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We develop a new image representation which combines support for coding and content-based retrieval. The algorithm minimizes a weighted sum of the expected compressed file size and query response time. Our approach leads to a progressive refinement retrieval by successively reducing the number of searched files as more bits are read. Furthermore, no distance computations are required during a query. Only simple bit pattern comparisons are required. In our experimental results based on a 1.4 Mbyte image database, the number of bits read during a typical retrieval was less than 1000 bytes (less than 0.08 of the database). The approach supports compressed data modification and low-bit-rate high quality browsing. © 1998 SPIE and IS&T.

Cardiac minimal invasive surgeries such as catheter based radio frequency ablation of atrial fibrillation requires high-precision tracking of inner cardiac surfaces in order to ascertain constant electrode-surface contact. Majority of cardiac motion tracking systems are either limited to outer surface or track limited slices/sectors of inner surface in echocardiography data which are unrealizable in MIS due to the varying resolution of ultrasound with depth and speckle effect. In this paper, a system for high accuracy real-time 3D tracking of both cardiac surfaces using sparse samples of outer-surface only is presented. This paper presents a novel approach to model cardiac inner surface deformations as simple functions of outer surface deformations in the spherical harmonic domain using multiple maximal-likelihood linear regressors. Tracking system uses subspace clustering to identify potential deformation spaces for outer surfaces and trains ML linear regressors using pre-operative MRI/CT scan based training set. During tracking, sparse-samples from outer surfaces are used to identify the active outer surface deformation space and reconstruct outer surfaces in real-time under least squares formulation. Inner surface is reconstructed using tracked outer surface with trained ML linear regressors. High-precision tracking and robustness of the proposed system are demonstrated through results obtained on a real patient dataset with tracking root mean square error ≤ (0.23 ± 0.04)mm and ≤ (0.30 ± 0.07)mm for outer & inner surfaces respectively.

Acoustic emission-based techniques are being used for the nondestructive inspection of mechanical systems. For reliable automatic fault monitoring related to the generation and propagation of cracks, it is important to identify the transient crack-related signals in the presence of strong time-varying noise and other interference. A prominent difficulty is the inability to differentiate events due to crack growth from noise of various origins. This work presents a novel algorithm for automatic clustering and separation of acoustic emission (AE) events based on multiple features extracted from the experimental data. The algorithm consists of two steps. In the first step, the noise is separated from the events of interest and subsequently removed using a combination of covariance analysis, principal component analysis (PCA), and differential time delay estimates. The second step processes the remaining data using a self-organizing map (SOM) neural network, which outputs the noise and AE signals into separate neurons. To improve the efficiency of classification, the short-time Fourier transform (STFT) is applied to retain the time-frequency features of the remaining events, reducing the dimension of the data. The algorithm is verified with two sets of data, and a correct classification ratio over 95% is achieved.

We propose a novel algorithm for high-quality data embedding in audio. The algorithm is based on changing the relative length of the middle segment between two successive maximum and minimum peaks to embed data. Spline interpolation is used to change the lengths. To ensure smooth monotonic behavior between peaks, a hybrid orthogonal and nonorthogonal wavelet decomposition is used prior to data embedding. The possible data embedding rates are between 20 and 30 bps. However, for practical purposes, we use repetition codes, and the effective embedding data rate is around 5 bps. The algorithm is invariant after time-scale modification, time shift, and time cropping. It gives high-quality output and is robust to mp3 compression.

Wavelets are a family of basis functions for the space of finite energy signals. In a wavelet decomposition, a signal is expanded in terms of translates and dilates of a single function. Wavelets provide a flexible decomposition of signals. This flexibility is the foundation of a new generation of advanced low‐bit‐rate, high‐quality audio coding procedures. By adaptively choosing the wavelet representations of consecutive segments of the audio signal one can optimally exploit the masking effect in human hearing. The performance of the resulting coding techniques surpasses that of known nonwavelet‐based approaches. Another important property of wavelet decompositions is the fact that one can relate the structure of the wavelet coefficients of a given signal to the local time and frequency domain characteristics of the signal. In sonar processing, this fact has been exploited by a new class of robust direction of arrival estimation algorithms that can deal with background correlated noise of an unknown correlation structure. These algorithms separate signal from noise by analyzing the structure of the wavelet coefficients of the array outputs.

Abstract: A non-destructive, real time device was developed to detect insect damage, sprout damage, and scab damage in kernels of wheat. Kernels are impacted onto a steel plate and the resulting acoustic signal analyzed to detect damage. The acoustic signal was processed using four different methods: modeling of the signal in the time-domain, computing time-domain signal variances and maximums in short-time windows, analysis of the frequency spectrum magnitudes, and analysis of a derivative spectrum. Features were used as inputs to a stepwise discriminant analysis routine, which selected a small subset of features for accurate classification using a neural network. For a network presented with only insect damaged kernels (IDK) with exit holes and undamaged kernels, 87% of the former and 98% of the latter were correctly classified. It was also possible to distinguish undamaged, IDK, sprout-damaged, and scab-damaged kernels. [Copyright &y& Elsevier]