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Experts-Shift is a novel statistical framework for keyframe-based video segmentation. Compared to existing video segmentation techniques with simple color models, our method proposes a probability mixture model coupling strong image classifiers (experts) with latent spatial configuration. In order to propagate image labels to the successive frames, our algorithm track all experts jointly by a efficient...
Restoring sight to the visually impaired poses significant challenges across multiple disciplines. In this demonstration, we present a prototype vision processing system to perform the external processing and to provide a technical user interface for a vision prosthesis. The system transforms an input video stream into intensity values suitable for transmission to a stimulation array implanted within...
In this paper, robust descriptors are extracted to detect video copies generated by complicated transformations. The main contribution of the proposed method lies in three aspects. Firstly, the complicated transformations on video copies are identified and tackled to guarantee the extraction of robust descriptors. Secondly, a motion classification approach is proposed to divide the video into video...
Firstly this paper discusses drawbacks of traditional adjacent frame difference motion detection method. Then we propose to restore video sequence background using image energy, higher order statistics theory, plus some background constrains criteria. Lastly, instead of using adjacent frame difference method, background based difference is used to detect moving regions. Our tests indicate the proposed...
This paper describes our algorithms for players tracking and ball detection for an automatic broadcast tennis video annotation. The system detects and tracks the players using a robust non-parametric procedure for estimating density gradients called the mean shift algorithm. The basic mean shift tracking algorithm assumes that the target object has to separate sufficiently from background, but this...
In this paper, we propose a robust video super-resolution reconstruction method based on spatial-temporal orientation-adaptive kernel regression. First, we propose a robust registration efficiency model to reflect the temporal information reliability. Second, we propose a spatial-temporal steering kernel considering motions between frames and structures in each low resolution frame. Simulation results...
Detection of moving objects in video streams is the first relevant step of information extraction in many computer vision applications. Aside from the intrinsic usefulness of being able to segment video streams into moving and background components, detecting moving objects provides a focus of attention for recognition, classification, and activity analysis, making these later steps more efficient...
This paper proposes an automatic video object segmentation with opacity estimate to obtain good alpha mattes and the foreground objects. In the proposed method, the trimap is automatically generated from the result obtained using video object segmentation based on motion information without background construction. Next, closed-form matting is used with the automatically generated trimaps to yield...
In order to obtain depth perception in computer vision, it is needed to process pairs of stereo images. This process is computationally challenging to be carried out in real-time, because it requires the search for matches between objects in both images. Such process is significantly simplified if the images are rectified. Stereo image rectification involves a matrix transformation which when done...
A novel video object segmentation algorithm is proposed based on mixtures of probabilistic principal component analysis (MPPCA) in this paper. The number of mixture components of MPPCA is estimated and the expectation maximization (EM) algorithm is initialized through segmentation projection after extracting feature. Then the EM algorithm is applied to estimate the distribution of feature vectors...
In this paper an adaptive Gaussian mixture model is introduced firstly to remove the shadow of regions of interest in the detection of moving human body from current video sequences. Then use a proposed method of obtaining ROI. From the view of the tracking effect, it can be concluded that this method of removal shadow of regions of interest can improve the precision rate of segment of moving people...
In this paper, a framework for recognition of Bangla ticker text1 from the Bangla news videos is presented. Tesseract OCR [1] has been used for Bangla script recognition. Tesseract OCR gives good results for text recognition in documents. But in case of images and videos, some processing is required beforehand. Approach here is to provide processed images to the Tesseract OCR to get better results...
Low-complexity error concealment techniques for missing macroblock (MB) recovery based on the boundary matching principle are extensively studied and evaluated. In this paper, an improved boundary matching algorithm (BMA) using adaptive search is presented to conceal channel errors in inter-frames of video images. The proposed scheme adaptively selects proper candidate regions to conceal the artifact...
A texture based object tracking algorithm is presented. The algorithm is an extension to famous mean-shift tracking method. It does not rely on color histogram. It incorporates both color histogram and texture histogram information to model tracking target. In order to extract textural information a novel similarity operator is introduced. It is fast to compute and gray-scale invariant. It would be...
In this paper we propose a novel algorithm for object tracking from Video images based on segmentation and Kernel based procedure. Many Kernel based object tracking algorithms have been developed during last few years. The computational complexity becomes very high in those kernel based techniques. In our proposed method the target localization problem is minimized using segmentation technique, instead...
In this paper, we present a novel scene change detection algorithm for mobile camera platforms. Our approach integrates sparse 3D scene background modelling and dense 2D image background modelling into a unified framework. The 3D scene background modelling identifies inconsistent clusters over time in a set of 3D cloud points as the scene changes. The 2D image background modelling further confirms...
Depth maps estimated using stereo matching between frames from different video views typically exhibit false contours and noisy artifacts around object boundaries. In this paper, iterative joint multilateral filtering is proposed to deal with these artifacts. The proposed filter consists of multiple filter kernels. Knowing that the estimated depth maps are erroneous, besides the kernels which measure...
Image quality assessment (IQA) is very important for many image and video processing applications, e.g. compression, archiving, restoration and enhancement. An ideal image quality metric should achieve consistency between image distortion prediction and psychological perception of human visual system (HVS). Inspired by that HVS is quite sensitive to image local orientation features, in this paper,...
Detection and classification of vehicles are the most challenging tasks of a video-based intelligent transportation system. Traditional detection and classification methods are based on subtraction of estimated still backgrounds from a video to find out the moving objects. In general, these methods are computationally highly expensive, and in many cases show poor detection and classification performance,...
This paper presented a video moving object segmentation and tracking system based on the active contour and the color classification models. First, the active contour model is applied to segment the target object in the initial frame. From the segmented object, the object and background regions are extracted. Then the object and the background regions are separately clustered according to color feature...
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