Many multispectral imaging systems are computational in nature and require processing of raw data in order to obtain radiance spectra. In this paper, we derive a fast and scalable spectral estimation algorithm based o...
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ISBN:
(纸本)9781479983407
Many multispectral imaging systems are computational in nature and require processing of raw data in order to obtain radiance spectra. In this paper, we derive a fast and scalable spectral estimation algorithm based on the Alternating Direction Method of Multipliers (ADMM). Using this approach we solve for the unknown surface spectral reflectance simultaneously for all pixels in the image. This global formulation allows us to incorporate spatial as well as spectral regularizers, such as total variation penalty or non-negativity. We show that the estimates derived with our solver are more accurate and more robust in the presence of noise.
There are four major components in application systems with internet-of-things (IoT): sensors, communications, computation and service, where large amount of data are acquired for ultra-big data analysis to discover t...
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ISBN:
(纸本)9781479977925
There are four major components in application systems with internet-of-things (IoT): sensors, communications, computation and service, where large amount of data are acquired for ultra-big data analysis to discover the context information and knowledge behind signals. To support such large-scale data size and computation tasks, it is not feasible to employ centralized solutions on cloud servers. Thanks for the advances of silicon technology, the cost of computation become lower, and it is possible to distribute computation on every node in IoT. In this paper, we take video sensing network as an example to show the idea of distributed computing in IoT. Existing related works are reviewed and the architecture of a system-on-a-chip solution for distributed smart cameras is proposed with coarse-grained reconfigurable image stream processing architecture. It can accelerate various computer vision algorithms for distributed smart cameras in IoT.
Fingerprinting is one of the most used biometrics for people identification, it relays on imageprocessing and classification algorithms. In this work we propose and test a framework that enables fingerprint detection...
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Fingerprinting is one of the most used biometrics for people identification, it relays on imageprocessing and classification algorithms. In this work we propose and test a framework that enables fingerprint detection using a set of image pre-processing algorithm. Concerning the features extraction, we propose the use of the number of bifurcations in image localities, and we propose the use of Artificial Neural Network (ANN) for the classification. The performance of our framework is evaluated for three different activation functions and show that we can reach an accuracy of 81%.
Diabetic Macular Edema (DME) is caused by accumulation of extracellular fluid from hyperpermeable capillaries within the macula. DME is one of the leading causes of blindness among Diabetes Mellitus (DM) patients. Ear...
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Diabetic Macular Edema (DME) is caused by accumulation of extracellular fluid from hyperpermeable capillaries within the macula. DME is one of the leading causes of blindness among Diabetes Mellitus (DM) patients. Early detection followed by laser photocoagulation can save the visual loss. This review discusses various imaging modalities viz. biomicroscopy, Fluorescein Angiography (FA), Optical Coherence Tomography (OCT) and colour fundus photographs used for diagnosis of DME. various automated DME grading systems using retinal fundus images, associated retinal imageprocessing techniques for fovea, exudate detection and segmentation are presented. We have also compared various imaging modalities and automated screening methods used for DME grading. The reviewed literature indicates that FA and OCT identify DME related changes accurately. FA is an invasive method, which uses fluorescein dye, and OCT is an expensive imaging method compared to fundus photographs. Moreover, using fundus images DME can be identified and automated. DME grading algorithms can be implemented for telescreening, Hence, fundus imaging based DME grading is more suitable and affordable method compared to biomicroscopy, FA, and OCT modalities. (C) 2015 Elsevier Ltd. All rights reserved.
The demand of 3D image is growing sharply in the recent trends. Digital imageprocessing performs imageprocessing on digital images by the use of computer algorithms. Strain measurement is a big challenge for 3D imag...
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ISBN:
(纸本)9781479968190
The demand of 3D image is growing sharply in the recent trends. Digital imageprocessing performs imageprocessing on digital images by the use of computer algorithms. Strain measurement is a big challenge for 3D images in the field of imageprocessing. This paper proposed a noble algorithm for strain measurement of 3D stereo image using imageprocessing for applications like non-invasive bio-sensing, non-contact measurements of space crafts, faults in pipeline industries, etc. Strain measurement is made possible with the help of filtration techniques and digital image correlation.
Since the computer vision technologies are used to extract features from the edges of images, the median filter is frequently used as a pre-processing operation to improve the quality of feature objects. However, the ...
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ISBN:
(纸本)9781479983407
Since the computer vision technologies are used to extract features from the edges of images, the median filter is frequently used as a pre-processing operation to improve the quality of feature objects. However, the median filter algorithms still have the limitation on integrating with the mobile systems due to the large computation overhead. Since the median filter accounts for a large proportion of total computation, this paper proposes a separable median filter to resolve the limitation. The proposed separable median filter is implemented with the sorting network, and reuses the data sorted at neighbor pixels, reducing duplicated comparisons.
Although many advances have been made in light-field and camera-array imageprocessing, there is still a lack of thorough analysis of the localisation accuracy of different multi-camera systems. By considering the pro...
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ISBN:
(纸本)9781479983407
Although many advances have been made in light-field and camera-array imageprocessing, there is still a lack of thorough analysis of the localisation accuracy of different multi-camera systems. By considering the problem from a frame-quantisation perspective, we are able to quantify the point localisation error of circular camera configurations. Specifically, we obtain closed form expressions bounding the localisation error in terms of the parameters describing the acquisition setup. These theoretical results are independent of the localisation algorithm and thus provide fundamental limits on performance. Furthermore, the new frame-quantisation perspective is general enough to be extended to more complex camera configurations.
image segmentation has an important role in many imageprocessing applications. Several methods exist for segmenting an image. However, this technique is still a relatively open topic for which various research works ...
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image segmentation has an important role in many imageprocessing applications. Several methods exist for segmenting an image. However, this technique is still a relatively open topic for which various research works are regularly presented. With the recent developments on complex networks theory, image segmentation techniques based on graphs has considerably improved. In this paper, we present a new perspective of image segmentation, by applying three of the most efficient community detection algorithms, Louvain, infomap and stability optimization based on the louvain algorithm, and we extract communities in which the highest modularity feature is achieved. After we show that this measure is invariant to non-structural change on image, which mean that the image segmentation is also invariant to rotation. Finally we evaluate the three proposed algorithms for Berkeley database images, and we show that our results can outperform other segmentation methods in terms of accuracy and can achieve much better segmentation results.
Every year lives and properties are lost in road accidents. About one-fourth of these accidents are due to low vision in foggy weather. At present, there is no algorithm that is specifically designed for the removal o...
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ISBN:
(纸本)9781627055864
Every year lives and properties are lost in road accidents. About one-fourth of these accidents are due to low vision in foggy weather. At present, there is no algorithm that is specifically designed for the removal of fog from videos. Application of a single-image fog removal algorithm over each video frame is a time-consuming and costly affair. It is demonstrated that with the intelligent use of temporal redundancy, fog removal algorithms designed for a single image can be extended to the real-time video application. Results confirm that the presented framework used for the extension of the fog removal algorithms for images to videos can reduce the complexity to a great extent with no loss of perceptual quality. This paves the way for the real-life application of the video fog removal algorithm. In order to remove fog, an efficient fog removal algorithm using anisotropic diffusion is developed. The presented fog removal algorithm uses new dark channel assumption and anisotropic diffusion for the initialization and refinement of the airlight map, respectively. Use of anisotropic diffusion helps to estimate the better airlight map estimation. The said fog removal algorithm requires a single image captured by uncalibrated camera system. The anisotropic diffusion-based fog removal algorithm can be applied in both RGB and HSI color space. This book shows that the use of HSI color space reduces the complexity further. The said fog removal algorithm requires pre- and post-processing steps for the better restoration of the foggy image. These pre- and post-processing steps have either data-driven or constant parameters that avoid the user intervention. Presented fog removal algorithm is independent of the intensity of the fog, thus even in the case of the heavy fog presented algorithm performs well. Qualitative and quantitative results confirm that the presented fog removal algorithm outperformed previous algorithms in terms of perceptual quality, color fidelity and execut
A review of modern optimal and quasi-optimal algorithms for radiometric signal processing, which provide images with a high spatial resolution is given. It is shown that the high resolution is achieved with ultra-wide...
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A review of modern optimal and quasi-optimal algorithms for radiometric signal processing, which provide images with a high spatial resolution is given. It is shown that the high resolution is achieved with ultra-wideband or multi-band time or space-time signal processing. Analytical expressions for the limiting error of image estimation, ambiguity functions and potential fluctuation sensitivity of systems designed according to the algorithms are derived.
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