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Frequently, a screen editor have to edit a video shot by cutting parts of a video clip and patching them onto a targeted video. Even though cut-and-paste method is a good one to do so for editing still images, it is not appropriate for the same purpose on editing video clips since the assigned patched area is fixed while contents continuously change. In this paper we propose an automatic video patching...
Accurate localization of landmarks in the vicinity of a robot is a first step towards solving the SLAM problem. In this work, we propose algorithms to accurately estimate the 3D location of the landmarks from the robot only from a single image taken from its on board camera. Our approach differs from previous efforts in this domain in that it first reconstructs accurately the 3D environment from a...
Swarm optimization has been proved suitable to solve various combinatorial optimization problems. Markov random field (MRF) based MRF-based early vision problem has higher dimensions, more complicate structure of solution space, and dynamic constrain conditions. Based on a dynamic multi-colony ant scheme, this paper proposes a dynamic cooperative swarm optimization model to estimate the labels fields...
An improved MAP-EM algorithm is proposed for Bayesian reconstruction in X-ray CT based upon Markov random field priors and the Poisson data model. The improved algorithm can yield better reconstruction than MAP-EM algorithm, and its convergence is faster. The improved method is verified by applications to computer simulation data and real X-ray CT data from two aluminous tubes scans. Experiments results...
We investigate the source separation problem of random fields within a Bayesian framework. The Bayesian formulation enables the incorporation of prior image models in the estimation of sources. Due to the intractability of the analytical solution, we resort to numerical methods for the joint maximization of the a posteriori distribution of the unknown variables and parameters. We construct the prior...
In this paper, we propose a new learning based approach for super-resolving an image captured at low spatial resolution. Given the low spatial resolution test image and a training set consisting of low and high spatial resolution images, all captured using the same camera, we obtain super-resolution for the test image. We propose a new wavelet based learning technique that learns the high frequency...
We propose a film directing semantics taxonomy grounded upon cinematographic elements to unlock the immense potential for motion indexing in the subtler and much neglected film domain. A novel ??directing-rule?? inspired MRF-based motion segmentation algorithm is formulated to extract salient motion descriptors for the classification of the proposed directing semantics. Experimental results validate...
In patch based face super-resolution method, the patch size is usually very small, and neighbor patchespsila relationship via overlapped regions is only to keep smoothness of reconstructed high-resolution image, so the prior is not always strong enough to regularize super-resolution when observed low-resolution image lose facial structure information. We propose to use Gaussian Mixture Model(GMM)...
Stereo matching has been one of the most active research areas in computer vision for decades. Many methods, ranging from similarity measures to local or global matching cost optimization algorithms, have been proposed. As we known, stereo matching can be formulated under the framework of Markov random field (MRF), and the global optimization in stereo matching can be approximated by inference procedure...
In this paper, we will present a new algorithm which is extended from the standard Gaussian mixture model to segment the noisy image based on the correlation among neighboring pixels. Firstly, we use the correlation between each centre pixel and its neighboring pixels in 3 times 3 window in building the prior probability, and this centre pixel is used to construct the conditional density function...
The high dynamic range image (HDRI) acquisition method based on Markov random field model is proposed. By combining multiple exposure images shot with different shutter speed, we estimate the irradiance value for each pixel. Our method estimates displacements, occlusion and saturated regions, and by using them construct the motion blur free HDRI with higher quality than other existing methods.
We present a novel probabilistic method to estimate the orientation field in fingerprint images. Traditional approach based on the smoothing of local image gradients usually generates unsatisfactory results in poor quality regions of fingerprint images. We show how to improve the orientation field estimation by first constructing a Markov random field (MRF) and then inferring the orientation field...
Object models based on bag-of-words representations can achieve state-of-the-art performance for image classification and object localization tasks. However, as they consider objects as loose collections of local patches they fail to accurately locate object boundaries and are not able to produce accurate object segmentation. On the other hand, Markov random field models used for image segmentation...
The observation models in tracking algorithms are critical to both tracking performance and applicable scenarios but are often simplified to focus on fixed level of certain target properties such as appearances and structures. In this paper, we propose a unified tracking paradigm in which targets are represented by Markov random fields of interest regions and introduce a new way to adapt observation...
Liver segmentation from scans of the abdominal area is an important step in several diagnostic processes. CT scans of the abdominal area contain several organs in close proximity exhibiting similar image characteristics. In this paper, we present preliminary results on an algorithm that uses Markov random fields to obtain an initial contour of the liver. Gradient vector fields (GVF) and active contours...
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