For objects with the same texture but different colors, it is difficult to discriminate them with the traditional scale invariant feature transform descriptor (SIFT), because it is designed for grayscale images only. Thus it is important to keep a high probability to make sure that the used key points are couples of correct pairs. In addition, mean distributed key points are much more expected than over dense and clustered key points for image match and other applications. In this paper, we analyze these two problems. First, we propose a color and scale invariant method to extract a more mean distributed key points relying on illumination intensity invariance but object reflectance sensitivity variance variable. Second, we modify the key point’s canonical direction accumulated error by dispersing each pixel’s gradient direction on a relative direction around the current key point. At last, we build the descriptors on a Gaussian pyramid and match the key points with our enhanced two-way matching regulations. Experiments are performed on the Amsterdam Library of Object Images dataset and some synthetic images manually. The results show that the extracted key points have better distribution character and larger number than SIFT. The feature descriptors can well discriminate images with different color but with the same content and texture.
As an effective information transmitting way, chart is widely used to represent scientific statistics datum in books, research papers, newspapers etc. Though textual information is still the major source of data, there has been an increasing trend of introducing graphs, pictures, and figures into the information pool. Text recognition techniques for documents have been accomplished using optical character recognition (OCR) software. Chart recognition techniques as a necessary supplement of OCR for document images are still an unsolved problem due to the great subjectiveness and variety of charts styles. This paper reviews the development process of chart recognition techniques in the past decades and presents the focuses of current researches. The whole process of chart recognition is presented systematically, which mainly includes three parts: chart segmentation, chart classification, and chart Interpretation. In each part, the latest research work is introduced. In the last, the paper concludes with a summary and promising future research direction.