Inability to speak is considered to be true disability. People with this disability use different modes to communicate with others, there are n number of methods available for their communication one such common method of communication is sign language. Sign language allows people to communicate with human body language; each word has a set of human actions representing a particular expression. The motive of the paper is to convert the human sign language to Voice with human gesture understanding and motion capture. This is achieved with the help of Microsoft Kinect a motion capture device from Microsoft. There are a few systems available for sign language to speech conversion but none of them provide natural user interface. For consideration if a person who has a disability to speak can stand perform the system and the system converts the human gestures as speech and plays it loud so that the person could actually communicate to a mass crowd gathering. Also the system is planned in bringing high efficiency for the users for improved communication.
The problem of reconstructing digital images from degraded measurements is regarded as a problem of importance in various fields of engineering and imaging science. The main goal of denoising is to restore a noisy image to produce a visually high quality image. In this paper, we propose a novel transform domain technique that uses multispinning for image denoising. The proposed method uses multiple cyclic shifted versions of an image, where each of them would capture more detail information during decomposition. Discrete wavelet transform (DWT) and contourlet transform (CT) in association with multispinning is used. The results are compared with traditional transform (soft thresholding) and spatial domain techniques. The visual and quantitative evaluation suggests that the proposed method yields better results.
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The problem of reconstructing digital images from degraded measurement is regarded as a problem of importance in various fields of engineering and imaging sciences. The main goal of denoising is to restore a noisy image to produce visually high quality image. In general, image denoising imposes a compromise between noise reduction and preserving significant image details. In this paper we propose to use multi-spinning based wavelet denoising for restoring images corrupted by Gaussian noise. Multi-spinning uses multiple cyclic shifted versions of image, where each version would capture more detail during decomposition. The visual and quantitative analysis suggest that the proposed method yields better results.
The presence of noise in digital images degrades the visual quality by corrupting the information associated with the image. The aim of denoising is to restore an image from its noisy version by preserving signal information. In this paper, we are considering an image corrupted by additive Gaussian noise. The image is modeled as Markov random field (MRF) and an estimation of maximum-a-posteriori (MAP) is obtained using graduated non-convexity. The results are compared with other spatial domain filtering methods. The discontinuity adaptive prior helps in preserving edge information. The results suggest that proposed method has an improved performance.
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