This paper shows a method of tracking feature points to update camera pose and generating a synchronous map for AR (Augmented Reality) system. Firstly we select the ORB (Oriented FAST and Rotated BRIEF) [1] detection algorithm to detect the feature points which have depth information to be markers, and we use the LK (Lucas-Kanade) optical flow [2] algorithm to track four of them. Then we compute the rotation and translation of the moving camera by relationship matrix between 2D image coordinate and 3D world coordinate, and then we update the camera pose. Last we generate the map, and we draw some AR objects on it. If the feature points are missing, we can compute the same world coordinate as the one before missing to recover tracking by using new corresponding 2D/3D feature points and camera poses at that time. There are three novelties of this study: an improved ORB detection, which can obtain depth information, a rapid update of camera pose, and tracking recovery. Referring to the PTAM (Parallel Tracking and Mapping) [3], we also divide the process into two parallel sub-processes: Detecting and Tracking (including recovery when necessary) the feature points and updating the camera pose is one thread. Generating the map and drawing some objects is another thread. This parallel method can save time for the AR system and make the process work in real-time. 

The aim of this research is to propose a methodology to classify the subjects into Alzheimer disease and normal control on the basis of visual features from hippocampus region. All three dimensional MRI images were spatially normalized to the MNI/ICBM atlas space. Then, hippocampus region was extracted from brain structural MRI images, followed by application of two dimensional Gabor filter in three scales and eight orientations for texture computation. Texture features were represented on slice by slice basis by mean and standard deviation of magnitude of Gabor response. Classification between Alzheimer disease and normal control was performed with linear support vector machine. This study analyzes the performance of Gabor texture feature along each projection (axial, coronal and sagittal) separately as well as combination of all projections. The experimental results from both single projection (axial) as well as combination of all projections (axial, coronal and sagittal), demonstrated better classification performance over other existing method. Hence, this methodology could be used as diagnostic measure for the detection of Alzheimer disease. 

Blurring of images is an unwelcome phenomenon that is difficult to avoid in many situations. It degrades the quality of a variety of images, including real life photographic images, astronomical images and medical images. In this paper a new image de-blurring algorithm is proposed using Lucy Richardson method. De-blurring is performed in two stages. To arrive at the best guestimate, an iterative method is employed as an initial step which computes the maximum likelihood estimate of the point spread function (PSF) without any prior information. In the second step, Lucy Richardson algorithm takes the PSF estimated in the initial step as its input parameter. In particular, for better processing of the image, suitable color space identification is done as a pre-processing step. This makes use of the idea of edge detectors. This paper, as a significant contribution, proposes a de-blurring technique, which uses a hybrid de-convolution method with a color space identification stage. This enables its application for a broad spectrum of images from real life photographic images to single photon emission computed tomography images as well. The performance of the algorithm is compared against other existing de-blurring algorithms and the results prove a better output in terms of blur reduction. Standard test images and real medical images are used for appraising the algorithm.

Due to the availability of various image processing tools forgery over an image can be performed very easily but very difficult to identify. In copy-move forgery, a segment is copied from the original image and pasted at some other location on the same image to hide significant objects of image or to bring additional information which is originally not present in image. Nowadays, this forgery technique is drawing researcher's attention. Till now many solutions are presented by researchers to detect such type of forgery in images. Several post-processing operations like rotation, alteration in intensity, noise addition, filtering and blurring can be applied over copy-moved segment which makes detection of forgery very difficult. Copy-move forgery detection is mainly based on finding similarity present in an image and establish a relationship between genuine image parts and pasted portion of the image. This paper is centralized towards providing survey to forgery detection techniques based on different block-based methods. In block-based methods image is divided in blocks of fixed dimension and further features are extracted corresponding to each block of image. Forged blocks are identified utilizing the similarity present between feature vectors. 

A time-domain sequence is converted into an equivalent frequency-domain sequence using discrete Fourier transform. The reverse operation converts a frequency-domain sequence into an equivalent time-domain sequence using inverse discrete Fourier transform. Based on the discrete Fourier transform. Fast Fourier transform (FFT) is an effective algorithm with few computations. FFT is used in everything from broadband to 3G and Digital TV to radio LAN's. To improve its architecture different efficient algorithms are developed. This paper gives an overview of the work done by a different FFT processor previously. The comparison of different architecture is also discussed. 

Steganography is the science that deals with conveying secret information by embedding into the cover object invisibly. In steganography, only the authorized party is aware of the existence of the hidden message to achieve secret communication. The image file is mostly used cover medium amongst various digital files such as image, text, audio and video. The proposed idea of this research work is to develop the robust image steganography. It is implemented using Least Significant Bit and Discrete Wavelet Transform techniques for digital image signal to improve the robustness & evaluate the performance of these algorithms. The parameters such as mean square error (MSE), bit error rate (BER), peak signal to noise ratio (PSNR) and processing time are considered here to evaluate the performance of the proposed work. In the proposed system, PSNR and MSE value ranges from 42 to 46 dB and 1.5 to 3.5 for LSB method respectively. For DWT method these results are further improved as it gives higher PSNR values between 49 to 57 dB and lower MSE values 0.2 to 0.7. 

Hexagonal structure is a different approach to represent an image rather than the traditional square structure. Hexagonal shaped pixels are used in hexagonal structure representation of images. The hexagonal structure closely resembles the structure of human visual systems (HVS) because the photo receptors found in human retina are arranged in a hexagonal manner. Also curved structure can be well represented using hexagonal structure. So if we could able to represent the image in hexagonal domain, the computer vision will be as close to human vision. But in the present scenario there is no hardware available to capture or display hexagonal images. So we have to simulate a hexagonal grid on a regular square pixel image for further processing in hexagonal domain. In this paper, a new method for constricting a pseudo hexagonal structure using square pixel is presented. This method preserves the important property of hexagonal architecture that each pixel has exactly six surrounding neighbors. This method also preserves the equidistance property of hexagonal pixels. 

A non-format compliant JPEG encryption algorithm is proposed which is based on a modification of the RSA encryption system. Firstly, an alternate form of entropy coding is described, which is more suited to the proposed algorithm, instead of the zigzag coding scheme used in JPEG. The algorithm for the encryption and decryption process is then elaborated. A variant of the algorithm, also based on the RSA algorithm is also described, which is faster than the original algorithm, but expands the bit stream slightly. Both the algorithms are shown to be scalable and resistant to 'sketch' attacks. Finally, the encrypted file sizes for both the algorithms are compared with the unencrypted JPEG compressed image file size. The encrypted image is found to be moderately expanded, but which is justified by the high security and most importantly, the scalability of the algorithm.