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.
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.
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.
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.
Pre-processing steps are critical in automated image analysis systems developed to aid in diagnosis of skin lesion images. The main areas of concern include, but are not limited to, hair on the skin, variations in ill...
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Pre-processing steps are critical in automated image analysis systems developed to aid in diagnosis of skin lesion images. The main areas of concern include, but are not limited to, hair on the skin, variations in illumination and skin tone, and alignment of successive skin images. These artifacts can partially or completely obstruct a lesion being analyzed causing errors in classification or diagnosis. This paper focuses on an independent quantitative evaluation of an open source hair removal algorithm [1]. The different input parameters to the algorithm were tested to determine their optimal values. Percent error and signal to noise ratio are utilized as the error metrics for the experimental results. Other essential pre-processing steps are considered and provided at the end of this paper.
Space field has experienced vigorous advancement with respect to evolution of vision system, image storage and processing. Real time imageprocessing has become one of the most important tools for navigation and landi...
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ISBN:
(纸本)9781538630051
Space field has experienced vigorous advancement with respect to evolution of vision system, image storage and processing. Real time imageprocessing has become one of the most important tools for navigation and landing for planetary and lunar missions. Information of horizontal velocity with high accuracy will be required to do accurate pin point landing. For the testability of such a system as well as to have the understanding of Lander dynamics, prior landing image sequences are required to initially testing the algorithm [1]. This paper deals with FPGA based processing on image sequence to find the relative velocity and also implements image Storage in a microSD card. Landing sequence is stored in SD card and post landing they are downloaded. These images provide a very useful information about lighting, lander dynamics to fine tune algorithm for future mission. Besides this image data serves the testability during various phases of testing during development.
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