Compressive light field photography using overcomplete dictionaries and optimized projections

Light field photography has gained a significant research interest in the last two decades; today, commercial light field cameras are widely available. Nevertheless, most existing acquisition approaches either multiplex a low-resolution light field into a single 2D sensor image or require multiple photographs to be taken for acquiring a high-resolution light field. We propose a compressive light field camera architecture that allows for higher-resolution light fields to be recovered than previously possible from a single image. The proposed architecture comprises three key components: light field atoms as a sparse representation of natural light fields, an optical design that allows for capturing optimized 2D light field projections, and robust sparse reconstruction methods to recover a 4D light field from a single coded 2D projection. In addition, we demonstrate a variety of other applications for light field atoms and sparse coding, including 4D light field compression and denoising.Natural Sciences and Engineering Research Council of Canada (NSERC postdoctoral fellowship)United States. Defense Advanced Research Projects Agency (DARPA SCENICC program)Alfred P. Sloan Foundation (Sloan Research Fellowship)United States. Defense Advanced Research Projects Agency (DARPA Young Faculty Award

VisionBlocks: A Social Computer Vision Framework

Vision Blocks (http://visionblocks.org) is an on demand, in-browser, customizable computer vision application publishing platform for masses. It empowers end-users (consumers)to create novel solutions for themselves that they would not easily obtain off-the-shelf. By transferring design capability to the consumers, we enable creation and dissemination of custom products and algorithms. We adapt a visual programming paradigm to codify vision algorithms for general use. As a proof of-concept, we implement computer vision algorithms such as motion tracking, face detection, change detection and others. We demonstrate their applications on real-time video. Our studies show that end users (non programmers) only need 50% more time to build such systems when compared to the most experienced researchers. We made progress towards closing the gap between researchers and consumers by finding that users rate the intuitiveness of the approach in a level 6% less than researchers. We discuss different application scenarios where such study will be useful and argue its benefit for computer vision research community. We believe that enabling users with ability to create application will be first step towards creating social computer vision applications and platform.Alfred P. Sloan Foundation (Research Fellowship

Gene expression prediction using low-rank matrix completion

Background: An exponential growth of high-throughput biological information and data has occurred in the past decade, supported by technologies, such as microarrays and RNA-Seq. Most data generated using such methods are used to encode large amounts of rich information, and determine diagnostic and prognostic biomarkers. Although data storage costs have reduced, process of capturing data using aforementioned technologies is still expensive. Moreover, the time required for the assay, from sample preparation to raw value measurement is excessive (in the order of days). There is an opportunity to reduce both the cost and time for generating such expression datasets. Results: We propose a framework in which complete gene expression values can be reliably predicted in-silico from partial measurements. This is achieved by modelling expression data as a low-rank matrix and then applying recently discovered techniques of matrix completion by using nonlinear convex optimisation. We evaluated prediction of gene expression data based on 133 studies, sourced from a combined total of 10,921 samples. It is shown that such datasets can be constructed with a low relative error even at high missing value rates (>50 %), and that such predicted datasets can be reliably used as surrogates for further analysis. Conclusion: This method has potentially far-reaching applications including how bio-medical data is sourced and generated, and transcriptomic prediction by optimisation. We show that gene expression data can be computationally constructed, thereby potentially reducing the costs of gene expression profiling. In conclusion, this method shows great promise of opening new avenues in research on low-rank matrix completion in biological sciences. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1106-6) contains supplementary material, which is available to authorized users

Context-Specific Ontology Integration: A Bayesian Approach

We introduce a principled computational framework and methodology for automated discovery of context-specific functional links between ontologies. Our model leverages over disparate free-text literature resources to score the model of dependency linking two terms under a context against their model of independence. We identify linked terms as those having a significant bayes factor (p < 0.01). To scale our algorithm over massive ontologies, we propose a heuristic pruning technique as an efficient algorithm for inferring such links. We have applied this method to translationalize Gene Ontology to all other ontologies available at National Center of Biomedical Ontology (NCBO) BioPortal under the context of Human Disease ontology. Our results show that in addition to broadening the scope of hypothesis for researchers, our work can potentially be used to explore continuum of relationships among ontologies to guide various biological experiments

Exploring space of four dimensional modulations inside traditional camera designs

Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2013.62Cataloged from PDF version of thesis.Includes bibliographical references (pages 95-99).Light field photography has gained a significant research in the last two decades: today, commercial light field cameras are widely available demonstrating capabilities such as post-capture refocus, 3D photography and view point changes. But, most traditional acquisition approaches either multiplex a low resolution light field into a single sensor image or require multiple photographs to be taken for acquiring high resolution light field. In this thesis, we design, implement and analyze a new light field camera architecture that allows capture and reconstruction of higher resolution light fields in a single shot. The proposed architecture comprises three key components: light field atoms as sparse representation of natural light fields, an optical design to allow capture of optimized 2D light field projections and robust sparse reconstruction methods to recover a 4D light field from a single coded 2D projection. In addition we also explore other applications including compressive focal stack reconstructions, light field compression and denoising.by Kshitij Marwah.S.M

Additional file 1 of Gene expression prediction using low-rank matrix completion

In this supplement, we provide additional discussion and further analysis on additional studies. (PDF 851 kb