Binary pattern

About: Binary pattern is a research topic. Over the lifetime, 1042 publications have been published within this topic receiving 25300 citations.

Multiresolution gray-scale and rotation invariant texture classification with local binary patterns

Abstract: Presents a theoretically very simple, yet efficient, multiresolution approach to gray-scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing that certain local binary patterns, termed "uniform," are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature. We derive a generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis. The proposed approach is very robust in terms of gray-scale variations since the operator is, by definition, invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table. Experimental results demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns.

Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition

Abstract: For years, researchers in face recognition area have been representing and recognizing faces based on subspace discriminant analysis or statistical learning. Nevertheless, these approaches are always suffering from the generalizability problem. This paper proposes a novel non-statistics based face representation approach, local Gabor binary pattern histogram sequence (LGBPHS), in which training procedure is unnecessary to construct the face model, so that the generalizability problem is naturally avoided. In this approach, a face image is modeled as a "histogram sequence" by concatenating the histograms of all the local regions of all the local Gabor magnitude binary pattern maps. For recognition, histogram intersection is used to measure the similarity of different LGBPHSs and the nearest neighborhood is exploited for final classification. Additionally, we have further proposed to assign different weights for each histogram piece when measuring two LGBPHSes. Our experimental results on AR and FERET face database show the validity of the proposed approach especially for partially occluded face images, and more impressively, we have achieved the best result on FERET face database.

Assessing the uncertainty in distributed model predictions using observed binary pattern information within GLUE

Abstract: In this paper we extend the generalized likelihood uncertainty estimation (GLUE) technique to estimate spatially distributed uncertainty in models conditioned against binary pattern data contained in flood inundation maps. Untransformed binary pattern data already have been used within GLUE to estimate domain-averaged (zero-dimensional) likelihoods, yet the pattern information embedded within such sources has not been used to estimate distributed uncertainty. Where pattern information has been used to map distributed uncertainty it has been transformed into a continuous function prior to use, which may introduce additional errors. To solve this problem we use here ‘raw’ binary pattern data to define a zero-dimensional global performance measure for each simulation in a Monte Carlo ensemble. Thereafter, for each pixel of the distributed model we evaluate the probability that this pixel was inundated. This probability is then weighted by the measure of global model performance, thus taking into account how well a given parameter set performs overall. The result is a distributed uncertainty measure mapped over real space. The advantage of the approach is that it both captures distributed uncertainty and contains information on global likelihood that can be used to condition predictions of further events for which observed data are not available. The technique is applied to the problem of flood inundation prediction at two test sites representing different hydrodynamic conditions. In both cases, the method reveals the spatial structure in simulation uncertainty and simultaneously enables mapping of flood probability predicted by the model. Spatially distributed uncertainty analysis is shown to contain information over and above that available from global performance measures. Overall, the paper highlights the different types of information that may be obtained from mappings of model uncertainty over real and n-dimensional parameter spaces. Copyright © 2002 John Wiley & Sons, Ltd.

Method and system operative to process monochrome image data

Abstract: A method and system operative to process monochrome image data are disclosed. In one embodiment, the method can comprise the steps of receiving monochrome image data, segmenting the input pixel values into pixel value ranges, assigning pixel positions in the lowest pixel value range an output pixel value of a first binary value, assigning pixel positions in the highest pixel value range an output pixel value of a second binary value, wherein the first and second binary values are different, and assigning pixel positions in intermediate pixel value ranges output pixel values that correspond to a spatial binary pattern. The resulting binary image data can be written to a file for subsequent storage, transmission, processing, or retrieval and rendering. In further embodiments, a system can be made operative to accomplish the same.

Statistical Pattern Integration for Mineral Exploration

Abstract: The method of statistical pattern integration used in this paper consists of reducing each set of mineral deposit indicator features on a map to a pattern of relatively few discrete states. In its simplest form the pattern for a feature is binary representing its presence or absence within a small unit cell; for example, with area of 1 km 2 on a 1:250,000 map. The feature of interest need not occur within the unit cell; its “presence” may indicate that the unit cell occurs within a given distance from a linear or curvilinear feature on a geoscience map. By using Bayes' rule, two probabilities can be computed that the unit cell contains a deposit. The log odds of the unit cell's posterior probability is obtained by adding weights W + or W − for presence or absence of the feature to the log odds of the prior probability. If a binary pattern is positively correlated with deposits, W + is positive and the contrast C=W + −W − provides a measure of the strength of this correlation. Weights for patterns with more than two states also can be computed and special consideration can be given to unknown data. Addition of weights from several patterns results in an integrated pattern of posterior probabilities. This final map subdivides the study region into areas of unit cells with different probabilities of containing a mineral deposit. In this paper, statistical pattern integration is applied to occurrence of gold mineralization in Meguma Terrane, eastern mainland Nova Scotia, Canada.