Abstract
Image feature detection and matching is a fundamental operation in image processing. As the detected and matched features are used as input data for high-level computer vision algorithms, the matching accuracy directly influences the quality of the results of the whole computer vision system. Moreover, as the algorithms are frequently used as a part of a real-time processing pipeline, the speed at which the input image data are handled is also a concern. The paper proposes an embedded system architecture for feature detection and matching. The architecture implements the FAST feature detector and the BRIEF feature descriptor and is capable of establishing key point correspondences in the input image data stream coming from either an external sensor or memory at a speed of hundreds of frames per second, so that it can cope with most demanding applications. Moreover, the proposed design is highly flexible and configurable, and facilitates the trade-off between the processing speed and programmable logic resource utilization. All the designed hardware blocks are designed to use standard, widely adopted hardware interfaces based on the AMBA AXI4 interface protocol and are connected using an underlying direct memory access (DMA) architecture, enabling bottleneck-free inter-component data transfers.
Highlights
Point correspondences found in sequences of images are the input data for a wide range of computer vision algo‐ rithms, including tracking [1, 2], 3D reconstruction [3, 4], image stitching [5], visual odometry [6, 7], video surveil‐ lance [8, 9] and simultaneous localization and mapping [10, 11]
We present a complete solution for the image feature detection and matching problem
Sets of images were downloaded to the DDR RAM memory. They were sent through the direct memory access (DMA) engine to the detection and description accelerator
Summary
Point correspondences found in sequences of images are the input data for a wide range of computer vision algo‐ rithms, including tracking [1, 2], 3D reconstruction [3, 4], image stitching [5], visual odometry [6, 7], video surveil‐ lance [8, 9] and simultaneous localization and mapping [10, 11]. As the quality of the input data directly influences the final results produced by the aforementioned algorithms, numerous solutions to the problem of automated image feature extraction and matching have been proposed by the research community. The matching is usually performed based on some kind of descriptor that capture the distinctive characteristics of the neighbourhood of the feature. As the image feature detectors and descriptors are often used as a part of a real-time image processing pipeline, the processing speed is an important parameter of this class of algorithms. The performance depends to a great extent on the processed image content (type of features, contrast, noise type and characteristics in the image, etc.) and the type and magni‐ tude of inter-frame transformations (in-plane rotations, perspective and affine distortions, scaling). While some of the algorithms offer high-quality results, their complexity makes them too slow for real-time applications
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