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Histogram estimation is one of the fundamental tasks in crowdsourcing data aggregation. Since contributing data reveal more or less information about individuals' identifications and activities, participants need to preserve privacy of data according to their own levels of privacy concern. However, most of the existing work only aggregates data with an identical privacy level. In this paper, we propose...
In order to have a rich representation for human action, we propose to combine two complementary features so that a human posture can be characterized in more details. In particular, the distance signal feature and the width feature are combined in an effective way to enhance each other's discriminating capability. The resulting feature vector is quantized into mid-level features using k-means clustering...
We propose a novel affinity matrix for image segmentation in this paper. The affinity matrix is constructed by using the Gaussian weighted Chi-square distance with neighborhood information, in which the vital spatial structure of the image is considered. An adaptive local scaling parameter is used to refine the segmentation rather than selecting a single scaling parameter. We demonstrate that graph-based...
This paper presents a novel scheme for human action recognition. First of all, we employ the curvature estimation to analyze human posture patterns and to yield the discriminative feature sequences. The feature sequences are further represented into sets of strings. Consequently, we can solve human action recognition problem by the string matching technique. In order to boost the performance of string...
This paper presents a human action recognition method using histogram of oriented gradient (HOG) of motion history image (MHI). First, the proposed method generates MHI with differential images which are obtained by frame difference of successive frames of a video. The histogram of oriented gradient (HOG) of the MHI is then computed. Finally, support vector machine (SVM) is applied to train an action...
Although interactive image segmentation has been widely exploited, current approaches present unsatisfactory results in medical image processing. This paper proposes a fast method for interactive CT image segmentation in which the tumor regions should be partitioned as foreground against the healthy tissues. In contrast to natural images, we have the following observation on CT images: (1) CT images...
infrared image pattern recognition system consists of image enhancement, segmentation and pattern classification. Image segmentation is realized through Threshold Segmentation. Generally speaking, there are several methods of threshold segmentation such as double-hump method, iterated method and maximum entropy method and the otsu method and so on. These segmentation methods are used for the automatic...
Gastroscopy is important tool for the clinical examination of gastric diseases, and the abnormality detection on the gastroscopic images will help physicians to diagnose. An improved patches assembled by local weights is presented in this paper. First, a series of classifiers on image patches with different sizes have been analyzed to find the suitable size. The boosted stumps are employed as the...
Gastroscope is important in gastric cancer diagnosis. However, the specular reflection is common existed in the gastroscope images and it's easily confused with ulcer. In this paper we develop a method for detecting specular reflection in gastroscopic images. First the Intensity-Saturation joint distribution of region of interest (ROI) is obtained in HSI color space. Then based on the analysis of...
This paper has accomplished an improved local accumulate histogram method of Thangka Image Retrieval. First, change the color space from RGB to HSV and divide similar area by the value of the hue reasonably, then it can get six similar intervals which the hue (H) is independent of the value (V) (Saturation (S) is assumed a constant in the beginning). Second, accumulate histogram is applied respectively...
This work proposed a reversible data hiding algorithm that is based on the interleaving max-min difference histogram. The method divides the cover image into non-overlapping identical blocks. In each block, the maximum or minimum pixel is selected to calculate absolute differences between the gray level value of the selected pixel and that of the other pixels. Then, these differences are used to generate...
Gastroscopy is widely used for the clinical examination of gastric diseases. The computerized methods capable to detect abnormal regions can help the physicians to identify the suspicious regions in gastroscopic images. The patch-based technique with the boosted stumps is adopted to detect all kinds of abnormalities in this paper. Considering that the responses of patch classifiers on the neighboring...
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