In image/video context processing, such as clustering, matting, or further editing and context-aware enhancement, the probability model on the basis of Markov property is usually employed, where the neighbors around t...
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In image/video context processing, such as clustering, matting, or further editing and context-aware enhancement, the probability model on the basis of Markov property is usually employed, where the neighbors around the center have stronger connection. To realize the optimization of such probability models encounters to solve a large linear system under the objective functional of L2-norm total variation (TV). the existing feasible methods can deal withthe problems with small or very large neighborhood, but there lacks of feasible method for solving linear system with intermediate neighborhood in an efficient and accurate way. In this paper, based on the theoretical analysis, we transform the optimization problem to a process with accumulated joint bilateral filtering. Both efficiency and accuracy are achieved with appropriate prove of validation. Finally, taking image soft segmentation as an example, the proposed optimization scheme is implemented on GPU with existing fast bilateral filter to show the feasibility.
this work addresses the twin issues of overlapping and blocking artifacts in Distributed Video Coding side information (SI). these both emanate from the block matching algorithm (BMA) used for motion vector (MV) gener...
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this work addresses the twin issues of overlapping and blocking artifacts in Distributed Video Coding side information (SI). these both emanate from the block matching algorithm (BMA) used for motion vector (MV) generation rather than pixel-wise processing. In temporal correlation exploitation and particularly in the formulation of higher-order piecewise temporal trajectory interpolation (HOPTTI), BMA has been applied due to its speed and simplicity. While HOPTTI has exhibited superior SI quality to other BMA-based algorithms, the adaptive overlapped block motion compensation (AOBMC) algorithm reduces the overlapping and blocky artifacts by adjusting the coefficients of a raised cosine overlapped window based on neighboring MV reliability. the aim of this paper is to investigate the benefits of combining HOPTTI with AOBMC. A mode switching (MS) mechanism is introduced to exploit the spatial-temporal correlation in a sequence to select between frames which will benefit from combining HOPTTI with AOBMC via a matching criterion. Experimental results confirm that selectively combining HOPTTI with AOBMC gives better SI quality, with on average up to 1.8dB improvement compared to using only HOPTII, and up to 3.6dB improvement over existing AOBMC-based algorithms.
the compression of video and subsequent partial loss of the compressed bitstream can dramatically reduce the accuracy of automated tracking algorithms. this is problematic for centralized applications such as transpor...
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the compression of video and subsequent partial loss of the compressed bitstream can dramatically reduce the accuracy of automated tracking algorithms. this is problematic for centralized applications such as transportation surveillance systems, where remotely captured and compressed video is transmitted over lossy wireless links to a central location for tracking. We propose a low-complexity method for protecting compressed video against channel loss such that the tracking accuracy of decoded and concealed video is maximized. Our algorithm leverages a previous method of video processingthat removes components of low tracking interest before compression to minimize bitrate, and uses some of the bitrate savings to introduce redundancy into the transmitted bitstream to reduce the probability of information loss. We show using a common tracker and loss concealment algorithm that our system allows for up to 100% increased tracking accuracy at a given bitrate, or 90% bitrate savings for comparable tracking quality.
Low-level features (also called descriptors) play a central role in content-based image retrieval (CBIR) systems. Features are various types of information extracted from the content and represent some of its characte...
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Low-level features (also called descriptors) play a central role in content-based image retrieval (CBIR) systems. Features are various types of information extracted from the content and represent some of its characteristics or signatures. However, especially the (low-level) features, which can be extracted automatically usually lack the discrimination power needed for accurate description of the image content and may lead to a poor retrieval performance. In order to efficiently address this problem, in this paper we propose a multi- dimensional evolutionary feature synthesis technique, which seeks for the optimal linear and non-linear operators so as to synthesize highly discriminative set of features in an optimal dimension. the optimality therein is sought by the multi-dimensional particle swarm optimization method along withthe fractional global-best formation technique. Clustering and CBIR experiments where the proposed feature synthesizer is evolved using only the minority of the image database, demonstrate a significant performance improvement and exhibit a major discrimination between the features of different classes.
Dynamic magnetic resonance imaging (MRI) is commonly used to observe dynamic physiological changes in tissue or to study organs with mobile structures such as the heart. In order to accurately capture spatiotemporal c...
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Dynamic magnetic resonance imaging (MRI) is commonly used to observe dynamic physiological changes in tissue or to study organs with mobile structures such as the heart. In order to accurately capture spatiotemporal changes, it is desirable to have dynamic images with high temporal resolution in addition to high spatial resolution. Due to the nature of data acquisition in current MRI systems, there exists a trade-off between temporal and spatial resolution. In this work, we present two methods for improving the spatiotemporal resolution in dynamic MRI using compressive sampling (CS). Experimental results illustrate that the proposed Bayes least squares-Gaussian scale mixtures (BLS-GSM) model-based CS algorithm compares favorably with other state-of-the-art compressive dynamic MRI techniques.
Denoising is one of the most common and important tasks in video processingsystems and abundant efforts have been made on video denoising nowadays. In this paper, we propose a novel denoising scheme based on minimum ...
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Denoising is one of the most common and important tasks in video processingsystems and abundant efforts have been made on video denoising nowadays. In this paper, we propose a novel denoising scheme based on minimum mean square error (MMSE) filter in the 2D transform domain, which we call 2DTD-MMSE. the current input noisy frame is processed block-by-block, and for every block, the current noisy observation and multiple prediction blocks found by motion estimation (ME) in denoised previous frames as well as noisy future frames constitute a 2D observed representation array. Afterwards, 2D transform is applied to every block in the representation array, and every transform coefficient of current block is estimated by weighted average of the coefficients in the same frequency position of all the transformed blocks. the weighting coefficients are adaptively determined through MMSE for every block after the estimation of statistical parameters in the transform domain. Experimental results on commonly used test sequences demonstrate that the proposed 2DTD-MMSE achieves comparable or favorable performance when compared to several state-of-the-art algorithms.
Super-resolution image reconstruction is an important technology in many imageprocessing areas such as image sensing, medical imaging, satellite imaging, and television signal conversion. It is also a key word of a r...
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Super-resolution image reconstruction is an important technology in many imageprocessing areas such as image sensing, medical imaging, satellite imaging, and television signal conversion. It is also a key word of a recent consumer HDTV set that utilizes the CELL processor. Among various super-resolution methods, the learning-based method is one of the most promising solutions. the problem of the learning-based method is its enormous computational time for image searching from the large database of training images. We have proposed a new Total Variation (TV) regularization super-resolution method that utilizes a learning-based super-resolution method. We have obtained excellent results in image quality improvement. However, our proposed method needs long computational time because of the learning-based method. In this paper, we examine two methods that reduce the computational time of the learning-based method. the resulting algorithms reduce complexity significantly while maintaining comparable image quality. this enables the adoption of learning-based super-resolution to the motion pictures such as HDTV and internet movies.
Video surveillance systems are often used to detect anomalies: rare events which demand a human response, such as a fire breaking out. Automated detection algorithms enable vastly more video data to be processed than ...
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Video surveillance systems are often used to detect anomalies: rare events which demand a human response, such as a fire breaking out. Automated detection algorithms enable vastly more video data to be processed than would be possible otherwise. this note presents a video analytics framework for the detection of amorphous and unstructured anomalies such as fire, targets in deep turbulence, or objects behind a smoke-screen. Our approach uses an off-line supervised training phase together with an on-line Bayesian procedure: we form a prior, compute a likelihood function, and then update the posterior estimate. the prior consists of candidate image-regions generated by a weak classifier. Likelihood of a candidate region containing an object of interest at each time step is computed from the photometric observations coupled with an optimal-mass-transport optical-flow field. the posterior is sequentially updated by tracking image regions over time and space using active contours thus extracting samples from a properly aligned batch of images. the general theory is applied to the video-fire-detection problem with excellent detection performance across substantially varying scenarios which are not used for training.
Sperm movement characteristic plays an important role in male fertility. Computer-aided Sperm Analysis (CASA) systems have tried to provide more accurate information about sperm motility and concentration. At the begi...
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Sperm movement characteristic plays an important role in male fertility. Computer-aided Sperm Analysis (CASA) systems have tried to provide more accurate information about sperm motility and concentration. At the beginning of this goal, achieving correct sperm trajectory is necessary. On the other hand, sperm tracking is a challenging matter because, sperms have same size and shape, move fast also there is an uncertainty in their motions. therefore, most of designers have attempted to reduce tracking mistakes especially, when sperms collide with each other. In this paper we try to present a sperm tracking method based on Watershed segmentation and Particle filter estimation which focuses on the sperm collision problem. In contrast of most sperm tracking algorithms based on sperm head orientation, this method tries to track sperms independent of sperm head orientation. Moreover, sperm videos were taken by common camera in order to ease availability in every laboratory. Consequently image sequences have low contrast so, processing section bears more burdens in proportion to the imaging. In this paper, the method is evaluated by tracking 10 sperms which had at least one collision along their paths. the results showed the method is robust in the cluttered scenes.
this paper presents a computationally efficient method for the measurement of a dense image correspondence vector field using supplementary data from an inertial navigation sensor. the application is suited to airborn...
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this paper presents a computationally efficient method for the measurement of a dense image correspondence vector field using supplementary data from an inertial navigation sensor. the application is suited to airborne imaging systems (such as on a UAV) where size, weight, and power restrictions limit the amount of onboard processing available. the limited processing will typically exclude the use of traditional, but expensive, optical flow algorithms such as Lucas-Kanade. Alternately, the measurements from an inertial navigation sensor lead to a closed-form solution to the correspondence field. Airborne platforms are also well suited to this application because they already possess inertial navigation sensors and global positioning systems (GPS) as part of their existing avionics package. We derive the closed form solution for the image correspondence vector field based on the inertial navigation sensor data. We then show experimentally that the inertial sensor solution outperforms traditional optical flow methods both in processing speed and accuracy.
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