Polarisation studies on the Raman spectra of cubic crystals - Part II: Calcium fluoride and potassium aluminium sulphate

Polarisation characters of the Raman lines of calcium fluoride (fluorspar) and potassium aluminium sulphate (alum) were investigated under the following conditions. Unpolarised light was incident normally on a face of the crystal making an angle 22.5° with a cubic face and the light scattered transversely along a cubic axis was analysed by a double image prism kept with its principal axes inclined at 45° to the vertical. Under these conditions the depolarisation factors of the Raman lines belonging to the totally symmetric (A), the doubly degenerate (E) and the triply degenerate (F) modes should be respectively =1, >1 and <1. The characteristic Raman line of CaF2 at 322 cm-1 exhibited a depolarisation value less than 1, showing thereby that the corresponding mode is a triply degenerate one (F). The Raman lines observed in the spectrum of K-alum were also classified and the results were compared with those given by previous investigators using standard crystal orientations.

The heat kernel on AdS(3) and its applications

We derive the heat kernel for arbitrary tensor fields on S-3 and (Euclidean) AdS(3) using a group theoretic approach. We use these results to also obtain the heat kernel on certain quotients of these spaces. In particular, we give a simple, explicit expression for the one loop determinant for a field of arbitrary spin s in thermal AdS(3). We apply this to the calculation of the one loop partition function of N = 1 supergravity on AdS(3). We find that the answer factorizes into left- and right-moving super Virasoro characters built on the SL(2, C) invariant vacuum, as argued by Maloney and Witten on general grounds.

Memory-based reasoning approach for pattern recognition of binary images

Template matching is concerned with measuring the similarity between patterns of two objects. This paper proposes a memory-based reasoning approach for pattern recognition of binary images with a large template set. It seems that memory-based reasoning intrinsically requires a large database. Moreover, some binary image recognition problems inherently need large template sets, such as the recognition of Chinese characters which needs thousands of templates. The proposed algorithm is based on the Connection Machine, which is the most massively parallel machine to date, using a multiresolution method to search for the matching template. The approach uses the pyramid data structure for the multiresolution representation of templates and the input image pattern. For a given binary image it scans the template pyramid searching the match. A binary image of N × N pixels can be matched in O(log N) time complexity by our algorithm and is independent of the number of templates. Implementation of the proposed scheme is described in detail.

External Bias Dependent Direct To Indirect Band Gap Transition in Graphene Nanoribbon

In this work, using self-consistent tight-binding calculations. for the first time, we show that a direct to indirect band gap transition is possible in an armchair graphene nanoribbon by the application of an external bias along the width of the ribbon, opening up the possibility of new device applications. With the help of the Dirac equation, we qualitatively explain this band gap transition using the asymmetry in the spatial distribution of the perturbation potential produced inside the nanoribbon by the external bias. This is followed by the verification of the band gap trends with a numerical technique using Magnus expansion of matrix exponentials. Finally, we show that the carrier effective masses possess tunable sharp characters in the vicinity of the band gap transition points.

Artificial database for character recognition research

This paper describes a technique for artificial generation of learning and test sample sets suitable for character recognition research. Sample sets of English (Latin), Malayalam, Kannada and Tamil characters are generated easily through their prototype specifications by the endpoint co-ordinates, nature of segments and connectivity.

Dislocation characterization in crystals of potash alum grown by seeded solution growth under conditions of low supersaturation

An analysis has been carried out of the genesis and character of growth dislocations present in all growth sectors of single crystals of potash alum. The crystals, grown from seeded solutions by the temperature lowering method under conditions of low supersaturation, presented the well-developed forms: {111} dominant, {100} and {110}. Growth dislocations formed predominately during refacetting of the edges and corners of the seed, rounded during preparation and insertion into the supersaturated solution. From here they become refracted into the {111} sectors which proved to be the most defective. Smaller numbers of dislocations form at the {111}, {100} and {110} seed interfaces and propagate in these sectors. In crystals of inferior quality, a number of inclusions were found predominantly in the fast growing {100} sectors which become the source of additional dislocations. Dislocations present in the original seed did not propagate across the interface into the developing crystal. Dislocations of all characters were observed. The principal Burgers vectors were found to be left angle bracket100right-pointing angle bracket, left angle bracket110right-pointing angle bracket and left angle bracket111right-pointing angle bracket.

Choice of Classifiers in Hierarchical Recognition of Online Handwritten Kannada and Tamil Aksharas

In this paper, we propose a novel dexterous technique for fast and accurate recognition of online handwritten Kannada and Tamil characters. Based on the primary classifier output and prior knowledge, the best classifier is chosen from set of three classifiers for second stage classification. Prior knowledge is obtained through analysis of the confusion matrix of primary classifier which helped in identifying the multiple sets of confused characters. Further, studies were carried out to check the performance of secondary classifiers in disambiguating among the confusion sets. Using this technique we have achieved an average accuracy of 92.6% for Kannada characters on the MILE lab dataset and 90.2% for Tamil characters on the HP Labs dataset.

Machine recognition of printed Kannada text

This paper presents the design of a full fledged OCR system for printed Kannada text. The machine recognition of Kannada characters is difficult due to similarity in the shapes of different characters, script complexity and non-uniqueness in the representation of diacritics. The document image is subject to line segmentation, word segmentation and zone detection. From the zonal information, base characters, vowel modifiers and consonant conjucts are separated. Knowledge based approach is employed for recognizing the base characters. Various features are employed for recognising the characters. These include the coefficients of the Discrete Cosine Transform, Discrete Wavelet Transform and Karhunen-Louve Transform. These features are fed to different classifiers. Structural features are used in the subsequent levels to discriminate confused characters. Use of structural features, increases recognition rate from 93% to 98%. Apart from the classical pattern classification technique of nearest neighbour, Artificial Neural Network (ANN) based classifiers like Back Propogation and Radial Basis Function (RBF) Networks have also been studied. The ANN classifiers are trained in supervised mode using the transform features. Highest recognition rate of 99% is obtained with RBF using second level approximation coefficients of Haar wavelets as the features on presegmented base characters.

Optimal feature extraction for bilingual OCR

Feature extraction in bilingual OCR is handicapped by the increase in the number of classes or characters to be handled. This is evident in the case of Indian languages whose alphabet set is large. It is expected that the complexity of the feature extraction process increases with the number of classes. Though the determination of the best set of features that could be used cannot be ascertained through any quantitative measures, the characteristics of the scripts can help decide on the feature extraction procedure. This paper describes a hierarchical feature extraction scheme for recognition of printed bilingual (Tamil and Roman) text. The scheme divides the combined alphabet set of both the scripts into subsets by the extraction of certain spatial and structural features. Three features viz geometric moments, DCT based features and Wavelet transform based features are extracted from the grouped symbols and a linear transformation is performed on them for the purpose of efficient representation in the feature space. The transformation is obtained by the maximization of certain criterion functions. Three techniques : Principal component analysis, maximization of Fisher's ratio and maximization of divergence measure have been employed to estimate the transformation matrix. It has been observed that the proposed hierarchical scheme allows for easier handling of the alphabets and there is an appreciable rise in the recognition accuracy as a result of the transformations.

Taxonomic Implications of a Field Study of Morphotypes of Hanuman Langurs (Semnopithecus entellus) in Peninsular India

The Hanuman langur is one of the most widely distributed and morphologically variable non-human primates in South Asia. Even though it has been extensively studied, the taxonomic status of this species remains unresolved due to incongruence between various classification schemes. This incongruence, we believe, is largely due to the use of plastic morphological characters such as coat color in classification. Additionally these classification schemes were largely based on reanalysis of the same set of museum specimens. To bring greater resolution in Hanuman langur taxonomy we undertook a field survey to study variation in external morphological characters among Hanuman langurs. The primary objective of this study is to ascertain the number of morphologically recognizable units (morphotypes) of Hanuman langur in peninsular India and to compare our field observations with published classification schemes. We typed five color-independent characters for multiple adults from various populations in South India. We used the presence-absence matrix of these characters to derive the pair-wise distance between individuals and used this to construct a neighbor-joining (NJ) tree. The resulting NJ tree retrieved six distinct clusters, which we assigned to different morphotypes. These morphotypes can be identified in the field by using a combination of five diagnostic characters. We determined the approximate distributions of these morphotypes by plotting the sampling locations of each morphotype on a map using GIS software. Our field observations are largely concordant with some of the earliest classification schemes, but are incongruent with recent classification schemes. Based on these results we recommend Hill (Ceylon Journal of Science, Colombo 21:277-305, 1939) and Pocock (Primates and carnivora (in part) (pp. 97-163). London: Taylor and Francis, 1939) classification schemes for future studies on Hanuman langurs.

Wavelet Transform Codec for Fast Retrieval of Slices of 3D Objects

We propose a method to encode a 3D magnetic resonance image data and a decoder in such way that fast access to any 2D image is possible by decoding only the corresponding information from each subband image and thus provides minimum decoding time. This will be of immense use for medical community, because most of the PET and MRI data are volumetric data. Preprocessing is carried out at every level before wavelet transformation, to enable easier identification of coefficients from each subband image. Inclusion of special characters in the bit stream facilitates access to corresponding information from the encoded data. Results are taken by performing Daub4 along x (row), y (column) direction and Haar along z (slice) direction. Comparable results are achieved with the existing technique. In addition to that decoding time is reduced by 1.98 times. Arithmetic coding is used to encode corresponding information independently

Online character recognition using regression techniques

This paper introduces a scheme for classification of online handwritten characters based on polynomial regression of the sampled points of the sub-strokes in a character. The segmentation is done based on the velocity profile of the written character and this requires a smoothening of the velocity profile. We propose a novel scheme for smoothening the velocity profile curve and identification of the critical points to segment the character. We also porpose another method for segmentation based on the human eye perception. We then extract two sets of features for recognition of handwritten characters. Each sub-stroke is a simple curve, a part of the character, and is represented by the distance measure of each point from the first point. This forms the first set of feature vector for each character. The second feature vector are the coeficients obtained from the B-splines fitted to the control knots obtained from the segmentation algorithm. The feature vector is fed to the SVM classifier and it indicates an efficiency of 68% using the polynomial regression technique and 74% using the spline fitting method.

Creation of a Huge Annotated Database for Tamil and Kannada OHR

This paper describes the efforts at MILE lab, IISc, to create a 100,000-word database each in Kannada and Tamil for the design and development of Online Handwritten Recognition. It has been collected from over 600 users in order to capture the variations in writing style. We describe features of the scripts and how the number of symbols were reduced to be able to effectively train the data for recognition. The list of words include all the characters, Kannada and Indo-Arabic numerals, punctuations and other symbols. A semi-automated tool for the annotation of data from stroke to word level is used. It segments each word into stroke groups and also acts as a validation mechanism for segmentation. The tool displays the stroke, stroke groups and aksharas of a word and hence can be used to study the various styles of writing, delayed strokes and for assigning quality tags to the words. The tool is currently being used for annotating Tamil and Kannada data. The output is stored in a standard XML format.

Online Handwriting Recognition of Tamil Script Using Fractal Geometry

We present a fractal coding method to recognize online handwritten Tamil characters and propose a novel technique to increase the efficiency in terms of time while coding and decoding. This technique exploits the redundancy in data, thereby achieving better compression and usage of lesser memory. It also reduces the encoding time and causes little distortion during reconstruction. Experiments have been conducted to use these fractal codes to classify the online handwritten Tamil characters from the IWFHR 2006 competition dataset. In one approach, we use fractal coding and decoding process. A recognition accuracy of 90% has been achieved by using DTW for distortion evaluation during classification and encoding processes as compared to 78% using nearest neighbor classifier. In other experiments, we use the fractal code, fractal dimensions and features derived from fractal codes as features in separate classifiers. While the fractal code is successful as a feature, the other two features are not able to capture the wide within-class variations.

Prototype learning methods for online handwriting recognition

In this paper, we study different methods for prototype selection for recognizing handwritten characters of Tamil script. In the first method, cumulative pairwise- distances of the training samples of a given class are used to select prototypes. In the second method, cumulative distance to allographs of different orientation is used as a criterion to decide if the sample is representative of the group. The latter method is presumed to offset the possible orientation effect. This method still uses fixed number of prototypes for each of the classes. Finally, a prototype set growing algorithm is proposed, with a view to better model the differences in complexity of different character classes. The proposed algorithms are tested and compared for both writer independent and writer adaptation scenarios.