The task of estimating the age of humans from facial image is a challenging one due to the non-linear and personalized pattern of aging differing from one individual to another. In this work, we investigated the problem of estimating the age of humans from their facial image using a GroupWise age ranking approach complemented by ageing pattern correlation learning. In our proposed GroupWise age-ranking approach, we constructed a reference image set grouped according to ages for each individual in the reference set and used this to obtain age-ranks for each age group in the reference set. The constructed reference set was used to obtain transformed LBP features called age-rank-biased LBP (arLBP) features which were used with attached age-ranks to train an age estimating function for predicting the ages of test images. Our experiments on the publicly available FG-NET dataset and a locally collected dataset (FAGE) shows the best known age estimation accuracy with MAE of 2.34 years on FG-NET using the leave-one-person-out strategy.

Photographic documentation of prepared rock core specimens may be required for scientific studies. For specimens that have surface features which vary circumferentially, it is advantageous to have a single photomosaic of the specimen surface rather than a series of surface photographs. A technique to develop a photomosaic from a series of overlapping images of prepared vesicular basalt core specimens is presented. The overlapping images of the specimen surface are subjected to an initial cropping, a geometric transformation, an intensity interpolation, a final cropping, and an image stitching algorithm. The final result is an undistorted photomosaic of the entire specimen surface. All steps except the initial cropping are implemented within MATLAB®.

In this paper, authors have proposed two novel techniques for occlusion detection and then localization of the occluded section from a given 3D face image if occlusion is present. For both of these methods, at first, the 2.5D or range face images are created from input 3D face images. Then for detecting the occluded faces, two approaches have been followed, namely: block based and threshold based. These two methods have been investigated individually on Bosphorus database for localization of occluded portion. Bosphorus database consists of different types of occlusions, which have been considered during our research work. If 2D and 3D images are compared then 3D images provide more reliable, accurate, valid information within digitized data. In case of 2D images each point, named as pixel, is represented by a single value. One byte for gray scale and three byte for color images in a 2D grid whereas in case of 3D, there is no concept of 2D grid. Each point is represented by three values, namely X, Y and Z. The 'Z' value in X-Y plane does not contain the reflected light energy like 2D images. The facial surface's depth data is included in Z's point set. The threshold or cutoff based technique can detect the occluded faces with the accuracy 91.79% and second approach i.e. block based approach can successfully detect the same with the success rate of 99.71%. The accuracy of the proposed occlusion detection scheme has been measured as a qualitative parameter based on subjective fidelity criteria.

Many researchers employ ESPRIT method as robust detection tool to identify fault frequency and amplitude in induction machines. However, this algorithm presents some limitation in terms of computational time and required data memory size. This drawback makes this technology unusable in real time diagnosis application. In the fact that wind turbine machine necessitates an on-line regular maintenance to guarantee an acceptable lifetime and to maximize its productivity. Thus, an improved version of ESPRIT-TLS method has been proposed and simulated to extract accurately fault frequencies and their magnitudes from the wind stator current with minimum computation time and less memory cost. The proposed approach has been evaluated by computer simulations under many fault kinds. Study outcomes prove the benefits and the performance of Fast-ESPRIT.

In this paper, Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) classification approaches were used to classify hyperspectral image of Georgia, USA, using Environment of Visualizing Images (ENVI). It is a software application used to process and analyze geospatial imagery. Spatial, spectral subset and atmospheric correction have been performed for SAM and SID algorithms. Results showed that classification accuracy using the SAM approach was 72.67%, and SID classification accuracy was 73.12%. Whereas, the accuracy of SID approach is better than SAM approach. Consequently, the two approaches (SID and SAM) have proven to be accurately converged in classification of hyperspectral image of Georgia, USA.

Determination of script type of document image is a complex real life problem for a multi-script country like India, where 23 official languages (including English) are present and 13 different scripts are used to write them. Including English and Roman those count become 23 and 13 respectively. The problem becomes more challenging when handwritten documents are considered. In this paper an approach for identifying the script type of handwritten document images written by any one of the Bangla, Devnagari, Roman and Urdu script is proposed. Two convolution based techniques, namely Gabor filter and Morphological reconstruction are combined and a feature vector of 20 dimensions is constructed. Due to unavailability of a standard data set, a corpus of 157 document images with an almost equal ratio of four types of script is prepared. During classification the dataset is divided into 2:1 ratio. An average identification accuracy rate of 94.4% is obtained on the test set. The average Bi-script and Tri-script identification accuracy rate was found to be 98.2% and 97.5% respectively. Statistical performance analysis is done using different well known classifiers.

Automatic Number Plate Recognition (ANPR) is becoming very popular and topic of research for the Intelligent Transportation System (ITS). Many researchers are working in this direction, as it is the topic of interest. In proposed system, we have presented a novel approach for number plate (NP) detection, which can be useful for Indian conditions. The system works well in different illumination conditions and 24 hours manner. Experiments achieved excellent accuracy of 98.88% of overall accuracy of NP detection on 90 vehicle images with different conditions and captured at different timing during day and night. Out of these 90 images, 89 images were segmented successfully. The minimum image size was 800 X 600 pixels. The system was developed using the Microsoft .NET 3.5 framework and Visual Studio 2008 as IDE with the Intel core i3 2.13 GHz processor having 3 GB RAM. Other systems discussed in this paper reported better processing time of less than 1s, but some of these systems work under restricted conditions and accuracy is also not as good as our system.

Most of the steganography techniques, based on pixel value difference, use the difference between the pixel values to hide the information. In this paper, an algorithm is proposed which combines the LSB and PVD steganography techniques to provide high data hiding capacity with acceptable stego image quality. Instead of using the original difference to hide the information, the difference is revised before it is used for hiding the information. This introduces an additional layer of security for the secret information. The algorithm divides the cover image in the blocks of 2x3 pixels. One of the pixels in the block is used as common pixel, which forms five pixel pairs with remaining five pixels in the block. The algorithm hides three secret bits in the common pixel using the LSB substitution method and then use the PVD based approach to hide data in five pixel pairs in each block. The algorithm determines the average (N) of the number of bits that can be hidden in the block. If the difference value allows M-bits to be hidden in the pair, then bits ≤ N are hidden in that pair. The result shows that the algorithm provides higher hiding capacity with better PSNR values as compared to other methods investigated in this study.