Exponential B-spline functions are more flexible than cardinal B-spline polynomials due to the extra degrees of freedom inherited by the arbitrary choice of its parameters. In this paper, independent simple proofs of some of the important features of Exponential B-spline functions are given. A novel efficient technique has also been proposed for decomposing a signal in terms of its exponential B-spline expansion. Applications of Exponential B-spline functions in spatial image compression are demonstrated. Our illustrative results show that Exponential B-splines outperform cardinal B-splines in image compression.

Many forensic techniques recently tried to detect the tampering and manipulation of JPEG compressed images that became a critical problem in image authentication and origin tracking. Some techniques indicated that a knowledgeable attacker can make it very hard to trace the image origin, while others indicated that portions of the compressed image that has been compressed at different quality factor quantization matrices are distinguishable if they are recompressed at a higher quality factor quantization matrix (with less quantization steps). In this paper, we pursue the idea of recompressing forensically suspect-able images with different compression parameters. We use different quantization matrix sizes that would indicate a DCT projection at different frequencies (horizontally, vertically, and diagonally), and would make it easier to track any tampering or hacking footprints. We show that a JPEG compressed image can make these footprints distinguishable if recompressed with a smaller size quantization matrix. Illustrative examples are presented.

B-splines caught interest of many engineering applications due to their merits of being flexible and provide a large degree of differentiability and cost/quality trade off relationship. However they have less impact with continuous time applications as they are constructed from piecewise polynomials. On the other hand, Exponential spline polynomials (E-splines) represent the best smooth transition between continuous and discrete domains as they are made of exponential segments. In this paper we present a technique for utilizing E-splines in image de-noising applications. This technique is based upon sub-band decomposition of the image through an E-spline based perfect reconstruction (PR) system. Different thresholdings are applied on the decomposition layers for de-noising purposes. Due to the selective nature of E-spline based decomposition, the performance of our E-spline based de-noising technique outperforms all other literature techniques.

B-splines caught interest of many engineering applications due to their merits of being flexible and provide a large degree of differentiability and cost/quality trade off relationship. However they have less impact with continuous time applications as they are constructed from piecewise polynomials. On the other hand, Exponential spline polynomials (E-splines) represent the best smooth transition between continuous and discrete domains as they are made of exponential segments. In this paper we present a technique for utilizing E-splines in image de-noising applications. This technique is based upon sub-band decomposition of the image through an E-spline based perfect reconstruction (PR) system. Different thresholdings are applied on the decomposition layers for de-noising purposes. Due to the selective nature of E-spline based decomposition, the performance of our E-spline based de-noising technique outperforms all other literature techniques.

B-splines caught interest of many engineering applications due to their merits of being flexible and provide a large degree of differentiability and cost/quality trade off relationship. However they have less impact with continuous time applications as they are constructed from piecewise polynomials. On the other hand, Exponential spline polynomials (E-splines) represent the best smooth transition between continuous and discrete domains as they are made of exponential segments. In this paper we present a technique for utilizing E-splines in image compression and de-noising applications. This technique is based upon sub-band decomposition of the image through an E-spline based perfect reconstruction (PR) system. Different thresholdings are applied on the decomposition layers for de-noising purposes. Due to the selective nature of E-spline based decomposition, the performance of our E-spline based de-noising technique outperforms all other literature techniques.

B-splines caught interest of many engineering applications due to their merits of being flexible and provide a large degree of differentiability and cost/quality trade off relationship. However they have less impact with continuous time applications as they are constructed from piecewise polynomials. On the other hand, Exponential spline polynomials (E-splines) represent the best smooth transition between continuous and discrete domains as they are made of exponential segments. In this paper we present a technique for utilizing E-splines in image compression and de-noising applications. This technique is based upon sub-band decomposition of the image through an E-spline based perfect reconstruction (PR) system. Different thresholdings are applied on the decomposition layers for de-noising purposes. Due to the selective nature of E-spline based decomposition, the performance of our E-spline based de-noising technique outperforms all other literature techniques.

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 were optimally chosen, to enhance the performance of image de-noising as well as image zooming schemes. The proposed technique is based on minimizing the total variation function of the detail coefficients of the E-spline based wavelet decomposition. In image de-noising schemes, apart from Espline parameter estimations, the thresholding levels of their detail coefficients, are also optimally chosen. In zooming applications, the quality of interpolated images are further improved and sharpened by applying ICA technique to them, in order to remove any dependency. 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 were optimally chosen, to enhance the performance of image de-noising as well as image zooming schemes. The proposed technique is based on minimizing the total variation function of the detail coefficients of the E-spline based wavelet decomposition. In image de-noising schemes, apart from Espline parameter estimations, the thresholding levels of their detail coefficients, are also optimally chosen. In zooming applications, the quality of interpolated images are further improved and sharpened by applying ICA technique to them, in order to remove any dependency. Illustrative examples are given to verify image enhancement of the proposed e-spline scheme, when compared with the existing approaches.

In this paper, we propose a novel design for a lifting based wavelet system that achieves the optimal trade off between compression and classification performances. In addition, it can also achieve a superior compression performance compared to existing wavelet kernels. The proposed system is based on bi-orthogonal filters and can operate in a scalable compression framework. In the proposed system, the trade off point between compression and classification is determined by the system, however the user can also fine-tune the relative performance using two controllers (one for compression and one for classification). Extensive simulations have been performed to demonstrate the superior compression and/or classification performance of our system in the context of the recent image compression standard, namely (JPEG2000). Our simulation result shows that the lifting based kernels, generated from the proposed system, are capable of achieving superior compression performance compared to the default kernels adopted in the JPEG2000 standard (with a classification rate of 70%). The generated kernels can also achieve a comparable compression quality with the JPEG2000 kernels whilst also provide a 99% classification rate. In other words the proposed lifting based system achieves the trade off between compression and classification performance in the wavelet domain.

Forensic odontology has long been carried out by forensic experts of law enforcement agencies for postmortem identification. We address the problem of developing an automated system for postmortem identification using dental records (dental radiographs). This automated dental identification system (ADIS) can be used by law enforcement agencies as well as military agencies throughout the United States to locate missing persons using databases of dental x rays of human remains and dental scans of missing or wanted persons. Currently, this search and identification process is carried out manually, which makes it very time-consuming in mass disasters. We propose a novel architecture for ADIS, define the functionality of its components, and describe the techniques used in realizing these components. We also present the performance of each of these components using a database of dental images.