Fractional Brownian Motion (fBm) has been successfully exploited to model an important number of physical phenomena and non-stationary processes such as medical images. These mathematical models closely describe essen...
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Fractional Brownian Motion (fBm) has been successfully exploited to model an important number of physical phenomena and non-stationary processes such as medical images. These mathematical models closely describe essential properties of natural phenomena, such as self-similarity, scale invariance and fractal dimension (FD). The use of wavelet analysis combined with fBm analysis may provide an interesting approach to compute key values for fBm processes, such as the fractal dimension FD. We propose two models to calculate the Hurst Coefficient H (and hence FD) for both one-dimensional and two-dimensional signals. The first approach is based on statistical properties of the signals, while the second one is based on their spectral characteristics. A formal extension of these two models to 2D processes is developed and implemented, and a comparison of both is presented. The proposed algorithms are tested using tomographic brain tumor data. Our simulation experiments offer promising results in the identification of such lesions.
In this paper, we focus on the leaky prediction based scalable coding (LPSC) structure and present a general framework for LPSC. We demonstrate the similarity between LPSC and motion compensation based multiple descri...
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In this paper, we focus on the leaky prediction based scalable coding (LPSC) structure and present a general framework for LPSC. We demonstrate the similarity between LPSC and motion compensation based multiple description coding scheme. We show that since the information contained in the enhancement layer in LPSC is actually a mismatch between two descriptions for each frame, it cannot be guaranteed that the enhancement layer always achieves superior reconstruction quality beyond that achieved by the base layer. We derive three reconstructions for each frame under the LPSC framework, and propose a maximum-likelihood (ML) estimation scheme for LPSC video reconstruction at the decoder. This generally achieves superior decoded video quality than both the enhancement layer and the base layer.
Driven by potential applications in military, biology, robot navigation and other areas, various methods for optical pattern recognition have been proposed and studied over the past decades. The key feature in current...
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Driven by potential applications in military, biology, robot navigation and other areas, various methods for optical pattern recognition have been proposed and studied over the past decades. The key feature in current real-time optical pattern recognition systems among these methods is the use of a spatial light modulator (SLM) at the Fourier transform plane in the so-called joint-Fourier transform correlation (JTC) system [C.S Weaver et al. (1966)]. We propose a novel hybrid (optical/electronic) processing technique to achieve real-time joint-transform correlation. The technique employs acousto-optic heterodyne scanning [T.-C. Poon et al., 1979] and it does not require a SLM at the Fourier plane as in other conventional real-time JTC systems. This departure from the conventional scheme is extremely important, as it does not depend on SLM design considerations and issues such as phase uniformity and contrast ratio. The effectiveness of the novel proposed system will be demonstrated with some experimental results. Future work will be focused on exploring scale-invariant, rotation-invariant and multiobject joint-transform correlation with this hybrid optical pattern recognition method.
Hyperspectral imagery offers a means of uncovering enormous spectral information that can be used for various applications in data exploitation. How effectively such information is used affects the way image analysis ...
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ISBN:
(纸本)0780383508
Hyperspectral imagery offers a means of uncovering enormous spectral information that can be used for various applications in data exploitation. How effectively such information is used affects the way image analysis algorithms are designed. In this paper, we take up this issue and focus on algorithms designed and developed for target detection and classification in hyperspectral imagery. In order to effectively characterize the information available before and after the data are processed, the a priori information and a posteriori information are used in accordance with how the information is obtained. A piece of information is referred to as a priori information if it is provided by known knowledge before data are processed. On the other hand, a piece of information is referred to as a posteriori information if it is unknown a priori, but can be obtained directly from the data in an unsupervised fashion during the course of data processing. Since a priori information is known beforehand, it can be further decomposed into two types of information, desired and undesired a priori information. The desired a priori information is the knowledge that will assist, improve and enhance data analysis, whereas the undesired a priori information is the knowledge that hinders, interferes or destructs analysis during data processing. This paper investigates how these three types of information play their roles in design and development of several hyperspectral target detection and classification algorithms. Experiments are also conducted to validate their utility.
Transmission of digital video signals over wireless networks demands efficient compression algorithms as well as reliable coding strategies. In this project, we provide a thorough evaluation of the joint source-channe...
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Transmission of digital video signals over wireless networks demands efficient compression algorithms as well as reliable coding strategies. In this project, we provide a thorough evaluation of the joint source-channel video coding methodology from two points of view: source coding design for error resilience and channel coding for error detection and recovery. We investigate the current ITU video compression standard, H.263+, for 3G wireless transmission. In particular, we concentrate on error resilient features provided within the standard and forward error correction (FEC) to find the optimal combination of various system parameters under different lossy channel conditions.
We present a clustering algorithm based on dynamic time warping (DTW) to automatically classify intracranial pressure (ICP) beats based on their morphology. The algorithm detects, classifies and labels each beat as a ...
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We present a clustering algorithm based on dynamic time warping (DTW) to automatically classify intracranial pressure (ICP) beats based on their morphology. The algorithm detects, classifies and labels each beat as a low-pressure or high-pressure beat based on morphology. The trend is removed during preprocessing to ensure the classifications are independent of the mean ICP. An ICP beat detection algorithm is used to automatically detect each beat. We measured the performance of the algorithm compared to expert classification of ICP beats acquired from intensive care unit patients using linear and nonlinear temporal alignment techniques. The algorithm achieved a superior performance using non-linear temporal alignment.
Protection of intellectual property is a critical issue in digital multimedia distribution systems. Cryptographic tools are commonly used for secure delivery of content and access keys to consumers via terrestrial, sa...
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Protection of intellectual property is a critical issue in digital multimedia distribution systems. Cryptographic tools are commonly used for secure delivery of content and access keys to consumers via terrestrial, satellite, cable and Internet transmissions. A third requirement is the distribution of the copyright or usage rights associated with the digital content. The integrity, as opposed to security, of this data is essential to prevent unauthorized modification. Two approaches have been proposed in the open literature: allocating special fields in the transport stream and embedding a watermark into multimedia content. We present two new methods, based on secret sharing, to create channels with guaranteed data integrity.
In this paper, adaptive directional zerotree image coding is introduced. This modified SPIHT algorithm called adaptive directional SPIRT (ADSPIHT) exploits an appropriate quadtree structure in order to take advantage ...
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In this paper, adaptive directional zerotree image coding is introduced. This modified SPIHT algorithm called adaptive directional SPIRT (ADSPIHT) exploits an appropriate quadtree structure in order to take advantage from horizontal and vertical edge encoding in separable wavelet transform. Moreover, this adaptive algorithm changes structure in order to enhance energy compaction without increasing side information rate. Compared to SPIRT and DSPIHT, ADSPIHT improves coding performance in PSNR, especially in images with highly vertical and horizontal edges.
We present a combined real-time face region tracking and highly accurate face recognition technique for an intelligent surveillance system. High-resolution face images are very important to achieving accurate identifi...
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We present a combined real-time face region tracking and highly accurate face recognition technique for an intelligent surveillance system. High-resolution face images are very important to achieving accurate identification of a human face. Conventional surveillance or security systems, however, usually provide poor image quality because they use only fixed cameras to record scenes passively. We have implemented a real-time surveillance system that tracks a moving face using four pan-tilt-zoom (PTZ) cameras. While tracking, the region-of-interest (ROI) can be obtained by using a low-pass filter and background subtraction with the PTZ. Color information in the ROI is updated to extract features for optimal tracking and zooming. FaceIt/sup /spl reg//, which is one of the most popular face recognition software packages, is evaluated and then used to recognize the faces from the video signal. Experimentation with real human faces showed highly acceptable results in the sense of both accuracy and computational efficiency.
In this paper, we present an analysis of the performance of three predictive fine granular SNR-scalable coders and compare them with their non-scalable version. In our study, we assume an exponential model for the qua...
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In this paper, we present an analysis of the performance of three predictive fine granular SNR-scalable coders and compare them with their non-scalable version. In our study, we assume an exponential model for the quantization noise and the use of linear prediction. Coders efficiency is assessed through the signal-to-noise ratio as a function of rate (SNR(R)) and the mean SNR. Validity of our analysis is tested by comparing theoretical results with simulations of the encoding of realizations of first order autoregressive processes. Results show that the use of coders which tolerate some prediction drift provides better results than other conventional scalable schemes.
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