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Sequential dictionary learning via the K-SVD algorithm has been revealed as a successful alternative to conventional data driven methods such as independent component analysis (ICA) for functional magnetic resonance imaging (fMRI) data analysis. fMRI data sets are however structured data matrices with notions of spatio-temporal correlation. This prior information has not been included in the K-SVD...
A new image specularity removal method is presented in this paper. This method is based on the polarization imaging through global energy minimization. Traditional color-based methods generate severe color distortions, and local-patch-based algorithms produce limited results without integrating the long range information. To handle these limitations, the proposed method uses polarization images to...
In the last years, the increasing availability of annotated data has facilitated the great success of supervised learning in real-world applications such as semantic labeling. However, the vast majority of data is nowadays unlabeled or partially annotated. In this paper, we develop an Expected Marginal Latent Structural SVM (EM-LSSVM) framework for performing structured learning in the presence of...
In this paper we propose a new method to automatically select the rank of linear transforms during supervised learning. Our approach relies on a sparsity-enforcing element-wise soft-thresholding operation applied after the linear transform. This novel approach to supervised rank learning has the important advantage that it is very simple to implement and incurs no extra complexity relative to linear...
Most edge-aware smoothing methods are based on the Euclidean distance to measure the similarity between adjacent pixels. This paper exploits the properties of the commute time to extend the notion of “similarity” in this context. The intuition is that since the commute time reflects the effect of all possible weighted paths between nodes (pixels), it can account for the global distribution of image...
The latest video compression standard HEVC sets new benchmarks concerning the efficiency for both video coding and also still image coding, i.e., pure intra picture coding. Nevertheless, its high complexity created by the rate-distortion optimization procedure is a serious drawback. To reduce this computational burden, several algorithms for fast mode decision have been proposed. However, most of...
This paper introduces row-column transforms (RCTs) which are 2D non-separable transforms defined with the aid of a set of 1-D linear transforms and a basis ordering permutation. We propose a novel method for the design of row-column transforms that approximate desired complex transforms (such as KLTs, SOTs, etc.) so that most of the performance of the approximated transforms is retained at significantly...
Video analysis often begins with background subtraction, which consists of creation of a background model that allows distinguishing foreground pixels. Recent evaluation of background subtraction techniques demonstrated that there are still considerable challenges facing these methods. Processing per-pixel basis from the background is not only time-consuming but also can dramatically affect foreground...
Many applications benefit from sampling algorithms where a small number of well chosen samples are used to generalize different properties of a large dataset. In this paper, we use diverse sampling for streaming video summarization. Several emerging applications support streaming video, but existing summarization algorithms need access to the entire video which requires a lot of memory and computational...
Data augmentation is the process of generating samples by transforming training data, with the target of improving the accuracy and robustness of classifiers. In this paper, we propose a new automatic and adaptive algorithm for choosing the transformations of the samples used in data augmentation. Specifically, for each sample, our main idea is to seek a small transformation that yields maximal classification...
Video retrieval and video copy detection are well studied problems. The goal is to find the matching video in a database from a given query video. Typically, these query videos are short and aligning the query video is of secondary importance. Short sequences can be aligned using dynamic time warping. But, since time and memory usage increases quadratically with the length of the sequences, such process...
In this paper, we consider a natural extension of the edge-preserving bilateral filter for vector-valued images. The direct computation of this non-linear filter is slow in practice. We demonstrate how a fast algorithm can be obtained by first approximating the Gaussian kernel of the bilateral filter using raised-cosines, and then using Monte Carlo sampling. We present simulation results on color...
A new algorithm, named Connected Oriented Image Foresting Transform (COIFT), is proposed, which provides global optimum solutions according to a graph-cut measure, subject to high-level boundary constraints. COIFT incorporates the connectivity constraint in the Oriented Image Foresting Transform (OIFT), ensuring the generation of connected objects, and can also handle simultaneously the boundary polarity...
We present new global convergence results for half-quadratic optimization in the context of image reconstruction. In particular, we do not assume that the inner optimization problem is solved exactly and we include the problematic cases where the objective function is nonconvex and has a continuum of stationary points. The inexact algorithm is modeled by a set-valued map defined from the majorization-minimization...
The explosion of computational imaging has seen the frontier of image processing move past linear problems, like denoising and deblurring, and towards non-linear problems such as phase retrieval. There has a been a corresponding research thrust into non-linear image recovery algorithms, but in many ways this research is stuck where linear problem research was twenty years ago: Models, if used at all,...
Denoising is an indispensable step in processing low-dose X-ray fluoroscopic images that requires development of specialized high-quality algorithms able to operate in near real-time. We address this problem with an efficient deep learning approach based on the process-centric view of traditional iterative thresholding methods. We develop a novel trainable patch-based multiscale framework for sparse...
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