Discrimination of Individual Tigers (\u3cem\u3ePanthera tigris\u3c/em\u3e) from Long Distance Roars

This paper investigates the extent of tiger (Panthera tigris) vocal individuality through both qualitative and quantitative approaches using long distance roars from six individual tigers at Omaha\u27s Henry Doorly Zoo in Omaha, NE. The framework for comparison across individuals includes statistical and discriminant function analysis across whole vocalization measures and statistical pattern classification using a hidden Markov model (HMM) with frame-based spectral features comprised of Greenwood frequency cepstral coefficients. Individual discrimination accuracy is evaluated as a function of spectral model complexity, represented by the number of mixtures in the underlying Gaussian mixture model (GMM), and temporal model complexity, represented by the number of sequential states in the HMM. Results indicate that the temporal pattern of the vocalization is the most significant factor in accurate discrimination. Overall baseline discrimination accuracy for this data set is about 70% using high level features without complex spectral or temporal models. Accuracy increases to about 80% when more complex spectral models (multiple mixture GMMs) are incorporated, and increases to a final accuracy of 90% when more detailed temporal models (10-state HMMs) are used. Classification accuracy is stable across a relatively wide range of configurations in terms of spectral and temporal model resolution

Algorithm for Fingerprint Verification System

Extraction of minutiae based features from good quality fingerprint images is more effective for fingerprint recognition in comparison with features from low quality fingerprint. In this paper, a new technique for fingerprint feature extraction based on ridge pattern is proposed. Robust features are extracted from fingerprint image notwithstanding the quality of the image. The variation within different person fingerprint is established using centre of gravity of the fingerprint image as the reference point for effective classification. Similarity measure in term of Euclidean distance is compute for test fingerprint image

Speech classification using combination virtual center of gravity and k-means clustering based on audio feature extraction

Voice recognition can be done in a variety of ways. Sound patterns can be recognized by performing sound feature extraction. The trainer sound data is built from the best sound data selection using a correlation coefficient based on the level of similarity between sound data for optimal sound features. Extraction of voting features on this research using the Virtual Center of Gravity method. This method calculates the distance between the sound data against the center point of gravity with visualizations in the 3-dimensional form of white, black, and grey pattern spaces. The preprocessing process generates a complex number of data consisting of real numbers and imaginary numbers. The number will be calculated the distance to the Virtual Center of Gravity's pattern space using Euclidean Distance. The sound feature testing is done using K-Means Clustering by means of a speech classification data based sound. The results showed an accuracy of 92.5%

Bark Classification of Trees Using K-Nearest Neighbor & Nearest Neighbor Algorithms

Pakistan is an agricultural country and less than 4 % of area secured with forests. Tree automatic classification based on computer science and it is the developing trend of classification. In this paper we examine how we can done bark classification of trees using k-nearest neighbor and nearest neighbor algorithms. There we discuss how these algorithms can be used to automatically classify trees from images of bark. We get the images of five kinds of different trees names suppose as A, B, C, D and E through using digital camera. We take ten different images of each kind of trees. The capability and information of inspectors are essential to perfectly achieve this process. The all the process will be done in computer vision image processing tool. In this tool we use the Histogram Features, Texture Features, and Pattern Classification. We achieved the final results of five kinds of different trees using nearest neighbor on distance two 82% average and on k-nearest neighbor when k=2 then the average result 82%, when k=3 the average result 82%, when k=4 then the average result 76% and when k=5 the average percentage 72% the result shows the maximum correct result and classifies the trees. These are the best percentage results using these algorithms for classification. In this way we can easily classify the different trees and also these methods provide opportunity to farmer and other people for identify and select the different better different trees for getting more benefit. Keywords: CVIP Tool, Histogram Features, Texture Features, Pattern Classification, Classification Algorithm

Vermiculite ore classification by texture analysis

In this paper, two micro-patterns descriptors are evaluated in the vermiculite ore classification: the Local Binary Pattern (LBP) and the Local Fuzzy Pattern (LFP). A micro-pattern is the gray-level pixels’ structure in an image neighborhood that describes the spatial specific context of various features, such as edges, lines, spots, blobs, corners or textures. In LBP approach is made a crisp comparison among the gray-levels values of the image pixel neighborhood and the LFP approach models the gray-level distribution of an image micro-pattern as a fuzzy set, and based on membership function generate fuzzy-codes that represents the membership degree of each neighborhood pixel to the central one. This is a quite appropriate alternative to deal with uncertainties by the acquisition process in digital images. The performance of the classification is evaluated using chi-square distance and the best result was obtained applying the LFP descriptor.FAPESPCAPE