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An autonomous license plate detection method

An autonomous license plate detection method

引用

IIH-MSP 2009 - 2009 5th International Conference on Intelligent Information Hiding and multimedia signal processing

作者： Kuo, Hsiao-Chen Lee, Jiann-Shu Kao, Sho-Tsung Department of Management Information Systems Far East University Tainan Taiwan Department of Computer Science and Information Engineering National University of Tainan Tainan Taiwan Department of Information and Learning Technology National University of Tainan Tainan Taiwan

ISBN: (纸本)9780769537627

Detection of license plate is an important process in intelligent transportation systems before license plate recognition. In this paper, we proposed an autonomous license plate detection system with computer vision instead of sensors. Using characteristics of dynamic images, our system rapidly identifies the license plate region. The system consists of two subsystems: car detection subsystem and plate extraction subsystem. Car detection subsystem uses MMADR and NDDR of dynamic images to find the location of cars on the screen. Plate extraction subsystem uses the characteristics of the plates and plate searching algorithm to extract plate image. Experimental results show that our system can effectively detect vehicles and locate the plates under different environments. © 2009 ieee.

关键词： Computer vision

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A car detection system based on hierarchical visual features

A car detection system based on hierarchical visual features

引用

ieee symposium on computational intelligence for multimedia signal and vision processing, CIMSVP

作者： Fok Hing Chi Tivive Abdesselam Bouzerdoum School of Electrical Computer and Telecommunications Engineering University of Wollongong Wollongong NSW Australia

In this paper, we address the problem of detecting and localizing cars in still images. The proposed car detection system is based on a hierarchical feature detector in which the processing units are shunting inhibitory neurons. To reduce the training time and complexity of the network, the shunting inhibitory neurons in the first layer are implemented as directional nonlinear filters, whereas the neurons in the second layer have trainable parameters. A multi-resolution processing scheme is implemented so as to detect cars of different sizes, and to reduce the number of false positives during the detection stage, an adaptive thresholding strategy is developed. Tested on the UIUC car database, the proposed method achieves better classification results than some of the existing car detection approaches.

关键词： Neurons Feature extraction Computer vision Gabor filters Pattern recognition Detectors Support vector machines Support vector machine classification Data mining Brain modeling

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Ultrasonic sensor for industrial inspection based on the acoustic impedance 20

Ultrasonic sensor for industrial inspection based on the aco...

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20th symposium on signal processing, Images and Computer vision, STSIVA 2015

作者： España, Juan José González Builes, Jovani Alberto Jiménez Tabares, Andrés Felipe Jején Department of Computer Science and Decision Research Group Artificial Intelligence in Education Universidad Nacional de Colombia Medellín Colombia

ISBN: (纸本)9781467394611

In this paper a signal processing method is proposed for the ultrasonic inspection based on the acoustic impedance called Peniel. This method has a low computational complexity which make it suitable for portable devices of small size. This method analyzes the acoustic impedance behavior of the sample inspected or process monitored to recognize defects or process evolution. Ultrasonic sensors based on the acoustic impedance measurement have important applications in industrial sectors such as energy, chemicals, aerospace, automobile, food, construction, among others. In this work is illustrated the implementation of the method for material identification using a microcontroller and its adaptation to recognize defects in bonded joints. © 2015 ieee.

关键词： Defects

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Maximizing neural responses leads to sensori-motor coordination of binocular vergence

Maximizing neural responses leads to sensori-motor coordinat...

引用

ieee symposium on computational intelligence for multimedia signal and vision processing, CIMSVP

作者： Yiwen Wang Bertram E. Shi Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong China

We present a simple optimization criterion based on the neural representation of the depth in the mammalian visual cortex that leads to self organization of a sensori-motor feedback loop. We study an active stereo vision system where the vergence angle between the two eyes is controlled by the output of a population of disparity selective neurons. We show that finding a control policy that maximizes the total response across an entire population of disparity selective neurons results in a system that automatically learns to track a target as it moves in depth. We characterized the tracking performance of the resulting policy using objects moving both sinusoidally and randomly in depth. The closed loop 3 dB tracking bandwidth of the system was 0.3 Hz.

关键词： Neurons Brain modeling Eyes Retina Learning Target tracking Stereo vision Automatic control Control systems Statistics

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A road traffic signal recognition system based on template matching employing tree classifier

A road traffic signal recognition system based on template m...

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International Conference on computational intelligence and multimedia Applications, ICCIMA 2007

作者： Varun, S. Singh, Surendra Sanjeev Kunte, R. Sudhaker Samuel, R.D. Philip, Bindu Department of Electronics and Communication S J College of Engineering Mysore - 570006 Karnataka India

ISBN: (纸本)0769530508

The traffic sign detection and recognition system is an essential module of the driver warning and assistance system. Smart vehicles are the order of the day. Such vehicles have the ability to warn drivers of pending situations, remind them of speed limits and even automatically take evasive action. Due to the visual nature of existing infrastructure, signs and line markings, image processing will play a large part in these systems. In this paper we present an automated Traffic sign recognition system allowing an invariance localization to changes in position, scale, rotation, weather conditions, partial occlusion, and the presence of other objects of the same color. The reliability demonstrated by the proposed method suggests that this system could be a part of an integrated driver warning and assistance system based on computer vision technology. © 2007 ieee.

关键词： Image recognition

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Dynamic vision sensor camera based bare hand gesture recognition

Dynamic vision sensor camera based bare hand gesture recogni...

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ieee symposium on computational intelligence for multimedia, signal and vision processing (CIMSIVP)

作者： Eun Yeong Ahn Jun Haeng Lee Tracy Mullen John Yen Information Sciences and Technology Pennsylvania State University University Park PA USA Advanced Samsung Institute of Technology Samsung Electronics Company Limited Yongin si Gyeonggi South Korea

This paper proposes a method to recognize bare hand gestures using a dynamic vision sensor (DVS) camera. Different from conventional cameras, DVS cameras only respond to pixels with temporal luminance differences, which can greatly reduce the computational cost of comparing consecutive frames to track moving objects. Due to differences in available information, conventional vision techniques for gesture recognition may not be directly applicable in DVS based applications. This paper attempts to classify three different hand gestures made by a player during rock-paper-scissors game. We propose novel methods to detect the point where the player delivers a throw, to extract hand regions, and to extract useful features for machine learning based classification. Preliminary results show that our method produces enhanced accuracy of hand gesture recognition.

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Proceedings: 2002 ieee International symposium on Circuits and Systems

Proceedings: 2002 IEEE International Symposium on Circuits a...

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2002 ieee International symposium on Circuits and Systems

This Volume 3 of 5 of the conference proceedings contains 225 papers. Topics discussed include global and local motion estimation, communication networks, adaptive signal processing, applications of chaos, communicating radio frequency circuits, neural networks, Nyquist data conversion, testing and verification, voltage controlled oscillators and phase locked loops, reference circuits, multimedia watermarking, frequency response masking techniques, VLSI circuit styles, wireless architecture, delta-sigma modulators, translinear and companding filters, vision chips, multidimensional signals and systems, digital circuits synthesis and optimization, video over networks, analysis, design and simulation of power electronics circuits, transforms and multirate systems, wireless LAN design, chaos and information processing, applications of digital signal processing in communications, network theory and linear systems, network theory and linear systems, VLSI arithmetic, modeling and analysis of nonlinear systems, low voltage amplifiers, advanced signal processing for MIMO systems, imaging architectures, delta-sigma modulators, circuit linearization, analog computing circuits, computational graph theory for computer and communication systems, device and interconnect modeling, video segmentation, DC switching mode power supplies, speech processing, rf filter design and implementation and chaotic communications and signal processing.

关键词： Electric network analysis

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Extracting salient lines by Visual Attention for omnidirectional image classification

Extracting salient lines by Visual Attention for omnidirecti...

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ieee symposium on computational intelligence for multimedia, signal and vision processing (CIMSIVP)

作者： AmirHossein Habibian Majid Nili-Ahmadabadi Babak Nadjar Araabi Robotics and AI Laboratory Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering Faculty of Engineering University of Tehran Tehran Iran School of Cognitive Sciences Institute for Research in Fundamental Sciences Tehran Iran

Representing an image as a set of its key and interesting lines facilitates the image understanding and classification. In this paper, we propose a method to extract the significant and interesting lines of the scene, which probably are useful in image classification. The proposed method is inspired from the Visual Attention, which is a perceptual mechanism in human and other primates that direct their perceptions to the limited regions of the scene. The attended regions are usually valuable in performing the task. Since the approach of using the lines to classify the images is particularly useful for omnidirectional images, we specialize our method to deal with these kinds of images. In the experiments, we demonstrate how our proposed methods improve the image classification performance with processing only small parts of the input images.

关键词： Feature extraction Visualization Image classification Gabor filters Image edge detection Histograms computational modeling

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Character recognition with two spiking neural network models on multicore architectures

Character recognition with two spiking neural network models...

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ieee symposium on computational intelligence for multimedia signal and vision processing, CIMSVP

作者： Mohammad A. Bhuiyan Rommel Jalasutram Tarek M. Taha Electrical and Computer Engineering Department Clemson University Clemson SC USA

This paper presents the use of the Izhikevich and Hodgkin Huxley neuron models for image recognition. The former is more biologically accurate than the commonly used integrate and fire neuron model but has similar low computational requirements. Brain scale cortex models tend to use the more biological neuron models. The results of this work show that the Izhikevich model can be used for image recognition and would be a good candidate for a large scale visual cortex model. Neural networks based on these models are developed and applied to character recognition. They were able to identify 48 24times24 images and their noisy versions. The networks were accelerated using modern multicore processors and showed significant speedups. Such processors are likely to be used for developing high performance, large scale implementations of these image recognition networks.

关键词： Character recognition Neural networks Multicore processing Biological system modeling Brain modeling Neurons Image recognition Biological neural networks Large-scale systems Computer architecture

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vision-based hand gesture recognition using PCA+Gabor filters and SVM

Vision-based hand gesture recognition using PCA+Gabor filter...

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IIH-MSP 2009 - 2009 5th International Conference on Intelligent Information Hiding and multimedia signal processing

作者： Huang, Deng-Yuan Hu, Wu-Chih Chang, Sung-Hsiang Department of Electrical Engineering Da-Yeh University Taiwan Department of Computer Science and Information Engineering National Penghu University Taiwan

ISBN: (纸本)9780769537627

In this paper we present a novel method for hand gesture recognition based on Gabor filters and support vector machine (SVM). Gabor filters are first convolved with images to acquire desirable hand gesture features. The principal components analysis (PCA) method is then used to reduce the dimensionality of the feature space. With the reduced Gabor features, SVM is trained and exploited to perform the hand gesture recognition tasks. To confirm the robustness of the proposed method, a dataset with large posed-angle (>45 deg.) of hand gestures is created. The experiment result shows that the recognition rate of 95.2% can be achieved when SVM is used. A real-time video system for hand gesture recognition is also presented with a processing rate of 0.2 s for every frame. This result proves the efficiency and superiority of the proposed Gabor-SVM method. © 2009 ieee.

关键词： Support vector machines

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