A system architecture is presented for document imaging in an open, distributed environment over networks, where various kinds of imaging devices can be interconnected remotely. The key components are two sets of imag...
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ISBN:
(纸本)3540321403
A system architecture is presented for document imaging in an open, distributed environment over networks, where various kinds of imaging devices can be interconnected remotely. The key components are two sets of imageprocessing operations to transform input images to (1) canonical image representations to absorb different visual appearance due to characteristics of imaging devices or image acquisition conditions (normalization), and (2) optimal image representations according to tasks and preferences of individual users (optimization). images captured through a diversity of input devices can be delivered to remote sites through networks, and then will be used for a variety of tasks such as printing on paper sheets, browsing on displays, and editing. These diversities can be resolved systematically by placing the normalizations at an upper end (routing servers) and the optimizations at a lower end (clients) of the data flow over networks. In view of this architecture, we describe some instances of the normalizations and optimizations associated with a particular task of highly legible printing of scanned document images. Three essential algorithms are mentioned for optimizing document images: adaptive tone mapping with background cleaning, text super-resolution, and text color clustering. The optimization process is mentioned for highly legible printing, along with some other potential applications and tasks.
Tools for Transform Coding in coding of video relied on DCT-II traditionally for mapping residuals of image/video signals. Residual mapping can be done to a domain where quantizing and encoding tools give better effic...
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The proceedings contain 33 papers. The topics discussed include: 3D imaging without range information;lock-in imaging with synchronous digital mirror demodulation;chromotomographic imager field demonstration results;m...
ISBN:
(纸本)9780819481320
The proceedings contain 33 papers. The topics discussed include: 3D imaging without range information;lock-in imaging with synchronous digital mirror demodulation;chromotomographic imager field demonstration results;multisensor airborne imagery collection and processing onboard small unmanned systems;use of compact synthetic aperture radar systems to assist with device detection and discrimination;compact survey and inspection day/night image sensor suite for small unmanned aircraft systems (EyePod);a simulation of wide area surveillance (WAS) systems and algorithm for digital elevation model (DEM) extraction;opto-mechanical review of a light weight compact visible zoom camera;robust component-based car detection and counting in aerial imagery based on the mean-shift color space clustering;advanced vehicle tracking in persistent aerial surveillance video;and directed area search using socio-biological vision algorithms and cognitive Bayesian reasoning.
Real-time video surveillance systems are widely deployed in various environments, including public areas, commercial buildings, and public infrastructures. Person detection is a key and crucial task in different video...
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Robust delineation of short-axis cardiac magnetic resonance images (MRI) is a fundamental precondition for functional heart diagnostics. Segmentation of the myocardium and the left ventricular blood pool allows for th...
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ISBN:
(纸本)9783540321361
Robust delineation of short-axis cardiac magnetic resonance images (MRI) is a fundamental precondition for functional heart diagnostics. Segmentation of the myocardium and the left ventricular blood pool allows for the analysis of important quantitative parameters. Modelbased segmentation methods based on representative image data provide an inherently stable tool for this *** present an implementation and evaluation of 3-D Active Appearance Models for the segmentation of the left ventricle using actual clinical case images. Models created from varying random data sets have been evaluated and compared with manual segmentations.
This paper presents a novel scheme to detect and discriminate landmines from other clutter objects during the image formation process for ultra-wideband (UWB) synthetic aperture radar (SAR) systems. By identifying lik...
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This paper presents a novel scheme to detect and discriminate landmines from other clutter objects during the image formation process for ultra-wideband (UWB) synthetic aperture radar (SAR) systems. By identifying likely regions containing the targets of interest, i.e., landmines, it is possible to speed up the overall formation time by pruning the processing to resolve regions that do not contain targets. The image formation algorithm is a multiscale approximation to standard backprojection known as the quadtree that uses a 'divide-and-conquer' strategy. The intermediate quadtree data admits multiresolution representations of the scene, and we develop a contrast statistic to discriminate structured/diffuse regions and an aperture diversity statistic to discriminate between regions containing mines and desert scrub. The potential advantages of this technique are illustrated using data collected at Yuma, AZ by the ARL BoomSAR system.
We design a new compactly-supported interpolating wavelet - distributed approximating functional (DAF) wavelet for biomedical signal/imageprocessing. DAF class is a smooth, continuous interpolating function system wh...
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We design a new compactly-supported interpolating wavelet - distributed approximating functional (DAF) wavelet for biomedical signal/imageprocessing. DAF class is a smooth, continuous interpolating function system which is symmetric and fast-decaying. DAF neural networks are designed for time varying electrocardiogram (EKG) signal filtering. The neural nets use the Hermite-DAF as the basis function and implement a 3-layer structure. DAF wavelets and the corresponding subband filters are constructed for imageprocessing. Edge-enhancement normalization and device-adapted visual group normalization algorithms are presented which sharpen the desired image features (especially for digital mammography) without prior knowledge of the spatial characteristics of the images. We design a nonlinear multiscale gradient-stretch method for feature extraction of mammograms (such as the detection of ill-defined borders and spiculated lesions, etc). A fractal technique is introduced to characterize microcalcifications in localized regions of breast tissue. We employ a DAF wavelet-based multiscale edge detection and Dijkstra fractal technique to identify microcalcification regions, and use a stochastic thresholding method to detect the calcified spots. The combined perceptual techniques (such as regularization, visual group normalization and contrast nonlinear enhancement) produce natural high-quality images based on the human vision system. The underlying technologies significantly facilitate the creation of generic signal processing and computer-aided diagnostic (CAD) systems. The system is implemented in the JAVA language, which is cross-platform friendly and is facilitated for telemedicine application.
Proportional-integral-derivative (PID) controllers are widely regarded as the most important tool for a control engineer. However, in today's era of computer science, MATLAB has acquired the position of an essenti...
ISBN:
(纸本)9781632401953
Proportional-integral-derivative (PID) controllers are widely regarded as the most important tool for a control engineer. However, in today's era of computer science, MATLAB has acquired the position of an essential tool for the modern system engineering. This book has been written in a manner to serve as a practical guide for both practicing engineers and engineering students. Numerous applications use MATLAB as the working framework which shows that it is an effective, comprehensive and handy technique for performing technical computations. This book discusses various prominent applications in which MATLAB is used: in imageprocessingalgorithms, and from Graphic User Interface (GUI) design for educational purposes to Simulink embedded systems.
We study the non-linear behavior of the KIII model for natural image classification. The KIII model is designed to be a dynamic computational model that simulates the sensory cortex. The KIII model has been explored f...
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We study the non-linear behavior of the KIII model for natural image classification. The KIII model is designed to be a dynamic computational model that simulates the sensory cortex. The KIII model has been explored for rudimentary pattern recognition and classification in noisy environment [1-3]. We extend the study of KIII models in understanding whether self-organized neural populations can be exploited into perceptual and memory producing systems such as in natural image classification. Our goal is to obtain a quantitative index on how well the KIII model behaves when it is assigned the task to identify and distinguish one class of natural image from the other based on color and texture features. For twenty training data, twenty validation data and eighty test data set for four image classes, we obtain 80% correct classification using the KIII. We compare a standard non linear neural network tools such as back propagation for the classification of the same set of natural images and obtain 65% correct classification. We conclude that dynamic neural computational models such as KIII may be suitable candidates for natural image classification.
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