Orientation (computer vision)

About: Orientation (computer vision) is a research topic. Over the lifetime, 17196 publications have been published within this topic receiving 358181 citations.

Computer aided drawing in three dimensions

Abstract: A computer system for aiding in the design of an object, comprising a first sensor representing the object and adapted to be moved in three dimensions in a work area by a designer, the first sensor providing signals to the computer system that indicate its position and orientation in the work area, and a second sensor adapted to be moved by the designer in three dimensions in the work area, the second sensor providing signals to the computer system that indicate its position and orientation in the work area; the computer system responds to the signals from the first and second sensors by creating a displayed image corresponding to a three-dimensional model of the object in a virtual work area that corresponds to the designer's work area, the model comprising lines that each have a shape and position that corresponds to movement by the designer of at least one of the sensors, and by controlling the position and orientation of the model in the virtual work area in accordance with the motion of at least one of the sensors

Translation, rotation, and scale invariant pattern recognition by high-order neural networks and moment classifiers

Abstract: The classification and recognition of two-dimensional patterns independently of their position, orientation, and size by using high-order networks are discussed. A method is introduced for reducing and controlling the number of weights of a third-order network used for invariant pattern recognition. The method leads to economical networks that exhibit high recognition rates for translated, rotated, and scaled, as well as locally distorted, patterns. The performance of these networks at recognizing types and handwritten numerals independently of their position, size, and orientation is compared with and found superior to the performance of a layered feedforward network to which image features extracted by the method of moments are presented as input. >

Document representation and its application to page decomposition

Abstract: Transforming a paper document to its electronic version in a form suitable for efficient storage, retrieval, and interpretation continues to be a challenging problem. An efficient representation scheme for document images is necessary to solve this problem. Document representation involves techniques of thresholding, skew detection, geometric layout analysis, and logical layout analysis. The derived representation can then be used in document storage and retrieval. Page segmentation is an important stage in representing document images obtained by scanning journal pages. The performance of a document understanding system greatly depends on the correctness of page segmentation and labeling of different regions such as text, tables, images, drawings, and rulers. We use the traditional bottom-up approach based on the connected component extraction to efficiently implement page segmentation and region identification. A new document model which preserves top-down generation information is proposed based on which a document is logically represented for interactive editing, storage, retrieval, transfer, and logical analysis. Our algorithm has a high accuracy and takes approximately 1.4 seconds on a SGI Indy workstation for model creation, including orientation estimation, segmentation, and labeling (text, table, image, drawing, and ruler) for a 2550/spl times/3300 image of a typical journal page scanned at 300 dpi. This method is applicable to documents from various technical journals and can accommodate moderate amounts of skew and noise.