Showing papers on "Corner detection published in 1992"

Scale-based detection of corners of planar curves

Abstract: A technique for detecting and localizing corners of planar curves is proposed. The technique is based on Gaussian scale space, which consists of the maxima of absolute curvature of the boundary function presented at all scales. The scale space of isolated simple and double corners is first analyzed to investigate the behavior of scale space due to smoothing and interactions between two adjacent corners. The analysis shows that the resulting scale space contains line patterns that either persist, terminate, or merge with a neighboring line. Next, the scale space is transformed into a tree that provides simple but concise representation of corners at multiple scales. Finally, a multiple-scale corner detection scheme is developed using a coarse-to-fine tree parsing technique. The parsing scheme is based on a stability criterion that states that the presence of a corner must concur with a curvature maximum observable at a majority of scales. Experiments were performed to show that the scale space corner detector is reliable for objects with multiple-size features and noisy boundaries and compares favorably with other corner detectors tested. >

Computer Vision and Image Processing

Abstract: On the use of morphological operators in a class of edge detectors, L. Hertz and R.W. Schafer a valley-seeking threshold selection technique, S.C. Sahasrabudhe and K.S. Das Gupta local characteristics of binary images and their application to the automatic control of low-level robot vision, P.W. Pachowicz corner detection and localization in a pyramid, S. Baugher and A. Rosenfeld parallel-hierarchical image partitioning and region extraction, G.N. Khan and D.F. Gillies invariant architectures for low-level vision, L. Jacobson and H. Wechsler representation - primitives chain code, L. O'Gorman generalized cones - useful geometric properties, K. Rao and G. Medioni vision-based rendering - image synthesis for vision feature algorithms, J.D. Yates, et al recognition - investigation of a number of character recognition algorithms, A.A. Verikas, et al log-polar mapping applied to pattern representation and recognition, J.C. Wilson and R.M. Hodgson pattern recognition of binary image objects using morphological shape decomposition, I. Pitas and N.D. Sidiropoulos a pattern classification approach to multi-level thresholding for image segmentation, J.G. Postaire and M. Ameziane KOR - a knowledge-based object recognition system, C.M. Lee, et al shape decomposition based on perceptual structure, H.S. Kim and K.H. Park three dimensional - the Frobenius metric in image registration, K. Zikan and T.M. Silberberg binocular fusion revisited utilizing a log-polar tessellation, N.C. Griswold, et al an expert system for recovering 3D shape and orientation from a single view, W.J. Shomar, et al integrating intensity and range sensing to construct 3D polyhedra representation, W.N. Lie, et al notes - texture segmentation using topographic labels, T.C. Pong, et al an improved algorithm for labelling connected components in a binary image, X.D. Yang a note on the paper "The Visual Potential - One Convex Polygon", A. Laurentini a string descriptor for matching partial shapes, H.C. Liu and M.D. Srinath formulation and error analysis for a generalized image point correspondence algorithm, S. Fotedar, et al a new surface tracking system in 3D binary images, L.W. Chang and M.J. Tsai.

A Matching and Tracking Strategy for Independently Moving Objects

Abstract: We present a robust and inherently parallel strategy for tracking “corner” features on independently moving (and possibly non-rigid) objects. The system operates over long, monocular image sequences and comprises two main parts. A matcher performs two-frame correspondence based on spatial proximity and similarity in local image structure, while a tracker maintains an image trajectory (and predictor) for every feature. The use of low-level features ensures an opportunistic and widely applicable algorithm. Moreover, the system copes with noisy data, predictor failure, and occlusion and disocclusion of scene structure. Motion and scene analysis modules can then be built onto this framework. The algorithm is aimed at applications with small inter-frame motion, such as videoconferencing.

A New Class of Corner Finder

Abstract: An accurate, stable and very fast corner finder (for feature based vision) has been developed, based on a novel definition of corners, using no image derivatives. This note describes the algorithm and the results obtained from its use.

Active Detection and Classsification of Junctions by Foveation with a Head-Eye System Guided by the Scale-Space Primal Sketch

Abstract: We consider how junction detection and classification can be performed in an active visual system. This is to exemplify that feature de-tection and classification in general can be done by both simple and robust methods, if the vision system is allowed to look at the world rather than at prerecorded images. We address issues on how to attract the attention to salient local image structures, as well as on how to characterize those.

Technique for edge/corner detection/tracking in image frames

Abstract: Edges/corners in an image can be detected and tracked over time by first sampling the image at periodic time intervals, and then processing the samples to obtain the intensity value for each pixel within each image acquired at a particular time. For each pixel, the bidirectional intraframe correlation of its intensity with the intensities of pixels along each of several directions within the same image is computed. Also, the bidirectional interframe correlation of the intensity of each pixel in each image with the intensities of pixels along each of several directions spanning several images is established. The intraframe and interframe intensity correlations are processed to obtain spatiotemporal tangent information about isobrightness surfaces and curves which are indicative of edges and corners, respectively, and their image motions.

A robust method for detecting image features with application to face recognition and motion correspondence

Abstract: The authors present an approach to feature detection, which is a fundamental issue in many intermediate-level vision problems such as stereo, motion correspondence, image registration, etc. The approach is based on a scale-interaction model of the end-inhibition property exhibited by certain cells in the visual- cortex of mammals. These feature detector cells are responsive to short lines, line endings, corners and other such sharp changes in curvature. In addition, this method also provides a compact representation of feature information which is useful in shape recognition problems. Application to face recognition and motion correspondence are illustrated. >

Statistical Analysis of a Stereo Matching Algorithm

Abstract: This paper discusses the problems of image processing algorithm design and comparison and suggests that a suitable approach may be to model algorithms. We introduce the corner matching algorithm which we have used to provide reliable data for 3D computation modules [5][6]. The development of a simple model of the matching process permits the understanding of the influence of various parameters in the matching algorithm. This model also allows optimisation of the algorithm using data distributions obtained from representative scenes.

A comparison of methods of extracting curvature features

Abstract: Examines the extraction of curvature features from the contours of 2D objects. General schemes for corner detection and particular methods from the recent OCR literature are considered. Eight approaches are compared with respect to their results on a set of 100 handwritten numerals of varying styles and sizes. Strengths and weaknesses are summarized for each method. >

ECG data compression by corner detection.

Abstract: An ECG sampled at a rate of 360, 500 samples s−1 or more produces a large amount of redundant data that are difficult to store and transmit. A process is therefore required to represent the signals with clinically acceptable fidelity and with the least code bits possible. In the paper, a real-time ECG data compressing algorithm, CORNER, is presented. CORNER is an efficient algorithm which locates significant samples and at the same time encodes the linear segments between them using linear interpolation. The samples selected include, but are not limited to, the samples that are significantly displaced from the encoded signal such that the allowed maximum error is limited to a constant ɛ which is specified by the users. The way in which CORNER computes the displacement of a sample from the encoded signal guarantees that the high activity regions are more accurately coded. The results are compared with those of the well known data compression algorithm, AZTEC, which is also a real-time algorithm. It is found that, under the same bit rate, a considerable improvement of the signal-to-noise ratio (SNR) and root mean square error (RMSerr) can be achieved by employing the proposed CORNER algorithm. An average value of SNR (RMSerr) of 27·0 dB (5·668) can be achieved even at an average bit rate of 0·79 bit sample−1 by employing CORNER, whereas the average value of SNR (RMSerr) achieved by AZTEC under the same bit rate is 16·60 dB (19·368).

Corner detection using spline-wavelets

Abstract: Detection of corners in an image is very useful in computer vision and pattern recognition. The existing algorithms for corner detection seem to be insufficient in many situations. The corner detection algorithm proposed in this paper is based on spline-wavelet decompositions. Corner and edge detectors are constructed from the 2-D wavelet transform coefficients. A somewhat sophisticated thresholding technique is applied to remove noise and minor irregularities in the images. Noise can be further reduced if additional processing is applied to the component images at all resolutions. Information on the edges and corners is contained in the component images in all the octaves to facilitate precise localization. A real-time wavelet decomposition algorithm is developed for the corner and edge detectors. It is very efficient and requires very little memory, since most of the computations involve only simple moving average operations and sub-sampling.

Multi-resolution shape description by corners

Abstract: A robust method for describing planar curves in multiple resolution using curvature information is presented. The method is developed by taking into account the discrete nature of digital images as well as the discrete aspect of a multiresolution structure (pyramid). The main contribution lies in the robustness of the technique, which is due to the additional information that is extracted from observing the behavior of corners in the whole pyramid. Furthermore, the resulting algorithm is conceptually simple and easily parallelizable. Theoretical results are developed analyzing the curvature of continuous curves in scale-space and showing the behavior of curvature extrema under varying scale. The results are used to eliminate any ambiguities that might arise from sampling problems due to the discreteness of the representation. Experimental results demonstrate the potential of the method. >

A structure-from-motion algorithm for robot vehicle guidance

Abstract: Reports a new structure-from-motion algorithm designed for autonomous robot vehicle guidance. This algorithm takes input from a binocular image sequence obtained from cameras mounted on the robot, and extracts feature points using a corner detection algorithm developed at Oxford. 3D information is instantiated from feature disparities given by feature-point matching using normalised correlation and disparity gradient techniques. Vehicle motion is estimated from the tracked feature points in the image sequence using a Kalman filter. The algorithm is partly implemented on a heterogeneous parallel machine PARADOX commissioned at Oxford. >

A parallel implementation of a structure-from-motion algorithm

Abstract: This paper describes the implementation of a 3D vision algorithm, Droid, on the Oxford parallel vision architecture, PARADOX, and the results of experiments to gauge the algorithm's effectiveness in providing navigation data for an autonomous guided vehicle. The algorithm reconstructs 3D structure by analysing image sequences obtained from a moving camera. In this application, the architecture delivers a performance of greater than 1 frame per second — 17 times the performance of a Sun-4 alone.

Curvature estimation and unique corner point detection for boundary representation

Abstract: Computing a curvature function on a digitized boundary is an ill-posed problem due to the discrete nature of the boundary. The authors use a constrained regularization technique to obtain the optimal smooth boundary before computing the curvature function. A corner sharpness is defined for robust corner point detection. Matching results in the presence of occlusion using a 2-D Hopfield neural network are also shown to produce excellent results using this boundary representation. The human cognition system recognizes both ideal corner points and slightly rounded segments as corner points. A criterion to mimic a human's capability of detecting corner points and to compensate for the smoothing effect of the preprocessing in detecting corner points in the curvature function space is established. >

Robustness of attractor networks and an improved convex corner detector

Abstract: The authors point out that by defining several notions of robustness for an attractor network, it is possible to augment previous results about the AdaTron algorithm by explicit values for the robustness of the optimal weights. It is shown that the symmetry of a problem is reflected by the invariance of the optimal weights. This enables one to deduce that a convex corner detection, using a discrete-time cellular neural network (DTCNN), cannot be accomplished with just one clock cycle, and an improved convex corner detector is proposed. >

Simple fast median filtering algorithm, with application to corner detection

Abstract: The Letter studies the speed of operaton of median filtering algorithms. An iterative approach is investigated, and then simplified to take the local neighbourhood centre pixel as a starting approximation. Overall speedup factors of 2–4 are obtained. The method is useful when the ‘running median’ technique cannot be used, as for ‘skimmed’ median-based corner detectors.

A sequential detection framework for feature tracking within computational constraints

Abstract: A unified decision-theoretic framework for automating the establishment of feature point correspondences in a temporally dense sequence of images is discussed. The approach extends a recent sequential detection algorithm to guide the detection and tracking of object feature points through an image sequence. The resulting extended feature tracks provide robust feature correspondences, for the estimation of three-dimensional structure and motion, over an extended number of image frames. >

Wavelet transform for corner detection

Abstract: A novel wavelet based corner detecting algorithm is proposed. Some intrinsic indicators implied in corners are extracted by utilizing the wavelet transform. Since these indicators are independent of the corner angle and corner curvature, they can be used to detect corners. In addition, several properties of corners in the multiscale wavelet transform are introduced. By applying these indicators and properties, corners can be detected correctly and efficiently. The experimental results show that the proposed algorithm achieves better accuracy than the conventional single-scale corner detectors. On the other hand, the algorithm is more computationally efficient and easier to implement than other multiscale corner detectors. >

Corner detector for 3D object recognition

Abstract: This paper presents an effective corner detector based on perceptual organization and a curve tracking scheme. The detector first finds 2-D corners among the curve partitioning points. It then locates 3-D corners by detecting the terminations of tracked curves intersecting at 2-D corners. It then assigns an attribute value according to its perceptual structure to each detected corner. The corner detector is a very important image analysis component in both 2-D and 3- D vision systems. Experimental results demonstrate its effectiveness and robustness.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Improvement of algorithm about corner detection in object feature extraction

Abstract: Improved algorithm for the corner detection in the object feature extraction is presented in thepaper. Due to the introduction of a novel linear transformation, the feature of the chain—code can be applied more reasonable, not only could the position of the corner be detected precisely, but the threshold be selected easily. 1.INTRODUCTTON If a picture consists of objects contours which can be obtained with extracting, it canbe simply represented by specifying these contours. It is convenient to approximate contours, for digital computer purpose, as polygons composed of short line segments. By taking these to be shortenough, this type of digitization permits contours to be reconstructed with any desired degreeof accuracy. The lengths and slopes of the segments can themselves be quantized to discrete setsof values, i.e. chain-codes which employs only horizental and vertical segments of some fixedlength d, togther with diagonal segments of length d-j . A given contour can be converted into a chain—code in a number of ways. Imaging, superimposed

The influence of object size on the regions of interest for edge detection-preliminary results

Abstract: Presents preliminary results from an investigation of edge detection with a principal components analysis. In this way the parameters of an edge detector can be derived from data about edges in images. The preliminary investigations study how the optimal region of interest varies with the size of objects in the image. >