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Visual tracking is important in computer vision. At present, although many algorithms of visual tracking have been proposed, there are still many problems which are needed to be solved, such as occlusion and frame speed. To solve these problems, this paper proposes a novel method which based on compressive tracking. Firstly, we make sure the occlusion happens if the testing result about image features by the classifiers is lower than a threshold value which is certain. Secondly, we mark the occluded image and record the occlusion region. In the next frame, we test both the classifier and the marked image. This algorithm makes sure the tracking is fast, and the result about solving occlusion is much better than other algorithms, especially compressive tracking.

This paper presents a novel approach for automatic pupil segmentation. The proposed algorithm uses local histogram and standard deviation based adaptive thresholding method that looks for the region that has the highest probability of having the pupil. We have tested our proposed algorithm on two public databases namely: CASIA v1.0 and MMU v1.0. Experimental results show that the proposed method has satisfying performance and good robustness against the reflection in the pupil.

Tomography is an important technique for non-invasive imaging, with applications in medicine, materials research and industry. Tomographic reconstructions are typically gray-scale images, that can possibly contain a wide spectrum of grey levels. Segmentation of these grey level images is an important step to obtain quantitative information from tomographic datasets. Thresholding schemes are often used in practice, as they are easy to implement and use. However, if the tomogram exhibits variations in the intensity throughout the image, it is not possible to obtain an accurate segmentation using a single, global threshold. Instead, local thresholding schemes can be applied that use a varying threshold, depending on local characteristics of the tomogram. Selecting the best local thresholds is not a straightforward task, as local image features (such as the local histogram) often do not provide sufficient information for choosing a proper threshold. In this paper, we propose a new criterion for selecting local thresholds, based on the available projection data, from which the tomogram. was initially computed. By reprojecting the segmented image, a comparison can be made with the measured projection data. This yields a quantitative measure of the quality of the segmentation. By minimizing the difference between the computed and measured projections, optimal local thresholds can be computed. Simulation experiments have been performed, comparing the result of our local thresholding approach with global thresholding. Our results demonstrate that the local thresholding approach yields segmentations that are significantly more accurate, in particular when the tomogram contains artifacts.

Special devices with higher quantization resolution are needed to display or process most medical images. In this paper, we compare three different quantization approaches for mammogram images in order to process them in 8 bits/pixel resolution. Since microcalcification is one of the most important indications of risk of breast cancer, a simple shift operation (uniform quantization) cannot retain this vital information. Quantization based on the local histogram will give better results but at the price of more computation.