Accepted Papers

  • ADAPTIVE LIFTING BASED IMAGE COMPRESSION SCHEME USING INTERACTIVE ARTIFICIAL BEE COLONY ALGORITHM
    1Vrinda Shivashetty and 2G.G Rajput,1Gulbarga University Gulbarga, India ,2Rani Channamma University, Belagavi, India
    ABSTRACT
    This paper presents image compression method using Interactive Artificial Bee Colony (IABC) optimization algorithm. The proposed method reduces storage and facilitates data transmission by reducing transmission costs. To get the finest quality of compressed image, utilizing local search, IABC determines different update coefficient, and the best update coefficient is chosen optimally. By using local search in the update step, we alter the center pixels with the co-efficient in 8-different directions with a considerable window size, to produce the compressed image, expressed in terms of both PSNR and compression ratio. The IABC brings in the idea of universal gravitation into the consideration of the affection between onlooker bees and the employed bees. By passing on different values of the control parameter, the universal gravitation involved in the IABC has various quantities of the single onlooker bee and employed bees. As a result when compared to existing methods, the proposed work gives better PSNR.
  • HINDI DIGITS RECOGNITION SYSTEM ON SPEECH DATA COLLECTED IN DIFFERENT NATURAL NOISE ENVIRONMENTS
    Babita Saxena and Charu Wahi , Birla Institute of Technology, Noida
    ABSTRACT
    This paper presents a baseline digits speech recognizer for Hindi language. The recording environment is different for all speakers, since the data is collected in their respective homes. The different environment refers to vehicle horn noises in some road facing rooms, internal background noises in some rooms like opening doors, silence in some rooms etc. All these recordings are used for training acoustic model. The Acoustic Model is trained on 8 speakers’ audio data. The vocabulary size of the recognizer is 10 words. HTK toolkit is used for building acoustic model and evaluating the recognition rate of the recognizer. The efficiency of the recognizer developed on recorded data, is shown at the end of the paper and possible directions for future research work are suggested.
  • Efficient Approach for static Hand Gesture Recognition using Sift Technique
    Mrs. Rajashree Patil and Dr. Shailaja Patil, University of Pune, India
    ABSTRACT
    There are number of images that are generated every day, which implies the necessity to classify, organise and access them using an easy, faster and efficient way. The classification of images into semantic categories is a challenging and important problem nowadays. In this way hand gesture image detection and recognition is also difficult task. Hence in this paper we have introduced a novel system which can be used for interaction with an application or videogame via hand gestures. In our system, first we extract key points of image using SIFT algorithm, after this by using k-means clustering a vector quantization technique will map key points from every training image into a unified dimensional histogram vector (bag-of-words), finally this histogram is treated as an input vector for a multiclass support vector matching to build the training classifier. In the testing stage, for every image captured from a digital webcam, we will perform actions same as training image and recognize the hand gesture.