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Traumatic brain injury (TBI) endangers many patients and lays great burden on the neural intensive-care units in the whole world. To improve the outcome of TBI patients, it is desirable to forecast the intracranial Pressure (ICP) so to enable timely or early interventions to control the ICP level. Past research mainly focused on ICP pulse morphology analysis and ICP waveform forecast, but results...
Volume registration is an essential technology for comparing medical volume data acquired in different days and for combining different types of volume data from various imaging devices. For accurate comparison, it is necessary to correct non-rigid deformation between volume data, since the complex deformation between medical volume data is observed even if they are taken from the same regions of...
This paper addresses the data corruption that occurs due to patient motion during a scan which is particularly a problem in perfusion weighted MRI due to long scan times. Motion correction is typically the rate-limiting step in processing as each volume has to be registered to a reference volume. This is compounded by the dynamically varying contrast in the volume series due to passage of an injected...
Early detection of Alzheimer's disease is expected to aid in the development and monitoring of more effective treatments. Classification methods have been proposed to distinguish Alzheimer's patients from normal controls using Magnetic Resonance Images. However, their performance drops when classifying patients at a prodromal stage, such as in Mild Cognitive Impairment. Most often, the features used...
We study the problem of cross-media retrieval, where the query and the returned results are of different modalities. A novel method is proposed to measure the similarity between heterogeneous media objects for cross-media retrieval. While existing methods only focus on the original low level feature spaces or the third common space, our proposed tri-space explores both of the two kinds of spaces....
In this paper, we propose a novel approach for cross-modal multimedia retrieval by jointly modeling the text and image components of multimedia documents. In this model, the image component is represented by local SIFT descriptors based on the bag-of-feature model. The text component is represented by a topic distribution learned from latent topic models such as latent Dirichlet allocation (LDA)....
Stereo matching is a challenging problem, especially in the presence of noise or of weakly textured objects. Using temporal information in a binocular video sequence to increase the discriminability for matching has been introduced in the recent past, but all the proposed methods assume either constant disparity over time, or small object motions, which is not always true. We introduce a novel stereo...
Fingerprint representation is important in fingerprint recognition systems and has great impact on its performance. In this paper, we first introduce complex continuous density functions named Complex Gaussian Mixture Model (CGMM) to represent the fingerprint minutiae. In this model, a Gaussian mixture model is constructed according to the positions of fingerprint minutiae, and the direction of minutiae...
Symbol retrieval for technical documents is still a hot challenge in the document analysis community. In this paper we propose another way to spot symbols. A pixel-based template operator which is an adaptation of the hit-or-miss transform is defined. This operator is robust to translation, rotation and reflection. Experimental results on a real application show the efficiency of our approach.
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 propose a generic 6d object localization approach based on surface normal images and CAD model data. Normal images or “normal maps” can be obtained using only one single camera shot of a simple camera-projector system. The advantages of this sensor setup are very short acquisition times and the exclusive use of consumer hardware, namely a projector and a grey value camera, making...
Diversity is a key element in the success of classifier ensembles, and has attracted much recent attention. It is typically measured by directly computing the amount of disagreement between ensemble classifiers at the decision level. This costly process usually involves evaluating output predictions of each classifier over some validation data set. Since most statistical and neural network classifiers...
Feature selection is an important issue in pattern recognition. In face recognition, one of the state-of-the-art methods is that some feature selection methods (e.g., AdaBoost) are first utilized to select the most discriminative features and then the subspace learning methods (e.g., LDA) are further applied to learn the discriminant subspace for classification. However, in these methods, the objective...
While there is a lot of research on change detection based on the streaming classification error, finding changes in multidimensional unlabelled streaming data is still a challenge. Here we propose to apply principal component analysis (PCA) to the training data, and mine the stream of selected principal components for change in the distribution. A recently proposed semi-parametric log-likelihood...
Recent years have witnessed a growing interest in the sparse representation problem. Prior work demonstrated that adaptive dictionary learning techniques can greatly improve the performance of sparse representation approaches. Existing techniques mainly focus on the reconstructive accuracies and the discriminative power of the learned dictionary, whereas the mutual incoherence between any two basis...
KNN-based image annotation method is proved to be very successful. However, it suffers from two issues: (1) high computational cost; (2) the difficulty of finding semantically similar images. In this paper, we propose a graph-based dimensionality reduction method to solve the two problems by adapting the locality sensitive discriminant analysis method [1] to multilabel setting. We first determine...
Semi-supervised learning is important when labeled data are scarce. In this paper, we develop a novel semi-supervised spectral feature selection technique using label regression and by using l\-norm regularized models for subset selection. Specifically, we propose a new two-step spectral regression technique for semi-supervised feature selection. In the first step, we use label propagation and label...
Canonical Correlation Analysis (CCA) is a powerful technique for finding the correlations between two sets of multidimensional variables. Due to its performance in practice, many extensions were brought forward such as least square CCA. However, there is no such a unified solution to compare their performance, i.e. in the sense of extracting canonical correlations. In this paper, we propose a framework...
We developed a motion blur restoration technique for surface orientation images using a correlation image sensor. This system consists of two components; one is ring-shaped modulation illumination for encoding surface orientation into the amplitude and phase of the reflected light intensity, and the other is the three-phase correlation image sensor (3PCIS) for demodulating the amplitude and phase...
In this paper, we propose a new method for remote sensing image pan-sharpening which is based on weighted red-black (WRB) wavelet and adaptive principal component analysis (PCA), where the adaptive PCA is used to reduce spectral distortions and the utilization of WRB wavelet is used to extract the spatial details in PAN images. To reduce the artifacts and spectral distortions in the pan-sharpened...
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