Authentication through the palmprint is a field of biometrics. Palmprint-based personal verification has quickly entered the biometric family. It has become increasingly popular in the recent years due to its ease of acquisition, reliability and high user acceptance. In this paper, we present an authentication system based on the palmprint. We are particularly interested in the feature extraction step. Three feature extraction techniques based on the discrete wavelet transform, the Gabor filters and the co-occurrence matrix are evaluated. The support vector machine is used for the classification step. The results have been validated on the PolyU database related to 400 users. The best results have been achieved with the wavelet decomposition.

Digital spectral analysis is the significant factor of consideration by which numerous applications importantly need of effective reception and analysis of signals, that is, the reception of signals is needed with improved spectral characteristics and simple techniques. To meet the above requirements, a novel technique is proposed in digital bandpass filter bank, supported by 'Modified Kaiser window' based Finite impulse response method in Multirate processing followed by Fast Fourier transform. The novel technique influences largely in the proposed method in such a way that it involves the modification of samples of input signal for deriving the advantages in respect of selectivity, stopband attenuation, peak output and constant width cum sharp rise of response apart from smooth spectral output when compared with existing methods. Further, reduction in computational complexity and hardware complexity are the additional features of the proposed method, henceforth; its spectral output is suitable in many of real time applications and moreover advantageous in digital hearing aids. The simulation results are drawn and its performance is compared to elucidate the advantages in the proposed method.

A novel hybrid restoration scheme of defocused image is presented, which uses multivariate generalized additive model (MGAM) which is a nonparametric statistical regression model with no curse of dimensionality and inverse filtering (InvF). In this algorithm, firstly the five features of wavelet domain in defocused digital image, which are very stable relationship with the point spread function (PSF) parameter, are extracted by training and fitting a multivariate generalized additive model which is to estimate defocused blurred parameter. After the point spread function parameter is obtained, inverse filtering, which is needed to known the point spread function and a non-blind restoration method, is applied to complete the restoration for getting the true image. Simulated and real blurred images are experimentally illustrated to evaluate performances of the presented method. Results show that the proposed defocused image hybrid restoration technique is effective and robust.

In this paper, we propose a multispectral analysis system using wavelet based Principal Component Analysis (PCA), to improve the brain tissue classification from MRI images. Global transforms like PCA often neglects significant small abnormality details, while dealing with a massive amount of multispectral data. In order to resolve this issue, input dataset is expanded by detail coefficients from multisignal wavelet analysis. Then, PCA is applied on the new dataset to perform feature analysis. Finally, an unsupervised classification with Fuzzy C-Means clustering algorithm is used to measure the improvement in reproducibility and accuracy of the results. A detailed comparative analysis of classified tissues with those from conventional PCA is also carried out. Proposed method yielded good improvement in classification of small abnormalities with high sensitivity/accuracy values, 98.9/98.3, for clinical analysis. Experimental results from synthetic and clinical data recommend the new method as a promising approach in brain tissue analysis.

Based on the information present in cumulative acoustic signal acquired from a roadside-installed single microphone, this paper considers the problem of vehicular traffic density state estimation. The occurrence and mixture weightings of traffic noise signals (Tyre, Engine, Air Turbulence, Exhaust, and Honks etc) are determined by the prevalent traffic density conditions on the road segment. In this work, we extract the short-term spectral envelope features of the cumulative acoustic signals using MFCC (Mel-Frequency Cepstral Coefficients). Support Vector Machines (SVM) is used as classifier is used to model the traffic density state as Low (40 Km/h and above), Medium (20-40 Km/h), and Heavy (0-20 Km/h). For the developing geographies where the traffic is non-lane driven and chaotic, other techniques (magnetic loop detectors) are inapplicable. SVM classifier with different kernels are used to classify the acoustic signal segments spanning duration of 20–40 s, which results in average classification accuracy of 96.67% for Quadratic kernel function and 98.33% for polynomial kernel function, when entire frames are considered for classification.

In this paper, we have proposed an Automatic Aerial Video Processing System for analyzing land surface features. Analysis of aerial video is done in three steps a) Image pre-processing b) Image registration and c) Image segmentation. Using the proposed system, we have identified Land features like Vegetation, Man-Made Structures and Barren Land. These features are identified and differentiated from each other to calculate their respective areas. Most important feature of this system is that it is an instantaneous video acquisition and processing system. In the first step, radial distortions of image are corrected using Fish-Eye correction algorithm. In the second step, the image features are matched and then images are stitched using Scale Invariant Feature Transform (SIFT) followed by Random Sample Consensus (RANSAC) algorithm. In the third step, the stitched images are segmented using Mean Shift Segmentation and different structures are identified using RGB model. Here we have used a hybrid system to identify Man-Made Structures using Fuzzy Edge Extraction along with Mean Shift segmentation. The results obtained are compared with the ground truth data, thus evaluating the performance of the system. The proposed system is implemented using Intel's OpenCV.

In present paper the effect of noise and error occurring due to noise in fractal dimension of digital images has been analyzed. For this purpose, three digital images have been used which are added by Gaussian noise, salt and pepper noise and speckle noise. The fractal dimension of both noisy and non-noisy images has been estimated and corresponding error is reported in terms of RMSE. The study shows that noise affects the fractal dimension and there is an increase in fractal dimension due to noise. The average percentage error in fractal dimension has been estimated and reported as an offset for finding actual fractal dimension from noisy images.

H.264/AVC is the latest video coding standard adopting variable block size, quarter-pixel accuracy and motion vector prediction and multi-reference frames for motion estimations. These new features result in higher computation requirements than that for previous coding standards.The computational complexity of motion estimation is about 60% in the H.264/AVC encoder. In this paper most significant bit (MSB first) arithmetic based bit serial Variable Block Size Motion Estimation (VBSME) hardware architecture is proposed. MSB first bit serial architecture main feature is, its early termination SAD computation compared to normal bit serial architectures. With this early termination technique, number computations are reduced drastically. Hence power consumption is also less compared to parallel architectures. An efficient bit serial processing element is proposed and developed 2D architecture for processing of 4x4 block in parallel .Inter connect structure is developed in such way that data reusability is achieved between PEs. Two types of adder trees are employed for variable block size SAD calculation with less number of adders. The proposed architecture can generate up to 41 motion vectors (MVs) for each macroblock. The inter connection complexity between PEs reduced drastically compared to parallel architectures. The architecture supports processing of SDTV (640x480) with 30fps at 172.8 MHz for search range [+8, -7]. We could reduce 14% of computations by using early termination technique.