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In a practical video encoder, a video sequence obtained from a CCD camera inevitably conveys noise, which degrades not only image quality but also coding efficiency. Based on the statistic analysis of noise signal, a noise variance adaptive two-stage successive elimination algorithm (NVA-SEA) for block motion estimation is presented. Simulation results demonstrate that the proposed algorithm can get...
We have improved our concurrent stereo matching (CSM) algorithm, which abandons the search for 'best' matches and determine matches that lie within admissible ranges using a noise model. We estimate photometric deviations between corresponding regions of stereo pairs with photometric transformations and mismatched or occluded regions. We allow for global, disparity dependent contrast and offset (gain...
We present a variational framework that integrates the statistical boundary shape models into a Level Set system that is capable of both segmenting and recognizing objects. Since we aim to recognize objects, we trace the active contour and stop it near real object boundaries while inspecting the shape of the contour instead of enforcing the contour to get a priori shape. We get the location of character...
Statistical background modeling is a fundamental and important part of many visual tracking systems and of other computer vision applications. In this paper, we presents an effective and adaptive background modeling method for detecting foreground objects in both static and dynamic scenes. The proposed method computes SAmple CONsensus (SACON) of the background samples and estimates a statistical model...
We focus on the problem of developing coupled statistical models that can be used to recover surface height from brightness images of faces. Our approach consists on using a simple model that assumes that the height eigenmodes are identical to the intensity eigenmodes. We recover the height function directly from the best-fit intensity parameters. As a result the computations involve only a straightforward...
In this paper we explore the use of complex numbers as means of representing angular statistics for surface normal data. Our aim is to use the representation to construct a statistical model that can be used to describe the variations infields of surface normals. We focus on the problem of representing facial shape. The fields of surface normals used to train the model are furnished by range images...
This paper presents a multi-state hierarchical approach for facial feature tracking. A hierarchical formulation of statistical shape models is proposed to characterize both global shape constraints of human faces and local structural details of facial components. Gabor wavelets and gray level profiles are integrated for effective and efficient representation of feature points. Furthermore, multi-state...
In many cases X-ray images are the only basis for surgery planning. Nevertheless it is desirable to draw conclusions about the 3D-anatomy of the patient from such data. This work presents a method to reconstruct 3D shapes from few digital X-ray images on the basis of 3D-statistical shape models. At the core of this method lies an algorithm which optimizes a similarity measure assessing the difference...
In this paper active feature models are proposed. They utilize local texture features and a statistical shape model for the reliable localization of landmarks in images. They are related to active appearance models, but instead of modelling the entire texture of an object they represent image texture by means of local descriptors. The approach has advantages with complex image data like anatomical...
This paper presents a novel LDA algorithm named 2DHLDA (2-dimensional heteroscedastic linear discriminant analysis). The proposed algorithms are applied on age-group classification using facial images under various lighting conditions. 2DHLDA significantly overcomes the singularity problem, so-called 'small sample size' problem (S3 problem), and the original feature space is split into useful dimensions...
Current word completion tools rely mostly on statistical or syntactic knowledge. Can using semantic knowledge improve the completion task? We propose a language-independent word completion algorithm which uses latent semantic analysis (LSA) to model the semantic context of the word being typed. We find that a system using this algorithm alone achieves keystroke savings of 56% and a hit rate of 42%...
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