Sparse Image Representation with Epitomes

Sparse coding, which is the decomposition of a vector using only a few basis elements, is widely used in machine learning and image processing. The basis set, also called dictionary, is learned to adapt to specific data. This approach has proven to be very effective in many image processing tasks. Traditionally, the dictionary is an unstructured "flat" set of atoms. In this paper, we study structured dictionaries which are obtained from an epitome, or a set of epitomes. The epitome is itself a small image, and the atoms are all the patches of a chosen size inside this image. This considerably reduces the number of parameters to learn and provides sparse image decompositions with shiftinvariance properties. We propose a new formulation and an algorithm for learning the structured dictionaries associated with epitomes, and illustrate their use in image denoising tasks.Comment: Computer Vision and Pattern Recognition, Colorado Springs : United States (2011

Multi-omics analysis identifies genes mediating the extension of cell walls in the Arabidopsis thaliana root elongation zone

Plant cell wall composition is important for regulating growth rates, especially in roots. However, neither analyses of cell wall composition nor transcriptomes on their own can comprehensively reveal which genes and processes are mediating growth and cell elongation rates. This study reveals the benefits of carrying out multiple analyses in combination. Sections of roots from five anatomically and functionally defined zones in Arabidopsis thaliana were prepared and divided into three biological replicates. We used glycan microarrays and antibodies to identify the major classes of glycans and glycoproteins present in the cell walls of these sections, and identified the expected decrease in pectin and increase in xylan from the meristematic zone (MS), through the rapid and late elongation zones (REZ, LEZ) to the maturation zone and the rest of the root, including the emerging lateral roots. Other compositional changes included extensin and xyloglucan levels peaking in the REZ and increasing levels of arabinogalactan-proteins (AGP) epitopes from the MS to the LEZ, which remained high through the subsequent mature zones. Immuno-staining using the same antibodies identified the tissue and (sub)cellular localization of many epitopes. Extensins were localized in epidermal and cortex cell walls, while AGP glycans were specific to different tissues from root-hair cells to the stele. The transcriptome analysis found several gene families peaking in the REZ. These included a large family of peroxidases (which produce the reactive oxygen species (ROS) needed for cell expansion), and three xyloglucan endo-transglycosylase/hydrolase genes (XTH17, XTH18, and XTH19). The significance of the latter may be related to a role in breaking and re-joining xyloglucan cross-bridges between cellulose microfibrils, a process which is required for wall expansion. Knockdowns of these XTHs resulted in shorter root lengths, confirming a role of the corresponding proteins in root extension growth

Review on Human Re-identification with Multiple Cameras

Human re-identification is the core task in most surveillance systems and it is aimed at matching human pairs from different non-overlapping cameras. There are several challenging issues that need to be overcome to achieve reidentification, such as overcoming the variations in viewpoint, pose, image resolution, illumination and occlusion. In this study, we review existing works in human re-identification task. Advantages and limitations of recent works are discussed. At the end, this paper suggests some future research directions for human re-identification