Prof. Dan E. Tamir Profile

Prof. Dan E. Tamir

Asociate Professor at Texas State Univ

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Publications (3)

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Proceedings Article | 24 September 2007 Paper

Recovery of data from damaged CD/DVD

Dan Tamir, Wilbon Davis, Larry Wolfe, Robert McNiece

Proceedings Volume 6696, 66962I (2007) https://doi.org/10.1117/12.735347

KEYWORDS: Compact discs, Image processing, Data acquisition, Data corrections, Binary data, Image fusion, Edge detection, Data modeling, Data storage, Forensic science

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This paper presents a novel system for automatic data recovery from damaged CD (or DVD). The system acquires a sequence of optically magnified high resolution digital images of partially overlapping CD-surface regions. Next, advanced image processing and pattern recognition techniques are used to extract the data encoded on the CD from image frames. Finally, forensic data recovery techniques are applied to provide the maximal usable CD data. Using the CD's error correction information, the entire data of a non-damaged CD can be extracted with 100% accuracy. However, if an image frame covers a damaged area, then the data encoded in the frame and some of its eight neighbor image frames may be compromised. Nevertheless, the effect of the frame overlapping, error correction code, forensic data recovery, and data fusion techniques can maximize the amount of data extracted from compromised frames. The paper analyzes low level image processing techniques, compromised frames scenarios, and data recovery results. An analytical model backed by experimental set up shows that there is high probability of recovering data despite certain damages. Current results should be of high interest to law enforcement and home-land-security agencies. They merit further research and investigation to cover additional applications of image processing of CD frames, such as encryption, parallel access, and zero-seek-time.

Proceedings Article | 17 September 2007 Paper

Multiresolution training of Kohonen neural networks

Dan Tamir

Proceedings Volume 6700, 67000B (2007) https://doi.org/10.1117/12.735394

KEYWORDS: Distortion, Quantization, Monte Carlo methods, Neural networks, Quality measurement, RGB color model, Image compression, MATLAB, Statistical analysis, Image quality

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This paper analyses a trade-off between convergence rate and distortion obtained through a multi-resolution training of a Kohonen Competitive Neural Network. Empirical results show that a multi-resolution approach can improve the training stage of several unsupervised pattern classification algorithms including K-means clustering, LBG vector quantization, and competitive neural networks. While, previous research concentrated on convergence rate of on-line unsupervised training. New results, reported in this paper, show that the multi-resolution approach can be used to improve training quality (measured as a derivative of the rate distortion function) on the account of convergence speed. The probability of achieving a desired point in the quality/convergence-rate space of Kohonen Competitive Neural Networks (KCNN) is evaluated using a detailed Monte Carlo set of experiments. It is shown that multi-resolution can reduce the distortion by a factor of 1.5 to 6 while maintaining the convergence rate of traditional KCNN. Alternatively, the convergence rate can be improved without loss of quality. The experiments include a controlled set of synthetic data, as well as, image data. Experimental results are reported and evaluated.

Proceedings Article | 17 September 2007 Paper

The validity of pyramid K-means clustering

Dan Tamir, Chi-Yeon Park, Wook-Sung Yoo

Proceedings Volume 6700, 67000D (2007) https://doi.org/10.1117/12.735436

KEYWORDS: Image segmentation, Distortion, Monte Carlo methods, Expectation maximization algorithms, Data centers, Image processing algorithms and systems, Quantization, Image processing, Statistical analysis, Detection and tracking algorithms

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K-means is a widely used objective optimization clustering method. It is generally implemented along with the minimum square error (MSE) minimization criteria. It has been shown empirically that the algorithm provides "good" MSE results. Nevertheless, K-means has several deficiencies; first, it is sensitive to the seeding method and may only converge to a local optimum. Second, the algorithms is known to be NP complete, hence, validating the quality of results may be intractable. Finally, the convergence rate of the algorithm is dependent on the seeding. Generally, low convergence rate is observed. This paper presents a multi-resolution K-means clustering method which applies the K-means algorithm to a sequence of monotonically increasing-resolution samples of the given data. The cluster-centers obtained from a low resolution stage are used as initial cluster-centers for the next stage which is a higher resolution stage. The idea behind this method is that a good estimation of the initial location of centers can be obtained through K-means clustering of a sample of the input data. This can reduce the convergence time of K-means. Alternatively the algorithm can be used to obtain better MSE in about the same time as the traditional K-means. The validity of pyramid K-means algorithm is tested using Monte Carlo simulations applied to synthetic data and to multi-spectral images and compared to traditional K-means. It is found that in the average case pyramid K-means improves the MSE by a factor of four to six. This may require only 1.35 more iterations than the traditional K-means. Alternatively, it can reduce the computation time by a factor of three to four with a slight improvement in the quality of clustering.

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