Basically, detecting convex and concave points on the boundary of an object plays an important role in computer vision, object recognition and image understanding. In this paper a method that combines boundary and skeleton information for detecting these critical points is proposed. Specifically, the method is developed with the aim of obtaining high performance and efficiency, and producing a more robust method in detecting concave and convex points with minimum cost of computation. Furthermore, for faster execution of the proposed method, the detection of convex and concave points can be run concurrently. In order to evaluate the performance of the proposed method, the results of the proposed method are compared with three related convex and concave points detection methods. The experimental results have shown that the proposed method provides better output and detection rate. � 2010 IEEE.

Many factors, such as noise level in the original image and the noise-removal methods that clean the image prior to performing a vectorization, may play an important role in affecting the line detection of raster-to-vector conversion methods. In this paper, we propose an empirical performance evaluation methodology that is coupled with a robust statistical analysis method to study many factors that may affect the quality of line detection. Three factors are studied: noise level, noise-removal method, and the raster-to-vector conversion method. Eleven mechanical engineering drawings, three salt-and-pepper noise levels, six noise-removal methods, and three commercial vectorization methods were used in the experiment. The Vector Recovery Index (VRI) of the detected vectors was the criterion used for the quality of line detection. A repeated measure ANOVA analyzed the VRI scores. The statistical analysis shows that all the studied factors affected the quality of line detection. It also shows that two-way interactions between the studied factors affected line detection. Copyright � 2011 The Institute of Electronics, Information and Communication Engineers.

In this paper an enhanced background subtraction approach for image segmentation is proposed in order to precisely detect and represent the objects in the images. It is based on automatic detection of the background by estimating the background and then subtracting it from the original image. This step is incorporated in the background subtraction approach in order to reduce the computational cost and overcome the problems where the environment is complex such as underwater images and when there are many kinds of objects. The segmentation results using this enhanced approach are compared with the recent background subtraction techniques in terms of speed and accuracy in order to show the efficiency and the effectiveness of the proposed approach. � 2010 IEEE.