Feature extraction and segmentation on multidimensional images is still a tedious task in the field of imageprocessing. images provide depth of reality and featuring the image interactively for which imageprocessing...
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ISBN:
(纸本)9781538623428;9781538623411
Feature extraction and segmentation on multidimensional images is still a tedious task in the field of imageprocessing. images provide depth of reality and featuring the image interactively for which imageprocessing is beneficial for extracting different features from any image by applying various algorithms and obtaining discrete results. These algorithm help to extract features like edges, texture and surface of an image. This paper attempts to evaluate efficiency of different edge detection algorithm and comparison of their result to find out the best operator for Edge Detection Technique.
A real-time video capture and motion recognition system based on FPGA is constructed. Based on the three-frame difference method, the fast median filter module and the Sobel edge detection module are designed. The exp...
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ISBN:
(纸本)9781538693902
A real-time video capture and motion recognition system based on FPGA is constructed. Based on the three-frame difference method, the fast median filter module and the Sobel edge detection module are designed. The experimental results show that the system can detect the moving object successfully, and has a faster detection speed and meets the real-time requirements.
Vehicular traffic is increasing rapidly in this world which is resulting in traffic congestion. The emergency vehicles such as ambulances, fire engines and police vehicles are given equal priority over other vehicles ...
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Vehicular traffic is increasing rapidly in this world which is resulting in traffic congestion. The emergency vehicles such as ambulances, fire engines and police vehicles are given equal priority over other vehicles and hence get stuck up in this traffic congestion. A methodology for priority based vehicle detection based on imageprocessing techniques is proposed in this paper. If an emergency vehicle is detected on the road, the lane in which this vehicle is will be given higher priority over all other lanes. The paper proposes an algorithm which will detect whether a vehicle is an emergency one or not.
This book (CCIS 837)constitutes the refereed proceedings of the Second International conference on Soft Computing systems, ICSCS 2018, held in Sasthamcotta, India, in April 2018. The 87 full papers were carefully revi...
ISBN:
(数字)9789811319365
ISBN:
(纸本)9789811319358
This book (CCIS 837)constitutes the refereed proceedings of the Second International conference on Soft Computing systems, ICSCS 2018, held in Sasthamcotta, India, in April 2018. The 87 full papers were carefully reviewed and selected from439 submissions. The papers are organized in topical sections on soft computing, evolutionary algorithms, imageprocessing, deep learning, artificial intelligence, big data analytics,data minimg, machine learning, VLSI, cloud computing, network communication, power electronics, green energy.
imageprocessing technique has been used to produce automated detection of Sickle Cell Anemia. A Laplacian of Gaussian (LoG) edge detection algorithm computed to detect sickle cells diseases at the early stage in diag...
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ISBN:
(纸本)9781538663288;9781538663271
imageprocessing technique has been used to produce automated detection of Sickle Cell Anemia. A Laplacian of Gaussian (LoG) edge detection algorithm computed to detect sickle cells diseases at the early stage in diagnosing patient. A MATLAB software able to demonstrate the abnormalities of the human Red Blood Cell (RBC) in the single shapes and quantities of sickle cells present in each dataset. A data samples of sickle cells from government Ampang Hospital has contributed this study to validate the results.
While machine learning algorithms become more and more accurate in imageprocessing tasks, their computation complexity becomes less important because they can be run on more and more powerful hardware. In this work, ...
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ISBN:
(纸本)9781538626344;9781538626337
While machine learning algorithms become more and more accurate in imageprocessing tasks, their computation complexity becomes less important because they can be run on more and more powerful hardware. In this work, we are considering the computation complexity of a machine learning algorithm training/classification phase as the major criterion. The main aim is given to the Principal Component Analysis algorithm, which is examined, its drawbacks are point-out and suppressed by the proposed combination with the F-transform technique. We show that the training phase of such a combination is very fast, which is caused by the fact that both PCA and F-transform algorithms reduce dimensionality. In the designed benchmark, we show that the success rate of the fast hybrid algorithm is the same as the original PCA, due to F-transform ability to capture spatial information and reduction of noise/distortion in an image. Finally, we demonstrate that PCA+FT is faster and can achieve a higher success rate than a standard Convolution Neural Network and nevertheless, it is slightly less accurate as a Capsule Neural Network for the chosen dataset, its training phase is 100000x faster and classification time is faster 9x.
Distributed processing and control are critical to supports distributed intelligence and autonomy of multi-degree-of-freedom motion systems. Measurements and fusion of spatiotemporal physical quantities imply data-int...
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ISBN:
(纸本)9781538663844
Distributed processing and control are critical to supports distributed intelligence and autonomy of multi-degree-of-freedom motion systems. Measurements and fusion of spatiotemporal physical quantities imply data-intensive computing and spatial distribution of computing resources to enable control and processing of large data sets from image and inertial sensors. We examine distributed and asynchronous processing nodes which process information independently deriving partial solutions. There are multiple sensing-and-processing nodes in each individual agent. Each node comprises solid-state or MEMS multi-degree-of-freedom sensors with ASICs which process and fuse data. On-node computing supports distributed processing. Adaptive bottom-up organization ensures data aggregation and data management with operation on sub-samples or hashed sets of large source datasets. Cooperative distributed processing is essential in centralized, decentralized and behavioral coordination. In the centralized organization, a central processor may not ensure adequacy. A network of semi-autonomous on-device processing sensors may interact to solve specific tasks and validate solutions. Problem allocation, partitioning, coordination and other tasks are implemented using software-and hardware-supported algorithms and protocols. This paper contributes to design of next generation of systems with distributed multi-node processing capabilities.
Modern vision systems include a television imageprocessing system. Improving the technical characteristics and expanding the capabilities of modern devices for recording television images makes it necessary to develo...
Modern vision systems include a television imageprocessing system. Improving the technical characteristics and expanding the capabilities of modern devices for recording television images makes it necessary to develop and create highly efficient algorithms for processing television images. Such systems process large amounts of information, therefore, the requirements for the reliability of processing large amounts of data at limited time intervals are constantly increasing. The rapid development of high-performance computing systems has recently created the conditions for expanding the range of tasks that can be solved with the help of vision systems. The research for improving the efficiency of methods and algorithms for digital imageprocessing significant. Currently, the processing of television images is divided into several steps. The first step is the formation of a digital representation of the image (discretization, quantization and input of the image into the computer memory). The second step is the pre-processing of images (restoration and filtering). The third step is the formation of a graphic preparation of the image (segmentation and contour detection). One of the most complex and relevant is the scientific and technical task of the contour detections of objects in television images on the background with additive noise. The purpose of this study is the development of the contour detection method on the image with impulse noise using mathematical modeling.
Aerial targets such as missiles and aircrafts at far distance projected on image plane as point or small targets in infra-red and visible video. These targets lack apriori information about target dynamic, shape and s...
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ISBN:
(纸本)9781728106489
Aerial targets such as missiles and aircrafts at far distance projected on image plane as point or small targets in infra-red and visible video. These targets lack apriori information about target dynamic, shape and size. Detection and tracking of such targets has been reported challenging task. Hence, point or small size target detection algorithms become focus of long range detection and tracking systems. Generally, pre-processing is carried out on the input frame to predict the background and consequently enhance the target signature. In some scenario, post-processingalgorithms are also required to reduce the false alarms. In this paper, we propose an efficient and innovative scheme for real-time implementation of point or small size target detection algorithm on PowerPC Single-Board Computer (SBC) by utilizing the parallel computing feature of AltiVec vector processing unit to achieve real-time processing. Results demonstrate the real-time processing of video with hardware results matching the simulation results.
This paper presents three fully convolutional neural network architectures which perform change detection using a pair of coregistered images. Most notably, we propose two Siamese extensions of fully convolutional net...
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This paper presents three fully convolutional neural network architectures which perform change detection using a pair of coregistered images. Most notably, we propose two Siamese extensions of fully convolutional networks which use heuristics about the current problem to achieve the best results in our tests on two open change detection datasets, using both RGB and multispectral images. We show that our system is able to learn from scratch using annotated change detection images. Our architectures achieve better performance than previously proposed methods, while being at least 500 times faster than related systems. This work is a step towards efficient processing of data from large scale Earth observation systems such as Copernicus or Landsat.
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