Showing papers on "Sketch recognition published in 1988"

Computer vision: basic principles

Abstract: The author provides a general introduction to computer vision. He discusses basic techniques and computer implementations, and also indicates areas in which further research is needed. He focuses on two-dimensional object recognition, i.e. recognition of an object whose spatial orientation, relative to the viewing direction is known. >

Chinese character recognition: a twenty-five-year retrospective

Abstract: The author assesses the current status of the field and places the problem of Chinese recognition into perspective with other areas of optical character recognition. Early experiments are briefly reviewed, and sources of more up-to-date information, including review articles, are indicated, advances in computer technology are discussed that have had a significant impact on the problem, and a sampling of relatively recent research on the classification of both printed and handprinted ideographs is presented. Included in the discussion are techniques of preprocessing (character location and segmentation) and hierarchical classification. >

Shape recognition: a landmark-based approach

Abstract: 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 neigh­ boring 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

Intelligent robotics: recognition and reasoning

Abstract: The functions of recognition and reasoning are examined for their fundamental importance to intelligent robotics. Pattern-based computation and inference operations are also analyzed, as they may lead to computation paradigms that have more efficient implementations than the discrete-symbol computation paradigm of serial digital computers. >

Object recognition in multi-resolution systems

Abstract: Pyramidal data structures provide several instances of the same image at different resolutions to allow the implementation of multi-resolution techniques, in particular planning strategies that are usually goal oriented, and bottom-up hierarchical procedures that are data driven. Various hardware architectures have been suggested to perform real-time multi-resolution computations. In the following, the match between the above mentioned strategies and these architectures will be discussed. This comparison will be based on two real-time vision tasks in the practical industrial environment: object recognition and scene labeling.

A Structural Recognition Algorithm For Handwritten Numerals

Abstract: A new structural recognition method for handwritten numerals is proposed that uses (i) a set of topological pattern primitives, (ii) an adaptive hierachical image segmentation technique and (iii) a tree automaton. Experimental results are also presented.

A Study On Description And Recognition Of Objects Based On Segment And Normal Vector Distributions

Abstract: Three dimensional (3-D) object recognition holds an important position in a study of artificial intelligence and has been investigated from various viewpoints. From the standpoint of an application, a visual sensor used in current industrial robots uses only two dimensional information of images to increase processing speed[1]. However to advance the progress of factory automation, it is necessary to improve the function of a robot, and to introduce 3-D object recognition as a fundamental technique.

Enhanced Autonomous Face Recognition Machine. Volume 1

Abstract: : This thesis continues work on the Autonomous Face Recognition Machine developed at AFIT in 1985. There were two major changes made to the system. The set of features extracted from the face for use in the recognition process, was changed. A higher dimensioned vector taken from the two-dimensional Discrete Fourier Transform of the face, was used in hope of increasing the separation of templates stored in the data base. Further research is needed to determine whether this change is beneficial to the system. The second change was to the decision rule used in recognition. The decision making portion of the system was replaced by a back propagation neural network. While providing equivalent recognition capability, this change provides a constant recognition time independent of the number of subjects trained into the system. Keywords: Pattern recognition, Image processing, Neural networks, Gesalt transforms.

Towards an automatic construction of object recognition strategies

Abstract: An approach to object recognition in an industrial environment is described. With the hypothesis that the universe of objects is limited and that the attitudes in space of any object are constrained by the working environment to a few possibilities, a method is obtained that determines minimal descriptions for each model/object pair. These descriptions, which are discriminant of each model, are used by the recognition strategy planner. It automatically determines which descriptions can be conveniently used to identify the object(s) looked for and in what order. Uncertainties due to overlapping are signaled and tentative classifications listed. >

Learning rules for 3D object recognition

Abstract: A fully autonomous computer-vision system needs the ability to automatically learn, or discover, from training views of objects the object information (models) needed for recognition. One approach to object recognition uses an evidence rulebase composed of objects with corresponding degrees of evidence for the various objects in the databases. A technique for automatically deriving the evidence rulebase from training views of objects is presented. This generated rulebase is shown to provide successful recognition of new views of those objects. >

Vertex space analysis and its application to model based object recognition

Abstract: The author discusses some of the properties of vertex space, including insensitivity to changes in scale, orientation, and partial object occlusion, and how these properties relate to problems in model-based object recognition. He also describes techniques developed for 2-D and 3-D object recognition using vertex space. The vertex-space approach to object recognition is powerful and efficient, deals well with missing information, and does not require conventional region segmentation. Results to date and an indication of future research directions are presented. >

Recognition of object domain by color distribution

Abstract: For the image processing of an object in its natural image, it is necessary to extract in advance the object to be processed from its image. To accomplish this the outer shape of an object is extracted through human instructions, which requires a great deal of time and patience. A method involving the setting of a model of color distribution on the surface of an object is described. This method automatically provides color recognition, a piece of knowledge that represents the properties of an object, from its natural image. A method for recognizing and extracting the object in the image according to the color recognized is also described.