Abstract
The objective of vision based gesture recognition is to design a system, which can understand the human actions and convey the acquired information with the help of captured images. An image restoration approach is extremely required whenever image gets blur during acquisition process since blurred images can severely degrade the performance of such systems. Image restoration recovers a true image from a degraded version. It is referred as blind restoration if blur information is unidentified. Blur identification is essential before application of any blind restoration algorithm. This paper presents a blur identification approach which categories a hand gesture image into one of the sharp, motion, defocus and combined blurred categories. Segmentation based fractal texture analysis extraction algorithm is utilized for featuring the neural network based classification system. The simulation results demonstrate the preciseness of proposed method.
Highlights
Gesture recognition is an area where meaningful physical movement of the fingers, hands, arms, face or body are used to convey information for human computer interaction [1]
This paper presents a blur identification approach which categories a degradation available in hand gesture image into one of the sharp, motion, defocus and combined blurred categories
The steps of the algorithm to classify blur are detailed in Figure 4 These are five major steps preprocessing of images, find logarithmic power spectrum, two-threshold binary decomposition, feature extraction, training of neural network classifier system and result analysis
Summary
Gesture recognition is an area where meaningful physical movement of the fingers, hands, arms, face or body are used to convey information for human computer interaction [1]. The optical lenses may be set in a way to clearly distinct two areas in the image: the blurry one and the non blurry one Such a method in which an automatic segmentation coupled to specific descriptors first allow to describe any region of the image and uses a supervised learning process that decides for each unknown region as “blurry" or "sharp” as suggested by Runga et al [10]. Bolan et al [16] proposed an image blurred region detection and classification method, which can automatically detect blurred image regions and blur type using singular value feature This scheme has analyzed the alpha channel information and classifies the blur type into defocus blur and motion blur categories.
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