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检索条件"任意字段=3rd International Conference on Computer Vision and Image Processing, CVIP 2018"

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Handwritten computer Program Recognition, Compilation & Execution Application

Handwritten Computer Program Recognition, Compilation & Exec...

引用

international conference for Convergence of Technology (I2CT)

作者： Manodhya Kumari Wanigapura Saminda Premaratne Department of Information Technology University of Moratuwa Sri Lanka

Make a computer program that does automatically recognize a handwritten computer program, compile and execution is extremely difficult. The reasons are the various types, shapes of handwritten characters of different peoples. Among other issues in optical character recognition, accurate handwritten identification is the main research issue. This paper provides an accurate handwritten computer program recognition method, which blend with the image processing and the language training for handwritten characters. Further, automatic error correction of handwritten program, compilation and execution of the program, will be discussed in detail throughout the paper. The final evaluation results show that the newly developed system can identify the handwritten computer program syntaxes with a higher level of accuracy.

关键词： Optical character recognition software Character recognition Handwriting recognition Feature extraction image segmentation Optical imaging Adaptive optics

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Change Detection Based on the Combination of Improved SegNet Neural Network and Morphology

Change Detection Based on the Combination of Improved SegNet...

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international conference on image, vision and Computing (ICIVC)

作者： Bin Zhu Hongmin Gao Xin Wang Mengxi Xu Xiaobin Zhu College of Computer and Information Hohai University Nanjing China School of Computer Engineering Nanjing Institute of Technology Nanjing China

ISBN: (纸本)9781538649923

Through the analysis of satellite remote sensing image data, the identification of newly added buildings in the same area can be realized to judge the use of land. The identification of newly added buildings based on remote sensing images, involving image object extraction, semantic segmentation and change detection. The difficulty is not only to identify the changes of remote sensing images in different periods, but also to identify the newly added buildings with the original buildings. Both of the recognition effect and the detection precision of the traditional method based on mathematical modeling need to be improved. SegNet neural network is a kind of deep convolution neural network. It shows good performance in dealing with the task of semantic segmentation of single image, but it is directly applied to building change detection with low accuracy. The simulation results show that the improved SegNet neural network method improves the accuracy of the quantitative evaluation index F 1 score by 8.6% compared with the conventional SegNet network in the newly added building detection effect in the same area in 2015 and 2017. In addition, the situation that the change detection result will produce a large number of noise, a combination of improved SegNet network and image morphological method is adopted to eliminate the noise and reduce the misjudgment. The simulation results show that the F 1 index increased further by 1.4% on the basis of 8.6%.

关键词： Buildings Remote sensing Convolution Training Machine learning Neural networks Morphology

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An Overview Of Feature Extraction Methods For Handwritten image Retrieval

An Overview Of Feature Extraction Methods For Handwritten Im...

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international conference on Smart City and Systems Engineering

作者： Gao ting Kamil Moydin Askar Hamdulla Institute of Information Science and Engineering Xinjiang University Urumqi China

ISBN: (纸本)9781728113678;9781728113661

In recent years, handwriting images have been widely used as research hotspots in various fields. Therefore, handwriting image retrieval has emerged. Before the handwriting image retrieval, the focus is on the feature extraction stage. In recent years, it has been a basic research problem of computer vision. The performance of feature extraction is not only the importance measure of text classification result, but also the document. Important steps before image retrieval. In this paper, the local feature extraction algorithm is summarized, and the improvement and innovation of common feature extraction algorithms at home and abroad are analyzed. Finally, the factors that should be considered in practical application are pointed out.

关键词： Feature extraction Corner detection Lighting Classification algorithms image retrieval Robustness Heuristic algorithms

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Classification of Posterior Fossa CT Brain Slices using Artificial Neural Network 3

Classification of Posterior Fossa CT Brain Slices using Arti...

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3rd international conference on computer Science and Computational Intelligence, ICCSCI 2018

作者： Muhd Suberi, Anis Azwani Wan Zakaria, Wan Nurshazwani Tomari, Razali Nik Fuad, Nik Farhan Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Parit Raja Batu Pahat Johor86400 Malaysia UKM Medical Centre Jalan Yaacob Latif Bandar Tun Razak Cheras Kuala Lumpur56000 Malaysia

The diagnosis of ischemic stroke in Posterior Fossa (PF) using conventional Computed Tomography (CT) is limited. Meanwhile, the identification of PF slices in CT is a very complicated task due to their varying structures for each slices. Traditionally, the identification of PF slices by the radiologist is based on the PF structure that present in the CT images. This procedure can introduce misidentification issue and time consuming. In this paper, a method for automated classification of PF slices in CT brain images is investigated to sort the slice that contains PF for further study of ischemic stroke detection. This framework employs 11 features of Gray Level Co-Occurrence Matrix (GLCM). All the obtained features have been passed through Artificial Neural Network (ANN) classifier using Multilayer Perceptron (MLP) architecture. The experimental results show that, the combination of GLCM with ANN is promising to be applied in the classification of PF slices as they can provide 93%, 92.5% and 94.2% of precision, recall, and accuracy average rate respectively with average processing time of 0.36 seconds per slices. © 2018 The Authors. Published by Elsevier Ltd.

关键词： image classification

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Beyond Retinal Layers: An Automatic Active Contour Model with Pre-Fitting Energy for Subretinal Fluid Segmentation in SD-OCT images

Beyond Retinal Layers: An Automatic Active Contour Model wit...

引用

international conference on image, vision and Computing (ICIVC)

作者： Nursultan Taubaldy Zexuan Ji School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781538649923

Automatically and accurately segment neurosensory retinal detachment (Nrd) associated subretinal fluid in spectral-domain optical coherence tomography (SD-OCT) is vital for the evaluation of central serous chorioretinopathy (CSC). A two-stage unsupervised fluid segmentation algorithm is proposed. In the first stage, the candidate fluid region is automatically estimated to obtain the initial curve of the fluid area for the level set method. In the second stage, the local Gaussian pre-fitting energy model is proposed to segment subretinal fluid. The testing data set with 23 longitudinal SD-OCT cube scans from 12 eyes of 12 patients are used to evaluate the proposed algorithm. Comparing with two independent experts' manual segmentations, our algorithm obtained a mean positive predicative value 94.0% and dice similarity coefficient 94.4%, respectively. Without retinal layer segmentation, the proposed algorithm can obtain high segmentation accuracy. Our model may provide reliable subretinal fluid segmentations for Nrd from SD-OCT images and shows the potential to improve clinical therapy for CSC.

关键词： Fluids image segmentation Retina Level set Computational modeling Fitting Gaussian distribution

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3D objects descriptors methods: Overview and trends 3

3D objects descriptors methods: Overview and trends

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3rd international conference on Advanced Technologies for Signal and image processing (ATSIP)

作者： H'roura, Jihad Bekkari, Aissam Mammass, Driss Bouzit, Ali Mansouri, Alamin Roy, Michael Le Goic, Gaetan Ibn Zohr Univ IRF SIC Lab Agadir Morocco Univ Bourgogne Franche Comte LE2I FRE CNRS ENSAM UBFC Lab 2005 Auxerre France

ISBN: (纸本)9781538605516

Object recognition or object's category recognition under varying conditions is one of the most astonishing capabilities of human visual system. The scientists in computer vision have been trying for decades to reproduce this ability by implementing algorithms and providing computers with appropriate tools. Hence, several intelligent systems have been proposed To act in this field, numerous approaches have been proposed In this paper we present an overview of the current trend in 3D objects recognition and describe some representative state of the art methods, highlighting their limits and complexity.

关键词： 3D objects Recognition computer vision skeleton Keypoints

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An Efficient Hardware Architecture for Activation Function in Deep Learning Processor

An Efficient Hardware Architecture for Activation Function i...

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international conference on image, vision and Computing (ICIVC)

作者： Lin Li Shengbing Zhang Juan Wu School of Computer Science and Engineering Northwestern Polytechnical University Xi'an China Xi'an Vocational and Technical College School of Animation Software and Education Xi'an China

ISBN: (纸本)9781538649923

In order to explore the efficient design and implementation of activation function in deep learning processor, this paper presents an efficient five-stage pipelined hardware architecture for activation function based on the piecewise linear interpolation, and a novel neuron data-LUT address mapping algorithm. Compared with the previous designs based on serial calculation, the proposed hardware architecture can achieve at least 3 times of acceleration. Four commonly used activation functions are designed based on the proposed hardware architecture, which is implemented on the XC6VLX240T of Xilinx. The LeNet-5 and AlexNet are selected as benchmarks to test the inference accuracy of different activation functions with different piecewise numbers on the MNIST and CIFAR-10 test sets in the deep learning processor prototype system. The experiment results show that the proposed hardware architecture can effectively accomplish the relevant calculation of activation functions in the deep learning processor and the accuracy loss is negligible. The proposed hardware architecture is adaptable for numerous activation functions, which can be widely used in the design of other deep learning processors.

关键词： Neurons Hardware Machine learning Biological neural networks Interpolation Table lookup

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A Novel Design of an Intangible Hand Gesture Controlled computer Mouse using vision Based image processing 3

A Novel Design of an Intangible Hand Gesture Controlled Comp...

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3rd international conference on Electrical Information and Communication Technology (EICT)

作者： Titlee, Rokhsana Rahman, Ashfaq Ur Zaman, Hasan U. Rahman, Hafiz Abdur North South Univ Dept Elect & Comp Engn Dhaka Bangladesh

ISBN: (纸本)9781538623077

In this paper we presented an intangible hand gesture based computer mouse control system. Our approach uses a novel skin color segmentation technique to control mouse movement. The system uses morphological operations like structuring elements and blob counting. Our system can remove other skin like objects from the background. One of the key features of the system is its simplicity. We have successfully tested our system for an intangible interface between human hand and computer mouse without any complex processing. Various mouse operations like cursor movements, right click and left click have been performed. The system has been implemented using webcam and MATLAB.

关键词： computer vision Hand gesture YCbCr Skin color detection blob structuring element

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Optimizing rice plant diseases recognition in image processing and decision tree based model 3rd

Optimizing rice plant diseases recognition in image processi...

引用

3rd international conference on Next Generation Computing Technologies, NGCT 2017

作者： Verma, Toran Dubey, Sipi RCET Chhattisgarh Swami Vivekanand Technical University BhilaiCG490024 India

ISBN: (纸本)9789811086595

The objective of this paper is to design rice plant diseases recognition system and optimize the recognition efficiency of the system for new test datasets. In this research, the images from rice plant field had been captured by Charged Couple Device digital camera in Joint Photographic Experts Group format in day lighting. The total 6 categories of images with 5 categories of disease infected and one category of non-infected images had been captured. These acquired images had been pre-processed and segmented using three-level of the threshold to extract hybrid features which are a combination of color, texture and discrete cosine coefficient. The hybrid features of each image represent unique feature pattern of individual categories. The inverse multi-quadrics radial basis function had been applied on extracted hybrid features to make features localized and non-singular to enhance the uniqueness of the feature patterns. These transformed features had been used to design rice plant diseases recognition system using a decision tree. The uses of radial function drastically optimize the average recognition efficiency of diseased and non-diseased rice plant from 16.67% to 83.34%. This method can be generalized to design a monitoring system for plant diseases to help farmers and government agencies for on-location inspection and assessment of severity of diseases and take precautionary measure to control the spread of diseases. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Decision trees

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Effect of Illumination and Blur Change on Markerless image Registration Methods

Effect of Illumination and Blur Change on Markerless Image R...

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international conference for Convergence of Technology (I2CT)

作者： Neetika Gupta Mukesh Kumar Rohil Department of Computer Science and Information Systems Birla Institute of Technology & Science Pilani Pilani India

image registration forms a basis for a wide variety of applications in computer vision. The methods used for image registration are generally divided into two categories: 1) Extrinsic: based on some external object placed in an image. 2) Intrinsic: based on image information. Intrinsic methods work upon image features, pixel intensity levels etc. to determine measurements with respect to the requirements of a particular application. This paper presents a comparative analysis of three widely used image registration methods i.e. SIFT, ASIFT and SURF, for intrinsic image registration process. Quality of images describing medical, natural and structured scenes with different illumination and blur conditions is correlated with the performance analysis of the three image registration methods. Results show that the total count of extracted features in an image and correspondences found between two images (for determining the correct number of matches between an image pair, RANSAC algorithm is used for eliminating outliers) decreases with decreasing quality in terms of both blur and illumination conditions. Also, ASIFT outperforms SIFT and SURF in these changing imaging conditions.

关键词： Feature extraction Lighting Detectors image registration image quality Ribs Distortion

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