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Content-based image retrieval (CBIR) systems normally return the retrieval results according to the similarity between features extracted from the query image and candidate images. In certain circumstance, however, users concern more about objects of their interest and only wish to retrieve images containing relevant objects, while ignoring irrelevant image areas (such as the background). Previous...
In content-based image retrieval, the representation of local properties in an image is one of the most active research issues. This paper proposes a salient region detector based on wavelet transform. The detector can extract the visually meaningful regions on an image and reflect local characteristics. An annular segmentation algorithm based on the distribution of salient regions is designed. It...
We present a simple method based on support vector machine (SVM) for Chinese license plate recognition. By firstly pre-processing the input images containing license plates, a set of normalized subimages can be obtained, each of which contains a number, an English letter or a Chinese character. We then transform these subimages into vectors by simply using pixel values. In this way, we can avoid the...
We propose two methods for efficient extraction of interference fringe information. The first method can restore the fringes in narrow strip areas through a series of operation: reducing noise, connecting broken points, extending short lines and polynomial fitting. The restoration process is self-adaptive and requires little user-defined parameters. The second method first employs distance histograms...
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