원문정보
초록
영어
The classification and measurement of rock fragments is very important in mining and construction engineering. The monitoring system acquires and analyses the fragment images from a gravitational falling stream at the end of a moving conveyor belt, and the key function of the system is to construct an image segmentation algorithm. To achieve this goal, an adaptive thresholding algorithm with fuzzy comprehensive evaluation is proposed. Firstly, a grabbed image is roughly segmented by using a global auto-thresholding algorithm. Then each of the objects is measured and analyzed if it includes the multiple fragments touching each other, based on the fuzzy comprehensive evaluation method in which the salient fragment features of area, perimeter, shape, gradient magnitude and gray-level flatness are extracted, and for each of the features, the membership function is constructed experimentally. Finally, each of the touching fragment regions, as one image, is auto-thresholded again, and this procedure is repeated until no region can be further separated. The experimental results show that compared to cluster analysis, graph based, and FCM image segmentation algorithms the new algorithm can make image segmentation well for the falling fragments on-line.
목차
1. Introduction
2. Image Preprocessing and Initial Segmentation
2.1 Image Preprocessing
2.2 Initial Thresholding Algorithm
3. Image Segmentation based on Fuzzy Math
3.1 Definitions of Parameters for a Cluster Region
3.2 Fuzzy Comprehensive Evaluation for Rock Fragment Parameters
4. Experiments and Comparison Anlaysis
4.1 Fuzzy Comprehensive Evaluation Results
4.2 Final Image Segmentation Results Analysis
4.3 Comparison to Other Algorithms
5. Conclusions
References