Iterative diffraction pattern retrieval from a single focal construct geometry image

Understanding the crystal structure of materials under extreme conditions of pressure and temperature has been revolutionized by major advances in laser-driven dynamic compression and in situ X-ray diffraction (XRD) technology. Instead of the well known Debye–Scherrer configuration, the focal construct geometry (FCG) was introduced to produce high-intensity diffraction data from laser-based in situ XRD experiments without increasing the amount of laser energy, but the resulting reflections suffered from profoundly asymmetrical broadening, leading to inaccuracy in determination of the crystal structure. Inspired by fast-neutron energy spectrum measurements, proposed here is an iterative retrieval method for recovering diffraction data from a single FCG image. This iterative algorithm restores both the peak shape and relative intensity with rapid convergence and requires no prior knowledge about the expected diffraction pattern, allowing the FCG to increase the in situ XRD intensity while simultaneously preserving the angular resolution. The feasibility and validity of the method are shown by successful recovery of the diffraction pattern from both a single simulated FCG image and a single laser-based nanosecond XRD measurement.

MUL-TIBIOMETRIC PATTERN RETRIEVAL USING INDEX CODE TO IMPROVE RESPONSE TIME

In a biometric identification system, the identity corresponding to the input data (probe) is typically determined by comparing it against the templates of all identities in a database (gallery). Exhaustive matching against a large number of identities increases the response time of the system and may also reduce the accuracy of identification. Onaway to reduce the response time is by designing biometric templates that allow for rapid matching, as in the case of Iris Codes. An alternative approach is to limit the number of identities against which matching is performed based on criteria that are fast to evaluate. We propose a method for generating fixed-length codes for indexing biometric databases. An index code is constructed by computing match scores between a biometric image and a fixed set of reference images. Candidate identities are retrieved based on the similarity between the index code of the probe image and those of the identities in the database. The proposed technique can be easily extended to retrieve pertinent identities from multimodal databases. Experiments on a chimeric face and fingerprint bimodal database resulted in an 84% average reduction in the search space at a hit rate of 100%. These results suggest that the proposed indexing scheme has the potential to substantially reduce the response time without compromising the accuracy of identification.

Repeat pattern segmentation of print fabric based on adaptive template matching

Patterned fabrics are generally constructed from the periodic repetition of a primitive pattern unit. Repeat pattern segmentation of printed fabrics has a very significant impact on the pattern retrieval and pattern defect detection. In this paper, we propose a new approach for repeat pattern segmentation by employing the adaptive template matching method. In contrast to the traditional method for template matching, the proposed algorithm first selects an adaptive size template image in the repeat pattern image based on the size of the original image and its local maximum edge density. Then it uses the sum of absolute differences as the matching features to identify the matched regions in the original image, and the minimum envelope border of the primitive pattern, typically as a parallelogram, can be determined from the results of the four adjacent matched templates. Finally, image traversal base on the obtained parallelogram is implemented over the original image using minimum information loss theory to produce a well-segmented primitive pattern with a complete edge structure. The results from the experiments conducted using an extensive database of real fabric images show that the proposed algorithm has the advantage of rotation invariance and scaling invariance and will not be affected when the background or foreground color is changed.

PAttern REtrieval or deNegation Testing Scheme (PARENTS) v.1.0 – Identifying the degree of presence of given patterns in spatial time series

The large number of spatially distributed earth observation products, i.e. time series of surface emissions and reflectances at different wavelengths with increasing spatial resolution, contribute to the derivation of surface characteristics, e.g. vegetation or soil parameters in the environmental sciences. These derivatives usually build upon complex algorithms consisting of atmospheric corrections and process descriptions. The testing scheme presented here seeks a different approach to identifying these surface characteristics that control the generation of such observation time series. Spatially distributed patterns of these characteristics of different persistence usually dominate parts of a time series because of their very specific reaction to and interaction with environmental influences. We test these characteristics' patterns for their existence in a rotated vector space of elementary patterns derived from a principal component analysis of an observational time series. With the result of this test we can then make valid assumptions, e.g. with regard to the importance of the surface properties for the emittance or reflectance, or their spatial uncertainties. We demonstrate the functionality of this rather simple test algorithm for a synthetic and fully traceable example, and an application in a medium hydrological catchment for a time series of thermal satellite data. Possible future applications for this scheme are the prioritization and improvement of model input, data assimilation, or the evaluation and validation of model output.