The effects of prayer on Muslim patients' well-being

The purpose of this study is to investigate the effects of Islamic prayer (du’a, dhikr, and Qur’anic recitation) on Muslim patients. Relying on the Qur’an and sunnah, Islamic scholars state that prayer has positive effects on patients’ psychological and physical well-being. To examine this, the principal investigator recruited 60 adult in-patients at Brigham and Women’s Hospital. Patients completed questionnaires that were used to assess the respondents’ psychological/emotional well being as well as determine the level of religiosity or spirituality (from an Islamic perspective). Vital sign recordings and self-report surveys were used before and after prayer sessions to measure effects of prayer. A non-religious text served as a control. Results support the hypothesis of the positive effects of prayer. A greater degree of religiosity/spirituality was associated with better psychological health. Physical changes were clinically insignificant but statistically meaningful.

Complying with the NIH Public Access Policy - Copyright Considerations and Options

On January 11, 2008, the National Institutes of Health ('NIH') adopted a revised Public Access Policy for peer-reviewed journal articles reporting research supported in whole or in part by NIH funds. Under the revised policy, the grantee shall ensure that a copy of the author's final manuscript, including any revisions made during the peer review process, be electronically submitted to the National Library of Medicine's PubMed Central ('PMC') archive and that the person submitting the manuscript will designate a time not later than 12 months after publication at which NIH may make the full text of the manuscript publicly accessible in PMC. NIH adopted this policy to implement a new statutory requirement under which: The Director of the National Institutes of Health shall require that all investigators funded by the NIH submit or have submitted for them to the National Library of Medicine's PubMed Central an electronic version of their final, peer-reviewed manuscripts upon acceptance for publication to be made publicly available no later than 12 months after the official date of publication: Provided, That the NIH shall implement the public access policy in a manner consistent with copyright law. This White Paper is written primarily for policymaking staff in universities and other institutional recipients of NIH support responsible for ensuring compliance with the Public Access Policy. The January 11, 2008, Public Access Policy imposes two new compliance mandates. First, the grantee must ensure proper manuscript submission. The version of the article to be submitted is the final version over which the author has control, which must include all revisions made after peer review. The statutory command directs that the manuscript be submitted to PMC 'upon acceptance for publication.' That is, the author's final manuscript should be submitted to PMC at the same time that it is sent to the publisher for final formatting and copy editing. Proper submission is a two-stage process. The electronic manuscript must first be submitted through a process that requires input of additional information concerning the article, the author(s), and the nature of NIH support for the research reported. NIH then formats the manuscript into a uniform, XML-based format used for PMC versions of articles. In the second stage of the submission process, NIH sends a notice to the Principal Investigator requesting that the PMC-formatted version be reviewed and approved. Only after such approval has grantee's manuscript submission obligation been satisfied. Second, the grantee also has a distinct obligation to grant NIH copyright permission to make the manuscript publicly accessible through PMC not later than 12 months after the date of publication. This obligation is connected to manuscript submission because the author, or the person submitting the manuscript on the author's behalf, must have the necessary rights under copyright at the time of submission to give NIH the copyright permission it requires. This White Paper explains and analyzes only the scope of the grantee's copyright-related obligations under the revised Public Access Policy and suggests six options for compliance with that aspect of the grantee's obligation. Time is of the essence for NIH grantees. As a practical matter, the grantee should have a compliance process in place no later than April 7, 2008. More specifically, the new Public Access Policy applies to any article accepted for publication on or after April 7, 2008 if the article arose under (1) an NIH Grant or Cooperative Agreement active in Fiscal Year 2008, (2) direct funding from an NIH Contract signed after April 7, 2008, (3) direct funding from the NIH Intramural Program, or (4) from an NIH employee. In addition, effective May 25, 2008, anyone submitting an application, proposal or progress report to the NIH must include the PMC reference number when citing articles arising from their NIH funded research. (This includes applications submitted to the NIH for the May 25, 2008 and subsequent due dates.) Conceptually, the compliance challenge that the Public Access Policy poses for grantees is easily described. The grantee must depend to some extent upon the author(s) to take the necessary actions to ensure that the grantee is in compliance with the Public Access Policy because the electronic manuscripts and the copyrights in those manuscripts are initially under the control of the author(s). As a result, any compliance option will require an explicit understanding between the author(s) and the grantee about how the manuscript and the copyright in the manuscript are managed. It is useful to conceptually keep separate the grantee's manuscript submission obligation from its copyright permission obligation because the compliance personnel concerned with manuscript management may differ from those responsible for overseeing the author's copyright management. With respect to copyright management, the grantee has the following six options: (1) rely on authors to manage copyright but also to request or to require that these authors take responsibility for amending publication agreements that call for transfer of too many rights to enable the author to grant NIH permission to make the manuscript publicly accessible ('the Public Access License'); (2) take a more active role in assisting authors in negotiating the scope of any copyright transfer to a publisher by (a) providing advice to authors concerning their negotiations or (b) by acting as the author's agent in such negotiations; (3) enter into a side agreement with NIH-funded authors that grants a non-exclusive copyright license to the grantee sufficient to grant NIH the Public Access License; (4) enter into a side agreement with NIH-funded authors that grants a non-exclusive copyright license to the grantee sufficient to grant NIH the Public Access License and also grants a license to the grantee to make certain uses of the article, including posting a copy in the grantee's publicly accessible digital archive or repository and authorizing the article to be used in connection with teaching by university faculty; (5) negotiate a more systematic and comprehensive agreement with the biomedical publishers to ensure either that the publisher has a binding obligation to submit the manuscript and to grant NIH permission to make the manuscript publicly accessible or that the author retains sufficient rights to do so; or (6) instruct NIH-funded authors to submit manuscripts only to journals with binding deposit agreements with NIH or to journals whose copyright agreements permit authors to retain sufficient rights to authorize NIH to make manuscripts publicly accessible.

Skin Color-Based Video Segmentation under Time-Varying Illumination

A novel approach for real-time skin segmentation in video sequences is described. The approach enables reliable skin segmentation despite wide variation in illumination during tracking. An explicit second order Markov model is used to predict evolution of the skin-color (HSV) histogram over time. Histograms are dynamically updated based on feedback from the current segmentation and predictions of the Markov model. The evolution of the skin-color distribution at each frame is parameterized by translation, scaling and rotation in color space. Consequent changes in geometric parameterization of the distribution are propagated by warping and resampling the histogram. The parameters of the discrete-time dynamic Markov model are estimated using Maximum Likelihood Estimation, and also evolve over time. The accuracy of the new dynamic skin color segmentation algorithm is compared to that obtained via a static color model. Segmentation accuracy is evaluated using labeled ground-truth video sequences taken from staged experiments and popular movies. An overall increase in segmentation accuracy of up to 24% is observed in 17 out of 21 test sequences. In all but one case the skin-color classification rates for our system were higher, with background classification rates comparable to those of the static segmentation.

Deformable Prototypes for Encoding Shape Categories in Image Databases

We describe a method for shape-based image database search that uses deformable prototypes to represent categories. Rather than directly comparing a candidate shape with all shape entries in the database, shapes are compared in terms of the types of nonrigid deformations (differences) that relate them to a small subset of representative prototypes. To solve the shape correspondence and alignment problem, we employ the technique of modal matching, an information-preserving shape decomposition for matching, describing, and comparing shapes despite sensor variations and nonrigid deformations. In modal matching, shape is decomposed into an ordered basis of orthogonal principal components. We demonstrate the utility of this approach for shape comparison in 2-D image databases.

Deformable Shape Detection and Description via Model-Based Region Grouping

A method for deformable shape detection and recognition is described. Deformable shape templates are used to partition the image into a globally consistent interpretation, determined in part by the minimum description length principle. Statistical shape models enforce the prior probabilities on global, parametric deformations for each object class. Once trained, the system autonomously segments deformed shapes from the background, while not merging them with adjacent objects or shadows. The formulation can be used to group image regions based on any image homogeneity predicate; e.g., texture, color, or motion. The recovered shape models can be used directly in object recognition. Experiments with color imagery are reported.

Fast, Reliable Head Tracking under Varying Illumination

An improved technique for 3D head tracking under varying illumination conditions is proposed. The head is modeled as a texture mapped cylinder. Tracking is formulated as an image registration problem in the cylinder's texture map image. To solve the registration problem in the presence of lighting variation and head motion, the residual error of registration is modeled as a linear combination of texture warping templates and orthogonal illumination templates. Fast and stable on-line tracking is then achieved via regularized, weighted least squares minimization of the registration error. The regularization term tends to limit potential ambiguities that arise in the warping and illumination templates. It enables stable tracking over extended sequences. Tracking does not require a precise initial fit of the model; the system is initialized automatically using a simple 2-D face detector. The only assumption is that the target is facing the camera in the first frame of the sequence. The warping templates are computed at the first frame of the sequence. Illumination templates are precomputed off-line over a training set of face images collected under varying lighting conditions. Experiments in tracking are reported.

Estimation and Prediction of Evolving Color Distributions for Skin Segmentation Under Varying Illumination

A novel approach for real-time skin segmentation in video sequences is described. The approach enables reliable skin segmentation despite wide variation in illumination during tracking. An explicit second order Markov model is used to predict evolution of the skin color (HSV) histogram over time. Histograms are dynamically updated based on feedback from the current segmentation and based on predictions of the Markov model. The evolution of the skin color distribution at each frame is parameterized by translation, scaling and rotation in color space. Consequent changes in geometric parameterization of the distribution are propagated by warping and re-sampling the histogram. The parameters of the discrete-time dynamic Markov model are estimated using Maximum Likelihood Estimation, and also evolve over time. Quantitative evaluation of the method was conducted on labeled ground-truth video sequences taken from popular movies.

Recursive Estimation of Motion and Planar Structure

A specialized formulation of Azarbayejani and Pentland's framework for recursive recovery of motion, structure and focal length from feature correspondences tracked through an image sequence is presented. The specialized formulation addresses the case where all tracked points lie on a plane. This planarity constraint reduces the dimension of the original state vector, and consequently the number of feature points needed to estimate the state. Experiments with synthetic data and real imagery illustrate the system performance. The experiments confirm that the specialized formulation provides improved accuracy, stability to observation noise, and rate of convergence in estimation for the case where the tracked points lie on a plane.

Inferring Body Pose without Tracking Body Parts

A novel approach for estimating articulated body posture and motion from monocular video sequences is proposed. Human pose is defined as the instantaneous two dimensional configuration (i.e., the projection onto the image plane) of a single articulated body in terms of the position of a predetermined set of joints. First, statistical segmentation of the human bodies from the background is performed and low-level visual features are found given the segmented body shape. The goal is to be able to map these, generally low level, visual features to body configurations. The system estimates different mappings, each one with a specific cluster in the visual feature space. Given a set of body motion sequences for training, unsupervised clustering is obtained via the Expectation Maximation algorithm. Then, for each of the clusters, a function is estimated to build the mapping between low-level features to 3D pose. Currently this mapping is modeled by a neural network. Given new visual features, a mapping from each cluster is performed to yield a set of possible poses. From this set, the system selects the most likely pose given the learned probability distribution and the visual feature similarity between hypothesis and input. Performance of the proposed approach is characterized using a new set of known body postures, showing promising results.

Index Trees for Efficient Deformable Shape-based Retrieval

An improved method for deformable shape-based image indexing and retrieval is described. A pre-computed index tree is used to improve the speed of our previously reported on-line model fitting method; simple shape features are used as keys in a pre-generated index tree of model instances. In addition, a coarse to fine indexing scheme is used at different levels of the tree to further improve speed while maintaining matching accuracy. Experimental results show that the speedup is significant, while accuracy of shape-based indexing is maintained. A method for shape population-based retrieval is also described. The method allows query formulation based on the population distributions of shapes in each image. Results of population-based image queries for a database of blood cell micrographs are shown.

3D Hand Pose Reconstruction Using Specialized Mappings

A system for recovering 3D hand pose from monocular color sequences is proposed. The system employs a non-linear supervised learning framework, the specialized mappings architecture (SMA), to map image features to likely 3D hand poses. The SMA's fundamental components are a set of specialized forward mapping functions, and a single feedback matching function. The forward functions are estimated directly from training data, which in our case are examples of hand joint configurations and their corresponding visual features. The joint angle data in the training set is obtained via a CyberGlove, a glove with 22 sensors that monitor the angular motions of the palm and fingers. In training, the visual features are generated using a computer graphics module that renders the hand from arbitrary viewpoints given the 22 joint angles. We test our system both on synthetic sequences and on sequences taken with a color camera. The system automatically detects and tracks both hands of the user, calculates the appropriate features, and estimates the 3D hand joint angles from those features. Results are encouraging given the complexity of the task.

Region Segmentation via Deformable Model-Guided Split and Merge

An improved method for deformable shape-based image segmentation is described. Image regions are merged together and/or split apart, based on their agreement with an a priori distribution on the global deformation parameters for a shape template. The quality of a candidate region merging is evaluated by a cost measure that includes: homogeneity of image properties within the combined region, degree of overlap with a deformed shape model, and a deformation likelihood term. Perceptually-motivated criteria are used to determine where/how to split regions, based on the local shape properties of the region group's bounding contour. A globally consistent interpretation is determined in part by the minimum description length principle. Experiments show that the model-based splitting strategy yields a significant improvement in segmention over a method that uses merging alone.