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The invention of wireless capsule endoscopy greatly helps physician to view small intestine images without causing much pain to patients. It becomes very popular around the world for its usability and performance. However, physician requires a long time (around 45 minutes) to examine a capsule endoscopy video generated from each examination. In this paper, we propose a new image processing method...
This paper presents a feature-driven, hierarchical shape registration algorithm. The central idea is to generate correspondences in multiple levels in a coarse-to-fine manner, with additional features incrementally inserted in each level. The registration starts from the coarsest resolution. Registration results obtained in one level serve as references for the registration in the next level. We adopt...
This paper tackles the matching problem of partial deformable shapes with changing boundary and varying topology. We compute high-order graph matching directly on manifolds, without global/local surface parameterization. In particular, we articulate the heat kernel tensor (HKT), which is a high-order potential of geometric compatibility between feature tuples measured by heat kernels within bounded...
The hand tremor is one of the most common motion disorders caused by various neurological diseases. Currently diagnostic procedures for tremor evaluation are subjective, and there are no examinations available that can accurately indicate whether tremors are present in a patient's daily life. Early detection of tremor is extremely important for the cure of the disease that causes the tremor. Thus,...
This study presents a recursive Kernel Density Estimation model (r-KDE) based method for the segmentation of dynamic scenes. In the algorithm, local maximum in the density functions is approximated recursively via mean shift method firstly. Via the proposed thresholding scheme, components and parameters in the mixture Gaussian distributions can be determined adaptively. The coarse foreground is obtained...
The well-known bilateral filter is used to smooth noisy images while keeping their edges. This filter is commonly used with Gaussian kernel functions without real justification. The choice of the kernel functions has a major effect on the filter behavior. We propose to use exponential kernels with L1 distances instead of Gaussian ones. We derive Stein's Unbiased Risk Estimate to find the optimal parameters...
In this paper we present an algorithm to detect text on video frames consisting of lecture slides. We begin by performing a multi-channel wavelet transform and then merge the channel components for the high frequency sub bands to obtain a composite energy map. Thresholding the energy map results in an edge map consisting of candidate text pixels — some of these correspond to actual text and others...
Many emerging application areas in video and image processing require real-time or faster visual concept detection. Examples include indexing of online user-generated video content and 24/7 archiving of TV broadcasts. The current state-of-the-art in concept detection uses bag-of-visual-words features with computationally heavy kernel-based classifiers. We argue that this approach is not feasible for...
In the literature of human action recognition, despite promising results have been obtained by the traditional bag-of-words model, the relationship among spatiotemporal points has rarely been considered. Furthermore, serious quantization error also exists in this kind of strategy. In this paper, we propose a novel coding strategy named contextual Fisher kernels to overcome these limitations. We add...
We describe a method for activity recognition based on distribution of human poses in a video. Pose estimation has shown to be sensitive to the priors given to the inference method; we use a collection of distinctive kinematic tree priors to model the variety of pose variations present in a video. Feature histograms are computed from vector quantized descriptors derived from the pose estimates. A...
Most of the state-of-the-art algorithms of restoring single blurred image are sensitive to image noise and artifacts. Our idea is to learn an adaptive filter for blind deconvolution to remedy this problem. We use this auxiliary filter to progressively suppress image noise in early stage of kernel estimation, leading to a robust kernel estimation algorithm. Our approach can naturally handle image noise...
Semi-supervised classification from pairwise constraints is a challenge in pattern recognition, since the constraints just represent the relationships between data pairs rather than the definite labels. In the last few years, several methods have been proposed, however, they still utilize either the discriminability within the constraints or the abundant unlabeled data insufficiently. In this paper,...
Real-life datasets are becoming larger and less linear separable. Divisive clustering methods with a computation time linear to the number of samples n can handle large data but mostly assume linear boundaries between the cluster in input space. Kernel based clustering methods are able to detect nonlinear boundaries in feature space but have a quadratic computation time O(n2). In this paper, we propose...
In this paper, we propose a new form of regularization that is able to utilize the label information of a data set for learning kernels. We first present the definition of extended ideal kernel for both labeled and unlabeled data of multiple classes. Based on this extended ideal kernel, we propose an ideal regularization which is a linear function of the kernel matrix to be learned. The ideal regularization...
Streaming data are any data that are sequentially presented to a system such that future data cannot be accessed. By their nature, streaming data are often large data sets and can quickly outgrow the working memory for a typical computer. Clustering is one of the primary tasks used in the pattern recognition and data mining communities and kernel k-means is a well-studied and popular algorithm. However,...
Computing similarities between data samples is a fundamental step in most Pattern Recognition (PR) tasks. Better similarity measures lead to more accurate prediction of labels. Computing similarities between video sequences has been a challenging problem for the PR community for long because videos have both spatial and temporal context which are hard to capture. We describe a novel approach that...
Hierarchical classification, decomposing the multi-class classification problem into binary ones hierarchically, is efficient when the class quantity getting large. Nowadays, the variety of features to describe data becomes huge and meanwhile the form of these features is diverse, which both make the task of feature fusion crucial for classification. In this paper, an adaptive kernel learning method,...
Motivated by the fact that data of each cluster are often well captured by distinct features, we propose a clustering approach called multiple kernel self-organizing map (MK-SOM) that integrates multiple kernel learning into the learning procedure of SOM, and carries out cluster-dependent feature selection simultaneously. MK-SOM is developed to reveal the intrinsic relation between features and clusters,...
This paper describes an efficient acceleration of GAT (Global Affine Transformation) correlation as a powerful technique of distortion-tolerant image matching. The key ideas are twofold: efficient calculation of optimal affine parameters that maximize the normalized cross-correlation value between an input image and a template via separation of variables in the original GAT computational model and...
In this paper, we present a new method for a locally adaptive region detector called Bilateral kernel-based Region Detector (BIRD). This work is to detect stable regions from images by consecutively computing a multiscale decomposition based on the bilateral kernel. The BIRD regards a region as covariant if it exhibits predictability in its photometric distance over spatial distance. Distinctiveness...
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