Connection flux statistical information extraction method and VLSI structure

Abstract

The invention discloses a connection flux statistical information extraction method and a VLSI (Very Large Scale Integration) structure. The connection flux statistical information extraction method comprises the following steps: simultaneously scanning two adjacent rows of a binary image; judging whether a connected area exists between the current row and the previous row; if yes, merging the area, connected with the current row, in the previous row into the current row according to an equivalent run pair merging rule; meanwhile, marking the area, not connected with the current row, in the previous row as an ended area, and outputting the information of the ended area; then updating the run number of the connected area in the current row; when the current row is the last one, according to the equivalent run pair merging rule, merging the connected area in the current row, then marking the merged and obtained area as the ended area, and outputting the information of the ended area, so as to obtain the connection flux statistical information. The connection flux statistical information extraction method disclosed by the invention can quickly process the binary image and extract the connection flux statistical information of the binary image, and is low in hardware resource consumption.

Description

technical field [0001] The invention belongs to the field of image processing technology and integrated circuit design, and relates to a method for extracting statistical information of connectivity and a VLSI structure. Background technique [0002] The connectivity information statistics of the binary image is to connect each other (4 neighbors or 8 Neighborhood) "1" value pixel set is extracted, the purpose is to find all connected regions in the image, and mark all pixels belonging to the same connected region with a unique label value, and count the characteristics of each connected region . This preprocessing operation is widely adopted in many fields of image processing and pattern recognition. Therefore, it is an important step to extract image features, target detection and recognition to distinguish each connected region by a certain method and study the characteristics of each connected region separately. [0003] The current regional connectivity algorithms ca...

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Problems solved by technology

[0007] It can be seen from the above that among the existing connected domain labeling algorithms, even the BBDT algorithm with the best performance can only achieve faster processing speed on high-performance PC machines, and often cannot meet the needs of high-speed real-time image processing, especially It is a miniaturized embedded image processing system, which requires a method for extracting statistical information of connectivity that is suitable for hardware acceleration and consumes less hardware resources.

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