vaishali kulkarni

Tooth caries or cavities diagnosing are concerned as the most significant research work, as this is the common oral disease suffered by humans. Many approaches have been proposed under the topics including demineralization and decaying as well. However, the imaging modalities often suffer from various critical or complex aspects that struggles the methods to attain accurate diagnosis. This article turns to introduce a new cavity diagnosis model with three phases: (i) pre-processing (ii) feature extraction (iii) classification. In the first phase, a new bi-histogram equalization with adaptive sigmoid functions (BEASF) is introduced to enhance the image quality followed by other enhancements models like grey thresholding and active contour. Then, the features are extracted using multilinear principal component analysis (MPCA). Further, the classification is done via neural network (NN) classifier. After the implementation, the proposed model compares its performance over other convent...

In this paper, three approaches for automatic Speaker Recognition based on Vector quantization are proposed and their performances are compared. Vector Quantization (VQ) is used for feature extraction in both the training and testing phases. Three methods for codebook generation have been used. In the 1st method, codebooks are generated from the speech samples by using the Linde-Buzo-Gray (LBG) algorithm. In the 2nd method, the codebooks are generated using the Kekre's Fast Codebook Generation (KFCG) algorithm and in the 3rd method, the codebooks are generated using the Kekre's Median Codebook Generation (KMCG) algorithm. For speaker identification, the codebook of the test sample is similarly generated and compared with the codebooks of the reference samples stored in the database. The results obtained for the three methods have been compared. The results show that KFCG gives better results than LBG, while KMCG gives the best results.

This paper presents a brief overview of the Speaker recognition process, its trends and applications. Further a simple technique based on the Euclidean distance comparison is proposed. The technique is applied for both text-dependent as well as text independent identification. Text dependent identification gives excellent results whereas text independent identification gives almost 80% matching accuracy. Introduction Human beings can identify a speaker based on his voice with a fairly good precision. With a large number of applications like voice dialing, phone banking, teleshopping, database access services, information services, voice mail, security systems and remote access to computers etc., the automated systems need to perform as well or even better, than humans. A lot of work in this regard has been done. But still there is lack of understanding of the characteristics of the speech signal that can uniquely identify a speaker. The speech signal gives various levels of informat...

In this paper we propose a unique approach to text dependent speaker identification using transformation techniques such as DCT (Discrete Cosine Transform) and WHT (Walsh and Hadamard Transform). The feature vectors for identification are extracted using two different techniques using the transforms, one without overlap and the other with overlap. The results show that accuracy increases as the feature vector size is increased from 64 onwards. But for feature vector size of more than 512 the accuracy again starts decreasing. The maximum accuracy without overlap is more than with overlap for both the transforms. Also the results show that DCT performs better than WHT. The maximum accuracy obtained for DCT is 94.28% for a feature vector size of 512.

ABSTRACT In this paper a novel approach to text dependent speaker identification based on feature vector reduction technique of the row mean is proposed. Five different Orthogonal Transform Techniques: Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT), Discrete Sine Transform (DST), Discrete Hartley Transform (DHT) and Walsh Hadamard Transform (WHT) are applied on the framed speech signal. Feature extraction in the testing and matching phases has been done by using feature vector reduction technique applied on the row mean vector of the magnitude of the transformed speech signal. Two similarity measures Euclidean distance and Manhattan distance are used for feature matching. The results indicate that the accuracy using both the similarity measures remains steady up to certain reduction in feature vector permitting to reduce feature vector size. This algorithm is tested using two databases: a locally created database and CSLU Database. It is observed that, DFT allows maximum percentage of feature vector reduction. It out performs other Transforms with a big margin.

ABSTRACT In this paper, closed set and open set Speaker Identification has been performed on two different databases. Feature extraction for the Identification has been done by using the amplitude distribution of four different Transforms i.e. DFT, DHT, DCT and DST. Two similarity measures i.e. Euclidean Distance (ED) and Manhattan Distance (MD) have been used for matching. The performance has been compared with respect to the best value of each Transform for following parameters: length of speech sample, similarity measure score, size of feature vector, FAR/FRR performance and data acquisition system. Amongst the transforms the best result is given by DFT at 99.06% for feature vector of size 32. Amongst similarity measures Manhattan distance outnumbers the Euclidean distance by 54 to 15, considering the results of all three lengths of speech. The best GAR is 90.65% with a threshold of 94.11% for DFT with MD as a similarity measure.

ABSTRACT In this paper a Sign Language Recognition system has been proposed. The first step of this system is to create a database of Indian Sign Language. This is done by acquiring the videos from the signers while they are performing the hand gestures. Next step is Hand tracking and Segmentation. This is performed in order to extract features from a particular gesture. A three step algorithm has been used in the proposed system to get better quality hand tracking and segmentation. This algorithm works on motion tracking, edge detection and skin color detection. The system is implemented successfully and results are presented in this paper. The results demonstrate working of motion tracking, edge detection and skin color detection individually as well as their combined effect.

Handwritten signatures are one of the oldest biometric traits for human authorization and authentication of documents. Majority of commercial application area deal with static form of signature. In this paper we present a method for off-line signature recognition. We have used morphological dilation on signature template for measurement of the pixel variance and hence the inter class and intra class

This paper proposes a multilevel Non-Return-to-Zero (NRZ) coding technique for the transmission of digital signals. The Multilevel Technique presented here helps in removing certain problems associated with Bipolar and Manchester coding techniques. This multilevel technique utilizes different D.C. levels for representing a ‘0’ and ‘1’ with a NRZ method. The PSD (power spectral density) of the encoded signal is analyzed and possible generation method is also shown.

In this paper, we propose automatic speaker recognition using circular DFT (Discrete Fourier Transform) sectors. In the first method, the feature vectors are extracted by dividing the complex DFT spectrum into circular sectors and then taking the weighted density count of the number of points in each of these sectors. In the second and third method, the circular sectors are