학술논문

Fast Run-Based Connected Components Labeling for Bitonal Images
Document Type
Conference
Source
2021 Joint 10th International Conference on Informatics, Electronics & Vision (ICIEV) and 2021 5th International Conference on Imaging, Vision & Pattern Recognition (icIVPR) Imaging, Vision & Pattern Recognition (icIVPR), 2021 Joint 10th International Conference on Informatics, Electronics & Vision (ICIEV) and 2021 5th International Conference on. :1-8 Aug, 2021
Subject
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Codes
Image processing
Merging
Benchmark testing
Hardware
Pattern recognition
Labeling
Connected Components Labeling
Binary Image Processing
Bitonal Images
Algorithms Optimization
Language
Abstract
Connected Components Labeling (CCL) is a fundamental task in binary image processing. Since its introduction in the sixties, several algorithmic strategies have been proposed to optimize its execution time. Most CCL algorithms in literature, including the current state-of-the-art, are designed to work on an input stored with 1-byte per pixel, even if the most memory-efficient format for a binary input only uses 1-bit per pixel. This paper deals with connected components labeling on 1-bit per pixel images, also known as 1bpp or bitonal images. An existing run-based CCL strategy is adapted to this input format, and optimized with Find First Set hardware operations and a smart management of provisional labels, giving birth to an efficient solution called Bit-Run Two Scan (BRTS). Then, BRTS is further optimized by merging pairs of consecutive lines through bitwise OR, and finding runs on this reduced data. This modification is the basis for another new algorithm on bitonal images, Bit-Merge-Run Scan (BMRS). When evaluated on a public benchmark, the two proposals outperform all the fastest competitors in literature, and therefore represent the new state-of-the-art for connected components labeling on bitonal images.