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An embedded software-reconfigurable color segmentation architecture for image processing systems

Chrysos Grigorios, Dollas Apostolos, Bourbakis, Nikolaos G

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Περίληψη

Image segmentation is one of the first important and difficult steps of image analysis and computer vision and it is considered as one of the oldest problems in machine vision. Lately, several segmentation algorithms have been developed with features related to thresholding, edge location and region growing to offer an opportunity for the development of faster image/video analysis and recognition systems. In addition, fuzzy-based segmentation algorithms have essentially contributed to synthesis of regions for better representation of objects. These algorithms have minor differences in their performance and they all perform well. Thus, the selection of one algorithm vs. another will be based on subjective criteria, or, driven by the application itself. Here, a low-cost embedded reconfigurable architecture for the Fuzzy-like reasoning segmentation (FRS) method is presented. The FRS method has three stages (smoothing, edge detection and the actual segmentation). The initial smoothing operation is intended to remove noise. The smoother and edge detector algorithms are also included in this processing step. The segmentation algorithm uses edge information and the smoothed image to find segments present within the image. In this work the FRS segmentation algorithm was selected due to its proven good performance on a variety of applications (face detection, motion detection, Automatic Target Recognition (ATR)) and has been developed in a low-cost, reconfigurable computing platform, aiming at low cost applications. In particular, this paper presents the implementation of the smoothing, edge detection and color segmentation algorithms using Stretch S5000 processors and compares them with a software implementation using the Matlab. The new architecture is presented in detail in this work, together with results from standard benchmarks and comparisons to alternative technologies. This is the first such implementation that we know of, having at the same time high throughput, excellent performance (at least in standard benchmarks) and low cost.

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