974 resultados para dictionary
Resumo:
Fingerprints are used for identification in forensics and are classified into Manual and Automatic. Automatic fingerprint identification system is classified into Latent and Exemplar. A novel Exemplar technique of Fingerprint Image Verification using Dictionary Learning (FIVDL) is proposed to improve the performance of low quality fingerprints, where Dictionary learning method reduces the time complexity by using block processing instead of pixel processing. The dynamic range of an image is adjusted by using Successive Mean Quantization Transform (SMQT) technique and the frequency domain noise is reduced using spectral frequency Histogram Equalization. Then, an adaptive nonlinear dynamic range adjustment technique is utilized to determine the local spectral features on corresponding fingerprint ridge frequency and orientation. The dictionary is constructed using spatial fundamental frequency that is determined from the spectral features. These dictionaries help in removing the spurious noise present in fingerprints and reduce the time complexity by using block processing instead of pixel processing. Further, dictionaries are used to reconstruct the image for matching. The proposed FIVDL is verified on FVC database sets and Experimental result shows an improvement over the state-of-the-art techniques. (C) 2015 The Authors. Published by Elsevier B.V.
Resumo:
Revista OJS
Resumo:
Edited by Andrea Abel, Chiara Vettori, Natascia Ralli.
Resumo:
Falileyev, Alexander, in collaboration with Ashwin E. Gohil and Naomi Ward, Dictionary of Continental Celtic Place-Names: A Celtic Companion to the Barrington Atlas of the Greek and Roman World (CMCS Publications: Aberystwyth, 2010) Editor: Falileyev, Alexander in collaboration with Ashwin E. Gohil and Naomi Ward
Resumo:
Rothwell, W., S. Gregory and David Trotter, Anglo-Norman Dictionary: revised edition, A-C; D-E (MHRA, 2005) RAE2008
Resumo:
http://ijl.oxfordjournals.org/cgi/reprint/ecp022?ijkey=FWAwWPvILuZDT1S&keytype=ref
Resumo:
A tree-based dictionary learning model is developed for joint analysis of imagery and associated text. The dictionary learning may be applied directly to the imagery from patches, or to general feature vectors extracted from patches or superpixels (using any existing method for image feature extraction). Each image is associated with a path through the tree (from root to a leaf), and each of the multiple patches in a given image is associated with one node in that path. Nodes near the tree root are shared between multiple paths, representing image characteristics that are common among different types of images. Moving toward the leaves, nodes become specialized, representing details in image classes. If available, words (text) are also jointly modeled, with a path-dependent probability over words. The tree structure is inferred via a nested Dirichlet process, and a retrospective stick-breaking sampler is used to infer the tree depth and width.
Resumo:
Review of: Vardah Shiloh, Millon 'Ivri-'Arami-'Aššuri bs-Lahag Yihude Zaxo (A New Neo-Aramaic Dictionary: Jewish Dialect of Zakho). Volume I: 'alef—nun\ Volume II: samex-tav. V. Shilo (16 Ben-Gamla Street), Jerusalem 1995. Pp. xiv + 488 (Vol. I); 489-963 (Vol. II). (Modern Hebrew, Zakho Jewish Neo-Aramaic). Hbk.