Automating the process of postmortem identification of individuals using dental records is receiving increased attention. Teeth segmentation from dental radiographic films is an essential step for achieving highly automated postmortem identification. In this paper, we offer a mathematical morphology approach to the problem of teeth segmentation.We also propose a grayscale contrast stretching transformation to improve the performance of teeth segmentation. We compare and contrast our approach with other approaches proposed in the literature based on a theoretical and empirical basis. The results show that in addition to its capability of handling bitewing and periapical dental radiographic views, our approach exhibits the lowest failure rate among all approaches studied.

Today’s multimedia applications demand sophisticated compression and classification techniques in order to store, transmit, and retrieve audio-visual information efficiently. Over the last decade, perceptually based image compression methods have been gaining importance. These methods take into account the abilities (and the limitations) of human visual perception (HVP) when performing compression. The upcoming MPEG 7 standard also addresses the need for succinct classification and indexing of visual content for efficient retrieval. However, there has been no research that has attempted to exploit the characteristics of the human visual system to perform both compression and classification jointly. One area of HVP that has unexplored potential for joint compression and classification is spatial frequency perception. Spatial frequency content that is perceived by humans can be characterized in terms of three parameters, which are:1) magnitude; 2) phase; and 3) orientation. While the magnitude of spatial frequency content has been exploited in several existing image compression techniques, the novel contribution of this paper is its focus on the use of phase coherence for joint compression and classification in the wavelet domain. Specifically, this paper describes a human visual system-based method for measuring the degree to which an image contains coherent (perceptible) phase information, and then exploits that information to provide joint compression and classification. Simulation results that demonstrate the efficiency of this method are presented.

This paper describes a new image watermarking technique based on the Natural Preserving Transform (NPT). The proposed watermarking scheme uses NPT to encode a gray scale watermarking logo image or text, into a host image at any location. NPT brings a unique feature which is uniformly distributing the logo across the host image in an imperceptible manner. The contribution of this paper lies is presenting two efficient non-blind and quasi-blind watermark extraction techniques. In the quasi blind case, the extraction algorithm requires little information about the original image that is already conveyed by the watermarked image. Moreover, the proposed scheme does not introduce visual quality degradation into the host image while still being able to extract a logo with a relatively large amount of data. The performance and robustness of the proposed technique are tested by applying common image-processing operations such as cropping, noise degradation, and compression. A quantitative measure is proposed to objectify performance; under this measure, the proposed technique outperforms most of the recent techniques in most cases. We also implemented the proposed technique on a hardware platform, digital signal processor (DSK 6713). Results are illustrated to show the effectiveness of the proposed technique, in different noisy environments.

This paper describes a new image watermarking technique based on the Natural Preserving Transform (NPT). The proposed watermarking scheme uses NPT to encode a gray scale watermarking logo image or text, into a host image at any location. NPT brings a unique feature which is uniformly distributing the logo across the host image in an imperceptible manner. The contribution of this paper lies is presenting two efficient non-blind and quasi-blind watermark extraction techniques. In the quasi blind case, the extraction algorithm requires little information about the original image that is already conveyed by the watermarked image. Moreover, the proposed scheme does not introduce visual quality degradation into the host image while still being able to extract a logo with a relatively large amount of data. The performance and robustness of the proposed technique are tested by applying common image-processing operations such as cropping, noise degradation, and compression. A quantitative measure is proposed to objectify performance; under this measure, the proposed technique outperforms most of the recent techniques in most cases. We also implemented the proposed technique on a hardware platform, digital signal processor (DSK 6713). Results are illustrated to show the effectiveness of the proposed technique, in different noisy environments.

This paper describes a new image watermarking technique based on the Natural Preserving Transform (NPT). The proposed watermarking scheme uses NPT to encode a gray scale watermarking logo image or text, into a host image at any location. NPT brings a unique feature which is uniformly distributing the logo across the host image in an imperceptible manner. The contribution of this paper lies is presenting two efficient non-blind and quasi-blind watermark extraction techniques. In the quasi blind case, the extraction algorithm requires little information about the original image that is already conveyed by the watermarked image. Moreover, the proposed scheme does not introduce visual quality degradation into the host image while still being able to extract a logo with a relatively large amount of data. The performance and robustness of the proposed technique are tested by applying common image-processing operations such as cropping, noise degradation, and compression. A quantitative measure is proposed to objectify performance; under this measure, the proposed technique outperforms most of the recent techniques in most cases. We also implemented the proposed technique on a hardware platform, digital signal processor (DSK 6713). Results are illustrated to show the effectiveness of the proposed technique, in different noisy environments.

In this paper, efficient one-dimensional (1-D) fast integer transform algorithm of the DCT matrix for the H.265 standard is proposed. Based on the symmetric property of the integer transform matrix and the matrix operations, along with using the dyadic symmetry modification on the standard matrix, the efficient fast 1-D integer transform algorithm is developed. Therefore, the computational complexities of the proposed fast integer transform are smaller than those of the direct method. In addition to computational complexity reduction the proposed algorithms provides transformation quality improvement. With lower complexity and better transformation quality, the proposed fast algorithm is suitable to accelerate the quality-demanding video coding computations

Exponential spline polynomials (E-splines) represent the best smooth transition between continuous and discrete domains. As they are constructed from convolution of exponential segments, there are many degrees of freedom to optimally choose the most convenient E-spline; suitable for a specific application. In this paper, the parameters of these Esplines are optimally chosen, to sharpen the performance of an interpolated high resolution systems HR derived from a given low resolution decimated one whether noisy or noiseless. The proposed technique is based on minimizing the aliasing effects due to the high frequency bands of the HR images. Illustrative examples are given to verify image enhancement of the proposed E-spline scheme, when compared with the existing approaches.

Exponential spline polynomials (E-splines) represent the best smooth transition between continuous and discrete domains. As they are constructed from convolution of exponential segments, there are many degrees of freedom to optimally choose the most convenient E-spline; suitable for a specific application. In this paper, the parameters of these Esplines are optimally chosen, to sharpen the performance of an interpolated high resolution systems HR derived from a given low resolution decimated one whether noisy or noiseless. The proposed technique is based on minimizing the aliasing effects due to the high frequency bands of the HR images. Illustrative examples are given to verify image enhancement of the proposed E-spline scheme, when compared with the existing approaches.

We consider the problem of sampling non band limited signals with a finite number of degrees of freedom such as non uniform splines or piecewise polynomials. These signals are typically called signals with finite rate of innovation (FRI). We propose a novel technique for perfectly reconstructing impulses of Diracs. These Diracs are our adopted non band limited signal and have been filtered specifically through a B-spline sampling kernel, and then been uniformly sampled with a period T. This B-spline sampling kernel has an impulse response that is similar to most linear acquisition sensors/devices. The novelty of our proposed approach lies in the fact it is robust in noisy environments, unlike many recent similar techniques, i.e. Dragotti el.[1] that may provide faster implementation but are very delicate with any type of noise. Our technique also does not have any restrictions on the number of perfectly reconstructed Diracs with respect to the sampling kernel order and achieves its reconstruction in a B-spline 2-channel perfect reconstruction (PR) framework. A comparison of our proposed B-spline based perfect reconstruction system with the recent technique given in [1], in terms of speed, complexity, and kernel order complexity, is provided.

Many forensic techniques recently tried to detect the tampering and manipulation of JPEG compressed images that became a critical problem in different imaging applications. Some techniques indicated that a knowledge able attacker can make it very hard to detect image tampering, while others indicated that portions of the compressed image that has been compressed at different compression parameters can be detected, if they are recompressed after changing some of these parameters. In this project, we pursue the idea of analyzing forensically suspect-able images to detect forgery. We study the effect of adopting Nonorthogonal Discrete Cosine Transform (NDCT) that is highly utilized in efficient media implementations, in detecting if different parts of the image have been modified. This is performed by measuring block convergence of different image parts and detecting its stability after recompressions.