127,990 research outputs found
Model-Based Edge Detector for Spectral Imagery Using Sparse Spatiospectral Masks
Two model-based algorithms for edge detection in spectral imagery are developed that specifically target capturing intrinsic features such as isoluminant edges that are characterized by a jump in color but not in intensity. Given prior knowledge of the classes of reflectance or emittance spectra associated with candidate objects in a scene, a small set of spectral-band ratios, which most profoundly identify the edge between each pair of materials, are selected to define a edge signature. The bands that form the edge signature are fed into a spatial mask, producing a sparse joint spatiospectral nonlinear operator. The first algorithm achieves edge detection for every material pair by matching the response of the operator at every pixel with the edge signature for the pair of materials. The second algorithm is a classifier-enhanced extension of the first algorithm that adaptively accentuates distinctive features before applying the spatiospectral operator. Both algorithms are extensively verified using spectral imagery from the airborne hyperspectral imager and from a dots-in-a-well midinfrared imager. In both cases, the multicolor gradient (MCG) and the hyperspectral/spatial detection of edges (HySPADE) edge detectors are used as a benchmark for comparison. The results demonstrate that the proposed algorithms outperform the MCG and HySPADE edge detectors in accuracy, especially when isoluminant edges are present. By requiring only a few bands as input to the spatiospectral operator, the algorithms enable significant levels of data compression in band selection. In the presented examples, the required operations per pixel are reduced by a factor of 71 with respect to those required by the MCG edge detector
MRI image segmantation based on edge detection
Cílem této práce je představit základní segmentační techniky používáné v oblasti medicínského zpracování obrazových dat a pomocí 3D prohlížeče schopného zobrazit 3D obrazy implementovat segmentační modul založený na hranové detekci a vyhodnotit výsledky. Navrhovaný prohlížeč je sestavený v prostředi Matlab GUI a je schopen načíst objem 3D snímků představující lidskou hlavu. Navrhovaný segmentační modul je založen na použití hranových detektorů, zejména Cannyho detektoru.The aim of this thesis is to present the basic segmentation techniques uses in the field of medical image processing and by using a 3D viewer able to visualize 3D images, implement a segmentation module based on edges detection and evaluate the results. The proposed viewer is a 3D viewer build using matlab GUI and is able to load a volume of images representing the human head. The proposed segmentation module is based on the use of edge detectors particularly the Canny algorithm.
Scene extraction in motion pictures
This paper addresses the challenge of bridging the semantic gap between the rich meaning users desire when they query to locate and browse media and the shallowness of media descriptions that can be computed in today\u27s content management systems. To facilitate high-level semantics-based content annotation and interpretation, we tackle the problem of automatic decomposition of motion pictures into meaningful story units, namely scenes. Since a scene is a complicated and subjective concept, we first propose guidelines from fill production to determine when a scene change occurs. We then investigate different rules and conventions followed as part of Fill Grammar that would guide and shape an algorithmic solution for determining a scene. Two different techniques using intershot analysis are proposed as solutions in this paper. In addition, we present different refinement mechanisms, such as film-punctuation detection founded on Film Grammar, to further improve the results. These refinement techniques demonstrate significant improvements in overall performance. Furthermore, we analyze errors in the context of film-production techniques, which offer useful insights into the limitations of our method
Peacock Bundles: Bundle Coloring for Graphs with Globality-Locality Trade-off
Bundling of graph edges (node-to-node connections) is a common technique to
enhance visibility of overall trends in the edge structure of a large graph
layout, and a large variety of bundling algorithms have been proposed. However,
with strong bundling, it becomes hard to identify origins and destinations of
individual edges. We propose a solution: we optimize edge coloring to
differentiate bundled edges. We quantify strength of bundling in a flexible
pairwise fashion between edges, and among bundled edges, we quantify how
dissimilar their colors should be by dissimilarity of their origins and
destinations. We solve the resulting nonlinear optimization, which is also
interpretable as a novel dimensionality reduction task. In large graphs the
necessary compromise is whether to differentiate colors sharply between locally
occurring strongly bundled edges ("local bundles"), or also between the weakly
bundled edges occurring globally over the graph ("global bundles"); we allow a
user-set global-local tradeoff. We call the technique "peacock bundles".
Experiments show the coloring clearly enhances comprehensibility of graph
layouts with edge bundling.Comment: Appears in the Proceedings of the 24th International Symposium on
Graph Drawing and Network Visualization (GD 2016
Interaction between high-level and low-level image analysis for semantic video object extraction
Authors of articles published in EURASIP Journal on Advances in Signal Processing are the copyright holders of their articles and have granted to any third party, in advance and in perpetuity, the right to use, reproduce or disseminate the article, according to the SpringerOpen copyright and license agreement (http://www.springeropen.com/authors/license)
Multiresolution community detection for megascale networks by information-based replica correlations
We use a Potts model community detection algorithm to accurately and
quantitatively evaluate the hierarchical or multiresolution structure of a
graph. Our multiresolution algorithm calculates correlations among multiple
copies ("replicas") of the same graph over a range of resolutions. Significant
multiresolution structures are identified by strongly correlated replicas. The
average normalized mutual information, the variation of information, and other
measures in principle give a quantitative estimate of the "best" resolutions
and indicate the relative strength of the structures in the graph. Because the
method is based on information comparisons, it can in principle be used with
any community detection model that can examine multiple resolutions. Our
approach may be extended to other optimization problems. As a local measure,
our Potts model avoids the "resolution limit" that affects other popular
models. With this model, our community detection algorithm has an accuracy that
ranks among the best of currently available methods. Using it, we can examine
graphs over 40 million nodes and more than one billion edges. We further report
that the multiresolution variant of our algorithm can solve systems of at least
200000 nodes and 10 million edges on a single processor with exceptionally high
accuracy. For typical cases, we find a super-linear scaling, O(L^{1.3}) for
community detection and O(L^{1.3} log N) for the multiresolution algorithm
where L is the number of edges and N is the number of nodes in the system.Comment: 19 pages, 14 figures, published version with minor change
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