Shape matching of two-dimensional objects

Journal ArticleIn this paper we present results in the areas of shape matching of nonoccluded and occluded two-dimensional objects. Shape matching is viewed as a "segment matching" problem. Unlike the previous work, the technique is based on a stochastic labeling procedure which explicitly maximizes a criterion function based on the ambiguity and inconsistency of classification. To reduce the computation time, the technique is hierarchical and uses results obtained at low levels to speed up and improve the accuracy of results at higher levels. This basic technique has been extended to the situation where various objects partially occlude each other to form an apparent object and our interest is to find all the objects participating in the occlusion. In such a case several hierarchical processes are executed in parallel for every object participating in the occlusion and are coordinated in such a way that the same segment of the apparent object is not matched to the segments of different actual objects. These techniques have been applied to two-dimensional simple closed curves represented by polygons and the power of the techniques is demonstrated by the examples taken from synthetic, aerial, industrial and biological images where the matching is done after using the actual segmentation methods

MKS: a multisensor kernel system

Journal ArticleThe multisensor kernel system (MKS) is presented as a means for multisensor integration and data acquisition. This system has been developed in the context of a robot work station equipped with various types of sensors utilizing three-dimensional laser range finder data and two-dimensional camera data. Specific goals that have been achieved include 1) Developing a suitable low-level representation of raw data and/or features extracted from the raw data of the various sensors; 2) Providing a method for efficient reconfiguration of the sensor system in terms of "logical" sensors which map onto physical sensors and computation; and 3) Providing a basis for high-level object modeling techniques

Point pattern matching by heuristic methods : a genetic algorithm and simulated annealing

The problem we consider is to find a subset of points in a pattern that best match to a subset of points in another pattern through a transformation in an optimal sense. Exhaustive search to find the best assignment mapping one set of points to another set is, if the number of points that are to be matched is large, computationally expensive. We propose two stochastic searching techniques - a genetic algorithm and simulated annealing to search for the best ( almost the best ) assignment efficiently. To make the comparison between GA and SA fair, we introduce a piece-wise linear cooling schedule for the SA. As compared to conventional searching techniques such as simple hill climbing and random search techniques, the proposed methods are able to attain better solutions much faster. The proposed methods can be applied to n-dimensional point patterns and any transformation, but we only present results for two-dimensional point patterns and similarity transformations

2-D scene analysis using split-level relaxation

technical reportWe present a new method for applying multiple semantic constraints based on discrete relaxation. A separate graph is maintained for each constraint relation and used in parallel to achieve a consistent labeling. This permits both local and global analysis without recourse to complete graphs. Here local means with respect to a paricular constraint graph, and thus actually includes global spatial relations on the features; e.g., parallel edges on an object will be neighbors in the parallel constraint graph even though they are far apart in Euclidean space. Another major result is a technique for handling occlusion by incorporating the use of spatially local feature sets in the relaxation-type updating method

Efficient Recognition of Partially Visible Objects Using a Logarithmic Complexity Matching Technique

An important task in computer vision is the recognition of partially visible two-dimensional objects in a gray scale image. Recent works addressing this problem have attempted to match spatially local features from the image to features generated by models of the objects. However, many algo rithms are considerably less efficient than they might be, typ ically being O(IN) or worse, where I is the number offeatures in the image and N is the number of features in the model set. This is invariably due to the feature-matching portion of the algorithm. In this paper we discuss an algorithm that significantly improves the efficiency offeature matching. In addition, we show experimentally that our recognition algo rithm is accurate and robust. Our algorithm uses the local shape of contour segments near critical points, represented in slope angle-arclength space (θ-s space), as fundamental fea ture vectors. These feature vectors are further processed by projecting them onto a subspace in θ-s space that is obtained by applying the Karhunen-Loève expansion to all such fea tures in the set of models, yielding the final feature vectors. This allows the data needed to store the features to be re duced, while retaining nearly all information important for recognition. The heart of the algorithm is a technique for performing matching between the observed image features and the precomputed model features, which reduces the runtime complexity from O(IN) to O(I log I + I log N), where I and N are as above. The matching is performed using a tree data structure, called a kD tree, which enables multidi mensional searches to be performed in O(log) time.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66975/2/10.1177_027836498900800608.pd

Recognizing Partially Occluded Objects Using Information Extracted From Polygonal Approximation.

This thesis addresses the problem of recognizing partially occluded two dimensional objects. The goal is to develop a system which is able to identify and locate several overlapping objects in the scene. To achieve this goal, the system must perform the following specific tasks: (1) storing useful information about objects in some format, which is often referred to as the process of object representation or model formation (2) matching procedure based on the object representation, and (3) efficient search of the best matching. This thesis presents a new approach to accomplish these tasks. Polygonal approximation is used to represent an object in this research. The accumulated lengths of line segments, s, and the accumulated sizes of turning angles, $\theta$, along the boundary from some starting point are extracted. The boundary of an object is then described as an equation $\theta$ = f(s). As algorithm shows, matching objects under s-$\theta$ space will be simple and effective. To avoid exhaustive matching in the recognition process, index diagrams of the features characterizing the boundary are established. Once the features of some unknown object are detected, the possible objects which might produce the best matching can be efficiently retrieved from this scheme

Shape Recognition: A Landmark-Based Approach

Shape recognition has applications in computer vision tasks such as industrial automated inspection and automatic target recognition. When objects are occluded, many recognition methods that use global information will fail. To recognize partially occluded objects, we represent each object by a Set of landmarks. The landmarks of an object are points of interest which have important shape attributes and are usually obtained from the object boundary. In this study, we use high curvature points along an object boundary as the landmarks of the object. Given a scene consisting of partially occluded objects, the hypothesis of a model object in the scene is verified by matching the landmarks of an object with those in the scene. A measure of similarity between two landmarks, one from a model and the other from a scene, is needed to perform this matching. One such local shape measure is the sphericity of a triangular transformation mapping the model landmark and its two neighboring landmarks to the scene landmark and its two neighboring landmarks. Sphericity is in general defined for a diffeomorphism. Its invariant properties under a group of transformation, namely, translation, rotation, and scaling are derived. The sphericity of a triangular transformation is shown to be a robust local shape measure in the sense that minor distortion in the landmarks does not significantly alter its value. To match landmarks between a model and a scene, a table of compatibility, where each entry of the table is the sphericity value derived from the mapping of a model landmark to a scene landmark, is constructed. A hopping dynamic programming procedure which switches between a forward and a backward dynamic programming procedure is applied to guide the landmark matching through the compatibility table. The location of the model in the scene is estimated with a least squares fit among the matched landmarks. A heuristic measure is then computed to decide if the model is in the scene

Automated image inspection using wavelet decomposition and fuzzy rule-based classifier

A general purpose image inspecting system has been developed for automatic flaw detection in industrial applications. The system has a general purpose image understanding architecture that performs local feature extraction and supervised classification. Local features of an image are extracted from the compactly supported wavelet transform of the image. The features extracted from the wavelet transform provide local harmonic analysis and multi-resolution representation of the image. Image segmentation is achieved by classifying image pixels based on features extracted within a local area near each pixel. The supervised classifier used in the segmentation process is a fuzzy rule-based classifier which is established from the training data. The fuzzy rule base that is used to control the performance of the classifier is optimized by combining similar training data into the same rule. Therefore an optimization is achieved for the established rule base to provide the maximum amount of information with the minimum amount of rules. The experimental results show that the features extracted from the wavelet decomposition give contextual information for the test images. The optimized fuzzy rule-based classifier gives the best performance in both the training and the classification stages. Flaws in the test images are detected automatically by the computer

Automatic visual recognition using parallel machines

Invariant features and quick matching algorithms are two major concerns in the area of automatic visual recognition. The former reduces the size of an established model database, and the latter shortens the computation time. This dissertation, will discussed both line invariants under perspective projection and parallel implementation of a dynamic programming technique for shape recognition. The feasibility of using parallel machines can be demonstrated through the dramatically reduced time complexity. In this dissertation, our algorithms are implemented on the AP1000 MIMD parallel machines. For processing an object with a features, the time complexity of the proposed parallel algorithm is O(n), while that of a uniprocessor is O(n2). The two applications, one for shape matching and the other for chain-code extraction, are used in order to demonstrate the usefulness of our methods. Invariants from four general lines under perspective projection are also discussed in here. In contrast to the approach which uses the epipolar geometry, we investigate the invariants under isotropy subgroups. Theoretically speaking, two independent invariants can be found for four general lines in 3D space. In practice, we show how to obtain these two invariants from the projective images of four general lines without the need of camera calibration. A projective invariant recognition system based on a hypothesis-generation-testing scheme is run on the hypercube parallel architecture. Object recognition is achieved by matching the scene projective invariants to the model projective invariants, called transfer. Then a hypothesis-generation-testing scheme is implemented on the hypercube parallel architecture