Showing papers on "3D single-object recognition published in 1984"

Pattern Recognition and Computer Vision

Abstract: With more powerful algorithms and greater computing power, the once \"unreachable\" pattern recognition and computer vision problems can now be resolved, simplifying complex decisions about input data. 274 In the last 20 years, interest in pattern recognition and computer vision problems has increased dramatically. This interest has in turn created a need for theoretical methods and experimental software and hardware to aid the design of computer vision and pattern recognition systems. Over 25 books have been published on these topics as have a number of conference proceedings and special issues of journals. * Pattern recognition machines and computer vision systems have been designed and built for everything from character recognition , target detection, medical diagnosis , analysis of biomedical signals and images, remote sensing, and identification of human faces and fingerprints , to reliability, socioeconomics, archaeology, speech recognition and understanding, machine part recognition , and automatic inspection. 1,2 In this article, we briefly review the most recent developments in pattern recognition and computer vision. Many definitions of pattern recognition have been proposed. We view pattern recognition here as being concerned primarily with the description and analysis of measurements taken from physical or mental processes. Pattern recognition often begins with some kind of preprocessing to remove noise and redundancy in the measurements , thereby ensuring an effective and efficient pattern description. Next, a set of characteristic measurements , numerical and/or nonnumeri-cal, and relations among these measurements are extracted to represent patterns. Patterns are then analyzed (classified and/or described) on the basis of the representation. Naturally, we need a good set of characteristic measurements and a firm idea of how they interrelate in representing patterns so that patterns can be easily recognized. Knowledge of the statistical and structural characteristics of patterns is vital to achieving this goal and should be fully utilized. From this point of view, then, pattern recognition means analyzing pattern characteristics as well as designing recognition systems.

Global And Partial Shape Discrimination For Computer Vision

Abstract: The use of Fourier descriptors for rapid recognition of complete shapes is discussed. A Fourier-Mellin correlation procedure for recognition of partial shapes is also presented. Some example recognition experiments, including industrial parts inspection and aircraft recognition, are shown.

On a non-classical recognition problem

Abstract: One of the models of recognition problems when the object presented is represented in the form of pieced data is considered.

Recognition Of Complex Three Dimensional Objects Using Three Dimensional Moment Invariants

Abstract: A technique for the recognition of complex three dimensional objects is presented. The complex 3-D objects are represented in terms of their 3-D moment invariants, algebraic expressions that remain invariant independent of the 3-D objects' orientations and locations in the field of view. The technique of 3-D moment invariants has been used successfully for simple 3-D object recognition in the past. In this work we have extended this method for the representation of more complex objects. Two complex objects are represented digitally; their 3-D moment invariants have been calculated, and then the invariancy of these 3-D invariant moment expressions is verified by changing the orientation and the location of the objects in the field of view. The results of this study have significant impact on 3-D robotic vision, 3-D target recognition, scene analysis and artificial intelligence.

A syntactic approach to three-dimensional object representation and recognition (computervision, pattern)

Abstract: The syntactic approach to pattern representation and scene analysis has received increasing attention due to its unique capability in handling pattern structures and their relationships. However, it has been applied mostly on one and two-dimensional pattern recognition problems. The difficulties of syntactic approach in dealing with three-dimensional objects or scenes are caused by (1) model primitives are described in an observer-centered coordinate system; (2) lack of the mechanisms for relating two-dimensional image to three-dimensional objects, and (3) projections of some primitives in the image are invisible or partially occluded. In this thesis, we propose a syntactic approach to three-dimensional object recognition from a single view. The system consists of two major parts: analysis and recognition. The analysis part consists of primitive surface patches selection and modeling grammar construction. The recognition part consists of preprocessing, image segmentation, visible primitive surface identification, camera model estimation, and structural analysis. In the modeling phase, a three-dimensional object model is represented by using surface patches as primitives and 3D-plex grammar rules as structural relationship descriptors. Several algorithms to extract useful information for recognition from a given 3D-plex grammar are presented. The recognition task starts with preprocessing and image segmentation. Then, the transformation from three-dimensional object space to two-dimensional image space is determined by a camera model estimation procedure. The final phase of the recognition is carried out by a semantic-directed top-down backtrack recognizer.

Three-dimensional object identification and registration (computer vision, recognition)

Abstract: During the past few years there have been great advances in the techniques of acquisition of range data from various sensing systems. The problem of speedily and reliably interpreting these range data for industrial object recognition is becoming critically important in the field of robotics and computer vision. This dissertation presents a methodology to meet such a challenge. It attempts to perform as much classification analysis as possible during the off-line learning process, while only a very small subset of the range data needs to be processed for the on-line recognition. It is assumed that there are only a small number of object types and the objects can be approximated by polyhedra. An adaptive matched filter is developed to extract an object's surface feature vectors using range measurement and surface normals. A sparse representation for each object category j is constructed for classification purpose in the form of a Rj-table of selected feature vectors. The approach, based on the concept of the generalized Hough Transform, classifies an object by examining the maximum votes which it receives from various Rj-tables. During the off-line learning phase an object tree is constructed for each prototype. The massive geometric data of a given prototype can be reordered through the tree traversal. An optimal selection rule is established for minimizing the misclassification probability. In this way the common feature vectors among object categories are removed as much as possible, while the distinctive feature vectors are selected in the Rj-table with statistical significance. Thus, it will result in a significant saving of the on-line processing time in comparison to the conventional three-dimensional scene segmentation approach. An experiment with simulated range images of nine categories demonstrated the success of the proposed methodology.