Dharmpal D. Doye

Wavelet Based Image Denoising Technique

This paper proposes different approaches of wavelet based image denoising methods. The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. Wavelet algorithms are useful tool for signal processing such as image compression and denoising. Multi wavelets can be considered as an extension of scalar wavelets. The main aim is to modify the wavelet coefficients in the new basis, the noise can be removed from the data. In this paper, we extend the existing technique and providing a comprehensive evaluation of the proposed method. Results based on different noise, such as Gaussian, Poissons, Salt and Pepper, and Speckle performed in this paper. A signal to noise ratio as a measure of the quality of denoising was preferred.

Hand Gesture Recognition by Thinning Method

Sign language is the most natural and expressive way for the hearing impaired. Its most appealing application is the development of more effective and friendly interfaces for human-machine interaction. Gestures are a natural and powerful way of communication. A hand gesture recognition system can provide an opportunity for a mute person to communicate with normal people without the need of an interpreter. In this paper we proposed a novel technology for hand gesture recognition which is based on thinning of segmented image.

Image denoising using wavelet transform

An image is often corrupted by noise in its acquisition and transmission. Removing noise from the original image is still a challenging problem for researchers. In this work new approach of threshold function developed for image denoising algorithms. It uses wavelet transform in connection with threshold functions for removing noise. Universal, Visu Shrink, Sure Shrink and Bayes Shrink, normal shrink are compared with our threshold function, it improves the SNR efficiently.

Colour and Texture Features for Content Based Image Retrieval

The novel approach combines colour and texture features for content based image retrieval. Features like colour and texture are obtained by computing the measure of standard deviation in combination with energy on each colour band of image and sub band of wavelet. Wavelet transform is used for decomposing the image into 2times2 sub-bands. Feature database in content-based image retrieval of 640 visual texture (VisTex) color images is constructed. It is observed that proposed method outperforms the other conventional histograms and standard wavelet decomposition techniques

Hand Gesture Recognition Using Object Based Key Frame Selection

The sign language recognition is the most popular research area involving computer vision, pattern recognition and image processing. It enhances communication capabilities of the mute person. In this paper, we present an object based key frame selection. Hausdorff distance and Euclidean distance are used for shape similarity for hand gesture recognition. We proposed the use of nonlinear time alignment model with key frame selection facility and gesture trajectory features for hand gesture recognition. Experimental results demonstrate the effectiveness of our proposed scheme for recognizing American Sign Language.

Speech recognition using modified fuzzy hypersphere neural network

In this paper, a modified fuzzy hypersphere neural network (MFHSNN) is proposed, which is an extension of the fuzzy hypersphere neural network (FHSNN) proposed by Kulkarni and Sontakke (2001). Its performance is compared with FHSNN for the recognition of spoken Marathi (the language spoken in the state of Maharashtra, India) digits and found to be superior with respect to the recall time and recognition rate.

General fuzzy hypersphere neural network

This paper describes a general fuzzy hypersphere neural network that uses supervised and unsupervised learning within a single training algorithm. It is an extension of fuzzy hypersphere neural network and can be used for pure classification, pure clustering or hybrid clustering/classification.

Spelled sign word recognition using key frame

In this study, the authors present a new system for sign language hand gesture recognition. Using video input, the system can recognise any spelled word or alphabetic sequence signed in American Sign Language. The three main steps in the recognition process include detection of the region of interest (the hands), detection of key frames and recognition of gestures from these key frames. The proposed segmentation algorithm distinguishes regions of interest from both uniform and non-uniform backgrounds with an efficiency of 95%. The proposed key frame detection algorithm achieves an efficiency of 96.50%. A rotation-invariant algorithm for feature extraction is additionally proposed, which provides an overall gesture recognition efficiency of 84.2%.

Spelled sentence recognition using radon transform

Various sign languages are used in India, but in schools for deaf, American Sign Language (ASL) is taught. So, the work is based on ASL. Sign recognition application is the development of more effective and friendly interfaces for human-machine interaction. It can provide an opportunity for a mute person to communicate with normal people without the need of an interpreter. We propose a novel system for recognition of spelled sentences from a video, based on radon transform. An algorithm is used to separate out key frames, which contain correct gestures from a video sequence. Segmentation is applied on key frames to separate out hand from complex and nonuniform background. Features are extracted by radon transform and gesture is recognized.

Sign recognition using key frame selection

This paper deals with static and dynamic hand gesture (digits) recognition. The method provides a threefold novel contribution: (1) segmentation algorithm gives better results on any skin colour and any size of hand on complex and non-uniform background; (2) key frame finding algorithm and (3) the recognition technique of signs (static digits, alphabets and dynamic digits). We separate out key frames from a sequence of static gestures, which include correct gestures from a video sequence. The recognition efficiency of key frame detection is 93% using the proposed algorithm. The segmentation efficiency is almost 95%. Features are extracted using the proposed feature extraction algorithm, and gestures are recognised. We propose a novel algorithm for static and dynamic gesture recognition. The proposed algorithm shows recognition efficiency of 94.8% for static gestures and 94% for dynamic gestures.