Similarity Measure for Matching Fuzzy Object Shapes

Similarity Measure for Matching Fuzzy Object Shapes

DOI: 10.4018/978-1-5225-3796-0.ch005
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Abstract

In this chapter, the Common Bin Similarity Measure (CBSM) is introduced to estimate the degree of overlapping between the query and the database objects. All available similarity measures fail to handle the problem of Integrated Region Matching (IRM). The technical procedure followed for extracting the objects from images is well defined with an example. The performance of CBSM is compared with well-known methods and the results are given. The effect of IRM with CBSM is also proved by the experimental results. In addition, the performance of CBSM in encoded feature is compared with similar approaches. Overall, the CBSM is a novel idea and very much suitable for matching objects and ranking on their similarities.
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Fuzzy Object Level (Fol) Similarity Measure

The procedure for obtaining the objects in images is shown in Figure 1. A similar traditional method, say, connected component detection is based on graph theory principle and the connected components are uniquely labelled for a given heuristic. Once the first pixel of a connected component is found, all the connected pixels of the connected component are labelled and the next pixel is considered. Data structures such as linked list or queue is required for processing the labelled pixels.

In this work, Canny edge detection is used along with the morphological operations, Dilation and Close with appropriate parameter using MATLAB tool. It is well-known that the Canny edge detection algorithm is an optimal edge detector and its error rate is very low. It is important that the edges occurring in images should not be missed and that response of non-edges should be very low with localization of edge points. The Canny operator always tries to minimize the distance between the estimated edge pixels and the actual edge with one response to a single edge. However, it is very difficult to completely eliminate the possibility of multiple responses to an edge. This issue has been handled by the Canny edge detector by smoothing the images to eliminate the noise and to find the image gradient to highlight regions with high spatial derivatives. The algorithm then tracks along these regions and suppresses any pixel that is not at the maximum and the gradient array is now further reduced by hysteresis. The hysteresis is used to track along the remaining pixels that have not been suppressed and two thresholds are used for edge detection. Because of the above mentioned procedural advantages, an optimal edge detector, the Canny edge detection algorithm is used. This mechanism substantially reduces the computational time and improves the performance.

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