With the availability of sensor technology across the broad electromagnetic spectrum, multi-spectral imaging is increasingly used in biometric systems. Especially for face recognition, multi-spectral imaging has gaine...
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ISBN:
(纸本)9780996452700
With the availability of sensor technology across the broad electromagnetic spectrum, multi-spectral imaging is increasingly used in biometric systems. Especially for face recognition, multi-spectral imaging has gained a lot of attention due to it's invariant property against variation caused by unknown illumination. However, obtaining best performance using multi-spectral imaging is still a challenge due to presence of a modality gap between the spectral imaging data and redundant band information. In this paper, we propose a fused band representation with a set of selected bands represented in Quaternion space for spectral band images to efficiently maintain the inter band relationship in spatial domain. The selection is based on measuring the information content in bands using entropy and fusion is carried out in Quaternion space for three best bands. The features from newly obtained image is collaboratively represented to achieve robust performance. The proposed approach is experimentally validated on the extended multi-spectral face database of 168 subjects, whose spectral band images are captured in 9 narrow spectral bands in visible and near infrared range (530nm to 1000nm). The quantitative performance analysis, obtained using the proposed method indicates 96.13% recognition rate at Rank-1, outperforming other state-of-the-art methods.
In this contribution, a real-time imaging system alongside its processing chain for millimeter wave (mmWave) synthetic aperture radar (SAR) sensors operating at 240 GHz is presented. In addition to real-time capabilit...
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ISBN:
(纸本)9781538617144
In this contribution, a real-time imaging system alongside its processing chain for millimeter wave (mmWave) synthetic aperture radar (SAR) sensors operating at 240 GHz is presented. In addition to real-time capability, the SAR imaging system is manually moved on a linear track without precise stepper motors. Instead, the current position is obtained by a second radar measurement at a far lower frequency with an additional reference target. By this means, the image of the considered scene can be obtained in mere seconds. To allow real-time processing of the measurement data, a signal processing chain based on separate threads is established and further enhanced by using a graphics-processing unit (GPU). Due to the large imaging bandwidth of 50 GHz, the image system can be used to obtain high-resolution sub-surface images of e.g. construction materials.
Industries are moving towards automation in order to increase productivity and ensure quality. Variety of electronic and electromagnetic systems are being employed to assist human operator in fast and accurate quality...
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ISBN:
(数字)9781510611221
ISBN:
(纸本)9781510611214;9781510611221
Industries are moving towards automation in order to increase productivity and ensure quality. Variety of electronic and electromagnetic systems are being employed to assist human operator in fast and accurate quality inspection of products. Majority of these systems are equipped with cameras and rely on diverse imageprocessingalgorithms. Information is lost in 2D image, therefore acquiring accurate 3D data from 2D images is an open issue. FAST, SURF and SIFT are well-known spatial domain techniques for features extraction and henceforth image registration to find correspondence between images. The efficiency of these methods is measured in terms of the number of perfect matches found. A novel fast and robust technique for stereo-imageprocessing is proposed. It is based on non-rigid registration using modified normalized phase correlation. The proposed method registers two images in hierarchical fashion using quad-tree structure. The registration process works through global to local level resulting in robust matches even in presence of blur and noise. The computed matches can further be utilized to determine disparity and depth for industrial product inspection. The same can be used in driver assistance systems. The preliminary tests on Middlebury dataset produced satisfactory results. The execution time for a 413 x 370 stereo-pair is 500ms approximately on a low cost DSP.
Medical image enhancement is an effective tool to improve visual quality of digital medical images. However, conventional linear image enhancement methods often suffers from problems such as over-enhancement and noise...
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ISBN:
(纸本)9781509018970
Medical image enhancement is an effective tool to improve visual quality of digital medical images. However, conventional linear image enhancement methods often suffers from problems such as over-enhancement and noise sensitivity. In this paper, we study nonlinear arithmetic frameworks designed to solve the common problems of linear enhancement methods, namely, LIP, PLIP and GLIP. We also introduce nonlinear unsharp masking algorithms based on the logarithmic imageprocessing models for medical image enhancement. Experiments are conducted to evaluate and compare the performance of the methods.
Circular Synthetic Aperture Radar (CSAR) can overcome some significant inherent drawbacks of conventional SAR imaging methods such as limited resolution and restricted aspect angle interval of the illuminated scene. I...
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Circular Synthetic Aperture Radar (CSAR) can overcome some significant inherent drawbacks of conventional SAR imaging methods such as limited resolution and restricted aspect angle interval of the illuminated scene. In addition, utilizing of different elevation acquisition has the capability to reduce undesired sidelobes of single flight CSAR. Because of the sparse nature of elevation aperture data of multi baseline CSAR, sparse signal processing approaches have been employed instead of typical algorithms. firstly, the number of samples for recording and processing can be reduced significantly by subaperture acquisition. Moreover, sparsity driven approaches such as Compressive Sensing (CS) represents considerable sidelobe reduction. Whereas CS demonstrates adequate resolution, reducing the number of tracks degrades the ultimate image abruptly. Consequently, we exploit the novel idea of Distributed Compressive Sensing (DCS) with joint sparsity in this context to improve resolution and reduce sidelobe effect substantially in the consequent full aperture 3D image. Furthermore, implementation of the state-of-the-art sparsity-driven algorithm improve our imaging result and reduce computational burden.
In imageprocessing area and segmentation algorithms based on thresholding, the intensity of the image (grayscale) is usually obtained in order to differentiate the regions of the objects and the background. The segme...
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ISBN:
(纸本)9781509050475
In imageprocessing area and segmentation algorithms based on thresholding, the intensity of the image (grayscale) is usually obtained in order to differentiate the regions of the objects and the background. The segmentation based on the threshold works well when the image has a high intensity in the contrast, this characteristic is key to make a good classification of the pixels. This document will explain some theoretical concepts to identify objects by means of their color (thresholding), this technique was implemented in the development of a game program. Furthermore, the thresholding range for the red, yellow and green colors was found in order to achieve a better approach in the object detection. This project used the python programming language, Pygame graphical interface libraries and the OpenCV library free open source about artificial vision.
In the present period of IT and correspondence innovation, use of video based data is expanding enormously. Efficient algorithms are very highly demanded Detection of scene text and caption text in the video in the ar...
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In this paper we propose a dictionary learning method that builds an overcomplete dictionary that is computationally efficient to manipulate, i.e., sparse approximation algorithms have sub-quadratic computationally co...
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In this paper we propose a dictionary learning method that builds an overcomplete dictionary that is computationally efficient to manipulate, i.e., sparse approximation algorithms have sub-quadratic computationally complexity. To achieve this we consider two factors (both to be learned from data) in order to design the dictionary: an orthonormal component made up of a fixed number of fast fundamental orthonormal transforms and a sparse component that builds linear combinations of elements from the first, orthonormal component. We show how effective the proposed technique is to encode image data and compare against a previously proposed method from the literature. We expect the current work to contribute to the spread of sparsity and dictionary learning techniques to hardware scenarios where there are hard limits on the computational capabilities and energy consumption of the computer systems.
With the development of computer technology, digital imageprocessing technologies have been applied to many areas of real life, blurred image restoration also has a rapid development, which gives a certain basis and ...
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With the development of computer technology, digital imageprocessing technologies have been applied to many areas of real life, blurred image restoration also has a rapid development, which gives a certain basis and conditions in the bridge crack detection. Aiming at the blurred crack image generated by camera shake, the paper studies the motion blur image restoration algorithms, and explores the parameter estimation methods of motion blur, where the direction and scale of the blur kernel function are estimated from the spectrum of the blurred image. The paper uses different image quality evaluation standards to compare the output, which can choose the best results and gets a more accurate point spread function. This method can obtain clearer crack images and provide more accurate crack information for bridge project research.
image segmentation is a vital task in imageprocessing/computer vision. However, no universally accepted quality measure exists for evaluating the performance of various segmentation algorithms or even different param...
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image segmentation is a vital task in imageprocessing/computer vision. However, no universally accepted quality measure exists for evaluating the performance of various segmentation algorithms or even different parameterizations of the same algorithm. This paper proposes a new segmentation evaluation measure, based on the fusion of HOG and Harris features, thus we call it the H2. It exploits local shape, corner and edge information to evaluate the similarity between a given segmentation and its respective ground truth, and thus belongs to the category of supervised evaluation measures. The results obtained from our experiments show accuracy of up to 95% for the H2.
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