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

CAD-based 3-D object recognition

Journal ArticleWe propose an approach to 3-D object recognition using CAD-based geometry models for freeform surfaces. Geometry is modeled with rational B-splines by defining surface patches and then combining these into a volumetric model of the object. Characteristic features are then extracted from this model and subjected to a battery of tests to select an "optimal" subset of surface features which are robust with respect to the sensor being used (e.g. laser range finder versus passive stereo) and permit recognition of the object from any viewing position. These features are then organized into a "strategy tree" which defines the order in which the features are sought, and any corroboration required to justify issuing a hypotheses. We propose the use of geometric sensor data integration techniques as a means for formally selecting surface features on free-form objects in order to build recognition strategies. Previous work has dealt with polyhedra and generalized cylinders, whereas here we propose to apply the method to more general surfaces

A microcomputer-based vision system to recognize and locate partially occluded parts in binary and gray level images

This paper presents a microcomputer-based machine vision system to recognize and locate partially occluded parts in binary or gray level images. The recognition process is restricted to untilted, two-dimensional objects;A new edge-tracking technique in conjunction with a straight-line approximation algorithm is used to identify the local features in an image. Corners and holes serve as local features. The local features identified in an image are matched against all the compatible features stored for the model parts. The algorithm computes, for all image and model features matches, a coordinate transformation that maps a model feature onto an image feature. A new clustering algorithm has been developed to identify consistent coordinate transformation clusters that serve as initial match hypotheses. A hypothesis verification process eliminates the match hypotheses that are not compatible with the image information;The system performance was compared to a vision system restricted to recognize nonoverlapping parts. Both systems require the same hardware configuration and share the basic image processing routines

Three dimensional pattern recognition using feature-based indexing and rule-based search

In flexible automated manufacturing, robots can perform routine operations as well as recover from atypical events, provided that process-relevant information is available to the robot controller. Real time vision is among the most versatile sensing tools, yet the reliability of machine-based scene interpretation can be questionable. The effort described here is focused on the development of machine-based vision methods to support autonomous nuclear fuel manufacturing operations in hot cells; This thesis presents a method to efficiently recognize 3D objects from 2D images based on feature-based indexing. Object recognition is the identification of correspondences between parts of a current scene and stored views of known objects, using chains of segments or indexing vectors. To create indexed object models, characteristic model image features are extracted during preprocessing. Feature vectors representing model object contours are acquired from several points of view around each object and stored. Recognition is the process of matching stored views with features or patterns detected in a test scene; Two sets of algorithms were developed, one for preprocessing and indexed database creation, and one for pattern searching and matching during recognition. At recognition time, those indexing vectors with the highest match probability are retrieved from the model image database, using a nearest neighbor search algorithm. The nearest neighbor search predicts the best possible match candidates. Extended searches are guided by a search strategy that employs knowledge-base (KB) selection criteria. The knowledge-based system simplifies the recognition process and minimizes the number of iterations and memory usage; Novel contributions include the use of a feature-based indexing data structure together with a knowledge base. Both components improve the efficiency of the recognition process by improved structuring of the database of object features and reducing data base size. This data base organization according to object features facilitates machine learning in the context of a knowledge-base driven recognition algorithm. Lastly, feature-based indexing permits the recognition of 3D objects based on a comparatively small number of stored views, further limiting the size of the feature database; Experiments with real images as well as synthetic images including occluded (partially visible) objects are presented. The experiments show almost perfect recognition with feature-based indexing, if the detected features in the test scene are viewed from the same angle as the view on which the model is based. The experiments also show that the knowledge base is a highly effective and efficient search tool recognition performance is improved without increasing the database size requirements. The experimental results indicate that feature-based indexing in combination with a knowledge-based system will be a useful methodology for automatic target recognition (ATR)

Parallel Evidence-Based Indexing of Complex Three-Dimensional Models Using Prototypical Parts and Relations (Dissertation Proposal)

This proposal is concerned with three-dimensional object recognition from range data using superquadric primitives. Superquadrics are a family of parametric shape models which represent objects at the part level and can account for a wide variety of natural and man-made forms. An integrated framework for segmenting dense range data of complex 3-D objects into their constituent parts in terms of bi-quadric surface patches and superquadric shape primitives is described in [29]. We propose a vision architecture that scales well as the size of its model database grows. Following the recovery of superquadric primitives from the input depth map, we split the computation into two concurrent processing streams. One is concerned with the classification of individual parts using viewpoint-invariant shape information while the other classifies pairwise part relationships using their relative size, orientation and type of joint. The major contribution of this proposal lies in a principled solution to the very difficult problems of superquadric part classification and model indexing. The problem is how to retrieve the best matched models without exploring all possible object matches. Our approach is to cluster together similar model parts to create a reasonable number of prototypical part classes (protoparts). Each superquadric part recovered from the input is paired with the best matching protopart using precomputed class statistics. A parallel, theoretically-well grounded evidential recognition algorithm quickly selects models consistent with the classified parts. Classified part relations (protorelations) are used to further reduce the number of consistent models and remaining ambiguities are resolved using sequential top-down search

CAGD-based computer vision

Journal ArticleThree-dimensional model-based computer vision uses geometric models of objects and sensed data to recognize objects in a scene. Likewise, Computer Aided Geometric Design (CAGD) systems are used to interactively generate three-dimensional models during the design process. Despite this similarity, there has been a dichotomy between these fields. Recently, the unification of CAGD and vision systems has become the focus of research in the context of manufacturing automation