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Recent advances in the brain computing technology have opened up novel paths for the development of various Brain-Computer-Interface (BCI) applications. It enables people to use the signals of BCI for silent communications, biometrics, security and to control various devices. Likewise, the progress in Smartphone technology facilitates everyone with a large number of applications including gaming,...
We consider the problem of parameter estimation and energy minimization for a region-based semantic segmentation model. The model divides the pixels of an image into non-overlapping connected regions, each of which is to a semantic class. In the context of energy minimization, the main problem we face is the large number of putative pixel-to-region assignments. We address this problem by designing...
Automatic identification of scripts, an imperative research problem during the last few decades, has posed many challenges in any multi-script environment. As India is a multilingual country, therefore, text documents containing more than one language are very familiar phenomenon here. But to digitize these multi-lingual documents using any Optical Character Recognition (OCR) engine, first it is required...
Script identification has long been the forerunner of many Optical Character Recognition (OCR) processes in a multi-lingual document environment. Script identification has numerous applications in the field of document image analysis, such as document sorting, indexing, retrieval and translation, etc. In this paper, we have developed a page-level script identification technique for handwritten documents...
Development of Optical Character Recognition (OCR) for printed Roman script is still an active area of research. Automatic Style Identification (ASI) can be used to improve the performance of OCR system and keyword spotting techniques for printed Roman script. This paper proposes a two stage font invariant technique for detection of italic, bold, underlined, normal and all capital styled words for...
Script identification from handwritten document images is an open document analysis problem especially for multilingual environment like India. To design the Optical Character Recognition (OCR) system for multi-script document pages, it is essential to recognize different scripts prior to employing an OCR engine of a particular script. The present work describes a texture based approach to word-level...
Object tracking is an important task within the field of computer vision. There are two key steps in video analysis: detection of interesting moving objects and tracking of such objects from frame to frame. Mean shift object tracking algorithm is a feature based algorithm. In this paper color information of object are extracted as well as texture information is also extracted by using local binary...
In this paper Wavelet Based Mel Frequency Cepstral Coefficient (WMFCC) features are proposed for speaker verification. The performance of WMFCC features is evaluated and compared with the performance of Mel Frequency Cepstral Coefficient (MFCC) features. A database of ten Hindi digits of sixteen speakers is used during simulation of results. Gaussian Mixture Models (GMMs) are used for maximum log...
In this paper a comparative study of Linear Prediction Coefficient (LPC) and Mel Frequency Cepstral Coefficient (MFCC) features is presented for text dependent speaker identification in clean and noisy environments. Noisy database was prepared by adding speech and F16 noises to clean database at -5 dB, 0 dB and 10 dB SNR levels. The speaker identification performance with MFCC and LPC features for...
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