visualization of hyperspectral images that combines the data from multiple sensors is a major challenge due to huge data set. An efficient image fusion could be a primary key step for this task. To make the approach c...
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ISBN:
(纸本)9781457702679
visualization of hyperspectral images that combines the data from multiple sensors is a major challenge due to huge data set. An efficient image fusion could be a primary key step for this task. To make the approach computationally efficient and to accommodate a large number of image bands, we propose a hierarchical fusion based on vector quantization and bilateral filtering. The consecutive image bands in the hyperspectral data cube exhibit a high degree of feature similarity among them due to the contiguous and narrow nature of the hyperspectral sensors. Exploiting this redundancy in the data, we fuse neighboring images at every level of hierarchy. As at the first level, the redundancy between the images is very high we use a powerful compression tool, vector quantization, to fuse each group. From second level onwards, each group is fused using bilateral filtering. While vector quantization removes redundancy, bilateral filter retains even the minor details that exist in individual image. The hierarchical fusion scheme helps in accommodating a large number of hyperspectral image bands. It also facilitates the midband visualization of a subset of the hyperspectral image cube. Quantitative performance analysis shows the effectiveness of the proposed method.
data structure visualization (or animation) has been studied for more than twenty years, though existing systems have not gained wide acceptance in the classroom by students and their instructors. The main reason is t...
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data structure visualization (or animation) has been studied for more than twenty years, though existing systems have not gained wide acceptance in the classroom by students and their instructors. The main reason is that animation preparation is too time consuming. A more technical reason is that when a particular data structure is encoded into an animation, it does not have the flexibility often needed in a classroom setting. There is also a pedagogical reason: a number of prior studies have found that using algorithm visualization in a classroom had no significant effect on student's performance. We believe that the Tablet PC, empowered by digital ink, will challenge the current boundaries imposed upon algorithm animation. One of the potential advantages of this new technology is that it allows the expression and exchange of ideas in an interactive environment using sketch based interfaces. In this paper we discuss teaching and learning Tablet PC based environment in which students using a stylus would draw a particular instance of a data structure and then invoke an algorithm to animate over this data structure. A completely natural way of drawing using a digital pen will generate a data structure model, which (once it is checked for correctness) will serve as a basis for execution of various computational algorithms.
An efficient term mining method to build a general term network is presented for entity relation visualization and exploration. Terms from each document in the corpus are first identified. They are subjected to an ana...
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An efficient term mining method to build a general term network is presented for entity relation visualization and exploration. Terms from each document in the corpus are first identified. They are subjected to an analysis for their association weights, which are accumulated over all the documents for each term pair. The resulting term association matrix is used to build a general term network. A set of terms having similar attributes can then be given to extract the desired sub-network from the general term network for visualization. This analysis scenario based on the collective terms of the similar type or from the same source enables evidence-based relation exploration. Some practical instances of crime investigations were demonstrated. Our application examples show that term relations, be it causality, coupling, or others, can be effectively revealed by our method and verified by the underlying corpus. This work contributes by presenting an efficient and effective term-relationship mining method and extending the applicability of term networks to a broader range of informatic tasks.
The number of China mobile phone users is growing very fast, which provides a good market foundation for developing data business of mobile communication market. It is necessary to provide a more convenient and flexib...
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ISBN:
(纸本)9781612848334
The number of China mobile phone users is growing very fast, which provides a good market foundation for developing data business of mobile communication market. It is necessary to provide a more convenient and flexible management system to realize automation management, because the wireless environment is constantly changing and the maintenance data of the ventilator station is huge. From the aspect of development and management status of the mobile communication industry, this article analyzes necessity and feasibility of developing mobile signal visual management system, discussing the supporting technology, proposing the whole system and database structure design in terms of demand analysis, discussing realization the typical module.
The proceedings contain 41 papers. The special focus in this conference is on Mathematical Morphology and Its Applications to Image and Signal Processing. The topics include: Frequent and Dependent Connectivities;stoc...
ISBN:
(纸本)9783642215681
The proceedings contain 41 papers. The special focus in this conference is on Mathematical Morphology and Its Applications to Image and Signal Processing. The topics include: Frequent and Dependent Connectivities;stochastic Multiscale Segmentation Constrained by Image Content;pattern Recognition Using Morphological Class Distribution Functions and Classification Trees;object Descriptors Based on a List of Rectangles: Method and Algorithm;ultimate Opening and Gradual Transitions;spatio-temporal Quasi-Flat Zones for Morphological Video Segmentation;primitive and Grain Estimation Using Flexible Magnification for a Morphological Texture Model;geodesic Attributes Thinnings and Thickenings;morphological Bilateral Filtering and Spatially-Variant Adaptive Structuring Functions;fuzzy Bipolar Mathematical Morphology: A General Algebraic Setting;general Adaptive Neighborhood Viscous Mathematical Morphology;spatially-Variant Structuring Elements Inspired by the Neurogeometry of the Visual Cortex;towards a Parallel Topological Watershed: First Results;Advances on Watershed Processing on GPU Architecture;incremental Algorithm for Hierarchical Minimum Spanning Forests and Saliency of Watershed Cuts;component-Hypertrees for Image Segmentation;fast Streaming Algorithm for 1-D Morphological Opening and Closing on 2-D Support;hierarchical analysis of Remote Sensing data: Morphological Attribute Profiles and Binary Partition Trees;self-dual Attribute Profiles for the analysis of Remote Sensing Images;concurrent Computation of Differential Morphological Profiles on Giga-Pixel Images;image Decompositions and Transformations as Peaks and Wells;hierarchical Segmentation of Multiresolution Remote Sensing Images;mathematical Morphology for Vector Images Using Statistical Depth;mathematical Morphology in Computer Graphics, Scientific visualization and Visual Exploration;surface Reconstruction Using Power Watershed;voxel-Based Assessment of Printability of 3D Shapes;a Comparison of Two Tree Re
In this paper, we propose a lossless data hiding algorithm for grayscale images. Specifically, our technique is based on the cluster-based difference expansion transform. The main scenario behind our technique is that...
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In this paper, we propose a lossless data hiding algorithm for grayscale images. Specifically, our technique is based on the cluster-based difference expansion transform. The main scenario behind our technique is that we use a recursive cluster construction technique to divide the input image into several clusters. In the data embedding process, a modified difference expansion transform is used to embed the secret message into the pixels cluster by cluster. Experimental results show that our technique can achieve high embedding capacity from 0.56 to 0.85 bpp while the PSNR value is over 30 db. The technique provides a reversible method and has been demonstrated to be feasible in image data hiding.
Of the 10 leading causes of death in the US, 6 are related to diet. Unfortunately, methods for real-time assessment and proactive health management of diet do not currently exist. There are only minimally successful t...
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Of the 10 leading causes of death in the US, 6 are related to diet. Unfortunately, methods for real-time assessment and proactive health management of diet do not currently exist. There are only minimally successful tools for historical analysis of diet and food consumption available. In this paper, we present an integrated database system that provides a unique perspective on how dietary assessment can be accomplished. We have designed three interconnected databases: an image database that contains data generated by food images, an experiments database that contains data related to nutritional studies and results from the image analysis, and finally an enhanced version of a nutritional database by including both nutritional and visual descriptions of each food. We believe that these databases provide tools to the healthcare community and can be used for data mining to extract diet patterns of individuals and/or entire social groups.
Compared to globality based supervised dimensionality reduction methods such as Fisher Discriminant analysis (FDA), locality based ones including Local Fisher Discriminant analysis (LFDA) have attracted increasing int...
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Compared to globality based supervised dimensionality reduction methods such as Fisher Discriminant analysis (FDA), locality based ones including Local Fisher Discriminant analysis (LFDA) have attracted increasing interests since they aim to preserve the intrinsic data structures and are able to handle multimodally distributed data. However, both FDA and LFDA are usually solved via a ratio trace form to approximate the trace ratio, which is the Fisher's original objective criterion. In this paper, a novel trace optimization framework is presented to solve the original trace ratio problem. It offers an exact solution via mathematical programming and recovers Fisher's maximal separability faithfully. The resulting maximum Local Fisher Discriminant analysis (maxLFDA) not only inherits the merits of LFDA, but also boosts the classification accuracy in each target subspace with expected maximum trace ratio value. Experiments on a toy example and real-world face databases validate the effectiveness of the proposed method.
The new-borns' cries are relevant for their health status. Their analysis may constitute a non-invasive diagnosis method. The paper presents a new tool (NEONAT) that allows digital processing of the vocal signal r...
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The new-borns' cries are relevant for their health status. Their analysis may constitute a non-invasive diagnosis method. The paper presents a new tool (NEONAT) that allows digital processing of the vocal signal representing the cry, visualizing the frequency spectrum and several values of interest as well as managing the data regarding mothers and babies. A protocol for performing recordings is proposed. The application has a facility to export data in ARFF format to be processed by the algorithms supplied by the data mining tool WEKA. Testing the data mining opportunities was made building a sex classifier based on the information extracted from the cry. Its performances have been good, and based on that, a process for the detection of neurologic suffering was suggested. The application runs in the Obstetrics and Gynaecology Clinique in the Emergency County Hospital from Timisoara.
The paper presents an intelligent approach to monitoring of the expected operating conditions on the basis of the self-organizing Kohonen maps and k-means method. The suggested approach makes it possible to effectivel...
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The paper presents an intelligent approach to monitoring of the expected operating conditions on the basis of the self-organizing Kohonen maps and k-means method. The suggested approach makes it possible to effectively follow changes in the current and expected operating conditions and predict heavy load conditions and/or development of emergency conditions almost in real time.
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