In this we propose an algorithm to automatically find objects in 3D Flash Lidar images. Three-dimensional Flash Lidar camera is a new technology which allows a single camera to take multiple images in a fast succession. It produces a series of range delimited imagery using a single laser flash. The imagery can distinguish between objects at different distances, such as the sea-surface and underwater objects. We apply a transformation to reduce the computational complexity and apply stable unsupervised multiphase segmentation for object identification. Numerical results are presented to validate the approach both using scalar and Victor models.

Fluorination of organic compounds is an important task which brings in changes in Physical and Chemical parameters in the organic material. Even the structural pattern of the molecule gets altered due to replacement of Hydrogen atoms by Fluorine atoms. Structural pattern recognition in case of molecules is an important task in the field of bio-engineering. Theoretical structure simulation is also employed in this technologically important field. An attempt has been made to look for the changes that take place due to fluorination in the technologically important polymer Polyformaldehyde. Electronic Density of States and Phonon modes in the material have been computed using First-principles calculations based on Density Functional Theory.

In the present investigation, the FTIR has been used as a structural pattern recognition tool to study the effect of gamma irradiation on the PE-PP block copolymer. The polymer has been subjected to gamma irradiation from 100 to 500 Mrad dosages. Characterization of the polymer using XRD and FTIR was done both before irradiation and after irradiation in each step. FTIR study shows that the sample loses C – C stretching mode of vibration and gains C=C stretching mode of vibration after irradiation.

Iris is one of the important biometrics techniques. The pupil may vary in from 10% to 80% of the iris diameter, but fundamental rods & cons structure for an individual remains unique till the life time. The important thing is its feature which has a unique pattern and not related to genetic feature. In our work we have localized, normalized and enhanced the image by using Daugman’s Integro-differential Operator, Cartesian to polar coordinates, Histogram equalization, reflectively Gaussian filter was applied and then iris code were generated. Hamming distance was used for pattern matching. The overall success rate achieved 80%.

Structural pattern recognition in case of molecules is an important task in the field of bio-engineering. Several techniques are employed in order to get the exact structural conformation and structural parameters of molecules. Present paper discusses some of the available techniques such as Fourier Infra-Red Spectrocopy, Raman Spectroscopy and Theoretical Structure simulation which are employed in this technologically important field. An attempt has been made to look for the exact structural conformation in case of Polyformaldehyde using First-principles calculations based on Density Functional Theory.

The binary mixture of Cholesteryl ethyl corbonate (CEC) and Terephthal-bis-p-n-Tetradecylaniline (TB14A) exhibits a twisted grain boundary (TGB) phase and smectic polymorphic phases. The mixture exhibits TGB-A and TGB-C phases in different concentrations and at different temperatures. Mixtures with higher concentrations of TB14A exhibit I – Ch - TGB-A - TGB-C - Sm A - Sm I - Sm F phases sequentially when the specimen is cooled from its isotropic melt. The existence of different TGB phases and polymorphic smectic phases are confirmed by DSC and optical microscopic studies.

Human generated patterns like handwritten characters are found to be fuzzy in nature upto certain extent. This presented work proposes a fuzzy conceptual approach to classify Handwritten Arabic Numerals based on invariant moments features and the divisions of numeral image into several parts. The Moment invariants features are well known for independence of size, slant, orientation, translation and other variations of handwritten characters. A database, created by American University in Cairo, of 7000 samples of each number from 700 different writers is used. Each image is normalized to 40X40 pixel size. Seven central invariant moments are evaluated for each image and its parts by dividing it by three different ways, i.e. three feature groups. The algorithm is experimented for 500 samples of each numeral image and 161 features were evaluated corresponding to each image. The performance rate of the method is found to be 95.14%.

In real life applications number of document contains printed as well as handwritten numerals in a single document. The process of recognition of such mixed numeral with respect to single OCR is complicated task. In this paper, we present a novel method for recognition of printed and handwritten Isolated Kannada numerals using single OCR system. We considered Directional Stroke and Directional density based feature for recognition system is proposed. The proposed system extracts Directional stroke on various angles and Directional density/profile on four side of the numeral image. Further, the stroke and density based extracted feature is feed for recognition systems. A Euclidian distance criteria and k-NN classifier is employed to classify the numeral class. A total 5000 numeral images, which includes 4000 for handwritten images and 1000 for printed images considered for experiments and overall accuracy found to 98.04%. The novelty of the proposed method is thinning free, and without size normalization.

Road accidents are a major factor for deaths world wide and recent studies show that driver fatigue is a key factor for road accidents. Drivers tend to drive vehicles even if they are extremely tired and due to this, there is chance of them falling asleep at the wheel which results in accidents causing major loss to precious human life & property. This implies there is a need for a driver alert system which monitors the driver’s wakefulness and gives an alarm. This paper proposes a novel technique to determine sleep stages by considering the available EEG Signals of the subject, calculate its FFT and determine the power spectrum. Using this we then plot the hypnogram from which the wakefulness and sleep stages can be determined and if the driver is found to move from a state of wakefulness to sleep stage, immediately an alarm is provided. This concept can be enhanced for real time application with hardware realization and thus helps to reduce road accidents to a great extent.

Breast cancer is one of the major causes of death among women. Segmentation refers to the process of partitioning a digital image into multiple segments (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries in images. The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image. Each of the pixels in a region is similar with respect to some characteristic or computed property, such as color, intensity, or texture. Adjacent regions are significantly different with respect to the same characteristic. We have used various segmentation algorithm methods. In this paper the comparison of the segmented images is done by taking the entropy and SNR information measures and it has been found that the lesion segmentation algorithm closely matches radiologists’ outlines of these lesions.

- License plate location is an important phase in vehicle license plate recognition for intelligent transport systems. The objective of this work is to design and implement an efficient method for License Plate Recognition (LPR) of Indian License Plates. This paper presents a robust method of license plate location, segmentation and reorganization of the characters present in the located plate. The images of various vehicles have been acquired manually and converted in to gray-scale images. Then wiener2 filter is used to remove noise present in the plates. The segmentation of gray scale image generated by finding edges using Sobel filter for smoothing image is used to reduce the number of connected component and then bwlabel is used to calculate the connected component. Finally, single character is detected. The results show that the proposed method achieved accuracy of 98% by optimizing various parameters with higher recognition rate than the traditional methods

In this paper, we consider the problem of remotely sensed image. Data recorded by sensors on a satellite or aircraft contain errors related to geometry and brightness values of the pixels in agriculture field. These errors are corrected using different image processing operations like image restoration, image enhancement, image transformation, image classification. Different image restoration techniques are used to extract useful information from the images. Aim of this research paper to provide a concise overview of the most useful restoration methods. Different type of image restoration techniques like spatial filter, median filter, wiener filter and wavelet based filter are described and the strengths and weaknesses of each approach are identified. Examples of remote sensing technique for agriculture field given. To provide guidelines for choosing a restoration technique for agriculture application, a comparison of the restoration techniques is made. The restoration methods are compared and evaluated based on quantitative and qualitative analysis.

A convenient and user –friendly interface for information retrieval through computer is an important issue. Speech being natural mode of communication among human beings has the potential of being a primary as well as convenient mode of interaction with computer. This is a good reason for building systems that communicate with users via speech. Marathi is one of the most widely used language is Maharashtra .An interesting domain for such speech understanding systems is information dialogs where the user wants to get some information by asking the system which takes the role of an “Intelligent Machine” in the field of interest. Speech recognition having much more application in health sector In order to make such a communication process possible we explore the new approach of Polly Clinic Inquiry System using Marathi Speech Recognition.

Mycobacterium Tuberculosis(M.TB) bacilli is causative agent of Tuberculosis. Manual detection method for M.TB is time consuming, tedious & sometime it may confuse with some stain residue & non tuberculosis bacilli. For this reason the need of atomization is required for exact identification of tuberculosis. We present a new method for M.TB bacilli cells recognition method using Moment invariant and Gaussian member function. The object i.e. M.TB bacilli cells are extracted using color segmentation method, from Ziehl-Neelsen (ZN) stained sputum smears images. These images have blue color background on which red color M.TB bacilli cells. The objects are extracted using color segmentation (thresholding) method and boundary detection. The various extracted shapes contain M. TB bacilli cells, non TB cells, stain residue etc, these extracted objects are preprocess using morphological dilation and then moment invariant feature extracted. Our main objective is to recognizes M.TB bacilli cells with the help of color segmentation & shape base feature moment invariants using Gaussian member function. The recognition rate of M.TB bacilli cells is obtaining 98.17%.

This paper proposes a Biometric Palm Identification of Individual using Statistical Properties of palm. The proposed work discusses the significance; since both the palm print and hand shape images are proposed to extract from the single hand image acquired from a sensor. We can discuss the new approach for palm print recognition is based on Statistical properties such as Mean square Error, Signal to Noise Ratio, Standard deviation. The comparison and combination of proposed statistical properties can be evaluated by considering the various statistical aspects are Mean Square Error (MSE), Signal to Noise Ratio (SNR), Standard Deviation.

In this paper, handwritten Kannada and Telugu digits recognition system is proposed based on zone features. The digit image is divided into 64 zones. For each zone, pixel density is computed. The KNN and SVM classifiers are employed to classify the Kannada and Telugu handwritten digits independently and achieved average recognition accuracy of 95.50%, 96.22% and 99.83%, 99.80% respectively. For bilingual digit recognition the KNN and SVM classifiers are used and achieved average recognition accuracy of 96.18%, 97.81% respectively.

Handwritten script has been the most acknowledged method of collecting, storing and transmitting information all the way through the centuries. In this paper an attempt is made to compare the offline handwritten character recognition system for the isolated Gujarati numerals. For feature extraction affine invariant moments based model is used. We are using KNN classifier and PCA (to reduce dimensions of feature space) and used Euclidean similarity measure to classify the numerals. KNN classifier yielded 90 % as recognition rate whereas PCA scored recognition rate of 84%. The comparison of KNN and PCA is made and it can be seen that KNN classifier has shown better results as compared to PCA classifier.

The detection and extraction of scene text from document images is one of the challenging research areas. Many researchers have detected and extracted the text from plain text background. But the multi-oriented scene text detection is one of the complex problems due to multi-oriented texts which have different orientation, font size, colors etc. In this work, we have proposed a new algorithm to detect and extract the multi-oriented scene text. Experiments have been carried out to find the robustness on the proposed method, by conducting various experiments on heterogeneous datasets. The proposed method achieves selectively high detection rate of 88.43% on the multi-oriented scene text.

Medical images do not contain sharp edges. Therefore segmentation of these images is a challenging. In this paper we propose the algorithm for interactive segmentation of endoscopic images using extension of original graph-cut method. In this work a more powerful, iterative version of the optimization is used; the power of the iterative algorithm is used to simplify substantially the user interaction and a robust algorithm for "border matting" has been developed to estimate simultaneously the alpha-matte around an object boundary and the colors of foreground pixels. This method is expected to be successful on a wide variety of images with foreground objects. The proposed algorithm is tested on endoscopic images containing tumors. The results of the proposed algorithm are encouraging.