as an effective input to high level vision systems concerned with scene understanding or object recognition. The performance of these systems is therefore greatly dependent on the accuracy of the line sketch. The work reported in this paper addresses the issues associated with generating accurate line sketches from gray level images. The methods described here have been implemented and tested with real and synthetic images and are compared to other vertex or corner detection techniques. The performance of the vertex detector is assessed using simulation runs on images with varied signal-to-noise ratios. The computational performance of this algorithm is evaluated and assessed by operating directly on the gray-scale image.

Detecting Corners of Polygonal and Polyhedral Objects

Abstract: In this paper, we proposed a novel corner detection algorithm, based on statistical properties of corners, to detect corners of both polygonal and polyhedral objects. By means of local histogram analysis, we first bilevel the subimage within a circular window, then compute the intensity mean for the bileveled subimage. From the intensity mean we can estimate the corner angle. We then calculate the theoretical position variance from the estimated angle. Comparing the position variance from the bileveled subimage with its theoretical value, we can identify whether or not the pixel at the center of the subimage is a corner. Finally, the corner orientation can be obtained from the position mean.

Gray-level-based corner detection by using wavelet transform

Abstract: Corners are very attractive features for many applications in computer vision. In this paper, a novel gray-level corner detection algorithm based on the wavelet transform is presented. First, we derived several properties of the corner and the edge embedded in the wavelet domain. Next, we apply these similar properties between the corner and the edge to extract the edge image. Then we employed these different properties between the corner and the edge to locate corners. Experiments have shown that our algorithm can detect corners accurately and effectively. >

Gray-level-based corner detection by using wavelet

Abstract: Corners are very attractive features for many applications in computer vision. In this paper, a novel gray-level corner detection algorithm based on the wavelet transform is presented. First, we derived several properties of the corner and the edge embedded in the wavelet domain. Next, we apply these similar properties between the corner and the edge to extract the edge image. Then we employed these different properties between the corner and the edge to locate corners. Experiments have shown that our algorithm can detect corners accurately and effectively.<>