Certain point target detectionalgorithms applied to digital images can be evaluated through a formalism involving the generation of threshold picture, i.e., replacing each actual pixel value with a threshold calculat...
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Certain point target detectionalgorithms applied to digital images can be evaluated through a formalism involving the generation of threshold picture, i.e., replacing each actual pixel value with a threshold calculated from the background. Methods to evaluate the strengths and weaknesses of each algorithm are presented using this threshold picture. Moreover, the calculation of an electro-optical system sensitivity parameter (minimum detectable signal) is presented, based on the threshold image.
In many problems of the initialization of objects' tracks, changepoint detectionalgorithms can be used. In the past when computational complexity was an issue, the K / N algorithm gained its popularity due to com...
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ISBN:
(纸本)9781728188300
In many problems of the initialization of objects' tracks, changepoint detectionalgorithms can be used. In the past when computational complexity was an issue, the K / N algorithm gained its popularity due to computational simplicity. Nowadays with the tremendous progress in computing technology, the problem of finding more efficient detection and track initiation algorithms is urgent. A substantially more efficient track initiation algorithm can be built based on the sequential changedetection technique. In this paper, we consider the Finite Moving Average algorithm. We compare the performance of the K / N algorithm and the Finite Moving Average algorithm. The optimality criterion is to maximize the probability of correct detection in a certain time interval under the given false alarm risk measured as the local probability of a false alarm. For performance, we obtain a theoretical estimate and an estimate by Monte Carlo (MC) simulations. The results show that the Finite Moving Average algorithm performs significantly better than the K / N procedure.
A data driven background subtraction algorithm where each background pixel is modeled with a representative set of samples is presented. The samples are pixel values observed in preceding frames. Each pixel in an inco...
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A data driven background subtraction algorithm where each background pixel is modeled with a representative set of samples is presented. The samples are pixel values observed in preceding frames. Each pixel in an incoming frame is classified as background or foreground by comparing the pixel value with the samples in pixel's background model. The background model is periodically updated by replacing the most outlying sample in the model with the current pixel value. Techniques for suppressing dynamic background and ghost regions are incorporated in the algorithm. Evaluation tests on a public dataset shows markedly faster ghost suppression and fewer false positives in dynamic background regions, as well as improved overall performance in terms of evaluation metrics, compared to the base method.
This paper addresses an iterative Expectation-Maximization (EM) time-frequency synchronization algorithm joint with channel estimation for MIMO-OFDM systems in frequency selective fading channels. The receivers iterat...
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This paper addresses an iterative Expectation-Maximization (EM) time-frequency synchronization algorithm joint with channel estimation for MIMO-OFDM systems in frequency selective fading channels. The receivers iterates between detection and estimation. For each iteration, we calculate first the expectation of ODFM symbols by using a posteriori probabilities provided by MAP decoder and second we maximise a proposed metric to obtain both frequency offset and then symbol-timing. The channel can be identified by means of these estimates. This algorithm can work in transmission mode and thus can be used to estimate the residual errors or track the change of the parameters. The performance of the proposed synchronization approach, in terms bit error rate and mean square error of the estimated frequency offset, is shown.
Two challenging situations for video frame rate up-conversion (FRUC) are first identified;namely, when the input video has abrupt illumination change and/or a low frame rate. Then, a low-complexity robust FRUC algorit...
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Two challenging situations for video frame rate up-conversion (FRUC) are first identified;namely, when the input video has abrupt illumination change and/or a low frame rate. Then, a low-complexity robust FRUC algorithm is proposed to address these two issues. The proposed algorithm employs a translational motion vector (MV) model of the first- and the second- order and detects the continuity of these motion vectors (MVs). The superior performance of the proposed algorithm has been tested extensively and representative examples are given in this work.
In this paper, we propose a time consistent video segmentation algorithm designed for real-time implementation. Our segmentation algorithm is based on a region merging process that combines both spatial and motion inf...
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In this paper, we propose a time consistent video segmentation algorithm designed for real-time implementation. Our segmentation algorithm is based on a region merging process that combines both spatial and motion information. The spatial segmentation takes benefit of an adaptive decision rule and a specific order of merging. Our method has proven to be efficient for the segmentation of natural images (¤at or textured regions) with few parameters to be set Temporal consistency of the segmentation is ensured by incorporating motion information through the use of an improved changedetection mask. This mask is designed using both illumination differences between frames, and region segmentation of the previous frame. By considering both pixel and region levels, we obtain a particularly efficient algorithm at a low computational cost, allowing its implementation in real-time on the TriMedia processor for CIF image sequences.
To store and retrieve large-scale video data sets effectively, the process of wipe detection is an essential step. In this paper, we propose a wipe scene-changedetection algorithm based on Visual Rhythm Spectrum (VRS...
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To store and retrieve large-scale video data sets effectively, the process of wipe detection is an essential step. In this paper, we propose a wipe scene-changedetection algorithm based on Visual Rhythm Spectrum (VRS). The VRS contains distinctive patterns or visual features for wipe effects. During a wipe, intensity change between incoming and the outgoing shots gives rise to abrupt intensity discontinuities on the VRS. The proposed algorithm is designed to detect such discontinuities.
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