This paper deals with real time segmentation of traffic images using a Mask R-CNN model. The aim is to improve the performance of real time image segmentation, so that it can be effective even with noisy images captur...
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This paper deals with real time segmentation of traffic images using a Mask R-CNN model. The aim is to improve the performance of real time image segmentation, so that it can be effective even with noisy images captured by traffic cameras. The approach developed here comprises of image preprocessing, object detection followed by segmentation. Mask R-CNN model not only segments the image, but also surrounds the image with bounding boxes and assigns class names to the individual objects e.g. Car, Truck, Bus, Bicycle, Person etc. The model is trained with the annotated MS COCO Training dataset. To improve the performance of Mask R-CNN over noisy images, here pre-processingalgorithms like Non Local Means (NLM) filter denoising and Median filter denoising are used. The testing is carried out on a subset of MS COCO Test dataset which comprises of only traffic images. The improved performance is demonstrated using parameters: increased correct object detections and corresponding confidence value, reduced incorrect object detections and corresponding confidence value, and an overall enhanced segment mask area accuracy.
Various Internet of Things (IoT) and Industry 4.0 use cases, such as city-wide monitoring or machine control, require low-latency distributed processing of continuous data streams. This fact has boosted research on ma...
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Various Internet of Things (IoT) and Industry 4.0 use cases, such as city-wide monitoring or machine control, require low-latency distributed processing of continuous data streams. This fact has boosted research on making Stream processing Frameworks (SPFs) IoT-ready, meaning that their cloud and IoT service management mechanisms (e.g., task placement, load balancing, algorithm selection) need to consider new requirements, e.g., ultra low latency due to physical interactions. The algorithm selection problem refers to selecting dynamically which internal logic a deployed streaming task should use in case of various alternatives, but it is not sufficiently supported in current SPFs. To the best of our knowledge, this work is the first to add this capability to SPFs. Our solution is based on i) architectural extensions of typical SPF middleware, ii) a new schema for characterizing algorithmic performance in the targeted context, and iii) a streaming-specific optimization problem formulation. We implemented our solution as an extension to Apache Storm and demonstrate how it can reduce stream processing latency by up to a factor of 2.9 in the tested scenarios.
This paper presents parallelization strategies for the implementation of imaging algorithms for synthetic aperture radar (SAR). Great emphasis is placed on time-domain based algorithms, namely the Global Backprojectio...
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ISBN:
(纸本)9781538631706
This paper presents parallelization strategies for the implementation of imaging algorithms for synthetic aperture radar (SAR). Great emphasis is placed on time-domain based algorithms, namely the Global Backprojection Algorithm (GBP) and its accelerated version, the Fast Factorized Backprojection Algorithm (FFBP). Multi-core platforms are selected for implementation as some combine good performance results with moderate power consumption. The implemented algorithms support several types of parallelization, as the stages of the algorithms can be handled sequentially or interleaved. For the GBP algorithm three different data distribution schemes are investigated. For the FFBP algorithm a successive stage calculation method is compared with a combined calculation method. The performance is exemplary evaluated on the low cost/energy, yet powerful multi-core platform Odroid-XU4. All parallelization strategies show an almost linear speed-up with the number of used cores. Even though a specific multi-core platform is investigated, the design decisions are applicable for general multi-core architectures.
Video sensors constitute a great innovation in the automotive sector and road safety as they contribute to the development of driver assistance systems. These video systems use imageprocessing techniques to inform dr...
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ISBN:
(纸本)9781509058150
Video sensors constitute a great innovation in the automotive sector and road safety as they contribute to the development of driver assistance systems. These video systems use imageprocessing techniques to inform drivers of impending dangers. One such development is the Lane Departure Warning System (LDWS) which play a key role in the prevention from accidents. The main function of this system is the detection of lane boundary lines using artificial vision. In this paper, we present a feature-based method for lane detection. We simplify the process of edge detection by using a horizontal differencing filter. The detected edge points are grouped into lines with a modified Hough transform.
imageprocessing and analysis is a useful tool for monitoring of activated sludge wastewater treatment plants. However, its effectiveness is dependent on performance of the segmentation algorithms. The activated sludg...
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ISBN:
(纸本)9781509011810
imageprocessing and analysis is a useful tool for monitoring of activated sludge wastewater treatment plants. However, its effectiveness is dependent on performance of the segmentation algorithms. The activated sludge wastewater plant can be monitored by imageprocessing and analysis of images acquired through microscope using bright field microscopy and phase contrast microscopy. In this paper, we have investigated three segmentation algorithms which are channel based segmentation, edge based segmentation and Bradley based segmentation. The performance of the algorithms is assessed using the performance metric of accuracy. Forty gold approximations of ground truth images are manually prepared for comparing with the result for segmentation. Half of the forty images are acquired at lOx and rest at 20x objective magnification of the microscope. Edge based segmentation gives better results compared to other algorithms with accuracy of 0.972.
In multiview systems, color plus depth format builds 3D representations of scenes within which the users can freely navigate by changing their viewpoints. In this paper we present a framework for view synthesis when t...
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In multiview systems, color plus depth format builds 3D representations of scenes within which the users can freely navigate by changing their viewpoints. In this paper we present a framework for view synthesis when the user requests an arbitrary viewpoint that is closer to the 3D scene than the reference image. On the target image plane, the requested view obtained via depth-image-based-rendering (DIBR) is irregularly structured and has missing information due to the expansion of objects. We propose a novel framework that adopts a graph-based representation of the target view in order to interpolate the missing image pixels under sparsity priors. More specifically, we impose that the target image is reconstructed with a few atoms of a graph-based dictionary. Experimental results show that the reconstructed views have better PSNR and MSSIM quality than the ones generated within the same framework with analytical dictionaries, and are comparable to the ones reconstructed with TV regularization and linear interpolation on graphs. Visual results, however, show that our method better preserves the details and results in fewer disturbing artifacts than the other interpolation methods.
Multi-beam scanning electron microscopy (MBSEM), has been developed to reduce the acquisition time by scanning multiple pixels simultaneously. The signal from the 14 x 14 beams is captured on a camera which reads out ...
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ISBN:
(纸本)9781467395557
Multi-beam scanning electron microscopy (MBSEM), has been developed to reduce the acquisition time by scanning multiple pixels simultaneously. The signal from the 14 x 14 beams is captured on a camera which reads out the position and intensity for each beam on the sample. But as we work with multiple beams and pixels we need a powerful technique for image acquisition and imageprocessingalgorithms. We use Field Programmable Gate Arrays (FPGA's), often used as an implementation platform for real time image acquisition and processing applications, because their structure is able to exploit spatial and temporal parallelism. This paper presents a technique for dealing with the various constraints of the camera and efficient mapping for image acquisition and processing operations on FPGA.
The ICCSCM 2017 (The 6th International conference on Computer Science and Computational Mathematics) has aimed to provide a platform to discuss computer science and mathematics related issues including Algebraic Geome...
The ICCSCM 2017 (The 6th International conference on Computer Science and Computational Mathematics) has aimed to provide a platform to discuss computer science and mathematics related issues including Algebraic Geometry, Algebraic Topology, Approximation Theory, Calculus of Variations, Category Theory; Homological Algebra, Coding Theory, Combinatorics, Control Theory, Cryptology, Geometry, Difference and Functional Equations, Discrete Mathematics, Dynamical systems and Ergodic Theory, Field Theory and Polynomials, Fluid Mechanics and Solid Mechanics, Fourier Analysis, Functional Analysis, Functions of a Complex Variable, Fuzzy Mathematics, Game Theory, General Algebraic systems, Graph Theory, Group Theory and Generalizations, imageprocessing, Signal processing and Tomography, Information Fusion, Integral Equations, Lattices, Algebraic Structures, Linear and Multilinear Algebra; Matrix Theory, Mathematical Biology and Other Natural Sciences, Mathematical Economics and Financial Mathematics, Mathematical Physics, Measure Theory and Integration, Neutrosophic Mathematics, Number Theory, Numerical Analysis, Operations Research, Optimization, Operator Theory, Ordinary and Partial Differential Equations, Potential Theory, Real Functions, Rings and Algebras, Statistical Mechanics, Structure Of Matter, Topological Groups, Wavelets and Wavelet Transforms, 3G/4G Network Evolutions, Ad-Hoc, Mobile, Wireless Networks and Mobile Computing, Agent Computing & Multi-Agents systems, All topics related image/Signal processing, Any topics related Computer Networks, Any topics related ISO SC-27 and SC- 17 standards, Any topics related PKI(Public Key Intrastructures), Artifial Intelligences(A.I.) & Pattern/image Recognitions, Authentication/Authorization Issues, Biometric authentication and algorithms, CDMA/GSM Communication Protocols, Combinatorics, Graph Theory, and Analysis of algorithms, Cryptography and Foundation of Computer Security, Data Base(D.B.) Management & Information Retr
Currently, robots are increasingly being used in every industry. One of the most high-tech areas is creation of completely autonomous robotic devices including vehicles. The results of various global research prove th...
Currently, robots are increasingly being used in every industry. One of the most high-tech areas is creation of completely autonomous robotic devices including vehicles. The results of various global research prove the efficiency of vision systems in autonomous robotic devices. However, the use of these systems is limited because of the computational and energy resources available in the robot device. The paper describes the results of applying the original approach for imageprocessing on reconfigurable computing environments by the example of morphological operations over grayscale images. This approach is prospective for realizing complex imageprocessingalgorithms and real-time image analysis in autonomous robotic devices.
Diabetic retinopathy (DR) is one of the most serious complications associated with chronic diabetes. High levels of sugar in blood results in long term damage of many vital organs like eyes. If untreated, diabetic ret...
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ISBN:
(纸本)9781538619599
Diabetic retinopathy (DR) is one of the most serious complications associated with chronic diabetes. High levels of sugar in blood results in long term damage of many vital organs like eyes. If untreated, diabetic retinopathy may even lead to partial or complete vision loss. With the advent of machine learning techniques in the field of imageprocessing, the severity of DR can now be estimated from eye fundus images which aids in timely appropriate treatment of the disease. In this paper we have compared the performance of different machine learning algorithms namely decision trees (bagged trees and boosted trees), weighted k-nearest neighbor (KNN), subspace discriminant analysis, and support vector machine (SVM) for the classification of eye fundus images into two classes. We have implemented binary particle swarm optimization (BPSO) to generate a reduced feature set on which we have applied our classification algorithms.
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