Showing papers on "Corner detection published in 2003"

Mean-shift blob tracking through scale space

Abstract: The mean-shift algorithm is an efficient technique for tracking 2D blobs through an image. Although the scale of the mean-shift kernel is a crucial parameter, there is presently no clean mechanism for choosing or updating scale while tracking blobs that are changing in size. We adapt Lindeberg's (1998) theory of feature scale selection based on local maxima of differential scale-space filters to the problem of selecting kernel scale for mean-shift blob tracking. We show that a difference of Gaussian (DOG) mean-shift kernel enables efficient tracking of blobs through scale space. Using this kernel requires generalizing the mean-shift algorithm to handle images that contain negative sample weights.

Phase Congruency Detects Corners and Edges

Abstract: There are many applications such as stereo matching, mo- tion tracking and image registration that require so called 'corners' to be detected across image sequences in a reliable manner. The Harris cor- ner detector is widely used for this purpose. However, the response from the Harris operator, and other corner operators, varies considerably with image contrast. This makes the setting of thresholds that are appropri- ate for extended image sequences difficult, if not impossible. This paper describes a new corner and edge detector developed from the phase con- gruency model of feature detection. The new operator uses the principal moments of the phase congruency information to determine corner and edge information. The resulting corner and edge operator is highly local- ized and has responses that are invariant to image contrast. This results in reliable feature detection under varying illumination conditions with fixed thresholds. An additional feature of the operator is that the corner map is a strict subset of the edge map. This facilitates the cooperative use of corner and edge information.

A simple and efficient algorithm for detection of high curvature points in planar curves

Abstract: A new algorithm is presented for detection of corners and other high curvature points in planar curves. A corner is defined as a location where a triangle with specified opening angle and size can be inscribed in the curve. The tests compare the new algorithm to four alternative algorithms for corner detection.

A Simple and Efficient Algorithm for Detection of High Curvature Points in Planar Curves

Abstract: A new algorithm is presented for detection of corners and other high curvature points in planar curves. A corner is defined as a location where a triangle with specified opening angle and size can be inscribed in the curve. The tests compare the new algorithm to four alternative algorithms for corner detection.

Face detection and tracking in a video by propagating detection probabilities

Abstract: This paper presents a new probabilistic method for detecting and tracking multiple faces in a video sequence. The proposed method integrates the information of face probabilities provided by the detector and the temporal information provided by the tracker to produce a method superior to the available detection and tracking methods. The three novel contributions of the paper are: 1) Accumulation of probabilities of detection over a sequence. This leads to coherent detection over time and, thus, improves detection results. 2) Prediction of the detection parameters which are position, scale, and pose. This guarantees the accuracy of accumulation as well as a continuous detection. 3) The representation of pose is based on the combination of two detectors, one for frontal views and one for profiles. Face detection is fully automatic and is based on the method developed by Schneiderman and Kanade (2000). It uses local histograms of wavelet coefficients represented with respect to a coordinate frame fixed to the object. A probability of detection is obtained for each image position and at several scales and poses. The probabilities of detection are propagated over time using a Condensation filter and factored sampling. Prediction is based on a zero order model for position, scale, and pose; update uses the probability maps produced by the detection routine. The proposed method can handle multiple faces, appearing/disappearing faces as well as changing scale and pose. Experiments carried out on a large number of sequences taken from commercial movies and the Web show a clear improvement over the results of frame-based detection (in which the detector is applied to each frame of the video sequence).

Performance assessment of feature detection algorithms: a methodology and case study on corner detectors

Abstract: We describe a generic methodology for evaluating the labeling performance of feature detectors. We describe a method for generating a test set and apply the methodology to the performance assessment of the three well-known corner detectors of L. Kitchen and A. Rosenfeld (1982), of K. Paler et al. (1984), and of C. Harris and M. Stephens (1988). The labeling deficiencies of each of these detectors is related to their discrimination ability between corners and various of the features which comprise the class of noncorners.

Visual odometer for pedestrian navigation

Abstract: This paper presents a visual odometer system using stereo cameras for pedestrian navigation. A novel method for pedestrian navigation based on the knowledge of gait analysis and robust ego-motion estimation is proposed. Two major problems of implementing the system on a pedestrian are stated. Firstly, the features collected from cameras attached on a walking pedestrian normally have winding trajectory resulting in inaccurate tracking. Secondly, the observed object moving independently leads to incorrect ego-motion estimation. Using gait analysis, capturing images at the same stage of the walking cycle produces a less winding trajectory that allows tracking without stabilizing the images. Robust ego motion is also introduced to eliminate outliers that are independently moving features, mismatched features in the stereo matching step and incorrectly assigned features in the tracking step. Data processing techniques including corner detection, stereo matching, triangulation, tracking, and ego-motion estimation are employed. The outcome is the estimated incremental ego motion of the stereo cameras. The approach not only enables the system to operate on walking users but also improves the accuracy of ego-motion estimation.

Integrated Edge and Junction Detection with the Boundary Tensor

Abstract: The boundaries of image regions necessarily consist of edges (in particular, step and roof edges), corners, and junctions. Currently, different algorithms are used to detect each boundary type separately, but the integration of the results into a single boundary representation is difficult. Therefore, a method for the simultaneous detection of all boundary types is needed. We propose to combine responses of suitable polar separable filters into what we will call the boundary tensor. The trace of this tensor is a measure of boundary strength, while the small eigenvalue and its difference to the large one represent corner / junction and edge strengths respectively. We prove that the edge strength measure behaves like a rotationally invariant quadrature filter. A number of examples demonstrate the properties of the new method and illustrate its application to image segmentation.

Bayesian Stochastic Mesh Optimization for 3D reconstruction.

Abstract: We describe a mesh based approach to the problem of structure from motion The input to the algorithm is a small set of images, sparse noisy feature correspondences (such as those provided by a Harris corner detector and cross correlation) and the camera geometry plus calibration The output is a 3D mesh, that when projected onto each view, is visually consistent with the images There are two contributions in this paper The first is a Bayesian formulation in which simplicity and smoothness assumptions are encoded in the prior distribution The resulting posterior is optimized by simulated annealing The second and more important contribution is a way to make this optimization scheme more efficient Generic simulated annealing has been long studied in computer vision and is thought to be highly inefficient This is often because the proposal distribution searches regions of space which are far from the modes In order to improve the performance of simulated annealing it has long been acknowledged that choice of the correct proposal distribution is of paramount importance to convergence Taking inspiration from RANSAC andimportance sampling we craft a proposal distribution that is tailored to the problem of structure from motion This makes our approach particularly robust to noise and ambiguity We show results for an artificial object and an architectural scene

Automatic Image Mosaic System Using Image Feature Detection and Taylor Series

Abstract: Image mosaicing has been collecting considerable attention in the field of computer vision and photogrammetry. Unlike previous methods using a tripod, we have developed which can handle images taken with a hand-held camera to accurately construct a panoramic image. This paper proposes the automatic image mosaic system implementation, which it sees to use feature detection of the image, which is extracted a feature point adjustment from continuous two images hour automatically from pixel price of image and in order to accomplish. Feature based image mosaics require the integration of corner detection, corner matching, motion parameters estimation, and image stitching. We used Taylor series for parameter and perspective transform estimation.

Corner detection based on adaptively filtered curvature function

Abstract: A fast new algorithm to detect corners defined at different scales is presented. It relies on calculating the shape curvature function using an adaptive filtering factor to remove as much noise as possible without losing corners. Corners are the peaks of the function and they can be detected by thresholding. Experimental results show that detected corners are very stable against noise, scale and rotation.

A neural model of early vision: contrast, contours, corners and surfaces - contributions toward an integrative architecture of form and brightness perception

Abstract: The thesis is concerned with the functional modeling of information processing in early and midlevel vision. The mechanisms can be subdivided into two systems, a system for the processing of discontinuities (such as contrast, contours and corners), and a complementary system for the representation of homogeneous surface properties such as brightness. For the robust processing of oriented contrast signals, a mechanism of dominating opponent inhibition (DOI) is proposed and integrated into an existing nonlinear simple cell model. We demonstrate that the model with DOI can account for physiological data on luminance gradient reversal. For the processing of both natural and artificial images we show that the new mechanism results in a significant suppression of responses to noisy regions, largely independent of the noise level. This adaptive processing is further examined by a stochastic analysis and numerical evaluations. We also show that contrast-invariant orientation tuning can be achieved in a purely feedforward model based on inhibition. DOI results in a sharpening of the tuning width of model simple cells which are in accordance with empirical findings. The results lead to the proposal of a new functional role of the dominant inhibition as observed empirically, namely to sharpen the orientation tuning and to allow for robust contrast processing under suboptimal, noisy viewing conditions. For the processing of contours, a model of recurrent colinear long-range interaction in V1 is proposed. The key properties of the model are excitatory long-range interactions between cells with colinear receptive fields, inhibitory unoriented short-range interactions and modulating feedback. In the model, initial noisy feedforward responses which are part of a more global contour are enhanced, while other responses are suppressed. We show for a number of artificial and natural images that the recurrent long-range processing results in a selective enhancement of coherent activity at contour locations. The competencies of the model are further quantitatively evaluated using two measures of contour saliency and orientation significance. The model also qualitatively reproduces empirical data on surround suppression and facilitation. We further suggest and examine a model variant using early feedback of grouped responses, showing an even stronger enhancement of contour saliency compared to the standard model. These results may suggest a functional role for the layout of different feedback projections in V1. Regions of intrinsically 2D structures such as corners and junctions are important for both biological and artificial vision systems. We propose a novel scheme for the robust and reliable representation and detection of junction points, where junctions are characterized by high responses at multiple orientations within an model hypercolumn. The recurrent long-range interaction results in a robust extraction of orientation information. A measurement of circular variance is used to detect and localize junctions. We show for a number of artificial and natural images that localization accuracy and positive correctness of the junction detection is improved compared to a purely feedforward computation of contour orientations. We also use ROC analysis to compare the new scheme with two other junction detector schemes based on Gaussian curvature and the structure tensor, showing that the new approach performs superior to the standard schemes. Brightness surfaces are reconstructed by a diffusive spreading or filling-in of sparse contrast measurements, which is locally controlled by contour signals. A mechanism of confidence-based fillingin is proposed, where a confidence measure ensures a robust selection of sparse contrast signals. We show that the model with confidence-based filling-in can generate brightness surfaces which are invariant against size and shape transformations and can also generate smooth brightness surfaces even from noisy contrast data, in contrast to standard filling-in. The model can also account for psychophysical data on human brightness perception. We further suggest a new approach for the reconstruction of reference levels, where sparse contrast signals are modulated to carry an additional luminance component. We show for a number of test stimuli that the newly proposed scheme can successfully predict human brightness perception. Overall, we show that basic tasks in early vision can be robustly and efficiently implemented by biologically motivated mechanisms. This leads to a deeper understanding of the functional role of the particular mechanisms and provides the basis for practical applications in technical vision systems.

Mosaic of a video shot with multiple moving objects

Abstract: In this paper we describe an application which takes a video shot as input and produces a compact representation composed by a background layer and segmented moving objects. We deal with the problems of global registration, super-resolution mosaicing, objects segmentation and tracking. Global registration is achieved with a graph-based technique that exploits situations when the camera returns to a previously seen area. Objects segmentation is based on motion analysis using a robust statistical model of the background. Tracking is based on blob matching using singular value decomposition.

A new approach to detecting the corners in digital images

Abstract: The corners are important features in images that are frequently used for scene analysis, image matching and object tracking. In this paper, a new corner detector is presented that is based on a theory that takes into account relatively complicated situations that may occur in the neighbourhoods of the corner candidates. The new detector was implemented, tested, and compared with several other existing algorithms that are often used to solve the problem. In the sense of successfulness and stability of detection, the new algorithm was better in the tests than the other algorithms that were used for comparison. The ANSI C code of the detector is available to the public.

Motion detection and object tracking in image sequences

Abstract: based oral presentations • Z.Zivkovic, ”Adaptive recursive probability density estimation for back- ground modeling ”, The Dutch Society for Pattern Recognition and Im- age Processing (NVPHBV) Spring meeting, 2003. • Z.Zivkovic, ”Adaptive recursive learning of finite mixture models”, NVPHBV Fall meeting, 2002. • Z.Zivkovic, ”Video tracking”, Invited speech, TNO Physics and Elec- tronics Laboratory, Den Haag, 2002. • Z.Zivkovic, ”Real-time 3D head tracking”, NVPHBV Spring Meet- ing, 2001, similar talks at: ASCI 2001 conference, Mechatronica cotact- groepdag 2002, etc. Demos • Z.Zivkovic, ”Virtual mirrors Shape sorting game and shooting game”, Presented at: IEEE ICME 2003, University of Twente open-day and Minor-markt, after NHPHBV Fall meeting 2002 etc. • R.van Mierlo and Z.Zivkovic, ”A traffic monitoring system”, Presented at: ICT knowledge congress 2002, Mechatronics Forum International Conference 2002, SUMMER workshop etc. • Z.Zivkovic, ”Real-time 3D head tracking”, Presented at: ICCV 2001, NVPHBVSpring Meeting 2001, ASCI conference 2001, Mechatronica cotactgroepdag 2002, etc.

Combined detector of locally-oriented structures and corners in images based on a scale-space tensor representation of local neighborhoods of pixels

Abstract: Detection of low-level image features such as edges or corners has been an essential task of image processing for many years. Similarly, detectors of such image features constitute basic building blocks of almost every image processing system. However, today's growing amount of vision applications requires at least twofold research directions: search for detectors that work better than the other, at least for a chosen group of images of interest, and - at the other hand - search for new image features, such as textons or oriented structures of local neighborhoods of pixels. In this paper we present a new approach to the old problem of corner detection, as well as detection of areas in images that can be characterized by the same angular orientation. Both detecting techniques are based on a scale-space tensor representation of local structures, and present computationally attractive image feature detectors.

Accurate L-corner measurement using USEF functions and evolutionary algorithms

Abstract: Corner feature extraction is studied in this paper as a global optimization problem. We propose a new parametric corner modeling based on a Unit Step Edge Function (USEF) that defines a straight line edge. This USEF function is a distribution function, which models the optical and physical characteristics present in digital photogrammetric systems. We search model parameters characterizing completely single gray-value structures by means of least squares fit of the model to the observed image intensities. As the identification results relies on the initial parameter values and as usual with non-linear cost functions in general we cannot guarantee to find the global minimum. Hence, we introduce an evolutionary algorithm using an affine transformation in order to estimate the model parameters. This transformation encapsulates within a single algebraic form the two main operations, mutation and crossover, of an evolutionary algorithm. Experimental results show the superiority of our L-corner model applying several levels of noise with respect to simplex and simulated annealing.

A New Algorithm for Chessboard Grid Corners Detection Based on Two Successive Radon Transform

Abstract: A novel method for grid corner detection is proposed. Two successive radon transform is adopted to find the lines grid comers lie on. Then linear fit technique is used to rectify these lines. Finally grid comers are acquired by solving the algebraic equations. The most attractive point of the method is that the region of the grid comers can be automatically detected. Thus the technique through interactive mode to select corners is not necessary, which is important to the study of dynamic calibration. It is proved, through the actual application, that the method presented in this paper is correct and reliable.

Improving harris corner detector

Abstract: This paper compares two important corner detectors:Harris corner detector and Frstner corner detector.The author proposes an approach to use the advantages of both corner detectors,which is effective in detecting accurate interest points.

A stereo vision system for pedestrian navigation

Abstract: In this paper we present the application of a stereo vision system for pedestrian navigation. Corner detection, stereo matching, triangulation, tracking, and robust ego-motion estimation are used to estimate incremental ego-motion of the stereo cameras with particular focus on implementation on pedestrians. A novel robust ego-motion estimation algorithm was utilized to eliminate outliers, which are independent, moving features, mismatched features in the stereo matching step and incorrect assigned features in the tracking step. We also introduce a new method based on the knowledge of gait analysis to capture images at the same stage of walking cycle. This leads to less winding trajectory, which can be tracked without increasing order and computational cost of the tracker. The whole navigation process has been experimentally verified.

A New Fuzzy Edge Detection Algorithm

Abstract: This paper proposes a simple and effective fuzzy edge detection algorithm. This novel algorithm is different from other fuzzy edge detection algorithm which is based on image enhancement technology ,it detects fuzzy edge based on shortening width of fuzzy edge. It overcomes some disadvantages of fuzzy edge detection algorithm which is based on image enhancement technology. At last, the paper gives the result of experiments of forward looking infra-red images. Experiments indicate that the algorithm is a simple, practical, effective corner detection algorithm.

Corner detection of contour images using continuous wavelet transform

Abstract: This paper presents a multiscale corner detection method based on continuous wavelet transform (CWT) of contour images. The corner points are detected from the local wavelet transform modulus maxima (WTMM) of the contour orientation. To reduce the side effects of the discretization and smoothing that are introduced by the preprocessing steps, we adopt a simple but efficient post processing algorithm: nonmaximum suppression. The proposed method detects sharp corners as well as subtle corners. Simulation results illustrate the better performance of the proposed corner detector compared to the other methods.

Skeletonization of Character Based on Wavelet Transform

Abstract: We propose a novel scheme for computing skeleton of characters using wavelet transform. The development of the method depends on a new wavelet function which is designed specifically for computing the medial axis and corner points of character strokes. Based on some certain desirable properties yielded by this particular wavelet function, wavelet skeleton is defined and computed. By using multiscale corner detection with new wavelet function, a set of modifying techniques are developed to remove the artifacts of primary skeletons. Experimental results show that the proposed algorithm overcomes some undesirable effects and limitations of previous methods.

A Practical Corner Finding Algorithm for Target Image

Abstract: Aiming at the special need of image processing in imaging fuze,a new algorithm of corner detection area ratios algorithm is presented.The position,orientation and angle magnitude of the corner can be obtained using this algorithm.The simulated result proves the validity of this algorithm.

Feature point extraction in compressed domain

Abstract: This document presents several approaches to extract interest points within compressed images (based on DCT compression methods). The goal is to minimize the stages and/or the calculation costs for image sequence indexing tasks or database retrieval from a significant MPEG file repository. Initially, only the fixed images (I-Frames) are take under consideration, motion will be integrated in further research. The traditional invariant feature points (Harris corner points, points with remarquable principal curvatures) are extracted from images using a gradient estimate (first order derivative) or the Laplacian (second-order derivative) of an image. So the first part of this paper handles in detail the derivation of the signal from DCT blocks. The trials to implement feature points detection as close as possible to the DCT coefficient are explained. Results provided by our last DCT-blockwise curvature estimatiorare also shown.

Image morphological processing

Abstract: Mathematical Morphology with applications in image processing and analysis has been becoming increasingly important in today's technology. Mathematical Morphological operations, which are based on set theory, can extract object features by suitably shaped structuring elements. Mathematical Morphological filters are combinations of morphological operations that transform an image into a quantitative description of its geometrical structure based on structuring elements. Important applications of morphological operations are shape description, shape recognition, nonlinear filtering, industrial parts inspection, and medical image processing. In this dissertation, basic morphological operations, properties and fuzzy morphology are reviewed. Existing techniques for solving corner and edge detection are presented. A new approach to solve corner detection using regulated mathematical morphology is presented and is shown that it is more efficient in binary images than the existing mathematical morphology based asymmetric closing for corner detection. A new class of morphological operations called sweep mathematical morphological operations is developed. The theoretical framework for representation, computation and analysis of sweep morphology is presented. The basic sweep morphological operations, sweep dilation and sweep erosion, are defined and their properties are studied. It is shown that considering only the boundaries and performing operations on the boundaries can substantially reduce the computation. Various applications of this new class of morphological operations are discussed, including the blending of swept surfaces with deformations, image enhancement, edge linking and shortest path planning for rotating objects. Sweep mathematical morphology is an efficient tool for geometric modeling and representation. The sweep dilation/erosion provides a natural representation of sweep motion in the manufacturing processes. A set of grammatical rules that govern the generation of objects belonging to the same group are defined. Earley's parser serves in the screening process to determine whether a pattern is a part of the language. Finally, summary and future research of this dissertation are provided.