We present a method for extracting text from images where the text plane is not necessarily fronto-parallel to the camera. Initially, we locate local image features such as borders and page edges. We then use perceptual grouping on these features to find rectangular regions in the scene. These regions are hypothesized to be pages or planes that may contain text. Edge distributions are then used for the assessment of these potential regions, providing a measure of confidence. It will be shown that the text may then be transformed to a fronto- parallel view suitable, for example, for an OCR system or other higher level recognition. The proposed method is scale independent (of the size of the text). We illustrate the algorithm using various examples.
This paper describes a parallel implementation of an image feature tracking system. The system is designed to operate as the front-end of a vision system for controlling autonomous guided vehicles (AGV). Image features or tokens (edge-based line segments in the example given here) are extracted from the image and allocated to individual tracking processes. Both the extraction and the tracking stages are performed by concurrent processes. Arbitrary tracking algorithms may be associated with each process. In the current implementation, a Kalman filter is used to track and predict tokens in subsequent image frames.
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Machine Vision Applications in Industrial Inspection XI