The paper proposes a numerical TTS-synthesis system in Marathi, Hindi and English languages. The system is in audible forms based on the sounds generated from several numeric units. A hybrid technique is newly launched for a numerical text-to-speech technology. The technique is divided into different phases. These numerical phases include pre-processing, numeric unit detection, numeric and speech unit matching; speech unit concatenation and speech generation. In order to enhance the syntactic generation of audible forms in three languages, two discipline tests were performed. The prosodic test has been obtained for evaluating on the statistical readings. Overall quality issue (OQI) test is a subjective test which is performed by various persons who are aware of three mentioned languages. On the basis of two distinct parameters of OQI test, all scores are positively provided. Initial parameter compromises with listening quality. The second parameter, awareness rate improves a level of the intelligibility. The ultimate satisfactory results of artificial sound generation in three unrelated languages were touched to humankind voice.

Multi-scale segmentation is one of the most important methods for object-oriented classification. The selection of the optimal scale segmentation parameters has become difficult and hot in current research certainly. This paper takes aerial images and IKONOS images as the experimental objects and proposes an automatic selection method of optimal segmentation scale for high resolution remote sensing image based on multi-scale MRF model. This method introduces the region feature into the object, and obtains the hierarchical structure of the image from the bottom up through the message propagation between the objects. Finally, the optimal segmentation scale is obtained automatically by computing the marginal probabilities of the objects in each scale image. Experimental results show that this method can effectively avoid the subjectivity and sidedness of the segmentation process, and improve the accuracy and efficiency of high resolution segmentation. 

Human pose detection in 2D/3D images plays a vital role in a large number of applications such as gesture recognition, video surveillance and human robot interaction. Joint human pose estimation in the 2D motion video sequence and 3D facial pose estimation is the challenging issue in computer vision due to noise, large deformation, illumination and complex background. Traditional directed and undirected graphical models such as the Bayesian Markov model, conditional random field have limitations with arbitrary pose estimation in 2D/3D images using the joint probabilistic model. To overcome these issues, we introduce an ensemble chaining graph model to estimate arbitrary human poses in 2D video sequences and facial expression evaluation in 3D images. This system has three main hybrid algorithms, namely 2D/3D human pose pre-processing algorithm, ensemble graph chaining segmented model on 2D/3D video sequence pose estimation and 3D ensemble facial expression detection algorithm. The experimental results on public benchmarks 2D/3D datasets show that our model is more efficient in solving arbitrary human pose estimation problem. Also, this model has the high true positive rate, low false detection rate compared to traditional joint human pose detection models. 

Fractal Dimension is a basic parameter of fractal geometry and it has been applied in many fields of application including image analysis, texture segmentation, and shape classification. Many fractal dimensions methods have been evolved depending upon different types of images that could be differentiated with greater precision. In this paper, we propose a color approach based on the modified differential box-counting method to estimate fractal dimension of color images in terms of its smoothness. Here we have experimented on four sets of color images like; sixteen number of real natural texture images, eight sets of controlled experimental fabric images with varied color and texture, twelve numbers of generated synthetic images and four smoothed images of known fractal dimension. The results demonstrated that the said proposed method shows accurate fractal dimension estimation of color texture image and also it indicates FD as 2 for smoothed images, which has already been developed in last decade and indicates higher roughness in color images, to check the accuracy of our proposed method, we used a set of twelve synthetic generated images. 

Gamma correction is an interesting method for improving image quality in uncontrolled illumination conditions case. This paper presents a new technique called Mean-Variance Gamma (MV-Gamma), which is used for estimating automatically the amount of gamma correction, in the absence of any information about environmental light and imaging device. First, we valued every row and column of image pixels matrix as a random variable, where we can calculate a feature vector of means/variances of image rows and columns. After that, we applied a range of inverse gamma values on the input image, and we calculated the feature vector, for each inverse gamma value, to compare it with the target one defined from statistics of good-light images. The inverse gamma value which gave a minimum Euclidean distance between the image feature vector and the target one was selected. Experiments results, on various test images, confirmed the superiority of the proposed method compared with existing tested ones.

QRS detection is becoming more popular in detecting the heart beat rate. The improvement is done by using the new filter. The data and control parallelism is used in order to improve the execution time and speed of the parallel based hybrid MAMEMI filter technique This research work focus on providing better performance in heart beat detection algorithm by using parallel hybrid filter.An enhanced algorithm has been proposed to enhance the performance of QRS detection. Different parameters are used for the performance analysis. Accuracy,F_Measure, and Detection_Error_rate are the parameters which are used to evaluate the performance of heart beat algorithm. The results of proposed algorithm are compared with existing heart beat detection algorithm for performance comparison. On the other hand the performance of the proposed method is also improved using parallelism. Parallel proposed method shows better results than Sequential proposed method. The Mean improvement in execution time is 0.80. 

This paper presents a robust watermarking for still digital images based on a Slant transform, discrete wavelet transforms and Singular Value Decomposition (SVD). The cover image is transformed using discrete wavelet transform and singular value decomposition while watermark image is transformed using slant transform. It is watermarked by replacing singular values of the high-frequency and low-frequency component of an original image by that of slant transformed watermarks. Experimental results show that our method guarantees the robust resistance against no attack and the robustness against common image processing attacks such as JPEG compression, cropping, histogram equalization, sharpening etc.