Showing papers on "Corner detection published in 1994"

Localization properties of direct corner detectors

Abstract: In the past, several approaches for directly determining corners in gray-value images have been introduced. The accuracy of an approach has usually been demonstrated experimentally by comparing its results with those obtained by previous schemes. In this contribution we analyze localization properties of existing direct corner detectors by using an analytical model of gray-value corners. For the different approaches we derive implicit equations constraining the corner points and numerically evaluate their locations. Since a gray-value corner is generally defined as the curvature extremum along the edge line, we also compute this position and take it as the reference location for a comparison of the investigated approaches.

Accuracy in image measure

Abstract: The aim of this paper is to show how image points can be extracted accurately. We will restrict our search to specific points identified by corners, which are stable given a sequence. Our approach makes us of a model-based corner detector. It matches a part of the image containing a corner against a predefined corner model. Once the fitting is accomplished, the position of the corner in the image can be deduced by the knowledge of the corner position in the image. The validity of our approach has been proven with 4 independent tests. It is shown that the accuracy which can be achieved is 1/10th of a pixel.

A practical solution to corner detection

Abstract: A new corner detection algorithm has been developed based on the observation of surface curvature. The algorithm utilizes a linear interpolation scheme for intermediate pixel addressing in the differentiation step, which results in improved accuracy of corner localisation and reduced computational complexity. Noise is reduced by a combination of Gaussian convolution, non-maximum suppression and false corner response suppression. The corner finder is applied to computing stereo disparities and structures from motion. It is implemented on a hybrid parallel processor PARADOX with a performance of 14 frames per second. >

Multiresolution skeletonization an electrostatic field-based approach

Abstract: Skeleton representation of an object is believed to be a powerful representation that captures both boundary and region information of the object. The skeleton of a shape is a representation composed of idealized thin lines that preserve the connectivity or topology of the original shape. Although the literature contains a large number of skeletonization algorithms, many open problems remain. A new skeletonization approach that relies on the electrostatic field theory (EFT) is proposed. Many problems associated with existing skeletonization algorithms are solved using the proposed approach. In particular, connectivity, thinness, and other desirable features of a skeleton are guaranteed. Furthermore, the electrostatic field-based approach captures notions of corner detection, multiple scale, thinning, and skeletonization all within one unified framework. Experimental results are very encouraging and are used to illustrate the potential of the proposed approach. >

A syntactic approach to scale-space-based corner description

Abstract: Planar curves are described by information about corners integrated over various levels of resolution. The detection of corners takes place on a digital representation. To compensate for ambiguities arising from sampling problems due to the discreteness, results about the local behavior of curvature extrema in continuous scale-space are employed. >

A constrained regularization approach to robust corner detection

Abstract: This paper presents a method of optimal boundary smoothing for curvature estimation and a method of corner detection for consistent representation of objects for computer vision applications. The existing methods for curvature estimation have a common problem in determining a unique smoothing factor. We propose a constrained regularization (CR) approach to overcome that problem. The curvature function computed on the preprocessed boundary, which is obtained by the CR approach, gives consistent corner detection results. Ideal corners rarely exist for a real boundary. They are often rounded due to the smoothing effects of the preprocessing. In addition, a human recognizes both sharp corners and slightly rounded segments as corners. Hence, we establish a criterion, called "corner sharpness", which is qualitatively similar to a human's capability to detect corners. >

Corner detection using the MAP technique

Abstract: This paper describes a corner detection method that obtains maximum a posteriori estimates for the corner location in a given sequence of points. The authors model an ideal corner as the intersection of two ideal line segments. The perturbations on the sample points in a given line segment are assumed to be i.i.d Gaussian random variables of zero mean and variance /spl sigma//sup 2/. Further, the perturbations on the points are assumed to be orthogonal to the ideal line. The paper discusses the theory of the corner detector and an algorithm that extends the basic theory to handle multilinear segment arcs. Experiments were conducted according to a specific protocol and performance curves showing the location error versus the noise variance, the included corner angle, and the arc length, are provided. Performance characterization of the corner detector is also performed by plotting the false alarm rate and the misdetect rate versus the context window length and included corner angle. It is shown that the experimental results match the theoretical error propagation.

A low-cost approach to real-time morphological edge detection

Abstract: Real-time edge-based image detection is an important task in many image analysis operations. Morphological-based edge detection operators have been shown to be effective as well as being efficiently implementable in many image processing applications. In this paper, the concept behind the development of various morphological edge detection operators is briefly described. A new edge detector, based on combining separable median filtering and morphological operations, is introduced. The performance of the proposed edge detector is compared with some other edge detectors. A low-cost methodology for implementing the morphological edge detectors is presented. >

Detection of moving corners in dynamic images

Abstract: Corners have become now one of the most commonly used features in image analysis, in particular, in motion analysis and stereo vision. There are many corner detection algorithms which can be classified into two groups. One is based on the extraction of object contours and followed by the analysis of their structures. The other works directly on the local grey values. In this paper, a moving corner detector for analysis of traffic movements is introduced which belongs to the second category. It comprises a corner response calculation component and a spatial temporal analysis part. The corner response calculation uses only the first order image derivatives, which is detected from Harris and Stephens' combined corner edge detector. Spatio-temporal analysis combines both spatial and temporal information to suppress noise and is achieved through more than two images. Apart from some random noises digitization, there are some slight shifts in space from frame to frame. These cause some stationary corners with high contrast being picked up as moving corners. Results showed that this moving corner detector is reasonably successful in separating these stationary corners from moving ones. >

New method for straight line and circular arc extraction

Abstract: We present a new method for linking edge points in a digital image, and segmenting the resulting edges into simple geometric elements. The initial linking procedure operates on the raw output of conventional edge detection algorithms, and links the pixels into sequences in a manner that guarantees the absence of branches. This linking requires no computationally expensive directional calculations to minimize branching. The resulting contours can, however, be of arbitrary length and complexity. In order to facilitate their later manipulation by higher-level algorithms, these contours are then segmented into straight line segments and circular arcs. The segmentation procedure relies on the overall symmetry of the detected segments, and avoids problems associated with the detection of corners or high curvature points. A contour segment is said to possess the considered overall symmetry property if, within certain tolerances, for any point on the segment, travelling an equal distance along the segment on each side of the point leads to contour points separated by equal straight-line chords from the central point. It appears that within the framework of digitized images, this property can only be satisfied by straight line segments and circular arcs. We describe an algorithm to extract, from arbitrary non-branching contours, segments verifying this symmetry property. After extraction, segments are classified as lines or arcs, and the radii and centers are estimated for the latter.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Parallel attentive visual tracking

Abstract: The research reported here addresses the problem of detecting and tracking independently moving objects from a moving observer in real-time, using corners as object tokens. Corners are detected using the Harris corner detector, and local image-plane constraints are employed to solve the correspondence problem. The approach relaxes the restrictive static-world assumption conventionally made, and is therefore capable of tracking independently moving and deformable objects. Tracking is performed without the use of any 3-dimensional motion model. The technique is novel in that, unlike traditional feature-tracking algorithms where feature detection and tracking is carried out over the entire image-plane, here it is restricted to those areas most likely to contain-meaningful image structure. Two distinct types of instantiation regions are identified, these being the “focus-of-expansion” region and “border” regions of the image-plane. The size and location of these regions are defined from a combination of odometry information and a limited knowledge of the operating scenario. The algorithms developed have been tested on real image sequences taken from typical driving scenarios. Implementation of the algorithm using T800 Transputers has shown that near-linear speedups are achievable, and that real-time operation is possible (half-video rate has been achieved using 30 processing elements).

Detection of corners on 3D space curves

Abstract: This paper presents a method for detecting corner points on 3D space curves. It is an extension of a previous work of distance accumulation for detecting corner points on 2D planar curves.6•7 A quantitative measure is proposed to define the "cornerness" of a 3D curve at a curve point, which has the advantage of being more stable and causing much less shape distortion than the traditional smoothing methods. In particular, this measure is invariant to scale, an attractive property for corner detection. Strategies are presented for reliable maximum selection. Experimental results with simulated data shows that the robustness and accuracy of the cornerness method. Keywords - Corner detection, curvature, distance accumulation, maxima selection, curve representation, 3D curves.

Automatic extraction and tracking of eye features from facial image sequences

Abstract: The dual issues of extracting and tracking eye features from video images are addressed in this dissertation. The proposed scheme is different from conventional intrusive eye movement measuring system and can be implemented using an inexpensive personal computer. The desirable features of such a measurement system are low cost, accuracy, automated operation, and non-intrusiveness. An overall scheme is presented for which a new algorithm is forwarded for each of the function blocks in the processing system. A new corner detection algorithm is presented in which the problem of detecting corners is solved by minimizing a cost function. Each cost factor captures a desirable characteristic of the corner using both the gray level information and the geometrical structure of a corner. This approach additionally provides corner orientations and angles along with corner locations. The advantage of the new approach over the existing corner detectors is that it is able to improve the reliability of detection and localization by imposing criteria related to both the gray level data and the corner structure. The extraction of eye features is performed by using an improved method of deformable templates which are geometrically arranged to resemble the expected shape of the eye. The overall energy function is redefined to simplify the minimization process. The weights for the energy terms are selected based on the normalized value of the energy term. Thus the weighting schedule of the modified method does not demand any expert knowledge for the user. Rather than using a sequential procedure, all parameters of the template are changed simultaneously during the minimization process. This reduces not only the processing time but also the probability of the template being trapped in local minima. An efficient algorithm for real-time eye feature tracking from a sequence of eye images is developed in the dissertation. Based on a geometrical model which describes the characteristics of the eye, the measurement equations are formulated to relate suitably selected measurements to the tracking parameters. A discrete Kalman filter is then constructed for the recursive estimation of the eye features, while taking into account the measurement noise. The small processing time allows this tracking algorithm to be used in real-time applications. This tracking algorithm is suitable for an automated, non-intrusive and inexpensive system as the algorithm is capable of measuring the time profiles of the eye movements. The issue of compensating head movements during the tracking of eye movements is also discussed. An appropriate measurement model was established to describe the effects of head movements. Based on this model, a Kalman filter structure was formulated to carry out the compensation. The whole tracking scheme which cascades two Kalman filters is constructed to track the iris movement, while compensating the head movement. The presence of the eye blink is also taken into account and its detection is incorporated into the cascaded tracking scheme. The above algorithms have been integrated to design an automated, non-intrusive and inexpensive system which provides accurate time profile of eye movements tracking from video image frames.

A Fast Corner Detection Algorithm Based on Area Deviation.

Abstract: A new algorithm, based on area-deviation, is proposed for the detection of corner points of digitized curves. The algorithm consists of two steps. In the first step, a fixed-length chord is moving along the digitized curve step by step, and the area between the chord and the associated curve segment is measured. The area values are used to represent the average curvature values for the corresponding curve segments. The curve segments with their area values having reached a local maximum and exceeded a threshold value will be identified. Each such curve segment will contain one corner point. In the second step, the exact position of the comer point in each identified curve segment is found by comparing the changes in the curve's directions at each point of the curve segment. A lot of graphical objects including Chinese and English fonts have been tested. The algorithm is proved to be efficient and gives results close to human expectations.

Electrostatic Field-Based Multiscale Corner Detection: A Physics-Motivated Approach

Abstract: Corners represent special features of interest in images. They are very useful in many vision problems such as optical ow, structure from motion, and motion correspondence. A corner may be deened 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 deenition of a corner is a point having an electrostatic eld extremum along an equipotential contour. Based on this deenition, a novel approach for corner detection developed based on the electrostatic eld theory (EFT) is proposed. The approach, which is motivated by the simple observation that the electrostatic eld is known to concentrate around pointed conductor edges, has the avor of the multiscale-based approach of corner detection. In this paper, relevant background in EFT is reviewed. Corner detection using the proposed approach is then presented and demonstrated experimentally , the results of which are very encouraging and are used to demonstrate the potential and merits of the EFT-based approach.

Dynamic object recognition using hit-or-miss approach

Abstract: We develop a new morphological hit-miss algorithm for detecting the features of binary images. The standard hit-miss algorithms use block structure patterns for matching features but in many practical situations this may cause problems because the size of the object is usually unknown. In the proposed algorithm we match the corners of the object instead of the block structure pattern -- this approach works nicely because most objects have different structure elements for the corners. The size of the object plays a less significant role in our recognition algorithm than in the standard algorithms. Our method can be easily adapted to recognize the features of different types of objects. We have implemented the proposed algorithm to recognize the muzzles of guns in military vehicles.© (1994) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.