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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems III"

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Considerations in the development of a foveated imaging system for unmanned aerial vehicles (UAVs) 5

Considerations in the development of a foveated imaging syst...

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conference on image Sensing Technologies - Materials, Devices, systems, and Applications V

作者： O'Neill, Andrew D. Davidson, Joshua C. Kane, Tim J. Narayanan, Ram M. Dhar, Nibir K. Penn State Univ Dept Elect Engn University Pk PA 16802 USA US Army RDECOM CERDEC Night Vis & Elect Sensors Directorate Ft Belvoir VA 22060 USA

ISBN: (数字)9781510618244

ISBN: (纸本)9781510618244

A fundamental problem in imaging remote sensing systems is that of scale and resolution. The ability to resolve an object at a distance requires a high resolution sensor, with pixels subtending a small portion of the total field-of-view (FOV) of the imaging system. Traditional approaches to addressing this challenge are fundamentally data limited. To this end, we implemented foveating data reduction models inspired by the bi-foveated vision of birds of prey. The development of such systems for multiple target detection and tracking for air-to-ground target acquisition is important for several defense applications. The relative merits and disadvantages of various optical imaging technologies as well as several image transformations, sampling schemes, and object tracking algorithms were explored. Variable focal lens controlled by pressure, external voltage, or microfluidics demonstrate potential for devices requiring high resolution within a specified range. The distortion, coma, and spherical aberrations that occur can be corrected through the use of adaptive optics and custom 3D printed lenses. In conjunction with the hardware aspects, algorithmic approaches were also considered. The use of dynamically generated, moving foveal regions was investigated for use in motion tracking and object detection algorithms. Through the use of imaging systems with exceptionally large fields of view and localized areas of high resolution, machine vision systems can be implemented with less computational and data overhead. The implementation of our system is suited to use in either unmanned aerial vehicle or autonomous vehicle applications.

关键词： foveated imaging image processing freeform optics transformation

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image Matching Algorithm with Color Information based on SIFT 10

Image Matching Algorithm with Color Information based on SIF...

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10th International conference on Digital image processing (ICDIP)

作者： Yu, Xiaoyong Lei, Hao Du, Yunfei Li, Baopeng Yuan, Xiaobin Gao, Wei Song, Zongxi Zheng, Peiyun Chinese Acad Sci Xian Inst Opt & Precis Mech Space Opt Lab Xian 710019 Shaanxi Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Key Lab Elect & Informat Technol Space Syst Beijing 100190 Peoples R China

ISBN: (数字)9781510622005

ISBN: (纸本)9781510622005

image matching is an important topic in the field of computer vision, in view of high robustness and accuracy, SIFT or the improved methods based on SIFT is generally used for image matching algorithms. The traditional SIFT method is implemented on grayscale images without regard to the color information of images, which may cause decreasing of the matching points and reduction of the matching accuracy. Prevailing color descriptors can effectively add color information into SIFT, however dramatically increase the complexity of algorithm. In this paper, a novel approach is proposed to take advantage of the color information for image matching based on SIFT. The proposed algorithm uses the gradient information of color channel as the compensation of luminance channel, which can effectively enhance the color information with SIFT. Experimental results show that the number of feature points and matching accuracy can be significantly promoted, while the complexity and performance of image matching algorithm are well trade-off.

关键词： image matching SIFT color compensation

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Collaborative Computation in Self-organizing Particle systems 1

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17th International conference on Unconventional Computation and Natural Computation (UCNC)

作者： Porter, Alexandra Richa, Andrea Stanford Univ Stanford CA 94305 USA Arizona State Univ Tempe AZ USA

ISBN: (数字)9783319924359

ISBN: (纸本)9783319924359;9783319924342

Many forms of programmable matter have been proposed for various tasks. We use an abstract model of self-organizing particle systems for programmable matter which could be used for a variety of applications, including smart paint and coating materials for engineering or programmable cells for medical uses. Previous research using this model has focused on shape formation and other spatial configuration problems (e.g., coating and compression). In this work we study foundational computational tasks that exceed the capabilities of the individual constant size memory of a particle, such as implementing a counter and matrix-vector multiplication. These tasks represent new ways to use these self-organizing systems, which, in conjunction with previous shape and configuration work, make the systems useful for a wider variety of tasks. They can also leverage the distributed and dynamic nature of the self-organizing system to be more efficient and adaptable than on traditional linear computing hardware. Finally, we demonstrate applications of similar types of computations with self-organizing systems to image processing, with implementations of image color transformation and edge detection algorithms.

关键词： image processing

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2018 Open Innovations conference, OI 2018

2018 Open Innovations Conference, OI 2018

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2018 Open Innovations conference, OI 2018

ISBN: (纸本)9781538653166

The proceedings contain 54 papers. The topics discussed include: a survey on propagation challenges in wireless communication networks over irregular terrains;e-learning adoption in rural-based higher education institutions in South Africa;voltage and frequency control of isolated pico-hydro system;innovative quality management system for flexible manufacturing systems;structuring of the terrorism problem in the digital age: a systems perspective;requirements elicitation techniques for dynamic parameterization of feature extraction algorithms in image processing: a survey;and usage of battery energy storage systems to defer substation upgrades.

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Pectoral muscle segmentation in breast tomosynthesis with deep learning

Pectoral muscle segmentation in breast tomosynthesis with de...

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conference on Medical Imaging - Computer-Aided Diagnosis

作者： Rodriguez-Ruiz, Alejandro Teuwen, Jonas Chung, Kaman Karssemeijer, Nico Chevalier, Margarita Gubern-Merida, Albert Sechopoulos, Ioannis Radboudumc Dept Radiol & Nucl Med Nijmegen Netherlands Univ Complutense Madrid Dept Radiol Madrid Spain Dutch Expert Ctr Screening LRCB Nijmegen Netherlands

ISBN: (数字)9781510616400

ISBN: (纸本)9781510616400

Digital breast tomosynthesis (DBT) has superior detection performance than mammography (DM) for population-based breast cancer screening, but the higher number of images that must be reviewed poses a challenge for its implementation. This may be ameliorated by creating a two-dimensional synthetic mammographic image (SM) from the DBT volume, containing the most relevant information. When creating a SM, it is of utmost importance to have an accurate lesion localization detection algorithm, while segmenting fibroglandular tissue could also be beneficial. These tasks encounter an extra challenge when working with images in the medio-lateral oblique view, due to the presence of the pectoral muscle, which has similar radiographic density. In this work, we present an automatic pectoral muscle segmentation model based on a u-net deep learning architecture, trained with 136 DBT images acquired with a single system (different BIRADS densities and pathological findings). The model was tested on 36 DBT images from that same system resulting in a dice similarity coefficient (DSC) of 0.977 (0.967-0.984). In addition, the model was tested on 125 images from two different systems and three different modalities (DBT, SM, DM), obtaining DSCs between 0.947 and 0.970, a range determined visually to provide adequate segmentations. For reference, a resident radiologist independently annotated a mix of 25 cases obtaining a DSC of 0.971. The results suggest the possibility of using this model for inter-manufacturer DBT, DM and SM tasks that benefit from the segmentation of the pectoral muscle, such as SM generation, computer aided detection systems, or patient dosimetry algorithms.

关键词： image segmentation Digital breast tomosynthesis Breast Mammography systems modeling 3D modeling Convolutional neural networks Detection and tracking algorithms Tissues image processing algorithms and systems

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Neural Architecture Optimization 32

Neural Architecture Optimization

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32nd conference on Neural Information processing systems (NIPS)

作者： Luo, Renqian Tian, Fei Qin, Tao Chen, Enhong Liu, Tie-Yan Univ Sci & Technol China Hefei Anhui Peoples R China Microsoft Res Beijing Peoples R China

Automatic neural architecture design has shown its potential in discovering powerful neural network architectures. Existing methods, no matter based on reinforcement learning or evolutionary algorithms (EA), conduct architecture search in a discrete space, which is highly inefficient. In this paper, we propose a simple and efficient method to automatic neural architecture design based on continuous optimization. We call this new approach neural architecture optimization (NAO). There are three key components in our proposed approach: (1) An encoder embeds/maps neural network architectures into a continuous space. (2) A predictor takes the continuous representation of a network as input and predicts its accuracy. (3) A decoder maps a continuous representation of a network back to its architecture. The performance predictor and the encoder enable us to perform gradient based optimization in the continuous space to find the embedding of a new architecture with potentially better accuracy. Such a better embedding is then decoded to a network by the decoder. Experiments show that the architecture discovered by our method is very competitive for image classification task on CIFAR-10 and language modeling task on PTB, outperforming or on par with the best results of previous architecture search methods with a significantly reduction of computational resources. Specifically we obtain 2.11% test set error rate for CIFAR-10 image classification task and 56.0 test set perplexity of PTB language modeling task. The best discovered architectures on both tasks are successfully transferred to other tasks such as CIFAR-100 and WildText-2. Furthermore, combined with the recent proposed weight sharing mechanism, we discover powerful architecture on CIFAR-10 (with error rate 3.53%) and on PTB (with test set perplexity 56.6), with very limited computational resources (less than 10 GPU hours) for both tasks.

关键词： image classification

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Proposed Model for an Expert System for Diagnosing Degenerative Diseases - Using Digital image processing with Neural Network 2nd

Proposed Model for an Expert System for Diagnosing Degenerat...

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2nd International conference on Information and Communication Technology for Intelligent systems (ICTIS)

作者： Desai, Mittal N. Dahiya, Vishal Singh, A. K. Parul Univ Parul Inst Comp Applicat Fac IT & Comp Sci Vadodara India Indus Univ Ahmadabad Gujarat India ISRO Space Applicat Ctr Ahmadabad Gujarat India

ISBN: (纸本)9783319636733;9783319636726

In the era of any information on fingertip or on one click, medical diagnosis is context in which wrong diagnosis should be avoided using extensive information related to patients and symptoms. There should be an efficient system in diagnosis in terms of expert diagnostic opinion within short span of time, so that disease should be prevented to become chronic. To streamline this expert diagnostic opinion process to the patients, in daily routine, Expert System (ES) using artificial neural network can be employed. It is the method which can simulate two very important characteristics of humans, learning and generalization. Using ANN algorithms various types of medical data are handled and output is achieved with defining various relations between that data. Radiology is one of the branches of medical science in which various medical imaging techniques are used to diagnose difference internal medical problems. Digital image processing is the science of processing various digital images: such that important information will be generated. An Expert System is also an efficient tool from which diagnosis can be made. Integrating outcomes of neural network from diseased X-ray, to the knowledge based expert system;an expert opinion of diagnosing disease can be generated. In this paper a model is proposed for diagnosing, seven lower lumbar problems as degenerative diseases.

关键词： Radiology Medical imaging techniques Artificial neural network Lower lumbar diagnosis Expert system

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Indian Sign Language converter using Convolutional Neural Networks

Indian Sign Language converter using Convolutional Neural Ne...

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International conference for Convergence of Technology (I2CT)

作者： Nishi Intwala Arkav Banerjee Meenakshi Nikhil Gala Mukesh Patel School of Technology Management and Engineering (Mumbai Campus) SVKM’s NMIMS University Mumbai Maharashtra India

ISBN: (数字)9781538680759

ISBN: (纸本)9781538680766

People with hearing and speech impairments have to face a lot of difficulties while communicating with the general public. Being a minority, the sign language used by them is not known to a majority of people. In this paper, an Indian sign language converter was developed using a Convolutional Neural Network algorithm with the aim to classify the 26 letters of the Indian Sign Language into their equivalent alphabet letters by capturing a real time image of that sign and converting it to its text equivalent. First a database was created in various backgrounds and various image pre-processing techniques were used to make the database ready for feature extraction. After feature extraction, the images were fed into the CNN using the python software. Several real time images were tested to find the accuracy and efficiency. The results showed a 96% accuracy for the testing images and an accuracy of 87.69% for real time images.

关键词： Assistive technology Gesture recognition image segmentation Convolutional neural networks Real-time systems Training Classification algorithms

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Scaling handwritten student assessments with a document image workflow system 31

Scaling handwritten student assessments with a document imag...

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31st Meeting of the IEEE/CVF conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018

作者： Rowtula, Vijay Bhargavan, Varun Kumar, Mohan Jawahar, C.V. IIIT Hyderabad India

With the increase in the number of students enrolled in the university system, regular assessment of student performance has become challenging. This is specially true in case of summative assessments, where one expects the student to write down an answer on paper, rather than selecting a correct answer from multiple choices. In this paper, we present a document image workflow system that helps in scaling the handwritten student assessments in a typical university setting. We argue that this improves the efficiency since the book keeping time as well as physical paper movement is minimized. An electronic workflow can make the anonymization easy, alleviating the fear of biases in many cases. Also, parallel and distributed assessment by multiple instructors is straightforward in an electronic workflow system. At the heart of our solution, we have (i) a distributed image capture module with a mobile phone (ii) image processing algorithms that improve the quality and readability (iii) image annotation module that process the evaluations/feedbacks as a separate layer. Our system also acts as a platform for modern image analysis which can be adapted to the domain of student assessments. This include (i) Handwriting recognition and word spotting [5] (ii) Measure of document similarity [6] (iii) Aesthetic analysis of handwriting [7] (iv) Identity of the writer [4] etc. With the handwriting assessment workflow system, all these recent advances in computer vision can become practical and applicable in evaluating student assessments. © 2019 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Students

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2nd International conference on Soft Computing systems, ICSCS 2018

2nd International Conference on Soft Computing Systems, ICSC...

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2nd International conference on Soft Computing systems, ICSCS 2018

ISBN: (纸本)9789811319358

The proceedings contain 87 papers. The special focus in this conference is on Soft Computing systems. The topics include: Ultrasonic Signal Modelling and Parameter Estimation: A Comparative Study Using Optimization algorithms;a Histogram Based Watermarking for Videos and images with High Security;enhanced Empirical Wavelet Transform for Denoising of Fundus images;kernelised Clustering algorithms Fused with Firefly and Fuzzy Firefly algorithms for image Segmentation;performance Analysis of Wavelet Transform Based Copy Move Forgery Detection;high Resolution 3D image in Marine Exploration Using Neural Networks - A Survey;ship Intrusion Detection System - A Review of the State of the Art;Novel Work of Diagnosis of Liver Cancer Using Tree Classifier on Liver Cancer Dataset (BUPA Liver Disorder);Performance Analysis and Error Evaluation Towards the Liver Cancer Diagnosis Using Lazy Classifiers for ILPD;a Weight Based Approach for Emotion Recognition from Speech: An Analysis Using South Indian Languages;exploring Structure Oriented Feature Tag Weighting Algorithm for Web Documents Identification;MQMS - An Improved Priority Scheduling Model for Body Area Network Enabled M-Health Data Transfer;data Compression Using Content Addressable Memories;heart Block Recognition Using image processing and Back Propagation Neural Networks;design and Development of Laplacian Pyramid Combined with Bilateral Filtering Based image Denoising;diabetes Detection Using Deep Neural Network;Multi-label Classification of Big NCDC Weather Data Using Deep Learning Model;object Recognition Through Smartphone Using Deep Learning Techniques;hot Spot Identification Using Kernel Density Estimation for Serial Crime Detection;analysis of Scheduling algorithms in Hadoop;smart Transportation for Smart Cities.

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