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Synthetically creating motion blur in two-dimensional (2D) images is a well-understood process and has been used in image processing for developing deblurring systems. There are no well-established techniques for synthetically generating arbitrary motion blur within three-dimensional (3D) images, such as depth maps and point clouds since their behavior is not as well understood. As a prerequisite, we have previously developed a method for generating synthetic motion blur in a plane that is parallel to the sensor detector plane. In this work, as a major extension, we generalize our previously developed framework for synthetically generating linear and radial motion blur along planes that are at arbitrary angles with respect to the sensor detector plane. Our framework accurately captures the behavior of the real motion blur that is encountered using a Time-of-Flight (ToF) sensor. This work uses a probabilistic model that predicts the location of invalid pixels that are typically present within depth maps that contain real motion blur. More specifically, the probabilistic model considers different angles of motion paths and the velocity of an object with respect to the image plane of a ToF sensor. Extensive experimental results are shown that demonstrate how our framework can be applied to synthetically create radial, linear, and combined radial-linear motion blur. We quantify the accuracy of the synthetic generation method by comparing the resulting synthetic depth map to the experimentally captured depth map with motion. Our results indicate that our framework achieves an average Boundary F1 (BF) score of 0.7192 for invalid pixels for synthetic radial motion blur, an average BF score of 0.8778 for synthetic linear motion blur, and an average BF score of 0.62 for synthetic combined radial-linear motion blur.

A limitation of traditional molecular dynamics (MD) is that reaction rates are difficult to compute. This is due to the rarity of observing transitions between metastable states since high energy barriers trap the system in these states. Recently the weighted ensemble (WE) family of methods have emerged which can flexibly and efficiently sample conformational space without being trapped and allow calculation of unbiased rates. However, while WE can sample correctly and efficiently, a scalable implementation applicable to interesting biomolecular systems is not available. We provide here a GPLv2 implementation called AWE-WQ of a WE algorithm using the master/worker distributed computing WorkQueue (WQ) framework. AWE-WQ is scalable to thousands of nodes and supports dynamic allocation of computer resources, heterogeneous resource usage (such as central processing units (CPU) and graphical processing units (GPUs) concurrently), seamless heterogeneous cluster usage (i.e., campus grids and cloud providers), and support for arbitrary MD codes such as GROMACS, while ensuring that all statistics are unbiased. We applied AWE-WQ to a 34 residue protein which simulated 1.5 ms over 8 months with peak aggregate performance of 1000 ns/h. Comparison was done with a 200 μs simulation collected on a GPU over a similar timespan. The folding and unfolded rates were of comparable accuracy.

Color sampling based matting methods find the best known samples for foreground and background colors of unknown pixels. Such methods do not perform well if there is an overlap in the color distribution of foreground and background regions because color cannot distinguish between these regions and hence, the selected samples cannot reliably estimate the matte. Furthermore, current sampling based matting methods choose samples that are located around the boundaries of foreground and background regions. In this paper, we overcome these two problems. First, we propose texture as a feature that can complement color to improve matting by discriminating between known regions with similar colors. The contribution of texture and color is automatically estimated by analyzing the content of the image. Second, we combine local sampling with a global sampling scheme that prevents true foreground or background samples to be missed during the sample collection stage. An objective function containing color and texture components is optimized to choose the best foreground and background pair among a set of candidate pairs. Experiments are carried out on a benchmark data set and an independent evaluation of the results shows that the proposed method is ranked first among all other image matting methods.

Multimedia applications such as image or video retrieval, copy detection, and so forth can benefit from saliency detection, which is essentially a method to identify areas in images and videos that capture the attention of the human visual system. In this paper, we propose a new spatio-temporal saliency detection framework on the basis of regularized feature reconstruction. Specifically, for video saliency detection, both the temporal and spatial saliency detection are considered. For temporal saliency, we model the movement of the target patch as a reconstruction process using the patches in neighboring frames. A Laplacian smoothing term is introduced to model the coherent motion trajectories. With psychological findings that abrupt stimulus could cause a rapid and involuntary deployment of attention, our temporal model combines the reconstruction error, regularizer, and local trajectory contrast to measure the temporal saliency. For spatial saliency, a similar sparse reconstruction process is adopted to capture the regions with high center-surround contrast. Finally, the temporal saliency and spatial saliency are combined together to favor salient regions with high confidence for video saliency detection. We also apply the spatial saliency part of the spatio-temporal model to image saliency detection. Experimental results on a human fixation video dataset and an image saliency detection dataset show that our method achieves the best performance over several state-of-the-art approaches.

This paper presents, for the first time, a unified blind method for multi-image super-resolution (MISR or SR), single-image blur deconvolution (SIBD), and multi-image blur deconvolution (MIBD) of low-resolution (LR) images degraded by linear space-invariant (LSI) blur, aliasing, and additive white Gaussian noise (AWGN). The proposed approach is based on alternating minimization (AM) of a new cost function with respect to the unknown high-resolution (HR) image and blurs. The regularization term for the HR image is based upon the Huber-Markov random field (HMRF) model, which is a type of variational integral that exploits the piecewise smooth nature of the HR image. The blur estimation process is supported by an edge-emphasizing smoothing operation, which improves the quality of blur estimates by enhancing strong soft edges toward step edges, while filtering out weak structures. The parameters are updated gradually so that the number of salient edges used for blur estimation increases at each iteration. For better performance, the blur estimation is done in the filter domain rather than the pixel domain, i.e., using the gradients of the LR and HR images. The regularization term for the blur is Gaussian (L2 norm), which allows for fast noniterative optimization in the frequency domain. We accelerate the processing time of SR reconstruction by separating the upsampling and registration processes from the optimization procedure. Simulation results on both synthetic and real-life images (from a novel computational imager) confirm the robustness and effectiveness of the proposed method.

We formulate the problem of salient object detection in images as an automatic labeling problem on the vertices of a weighted graph. The seed (labeled) nodes are first detected using Markov random walks performed on two different graphs that represent the image. While the global properties of the image are computed from the random walk on a complete graph, the local properties are computed from a sparse k-regular graph. The most salient node is selected as the one which is globally most isolated but falls on a locally compact object. A few background nodes and salient nodes are further identified based upon the random walk based hitting time to the most salient node. The salient nodes and the background nodes will constitute the labeled nodes. A new graph representation of the image that represents the saliency between nodes more accurately, the "pop-out graph" model, is computed further based upon the knowledge of the labeled salient and background nodes. A semisupervised learning technique is used to determine the labels of the unlabeled nodes by optimizing a smoothness objective label function on the newly created "pop-out graph" model along with some weighted soft constraints on the labeled nodes.

The left ventricular ejection fraction (LVEF) determined by invasive ventriculography (routine cardiac cath; LV-gram) was compared with that determined by echocardiography in 100 patients scheduled for angiography (86% had LV-gram and 2DE during same hospital admission). Seventy percent of patients had at least single-vessel obstructive coronary artery disease, defined as more than 50% stenosis. By all estimates, the LVEF was higher in patients without coronary artery disease (CAD) compared to patients with CAD. There was an excellent correlation between the LVEF by cath and echo, but this correlation was noticeably less strong in patients with CAD, especially with involvement of the left circumflex artery.

Cloud provides plenty of storage that makes enterprises to move their precious information/documents to the cloud. Since the user moves their valued data to the cloud, security is a major concern between storage service provider and user. To ascertain the security, the cryptography plays vital role in the searchable encryption based document. In this paper, Dynamic multi keyword based search Algorithm is proposed that contain modified fully homomophic encryption and prims Algorithm. Based on keyword frequency present in the document, keywords are sorted and built searchable index for keyword and files. The dynamic tree is constructed using prim's algorithm based on keyword and file. The encrypted files in the tree are indexed to keywords using two way Hash table for efficient search and retrival of documents. The files are encrypted using modified ring based fully homomorphic encryption. The experimental result shows that proposed work provides 80 and 86% encryption and decryption throughput and take less indexing time to index the files than existing RC4+. [ABSTRACT FROM AUTHOR]