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Discriminating photorealistic computer graphics from natural images is an important problem in image forensics. A new distinguishing method using second-order difference statistics is proposed in this paper. Firstly, the second-order difference signals and predicting error signals of both original and calibrated images are extracted in the HSV color space, and then the variance and kurtosis of second-order...
Identifying moving objects from a video sequence is a fundamental and critical task in many computer vision applications. We propose a three stage adaptive object segmentation algorithm for color surveillance videos. In the first stage, background is modeled using Multiple Correlation Coefficient (Ra,bc) using pixel-level based approach for motion segmentation. Segmented foreground objects generally...
A technique for intelligent processing is proposed for the analysis of brain magnetic resonance images. This paper presents segmentation and detection technique of tumor, edema and healthy tissues from fluid attenuated inversion recovery magnetic resonance images of brain with the help of composite feature vectors comprising of empirically developed functions of higher order wavelets and statistical...
Artificial neural network (ANN) is an important part of artificial intelligence, it has been widely used in remote sensing classification research field. Wetlands remote sensing classification based on ANN is difficult, because of the complex feature of wetlands areas. The purity of training samples for remote sensing image supervised classification is difficult to guarantee that will affect the classification...
This paper proposes an extension to the mean shift tracking. We introduce the color connectedness degrees (CCD) which, more than providing statistical information about the target to track, embeds information about the amount of connectedness of the color intervals which compose the target. With a low increase of complexity, this approach provides a better robustness and quality of the tracking compared...
In this paper, we present an approach for symbol representation and recognition in line drawings, integrating both the vector-based structural description and pixel-level statistical features of the symbol. For the former, a vectorial template is defined on the basis of the vectorization model and exploited in segmenting symbols from the line network. For the latter, a Radon-transform-based signature...
In this paper, we present an effective method for human action recognition using statistical models based on optical flow orientations. We compute a distribution mixture over motion orientations at each spatial location of the video sequence. The set of estimated distributions constitutes the direction model, which is used as a mid-level feature for the video sequence. We recognize human actions using...
In this paper, we propose a novel appearance-based method for person re-identification, that condenses a set of frames of the same individual into a highly informative signature, called Histogram Plus Epitome, HPE. It incorporates complementary global and local statistical descriptions of the human appearance, focusing on the overall chromatic content, via histograms representation, and on the presence...
Spoof detection is a critical function for iris recognition because it reduces the risk of iris recognition systems being forged. Despite various counterfeit artifacts, cosmetic contact lens is one of the most common and difficult to detect. In this paper, we proposed a novel fake iris detection algorithm based on improved LBP and statistical features. Firstly, a simplified SIFT descriptor is extracted...
This paper presents an algorithm for segmenting the hair region in uncontrolled, real life conditions images. Our method is based on a simple statistical hair shape model representing the upper hair part. We detect this region by minimizing an energy which uses active shape and active contour. The upper hair region then allows us to learn the hair appearance parameters (color and texture) for the...
Robust background subtraction under sudden illumination changes is a challenging problem. In this paper, we propose an approach to address this issue, which combines the Eigenbackground algorithm together with a statistical illumination model. The first algorithm is used to give a rough reconstruction of the input frame, while the second one improves the foreground segmentation. We introduce an online...
Illumination and view dependent texture provide ample information on the appearance of real materials at the cost of enormous data storage requirements. Hence, past research focused mainly on compression and modelling of these data, however, few papers have explicitly addressed the way in which humans perceive these compressed data. We analyzed human gaze information to determine appropriate texture...
In this paper, a novel human articulated pose estimation method based on AdaBoost algorithm is presented. The human articulated pose is estimated by locating major human joint positions. We learn the classifiers on a normalized image for classifying each pixel position into a certain category. Two different kinds of classifiers, bottom-up joint position classifier and top-down skeleton classifier,...
This paper presents a new method for automatic text-line extraction from Arabic historical handwritten documents presenting an overlapping and multi-touching characters problems. Our approach is based on block covering analysis using unsupervised technique. This algorithm performs firstly a statistical block analysis which computes the optimal number of document decomposition into vertical strips...
This paper proposes a method for period-based gait trajectory matching in the eigenspace using phase synchronization for low frame-rate videos. First, a gait period is detected by maximizing the normalized autocorrelation of the gait silhouette sequence for the temporal axis. Next, a gait silhouette sequence is expressed as a trajectory in the eigenspace and the gait phase is synchronized by time...
Research on complex shape recognition showed that the shape context algorithm is sensitive to relative position variation of articulation. Aimed at this problem, a shape recognition method is proposed based on local shape filling rate of various object silhouettes. We take each landmark point as a circle center and use as its radius. Then, under a particular radius, the ratio between the covered silhouette...
We propose an edge segment based statistical background modeling algorithm and a moving edge detection framework for the detection of moving objects. We analyze the performance of the proposed segment based statistical background model with traditional pixel based, edge pixel based and edge segment based approaches. Existing edge based moving object detection algorithms fetches difficulty due to the...
In this paper we show how to render the computation of polarisation information from multiple polariser angle images robust. We make two contributions. First, we show how to use M-estimators to make robust moments estimates of the mean intensity, polarisation and phase. Second, we show how directional statistics can be used to smooth the phase-angle, and to improve its estimation when the polarisation...
In this paper, we present a new solution to the problem of matching groups of people across multiple non-overlapping cameras. Similar to the problem of matching individuals across cameras, matching groups of people also faces challenges such as variations of illumination conditions, poses and camera parameters. Moreover, people often swap their positions while walking in a group. In this paper, we...
When computing optical flow with region-based matching, very few of them can be reliably obtained, especially for the high-contrast areas or those with little texture. Instead of using a single pixel from the reference frame, non-deterministic motion utilizes multiple pixels within a neighborhood to represent the corresponding pixel in the current frame. Although remarkable improvement has been made...
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