Aiming at the problem of low contrast between target and background in fusion of infrared and visible images, a fusion method of infrared and visible images based on IHS transform and wavelet region transformation is ...
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Aiming at the problem of low contrast between target and background in fusion of infrared and visible images, a fusion method of infrared and visible images based on IHS transform and wavelet region transformation is proposed. The first of the visible light IHS transform the luminance component of infrared image gray processing, then the luminance component of visible light and infrared image of three level wavelet transform to obtain high frequency component and low frequency component respectively, the low frequency sub-band coefficients fusion rules comparing the area difference, and then calculate the weighted wavelet coefficients; in the high frequency sub-band coefficients using regional variance adaptive fusion rules. The experimental results show that compared with other algorithms, the fusion image obtained by this method enhances the contrast between the thermal target and the background, and keeps the details of the background, which is more consistent with human vision perception.
In this paper, hardware implementation of edge detection at real time video signals using Sobel, Robert, Prewitt and Laplacian filters based on FPGA is explained. Besides, filters are compared in many ways. Edge detec...
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ISBN:
(纸本)9781509064946
In this paper, hardware implementation of edge detection at real time video signals using Sobel, Robert, Prewitt and Laplacian filters based on FPGA is explained. Besides, filters are compared in many ways. Edge detection is an elemantary and fundamental tool for image segmentation and feature extraction. Very high speed hardware like FPGA's are used to implement the image and video processingalgorithms for improving the performance of processingsystems. algorithms are implemented on the Xilinx Zynq 7000. The video input signals come from a laptop's HDMI interface to FPGA in order to filter and the detected edges arc displayed on a HDMI display screen.
In present scenario, agriculture forms a vital part in India's economy. More than 50 % of India's population is dependent (directly or indirectly) on agriculture for their livelihood. In India many crops are c...
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ISBN:
(纸本)9789811016752;9789811016745
In present scenario, agriculture forms a vital part in India's economy. More than 50 % of India's population is dependent (directly or indirectly) on agriculture for their livelihood. In India many crops are cultivated, out of which wheat being one of the most important food grain that this country cultivates and exports. Thus it can be seen that wheat forms a major part of the Indian agricultural system and India's economy. Hence, maintenance of the steady production of above stated crop is very important. The main idea of this project is to provide a system for detecting wheat leaf diseases. The given system will study the leaf image of a wheat plant through imageprocessing and pattern recognition algorithms. Former algorithms are used for extracting vital information from the leaf and the latter is used for detecting the disease that it is infected with. For imageprocessing and segmentation usage of k-means algorithm and canny filter has been suggested. Pattern recognition is achieved through PCA or GLCM and classification through SVM or ANN.
Ellipse detection is an important task in machine vision and can be used to solve other high-level visual problems. In the past, many studies have been conducted by researchers at high precision ellipse fitting, but m...
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Ellipse detection is an important task in machine vision and can be used to solve other high-level visual problems. In the past, many studies have been conducted by researchers at high precision ellipse fitting, but most of the proposed solutions are not fast enough to be applied in real-time detection for larger images or with limited hardware resources. This paper improves the accuracy of ellipse detection by improving the candidate ellipse scoring method and geometric constraints. Combined with ellipse fitting algorithm, the proposed method can perform real-time ellipse and semi ellipse detection. The efficiency of the algorithm is proven by using three popular datasets which shows the state of the art ellipse and semi-ellipse detection performance.
The basic principles of an algorithm of Detection of objects in the onboard systems of unmanned aerial vehicles using information about changes in the image of the terrain is considered in the paper. The possibility o...
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ISBN:
(纸本)9781538675328
The basic principles of an algorithm of Detection of objects in the onboard systems of unmanned aerial vehicles using information about changes in the image of the terrain is considered in the paper. The possibility of obtaining additional information about the objects determines the prospects of the technology for allocating changes for use in high-resolution aviation surveillance systems.
The fuzzy co-clustering algorithms are considered as effective technique for clustering the complex data, such as high-dimensional and large size. In general, features of data objects are considered the same importanc...
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ISBN:
(纸本)9781509060344
The fuzzy co-clustering algorithms are considered as effective technique for clustering the complex data, such as high-dimensional and large size. In general, features of data objects are considered the same importance. However, in reality, the features have different roles in data analyses;even some of them are considered redundancy in the individual case for data sets. Removing these features is a way for the dimensionality reduction, which needs to improve the performance of data processingalgorithms. In this paper, we proposed an improved fuzzy co-clustering algorithm called feature-reduction fuzzy co-clustering (FRFCoC), which can automatically calculate the weight of features and put them out of the data processing. We considered the objective function of the FCoC algorithm with feature-weighted entropy and build a learning procedure for components of the objective function, then reducing the dimension of data by eliminating irrelevant features with small weights. Experiments were conducted on synthetic data sets and hyperspectral image using the robust assessment indexes. Experimental results demonstrated the proposed algorithm outperformed the previous algorithms.
Reversible data hiding has its own significance in medical imagesystems. Medical imaging has key applications in healthcare systems, where the private information about a person needs to be shared among the authentic...
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ISBN:
(纸本)9781509067343
Reversible data hiding has its own significance in medical imagesystems. Medical imaging has key applications in healthcare systems, where the private information about a person needs to be shared among the authenticated persons only without any distortion during data hiding and transmission. RDH is one of most practicable techniques of privacy preservation with lossless recovery at receiver's end. Numerous RDH techniques are proposed on different image domains like, compressed and uncompressed. This paper summarizes the existing RDH algorithms for medical images.
This paper explores the feasibility of using a lowcost embedded ARM-based system for real-time vehicle detection, classification and counting through imageprocessingalgorithms with the aim of knowing information abo...
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This paper explores the feasibility of using a lowcost embedded ARM-based system for real-time vehicle detection, classification and counting through imageprocessingalgorithms with the aim of knowing information about vehicular traffic in different roads and highways to improve the management of mobility and the functioning of cities. This paper proposes the implementation of a low cost system to identify and classify vehicles using an Embedded ARM based platform (ODROID XU-4) with Ubuntu operating system. The algorithms used are based on the Open-source library (Intel OpenCV) and implemented in Python programming language. The experimentation carried out proved that the efficiency of the algorithm implemented was 95.35%, but it can be improved by increasing the training sample.
Cloud computing offers the opportunity to minimize the evaluation time of complex algorithms - e.g. needed for computational imaging - by horizontal scaling of the available computing resources. By this way, new image...
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ISBN:
(纸本)9781538608371
Cloud computing offers the opportunity to minimize the evaluation time of complex algorithms - e.g. needed for computational imaging - by horizontal scaling of the available computing resources. By this way, new image analyzing algorithms can be employed in weak real-time conditions, like inline quality analysis in production with time stamps in the order of several tens of seconds. The cloud offers a platform to merge sensor data of all production processes to analyze quality data comprehensively, e.g. for methods like predictive maintenance. Typically, cloud environments are applied for the Internet of things (IoT) or Big Data analysis. But IoT-applications usually generate very small data packages (like sensor values with a size much less than 1 megabyte), while Big Data applications deal with very high data volume (terra- or petabyte). imageprocessing requires an environment, which is optimized for medium size data streaming, composed of images with a size in the lower megabyte range. In this paper, a sensor to-cloud architecture as a platform for imageprocessing is described. This approach is upward compatible, because it is not necessary to change the sensor hardware, e.g. if algorithms with considerable higher computing complexity are desired (like for a smart camera), so algorithms can be exchanged in the cloud without interrupting the production process.
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