In this paper, we develop and study a new algorithm to recognize and precisely measure keys for the ultimate purpose of physically duplicating them. The main challenge comes from the fact that the proposed algorithm must use a single picture of the key obtained from a regular desktop scanner without any special preparation. It does not use the special lasers, lighting systems, or camera setups commonly used for the purpose of key measuring, nor does it require that the key be placed in a precise position and orientation. Instead, we propose an algorithm that uses a wide range of image processing methods to discover all the information needed to identify the correct key blank and to find precise measures of the notches of the key shank from the single scanned image alone. Our results show that our algorithm can correctly differentiate between different key models and can measure the dents of the key with a precision of a few tenths of a millimeter.

An image scrambling method based on the itinerary of the improved 3D Baker map is proposed in this paper. The standard 3D Baker map is improved by the tent map, so that the itinerary becomes more complicated and can be used to encode the image pixel positions to scramble the image. The scrambling method is applied to the preprocessing in watermarking. The watermark bits are embedded in the discrete wavelet transform (DWT) spectral domain based on the scrambling of watermark, the odd-even adjustment rule and the neighbor mean value. The watermark bits are embedded in medium coefficients in DWT domain of the host image. Experimental results show that the watermarked image looks visually identical to the original one and the watermark can be effectively extracted upon image processing attacks, which demonstrates strong robustness against a variety of attacks.

This paper proposes an emotion recognition system using EEG signals and higher order spectra. A visual induction based acquisition protocol is designed for recording the EEG signals in five channels (FP1, FP2, T3, T4 and Pz) under two emotional states of participants, calm-neutral and negatively exited. After pre-processing the signals, higher order spectra are employed to extract the features for classifying human emotions. We used Genetic Algorithm (GA) and Support vector machine (SVM) for optimum features selection for the classifier. In this research, we achieved an average accuracy of 82.32% for the two emotional states using Linear Discriminant Analysis (LDA) classifier. We concluded that, HOS analysis could be an accurate tool in the assessment of human emotional states. We achieved to same results compared to our previous studies.

Most of the thresholding procedures involved setting of boundaries based on grey values or intensities of image pixels. In this paper, the thresholding is to be done based on color values in natural images. The color thresholding technique is being carried out based on the adaptation and slight modification of the grey level thresholding algorithm. Multilevel thresholding has been conducted to the RGB color information of the object extract it from the background and other objects. Different natural images have been used in the study of color information. The results showed that by using the selected threshold values, the image segmentation technique has been able to separate the object from the background.

The main topic of this paper is to segment brain tumors, their components (edema and necrosis) and internal structures of the brain in 3D MR images. For tumor segmentation we propose a framework that is a combination of region-based and boundary-based paradigms. In this framework,segment the brain using a method adapted for pathological cases and extract some global information on the tumor by symmetry based histogram analysis. We propose a new and original method that combines region and boundary information in two phases: initialization and refinement. The method relies on symmetry-based histogram analysis.The initial segmentation of the tumor is refined relying on boundary information of the image. We use a deformable model which is again constrained by the fused spatial relations of the structure. The method was also evaluated on 10 contrast enhanced T1-weighted images to segment the ventricles,caudate nucleus and thalamus.

A special member of the emerging family of multi scale geometric transforms is the contourlet transform which was developed in the last few years in an attempt to overcome inherent limitations of traditional multistage representations such as curvelets and wavelets. The biomedical images were denoised using firstly wavelet than curvelets and finally contourlets transform and results are presented in this paper. It has been found that contourlets transform outperforms the curvelets and wavelet transform in terms of signal noise ratio

In Recent days Semi supervised image segmentation techniques play a noteworthy role in image processing. Semi supervised image segmentation needs both labeled data and unlabeled data. It means that a Small amount of human assistance or Prior information is given during clustering process. This paper discusses an enhanced semi supervised image segmentation method from labeled image. It uses both a background selection marker and fore ground object selection marker separately. The EM (Expectation Maximization) algorithm is used for clustering along with must link constraints. The proposed method is applied for natural images using MATLAB 7. Thus the proposed method extracts Object of Interest (OOI) from OONI (Object of Not Interest) efficiently and the experimental results are compared with Standard K Means and EM Algorithm also. The results show that the proposed system gives better results than the other two methods. It may also be suitable for object extraction from natural images and medical image analysis.

This paper deals with the performance improvement of a mono modal face identification. A statistical study of various structures of the LBPs (Local Binary Patterns) features associated to two metrics is performed to find out those committing errors on different subjects. Then, during the identification stage, these optimal variants are used, and a simple score level fusion is adopted. The score fusion is done after min-max normalization. The main contribution of this paper consists in the association of multiple LBP schemes with different metrics using simple fusion operation. The overall identification rating up to 99% using AT&T database is achieved.