319,797 research outputs found

    Nearness to Local Subspace Algorithm for Subspace and Motion Segmentation

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    There is a growing interest in computer science, engineering, and mathematics for modeling signals in terms of union of subspaces and manifolds. Subspace segmentation and clustering of high dimensional data drawn from a union of subspaces are especially important with many practical applications in computer vision, image and signal processing, communications, and information theory. This paper presents a clustering algorithm for high dimensional data that comes from a union of lower dimensional subspaces of equal and known dimensions. Such cases occur in many data clustering problems, such as motion segmentation and face recognition. The algorithm is reliable in the presence of noise, and applied to the Hopkins 155 Dataset, it generates the best results to date for motion segmentation. The two motion, three motion, and overall segmentation rates for the video sequences are 99.43%, 98.69%, and 99.24%, respectively

    The evolution of bits and bottlenecks in a scientific workflow trying to keep up with technology: Accelerating 4D image segmentation applied to nasa data

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    In 2016, a team of earth scientists directly engaged a team of computer scientists to identify cyberinfrastructure (CI) approaches that would speed up an earth science workflow. This paper describes the evolution of that workflow as the two teams bridged CI and an image segmentation algorithm to do large scale earth science research. The Pacific Research Platform (PRP) and The Cognitive Hardware and Software Ecosystem Community Infrastructure (CHASE-CI) resources were used to significantly decreased the earth science workflow's wall-clock time from 19.5 days to 53 minutes. The improvement in wall-clock time comes from the use of network appliances, improved image segmentation, deployment of a containerized workflow, and the increase in CI experience and training for the earth scientists. This paper presents a description of the evolving innovations used to improve the workflow, bottlenecks identified within each workflow version, and improvements made within each version of the workflow, over a three-year time period

    Segmentation ART: A Neural Network for Word Recognition from Continuous Speech

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    The Segmentation ATIT (Adaptive Resonance Theory) network for word recognition from a continuous speech stream is introduced. An input sequeuce represents phonemes detected at a preproccesing stage. Segmentation ATIT is trained rapidly, and uses a fast-learning fuzzy ART modules, top-down expectation, and a spatial representation of temporal order. The network performs on-line identification of word boundaries, correcting an initial hypothesis if subsequent phonemes are incompatible with a previous partition. Simulations show that the system's segmentation perfonnance is comparable to that of TRACE, and the ability to segment a number of difficult phrases is also demonstrated.National Science Foundation (NSF-IRI-94-01659); Office of Naval Research (N00014-95-1-0409, N00014-95-1-0G57

    Content-based Propagation of User Markings for Interactive Segmentation of Patterned Images

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    Efficient and easy segmentation of images and volumes is of great practical importance. Segmentation problems that motivate our approach originate from microscopy imaging commonly used in materials science, medicine, and biology. We formulate image segmentation as a probabilistic pixel classification problem, and we apply segmentation as a step towards characterising image content. Our method allows the user to define structures of interest by interactively marking a subset of pixels. Thanks to the real-time feedback, the user can place new markings strategically, depending on the current outcome. The final pixel classification may be obtained from a very modest user input. An important ingredient of our method is a graph that encodes image content. This graph is built in an unsupervised manner during initialisation and is based on clustering of image features. Since we combine a limited amount of user-labelled data with the clustering information obtained from the unlabelled parts of the image, our method fits in the general framework of semi-supervised learning. We demonstrate how this can be a very efficient approach to segmentation through pixel classification.Comment: 9 pages, 7 figures, PDFLaTe

    How to collect high quality segmentations: use human or computer drawn object boundaries?

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    High quality segmentations must be captured consistently for applications such as biomedical image analysis. While human drawn segmentations are often collected because they provide a consistent level of quality, computer drawn segmentations can be collected efficiently and inexpensively. In this paper, we examine how to leverage available human and computer resources to consistently create high quality segmentations. We propose a quality control methodology. We demonstrate how to apply this approach using crowdsourced and domain expert votes for the "best" segmentation from a collection of human and computer drawn segmentations for 70 objects from a public dataset and 274 objects from biomedical images. We publicly share the library of biomedical images which includes 1,879 manual annotations of the boundaries of 274 objects. We found for the 344 objects that no single segmentation source was preferred and that human annotations are not always preferred over computer annotations. These results motivated us to examine the traditional approach to evaluate segmentation algorithms, which involves comparing the segmentations produced by the algorithms to manual annotations on benchmark datasets. We found that algorithm benchmarking results change when the comparison is made to consensus-voted segmentations. Our results led us to suggest a new segmentation approach that uses machine learning to predict the optimal segmentation source and a modified segmentation evaluation approach.National Science Foundation (IIS-0910908

    Overview: Computer vision and machine learning for microstructural characterization and analysis

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    The characterization and analysis of microstructure is the foundation of microstructural science, connecting the materials structure to its composition, process history, and properties. Microstructural quantification traditionally involves a human deciding a priori what to measure and then devising a purpose-built method for doing so. However, recent advances in data science, including computer vision (CV) and machine learning (ML) offer new approaches to extracting information from microstructural images. This overview surveys CV approaches to numerically encode the visual information contained in a microstructural image, which then provides input to supervised or unsupervised ML algorithms that find associations and trends in the high-dimensional image representation. CV/ML systems for microstructural characterization and analysis span the taxonomy of image analysis tasks, including image classification, semantic segmentation, object detection, and instance segmentation. These tools enable new approaches to microstructural analysis, including the development of new, rich visual metrics and the discovery of processing-microstructure-property relationships.Comment: submitted to Materials and Metallurgical Transactions
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