An Overview of Video Shot Clustering and Summarization Techniques for Mobile Applications

The problem of content characterization of video programmes is of great interest because video appeals to large audiences and its efficient distribution over various networks should contribute to widespread usage of multimedia services. In this paper we analyze several techniques proposed in literature for content characterization of video programmes, including movies and sports, that could be helpful for mobile media consumption. In particular we focus our analysis on shot clustering methods and effective video summarization techniques since, in the current video analysis scenario, they facilitate the access to the content and help in quick understanding of the associated semantics. First we consider the shot clustering techniques based on low-level features, using visual, audio and motion information, even combined in a multi-modal fashion. Then we concentrate on summarization techniques, such as static storyboards, dynamic video skimming and the extraction of sport highlights. Discussed summarization methods can be employed in the development of tools that would be greatly useful to most mobile users: in fact these algorithms automatically shorten the original video while preserving most events by highlighting only the important content. The effectiveness of each approach has been analyzed, showing that it mainly depends on the kind of video programme it relates to, and the type of summary or highlights we are focusing on

Hierarchical Structuring of Video Previews by Leading-Cluster-Analysis

3noClustering of shots is frequently used for accessing video data and enabling quick grasping of the associated content. In this work we first group video shots by a classic hierarchical algorithm, where shot content is described by a codebook of visual words and different codebooks are compared by a suitable measure of distortion. To deal with the high number of levels in a hierarchical tree, a novel procedure of Leading-Cluster-Analysis is then proposed to extract a reduced set of hierarchically arranged previews. The depth of the obtained structure is driven both from the nature of the visual content information, and by the user needs, who can navigate the obtained video previews at various levels of representation. The effectiveness of the proposed method is demonstrated by extensive tests and comparisons carried out on a large collection of video data. of digital videos has not been accompanied by a parallel increase in its accessibility. In this context, video abstraction techniques may represent a key components of a practical video management system: indeed a condensed video may be effective for a quick browsing or retrieval tasks. A commonly accepted type of abstract for generic videos does not exist yet, and the solutions investigated so far depend usually on the nature and the genre of video data.openopenBenini, Sergio; Migliorati, Pierangelo; Leonardi, RiccardoBenini, Sergio; Migliorati, Pierangelo; Leonardi, Riccard

Biochemical and Functional Analysis of the PREP1 Interactor: p160 Myb Binding Protein 1

p160 myb binding protein 1a (p160) is a nucleolar protein the function of which is poorly understood. To date, only a few interactors, mainly transcription factors (e.g. Myb, Jun), have been characterized. One of these is Prep1, an homeodomain transcription factor essential for embryonic development, involved, in particular, in haemopoiesis, angiogenesis, and oculogenesis, as well as in apoptosis and tumorigenesis. In this thesis work I show that p160 is capable of binding Prep1 through the same region as another Prep1 interactor, Pbx, and that such binding inhibits Prepl action on the HoxB enhancer. I demonstrate that the two proteins, Prep1 and p160, interact mainly in the nucleoplasm and hence only under conditions that favor p160 translocation from the nucleolus to the nucleoplasm (e.g. after treatment with Actinomycin D). Moreover I studied p160 during cell-cycle, since during mitosis p160 assumes a para-chromosomal localization. In HeLa cells, depletion of p160 by siRNA transfection decreases the proliferation rate and increases cell-death, mostly via apoptosis. p160-depleted HeLa cells accumulate in the G2/M phases of the cell-cycle and show a variety of mitotic defects: extended division time and structural alterations of the metaphasic plate. All these data suggest that p160 is essential for proper cell division. This phenotype is also in agreement with my observations that p160 down-regulated NIH 3T3 cells are more sensitive to transformation by Ras and Myc and undergo spontaneous neoplastic transformation even in the absence of oncogenes

A scalable approach to video summarization and adaptation

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, octubre de 201

Developing Public Interest in the Natural Sciences at the ZHAW, Wädenswil, Switzerland

This project investigated methods for the conservation and presentation of artifacts from the ZHAW Chemistry department. TThe artifacts were inventoried and analyzed based on condition and historical value. Through extensive research, museums visits, and interviews conducted internationally, we gathered paramount information to best generate interest in the natural sciences. With the material collected, we were able to prepare a recommendation for an exhibition design, which incorporates all areas of the ZHAW collection. We developed implementation plans in an effort to conserve and convey the collection of samples and experiments. This plan is presented in multiple phases, so as to keep the project executable for the ZHAW

From nanometers to centimeters: Imaging across spatial scales with smart computer-aided microscopy

Microscopes have been an invaluable tool throughout the history of the life sciences, as they allow researchers to observe the miniscule details of living systems in space and time. However, modern biology studies complex and non-obvious phenotypes and their distributions in populations and thus requires that microscopes evolve from visual aids for anecdotal observation into instruments for objective and quantitative measurements. To this end, many cutting-edge developments in microscopy are fuelled by innovations in the computational processing of the generated images. Computational tools can be applied in the early stages of an experiment, where they allow for reconstruction of images with higher resolution and contrast or more colors compared to raw data. In the final analysis stage, state-of-the-art image analysis pipelines seek to extract interpretable and humanly tractable information from the high-dimensional space of images. In the work presented in this thesis, I performed super-resolution microscopy and wrote image analysis pipelines to derive quantitative information about multiple biological processes. I contributed to studies on the regulation of DNMT1 by implementing machine learning-based segmentation of replication sites in images and performed quantitative statistical analysis of the recruitment of multiple DNMT1 mutants. To study the spatiotemporal distribution of DNA damage response I performed STED microscopy and could provide a lower bound on the size of the elementary spatial units of DNA repair. In this project, I also wrote image analysis pipelines and performed statistical analysis to show a decoupling of DNA density and heterochromatin marks during repair. More on the experimental side, I helped in the establishment of a protocol for many-fold color multiplexing by iterative labelling of diverse structures via DNA hybridization. Turning from small scale details to the distribution of phenotypes in a population, I wrote a reusable pipeline for fitting models of cell cycle stage distribution and inhibition curves to high-throughput measurements to quickly quantify the effects of innovative antiproliferative antibody-drug-conjugates. The main focus of the thesis is BigStitcher, a tool for the management and alignment of terabyte-sized image datasets. Such enormous datasets are nowadays generated routinely with light-sheet microscopy and sample preparation techniques such as clearing or expansion. Their sheer size, high dimensionality and unique optical properties poses a serious bottleneck for researchers and requires specialized processing tools, as the images often do not fit into the main memory of most computers. BigStitcher primarily allows for fast registration of such many-dimensional datasets on conventional hardware using optimized multi-resolution alignment algorithms. The software can also correct a variety of aberrations such as fixed-pattern noise, chromatic shifts and even complex sample-induced distortions. A defining feature of BigStitcher, as well as the various image analysis scripts developed in this work is their interactivity. A central goal was to leverage the user's expertise at key moments and bring innovations from the big data world to the lab with its smaller and much more diverse datasets without replacing scientists with automated black-box pipelines. To this end, BigStitcher was implemented as a user-friendly plug-in for the open source image processing platform Fiji and provides the users with a nearly instantaneous preview of the aligned images and opportunities for manual control of all processing steps. With its powerful features and ease-of-use, BigStitcher paves the way to the routine application of light-sheet microscopy and other methods producing equally large datasets