58 resultados para regional feature

em Indian Institute of Science - Bangalore - Índia


Relevância:

30.00% 30.00%

Publicador:

Resumo:

Feature selection is an important first step in regional hydrologic studies (RHYS). Over the past few decades, advances in data collection facilities have resulted in development of data archives on a variety of hydro-meteorological variables that may be used as features in RHYS. Currently there are no established procedures for selecting features from such archives. Therefore, hydrologists often use subjective methods to arrive at a set of features. This may lead to misleading results. To alleviate this problem, a probabilistic clustering method for regionalization is presented to determine appropriate features from the available dataset. The effectiveness of the method is demonstrated by application to regionalization of watersheds in conterminous United States for low flow frequency analysis. Plausible homogeneous regions that are formed by using the proposed clustering method are compared with those from conventional methods of regionalization using L-moment based homogeneity tests. Results show that the proposed methodology is promising for RHYS.

Relevância:

30.00% 30.00%

Publicador:

Resumo:

Clustering techniques are used in regional flood frequency analysis (RFFA) to partition watersheds into natural groups or regions with similar hydrologic responses. The linear Kohonen's self‐organizing feature map (SOFM) has been applied as a clustering technique for RFFA in several recent studies. However, it is seldom possible to interpret clusters from the output of an SOFM, irrespective of its size and dimensionality. In this study, we demonstrate that SOFMs may, however, serve as a useful precursor to clustering algorithms. We present a two‐level. SOFM‐based clustering approach to form regions for FFA. In the first level, the SOFM is used to form a two‐dimensional feature map. In the second level, the output nodes of SOFM are clustered using Fuzzy c‐means algorithm to form regions. The optimal number of regions is based on fuzzy cluster validation measures. Effectiveness of the proposed approach in forming homogeneous regions for FFA is illustrated through application to data from watersheds in Indiana, USA. Results show that the performance of the proposed approach to form regions is better than that based on classical SOFM.

Relevância:

20.00% 20.00%

Publicador:

Resumo:

Over the last few decades, there has been a significant land cover (LC) change across the globe due to the increasing demand of the burgeoning population and urban sprawl. In order to take account of the change, there is a need for accurate and up-to-date LC maps. Mapping and monitoring of LC in India is being carried out at national level using multi-temporal IRS AWiFS data. Multispectral data such as IKONOS, Landsat-TM/ETM+, IRS-ICID LISS-III/IV, AWiFS and SPOT-5, etc. have adequate spatial resolution (similar to 1m to 56m) for LC mapping to generate 1:50,000 maps. However, for developing countries and those with large geographical extent, seasonal LC mapping is prohibitive with data from commercial sensors of limited spatial coverage. Superspectral data from the MODIS sensor are freely available, have better temporal (8 day composites) and spectral information. MODIS pixels typically contain a mixture of various LC types (due to coarse spatial resolution of 250, 500 and 1000 in), especially in more fragmented landscapes. In this context, linear spectral unmixing would be useful for mapping patchy land covers, such as those that characterise much of the Indian subcontinent. This work evaluates the existing unmixing technique for LC mapping using MODIS data, using end-members that are extracted through Pixel Purity Index (PPI), Scatter plot and N-dimensional visualisation. The abundance maps were generated for agriculture, built up, forest, plantations, waste land/others and water bodies. The assessment of the results using ground truth and a LISS-III classified map shows 86% overall accuracy, suggesting the potential for broad-scale applicability of the technique with superspectral data for natural resource planning and inventory applications. Index Terms-Remote sensing, digital

Relevância:

20.00% 20.00%

Publicador:

Resumo:

With the availability of a huge amount of video data on various sources, efficient video retrieval tools are increasingly in demand. Video being a multi-modal data, the perceptions of ``relevance'' between the user provided query video (in case of Query-By-Example type of video search) and retrieved video clips are subjective in nature. We present an efficient video retrieval method that takes user's feedback on the relevance of retrieved videos and iteratively reformulates the input query feature vectors (QFV) for improved video retrieval. The QFV reformulation is done by a simple, but powerful feature weight optimization method based on Simultaneous Perturbation Stochastic Approximation (SPSA) technique. A video retrieval system with video indexing, searching and relevance feedback (RF) phases is built for demonstrating the performance of the proposed method. The query and database videos are indexed using the conventional video features like color, texture, etc. However, we use the comprehensive and novel methods of feature representations, and a spatio-temporal distance measure to retrieve the top M videos that are similar to the query. In feedback phase, the user activated iterative on the previously retrieved videos is used to reformulate the QFV weights (measure of importance) that reflect the user's preference, automatically. It is our observation that a few iterations of such feedback are generally sufficient for retrieving the desired video clips. The novel application of SPSA based RF for user-oriented feature weights optimization makes the proposed method to be distinct from the existing ones. The experimental results show that the proposed RF based video retrieval exhibit good performance.

Relevância:

20.00% 20.00%

Publicador:

Resumo:

The minimum cost classifier when general cost functionsare associated with the tasks of feature measurement and classification is formulated as a decision graph which does not reject class labels at intermediate stages. Noting its complexities, a heuristic procedure to simplify this scheme to a binary decision tree is presented. The optimizationof the binary tree in this context is carried out using ynamicprogramming. This technique is applied to the voiced-unvoiced-silence classification in speech processing.

Relevância:

20.00% 20.00%

Publicador:

Resumo:

A global climate model experiment is performed to evaluate the effect of irrigation on temperatures in several major irrigated regions of the world. The Community Atmosphere Model, version 3.3, was modified to represent irrigation for the fraction of each grid cell equipped for irrigation according to datasets from the Food and Agriculture Organization. Results indicate substantial regional differences in the magnitude of irrigation-induced cooling, which are attributed to three primary factors: differences in extent of the irrigated area, differences in the simulated soil moisture for the control simulation (without irrigation), and the nature of cloud response to irrigation. The last factor appeared especially important for the dry season in India, although further analysis with other models and observations are needed to verify this feedback. Comparison with observed temperatures revealed substantially lower biases in several regions for the simulation with irrigation than for the control, suggesting that the lack of irrigation may be an important component of temperature bias in this model or that irrigation compensates for other biases. The results of this study should help to translate the results from past regional efforts, which have largely focused on the United States, to regions in the developing world that in many cases continue to experience significant expansion of irrigated land.

Relevância:

20.00% 20.00%

Publicador:

Resumo:

previous termBull spermnext term heads and tails have been separated by proteolytic digestion (trypsin) and previous termplasma membranesnext term have been isolated, using discontinuous sucrose density gradient centrifugation. previous termPlasma membranenext term bound Ca2+-ATPase is shown to be associated mostly with the tail previous termmembranes.next term Pyrene excimer fluorescence and diphenylhexatriene fluorescence polarization experiments indicate a more fluid lipid phase in the tail region. Differences in surface charge distribution have been found, using 1-anilinonaphthalene-8-sulfonate and Tb3+ as fluorescent probes.

Relevância:

20.00% 20.00%

Publicador:

Resumo:

Bull sperm heads and tails have been separated by proteolytic digestion (trypsin) and plasma membranes have been isolated, using discontinuous sucrose density gradient centrifugation. Plasma membrane bound Ca2+-ATPase is shown to be associated mostly with the tail membranes. Pyrene excimer fluorescence and diphenylhexatriene fluorescence polarization experiments indicate a more fluid lipid phase in the tail region. Differences in surface charge distribution have been found, using 1-anilinonaphthalene-8-sulfonate and Tb3+ as fluorescent probes.

Relevância:

20.00% 20.00%

Publicador:

Resumo:

The concept of feature selection in a nonparametric unsupervised learning environment is practically undeveloped because no true measure for the effectiveness of a feature exists in such an environment. The lack of a feature selection phase preceding the clustering process seriously affects the reliability of such learning. New concepts such as significant features, level of significance of features, and immediate neighborhood are introduced which result in meeting implicitly the need for feature slection in the context of clustering techniques.

Relevância:

20.00% 20.00%

Publicador:

Resumo:

The concept of feature selection in a nonparametric unsupervised learning environment is practically undeveloped because no true measure for the effectiveness of a feature exists in such an environment. The lack of a feature selection phase preceding the clustering process seriously affects the reliability of such learning. New concepts such as significant features, level of significance of features, and immediate neighborhood are introduced which result in meeting implicitly the need for feature slection in the context of clustering techniques.

Relevância:

20.00% 20.00%

Publicador:

Resumo:

This paper presents two algorithms for smoothing and feature extraction for fingerprint classification. Deutsch's(2) Thinning algorithm (rectangular array) is used for thinning the digitized fingerprint (binary version). A simple algorithm is also suggested for classifying the fingerprints. Experimental results obtained using such algorithms are presented.

Relevância:

20.00% 20.00%

Publicador:

Resumo:

Mixed-species bird flocks are attractive models for the investigation of geographical variation in animal communities, as they represent a subset of the avifauna in most forested regions of the world. Yet studies of the regional variation in flock size and the composition of flocks are few, due to the predominance of studies carried out at single study site. Here, we review nine studies of mixed-species flocks conducted at 16 sites along the Western Ghats in India and in Sri Lanka. We find that flock size varies as much within this region as it does globally, with observation time being a confounding variable. Flock composition, however, is predictably related to elevation. Flocks at high elevations (>1200 m) in the Western Ghats strongly resemble flocks at high elevations in the mountain ranges of Sri Lanka in their composition, especially at the family level. We compare these flocks to flocks of other regions and make recommendations on study methodology that can facilitate comparisons across studies.

Relevância:

20.00% 20.00%

Publicador:

Resumo:

In this paper, we present a new feature-based approach for mosaicing of camera-captured document images. A novel block-based scheme is employed to ensure that corners can be reliably detected over a wide range of images. 2-D discrete cosine transform is computed for image blocks defined around each of the detected corners and a small subset of the coefficients is used as a feature vector A 2-pass feature matching is performed to establish point correspondences from which the homography relating the input images could be computed. The algorithm is tested on a number of complex document images casually taken from a hand-held camera yielding convincing results.

Relevância:

20.00% 20.00%

Publicador:

Resumo:

We describe a novel method for human activity segmentation and interpretation in surveillance applications based on Gabor filter-bank features. A complex human activity is modeled as a sequence of elementary human actions like walking, running, jogging, boxing, hand-waving etc. Since human silhouette can be modeled by a set of rectangles, the elementary human actions can be modeled as a sequence of a set of rectangles with different orientations and scales. The activity segmentation is based on Gabor filter-bank features and normalized spectral clustering. The feature trajectories of an action category are learnt from training example videos using dynamic time warping. The combined segmentation and the recognition processes are very efficient as both the algorithms share the same framework and Gabor features computed for the former can be used for the later. We have also proposed a simple shadow detection technique to extract good silhouette which is necessary for good accuracy of an action recognition technique.

Relevância:

20.00% 20.00%

Publicador:

Resumo:

Guo and Nixon proposed a feature selection method based on maximizing I(x; Y),the multidimensional mutual information between feature vector x and class variable Y. Because computing I(x; Y) can be difficult in practice, Guo and Nixon proposed an approximation of I(x; Y) as the criterion for feature selection. We show that Guo and Nixon's criterion originates from approximating the joint probability distributions in I(x; Y) by second-order product distributions. We remark on the limitations of the approximation and discuss computationally attractive alternatives to compute I(x; Y).