By a car license plate recognition, we mean a software system processingimages and providing an alphanumeric transcription of car plates included in an image. We divide the task into four sub-tasks: license plate loc...
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ISBN:
(纸本)9781509049189
By a car license plate recognition, we mean a software system processingimages and providing an alphanumeric transcription of car plates included in an image. We divide the task into four sub-tasks: license plate localization, license plate extraction, characters segmentation and characters recognition. All four sub-tasks are discussed in the context of standard approaches and own solution based on a chain of standard and soft computing imageprocessingalgorithms is presented. In this chain, the F-transform approximate pattern matching algorithm plays the crucial role. For the solution, we presented recognition ability for a dataset which includes 500 images with difficult conditions.
One of the key technologies of the large-size electronic whiteboard is pen locating, but most existing technologies have their weaknesses. In this paper, a pen locating algorithm and its prototype are proposed. Using ...
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One of the key technologies of the large-size electronic whiteboard is pen locating, but most existing technologies have their weaknesses. In this paper, a pen locating algorithm and its prototype are proposed. Using Spatial-based watermark embedding method, the pen-locating algorithm generates watermarked blocks by embedding watermark information into the background image on the screen. The pen detects the watermarked blocks on the screen and when the watermarked blocks are detected, the pen can be located through the analysis of the image block location and the distance between the pen and the image block. The algorithm is tested by simulation and the locating error of the prototype system is 1.7 pixels approximately.
In this article, it is developed some area of issues related to data compression algorithms in the field of imageprocessing. imageprocessing area is very commonly used today with multiple applications in different f...
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ISBN:
(纸本)9781450347891
In this article, it is developed some area of issues related to data compression algorithms in the field of imageprocessing. imageprocessing area is very commonly used today with multiple applications in different fields, but also, the image compression methods or algorithms for imaging are used every day by the computer users. This paper will highlight the result of reports over the investigations or comparisons on image compression methods and will provide conclusions and ideas for further research in this area, with intense uses. Today there are a lot of different data compression methodologies, which are used to compress different data formats like, video, audio, image files. This article represents a comparison of several compression methods based on previous research and the analysis in the context of their current needs. In conclusion, this topic combines imageprocessingsystems, the advantage of using parallel programming, the benefits and results of the image compression and also the importance of some fundamental algorithms in this area.
Security is an important issue during communication and data transmission. There are many ways to provide security. One method to ensure security is the use of cryptographic algorithms such as DES, AES, RC5, Blowfish ...
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ISBN:
(纸本)9781509047611;9781509047604
Security is an important issue during communication and data transmission. There are many ways to provide security. One method to ensure security is the use of cryptographic algorithms such as DES, AES, RC5, Blowfish etc. Cryptography is a method used for encoding the data which may be hacked by the unauthorized person. In this paper FPGA based design and implementation of Blowfish algorithm has been proposed. For RTL coding VHDL has been used and Virtex-5XC5VLX50T FPGA device used as a reconfigurable platform for implementation of Blowfish algorithm. The aim of this system is to evaluate performance of Blowfish algorithm on reconfigurable platform in terms of power consumption and throughput. For testing purpose image data and ECG data has been used as plaintext.
Summary form only given. Digital imageprocessing refers to the set of algorithms used to transform, filter, enhance, modify, analyze, distort, fuse, etc., digital images. Most of these algorithms are designed to mimi...
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ISBN:
(纸本)9781509042548
Summary form only given. Digital imageprocessing refers to the set of algorithms used to transform, filter, enhance, modify, analyze, distort, fuse, etc., digital images. Most of these algorithms are designed to mimic an underlying physical operation defined in the continuous illumination domain and formerly achieved via optical or electronic filters or through manipulations, including painting, cutting, moving or pasting of image patches. It also allows more sophisticated transformations (associated to more or less complex algorithms) which would be impossible to process by analog means. It may be quite hard to completely transpose an operation from the continuous to the discrete domain. Such a transposition usually relies on methods that ensure a kind of interplay between continuous and discrete domains. The interplay between the continuous and the discrete domain usually involves a convolution with a point spread function, when the measurement model is supposed to be linear, while the interplay between the discrete and the continuous domain is ensured by interpolation or more generally approximation methods, which also involve a convolution with a reconstruction kernel. Performing a precise identification of the point spread function of an imager is usually pretty challenging. Moreover, modeling the imaging process by a point spread function could be considered as an approximation of a more complex (and not shift-invariant) phenomenon (e.g. radial distortion or chromatic aberrations).
The proceedings contain 25 papers. The special focus in this conference is on Soft Computing. The topics include: A multilevel genetic algorithm for the maximum constraint satisfaction problem;models and simulations o...
ISBN:
(纸本)9783319580876
The proceedings contain 25 papers. The special focus in this conference is on Soft Computing. The topics include: A multilevel genetic algorithm for the maximum constraint satisfaction problem;models and simulations of queueing systems;influence of random number generators on GPA-ES algorithm efficiency;genetic algorithm based random selection-rule creation for ontology building;improving artificial fish swarm algorithm by applying group escape behavior of fish;genetic programming algorithm creating and assembling subtrees for making analytical functions;comparison of parallel linear genetic programming implementations;geometric particle swarm optimization and reservoir computing for solar power forecasting;walkSAT based-learning automata for MAX-SAT;a self-adaptive artificial bee colony algorithm with incremental population size for large scale optimization;lie algebra-valued bidirectional associative memories;guaranteed training set for associative networks;Markov chain for author writing style profile construction;maximum traveling salesman problem by adapted neural gas;conjugate gradient algorithms for quaternion-valued neural networks;evaluating suitable job applicants using expert system;a computationally efficient approach for mining similar temporal patterns;estimating prevalence bounds of temporal association patterns to discover temporally similar patterns;an approach for imputation of medical records using novel similarity measure;implementation of particle filters for mobile robot localization;direct point cloud visualization using T-spline with edge detection and development of methods of the fractal dimension estimation for the ecological data analysis.
Different information visualization techniques can be found in the literature due to the quantity and variety of data stored in computational systems. In this context, the classification of chart images becomes import...
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Different information visualization techniques can be found in the literature due to the quantity and variety of data stored in computational systems. In this context, the classification of chart images becomes important because it allows various types of graphs to be detected automatically in different contexts, allowing a more specific processing for each type of visualization, for example, data extraction. Several techniques of image classification can be used, where the most common are based on the extraction of features of the images, and a later classification using these features. However, one technique that has been gaining prominence in the context of image classification is the Convolutional Neural Network (CNN). This technique is based on deep learning and, in a way, encapsulates the feature extraction process. In this way, the proposal of this article is to use an architecture of a client-server based model to do the chart image classification and later data extraction from this image. The main advantage is doing the CNN processing on the server side, so the application does not rely on client device limitations. For this, an image dataset was generated from the web, and it has ten classes of graphs. From the experiments done, it was seen that the use of this technique was feasible, and modifications in the architecture can be made as a proposal to improve the accuracy of the model.
Cataract is one of the most common diseases that might cause blindness. Previous research shows that cataract occupies almost 50% in severe visually impairments. Fundus image is a significant reference for the diagnos...
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Cataract is one of the most common diseases that might cause blindness. Previous research shows that cataract occupies almost 50% in severe visually impairments. Fundus image is a significant reference for the diagnosis of the cataract disease. The classification of fundus images mainly consists of four parts: pre-processing of fundus images, features extraction, features weighting and classification. In this paper, firstly a whole fundus image is divided into 17 images evenly, secondly features are extracted features from each sub-image, then the feature vectors are weighted with the result of genetic algorithm and finally support vector machine is used to train and classify the fundus images. The experimental result shows that the accuracy of four-class classification can reach 87.52%.
Modern scientific instruments, such as detectors at synchrotron light sources, can generate data at 10s of GB/sec. Current experimental protocols typically process and validate data only after an experiment has comple...
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ISBN:
(纸本)9781538626863
Modern scientific instruments, such as detectors at synchrotron light sources, can generate data at 10s of GB/sec. Current experimental protocols typically process and validate data only after an experiment has completed, which can lead to undetected errors and prevents online steering. Real-time data analysis can enable both detection of, and recovery from, errors, and optimization of data acquisition. We thus propose an autonomous stream processing system that allows data streamed from beamline computers to be processed in real time on a remote supercomputer, with a control feed-back loop used to make decisions during experimentation. We evaluate our system using two iterative tomographic reconstruction algorithms and varying data generation rates. These experiments are performed in a real-world environment in which data are streamed from a light source to a cluster for analysis and experimental control. We demonstrate that our system can sustain analysis rates of hundreds of projections per second by using up to 1,200 cores, while meeting stringent data quality constraints.
Installed Onboard cameras considering cases of off road unmanned navigating ground vehicles experience severe jitter and vibration. This leads to the prerequisite that the video images acquired from these platforms ne...
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ISBN:
(纸本)9781509047611;9781509047604
Installed Onboard cameras considering cases of off road unmanned navigating ground vehicles experience severe jitter and vibration. This leads to the prerequisite that the video images acquired from these platforms need to be heavily preprocessed to eliminate the jitter induced variations before human analysis. Digital Video stabilization system is the process of using electronic processing to control the image stability. That is, only software algorithms are used rather than hardware components such as motion sensors, actuators or floating lenses to compensate the disturbances. This makes digital stabilization more portable and cost effective among other methods. Digital stabilization can be used for real time and offline applications if the algorithms are optimized. This literature discusses the state of the art in the field of DVS with an implementation aspect of its use in challenging environment of unmanned ground vehicles where due to the dynamic nature of the vehicle, vibrations and oscillations are affect the camera resulting in a shaky and unstable video feed.
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