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Lung 4D-CT provides important anatomical structure and motion information, which can be crucial in radiation therapy for lung cancer. However, radiation dose concerns limit the number of axial slices in 4D-CT, resulting in low superior-inferior resolution. We propose an approach to estimate the intermediate slices for resolution enhancement of 4D-CT. We explore the lung-motion-induced locally complimentary...
In this article we address the issue of adopting a local sparse coding representation (Histogram of Sparse Codes), in a part-based framework for inferring the locations of facial landmarks. The rationale behind this approach is that unsupervised learning of sparse code dictionaries from face data can be an effective approach to cope with such a challenging problem. Results obtained on the CMU Multi-PIE...
Human activity recognition is a crucial area of computer vision research and applications. The goal of human activity recognition aims to automatically analyze and interpret ongoing events and their context from video data. Recently, the bag of visual words (BoVW) approach has been widely applied for human action recognition. Generally, a representative corpus of videos is used to build the Visual...
Compressive sensing (CS) is well known for its robust sparse signal reconstruction ability from a smaller set of linear projections taken over an incoherent basis. For mutually correlated signals, a variant of CS called distributed compressive sensing (DCS) is employed. In this work, DCS is proposed to exploit the underlying correlation structure between different channels of multichannel electrocardiogram...
We explore the concept of dictionary learning and sparse coding applied to audio spectrograms. First, we statistically generate a dictionary of feature vectors by sampling many columns of input spectrograms. Then, using ℓ1-regularized least-squares optimization, we transform the columns of the spectrogram into sparse coefficient vectors. Hence, the learned dictionary column features act as an overcomplete...
We present a novel blind image quality assessment (BIQA) algorithm inspired by the sparse representation of natural images in the human visual system (HVS). The hypothesis behind the proposed method is that the properties of natural images that afford their sparse representation are altered in the presence of distortion. We attempt to quantify this change in sparsity and show that it is indeed a measure...
In this paper, we propose a novel method for fingerprint indexing based on local patterns of ridge flow centered on minutiae. These local descriptors are projected on a learned dictionary of ridge flow patches, with a sparsity-inducing algorithm. We show that this sparse decomposition allows to replace the ridge flow patches by a compressed signature with a reduced loss of accuracy. We experimented...
This paper focuses on detecting activated voxels in fMRI data by exploiting the sparsity of the BOLD signal. Due to the large volume of the data, we propose to learn a dictionary from the compressed measurements of the BOLD signal. The solution to the inverse problem induced by the General Linear Model is then sought through sparse coding using the double sparsity model, where sparsity is imposed...
In this paper we propose a robust sparse based visual tracking method by exploiting local representations in a particle filter framework. We construct a Multi-level Local Dictionary which consists of positive templates and negative templates for discriminative model, Which divide the positive and negative dictionary into two levels called static templates and dynamic templates, respectively, thus...
In this paper, we investigate the generation of diagnostic test vectors targeting the intra-cell defects. Experimental results carried out on an industrial circuit show that we actually increase the diagnosis resolution by adding few more diagnostic test patterns.
In this paper we proposed a dictionary learning and dimensionality reduction (DLDR) scheme for image steganalysis. We construct a structural discriminative dictionary which is learned from the reduced dimension space and exploit the discriminative information in stego-images. Simulation results verify the effectiveness of the proposed approach and the performance is considerable. Both the dictionary...
In this communication we propose and discuss comparatively several techniques for ECG signal compression inspired from the fundamentals of compressed sensing (CS) theory, focusing on acquisition techniques, projection matrices and reconstruction dictionaries and on the effects of the preprocessing involved. Essentially, we investigate and discuss two approaches. The first approach for ECG signal compression...
Image set based face recognition provides more opportunities compared to single mug-shot face recognition. However, modelling the variations in an image set is a challenging task. We propose a computationally efficient and accurate image set modelling technique. The idea is to reconstruct each image set sample with an unlabeled dictionary using the computationally efficient regularized least squares...
This paper proposes a model which approximates full covariance matrices in Gaussian mixture models (GMM) with a reduced number of parameters and computations required for likelihood evaluations. In the proposed model inverse covariance (precision) matrices are approximated using sparsely represented eigenvectors, i.e. each eigenvector of a covariance/precision matrix is represented as a linear combination...
Text clustering is important in many application of information retrieval. This paper presents a study of clustering short texts in Bahasa Indonesia using semantic similarity approach where dictionary of synonyms and hyponyms is used to get information on word relatedness. We compare sentence similarity calculations based on lexical matching and word similarity. More than 250 sentences are involved...
In this paper, we describe an algorithm to improve dictionary based lossless data compression on GPGPUs. The presented algorithm uses bit-wise computations and leverages bit parallelism for the core part of the algorithm which is the longest prefix match calculations. Using bit parallelism, also known as bit-vector approach, is a fundamentally new approach for data compression and promising in performance...
In order to better fuse the CT and MR images, based on the classical image fusion method, an image feature extraction and fusion algorithm based on K-SVD is presented. The images are sparse representation. The images are divided into blocks via the sliding window. The dictionary is compiled the column vectors. The redundant dictionary is learned by the K-singular value decomposition (K-SVD) algorithm...
Sentence similarity measures play an increasingly important role in text-related research and applications in areas such as text mining, Web page retrieval, and dialogue systems. Existing methods for computing sentence similarity have been adopted from approaches used for long text documents. These methods process sentences in a very high-dimensional space and are consequently inefficient, require...
Compressive Sensing (CS) is a newly introduced signal processing technique that enables to recover sparse signals from fewer samples than the Shannon sampling theorem would typically require. It is based on the assumption that, for a sparse signal, a small collection of linear measurements contains enough information to allow its reconstruction. Combining the acquisition and compression stages, CS...
Cloud computing is emerging as a revolutionary computing paradigm which provides a flexible and economic strategy for data management and resource sharing. Security and privacy become major concerns in the cloud scenario, for which Searchable Encryption (SE) technology is proposed to support efficient keyword based queries and retrieval of encrypted data. However, the absence of personalized search...
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