система технічного зору

Detecting objects in a video stream is a typical problem in modern computer vision systems that are used in multiple areas. Object detection can be done on both static images and on frames of a video stream. Essentially, object detection means finding color and intensity non-uniformities which can be treated as physical objects. Beside that, the operations of finding coordinates, size and other characteristics of these non-uniformities that can be used to solve other computer vision related problems like object identification can be executed.

We propose a structure of special processor implementing feature detection in a video stream based on the SURF algorithm to be used in computer vision systems.

We propose a computer vision system based on multi-processor architecture that uses multi-port memory with equal access to all special microprocessors.

We consider a microcontroller implementation of an algorithm for extracting SURF features of an object in a video stream to be used in a specialized computer vision system.

Splines have been used for the solution of the considered problems. This allows getting a single model for the three tasks and combine flexibility of the model with ease of calculations. For filtering and segmentation of acoustic signals spline filters that are similar to Savitsky-Golay filters have been used. Various widths of spline fragments provides a possibility to have different smoothness and select fragments of varying detalization.

The method BOX transformation that reflects lines on raster images for the points of intersection circumscribing the square image. Displaying is carried out by lines with distinct pairs of crossing points and has N2 complexity. The number of pairs of points lying on a straight line accumulates at a point of reflection. This selection allows you to direct the number of dots on them and perform filtering of individual points. The algorithms of direct and inverse conversion and the examples of images conversion have been demonstrated.