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检索条件"任意字段=Conference on Applications of Artificial Neural Networks in Image Processing II"

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BP-DIP: A Backprojection based Deep image Prior

BP-DIP: A Backprojection based Deep Image Prior

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European Signal processing conference (EUSIPCO)

作者： Jenny Zukerman Tom Tirer Raja Giryes Tel Aviv University Tel Aviv Israel School of Electrical Engineering Tel Aviv University Tel Aviv Israel

ISBN: (数字)9789082797053

ISBN: (纸本)9781728150017

Deep neural networks are a very powerful tool for many computer vision tasks, including image restoration, exhibiting state-of-the-art results. However, the performance of deep learning methods tends to drop once the observation model used in training mismatches the one in test time. In addition, most deep learning methods require vast amounts of training data, which are not accessible in many applications. To mitigate these disadvantages, we propose to combine two image restoration approaches: (i) Deep image Prior (DIP), which trains a convolutional neural network (CNN) from scratch in test time using the given degraded image. It does not require any training data and builds on the implicit prior imposed by the CNN architecture; and (ii) a backprojection (BP) fidelity term, which is an alternative to the standard least squares loss that is usually used in previous DIP works. We demonstrate the performance of the proposed method, termed BP-DIP, on the deblurring task and show its advantages over the plain DIP, with both higher PSNR values and better inference run-time.

关键词： Deep learning Training Training data Tools image restoration Task analysis Electronics packaging

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image Based Detection of Cotton Plant Diseases Using Discrete Wavelet Transform Features

Image Based Detection of Cotton Plant Diseases Using Discret...

引用

IEEE International conference on Intelligent Techniques in Control, Optimization and Signal processing (INCOS)

作者： Shalini Mohan Lakshmanan Muthulakshmi Devaraj D Department of Biotechnology Kalasalingam Academy of Research and Education Srivilliputtur India Department of Electrical and Electronics Engineering Kalasalingam Academy of Research and Education Srivillliputur India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

Cotton is one of the most vital cash crops cultivated around the globe and its yield directly influences the economy and the livelihood of huge number of people associated with it. The major loss of cotton occurs due to damage incurred by microbial pathogens that infect the cotton crop. The course of infection and their effect on the growth and yield of the plant varies depending upon the type of disease. Appropriate treatment for the specific type of disease could prevent the spread of infection and also reduce the use of pesticide that is usually done during the empirical treatment techniques. Identification of the type of cotton plant disease is critical and needs to be done at a faster pace. image processingbased machine learning approach is found to be a better option for this application. This article deals with development of the machine learning model capable of identifying the different types of cotton plant diseases using image processing. Foliar images of diseased cotton plants are collected from public databases and preprocessing operations like resizing, filtering and contrast enhancement are performed followed by k-means clustering for segmentation of diseased part of the leaf. image decomposition is performed using discrete wavelet transform with Daubechies wavelet at five levels and the mean, standard deviation, and entropy of the coefficients are used for building the machine learning model. Classification with artificial neural Network trained with backpropagation algorithm has a classification accuracy of 97% indicating the veracity of the model. Hence, the developed model could be used as for developing a user-friendly interface that could be tapped for real-time applications.

关键词： Plant diseases image segmentation Crops Machine learning Backpropagation algorithms artificial neural networks Discrete wavelet transforms

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Detail-preserving restoration of impulse noise corrupted images by a switching median filter controlled by a neuro-fuzzy network

Detail-preserving restoration of impulse noise corrupted ima...

引用

IEEE Signal processing and Communications applications (SIU)

作者： M.E. Yuksel A. Basturk E. Besdok M.T. Yildirim Müh. Fak. Elektronik Müh. Bölümü Erciyes Üniversitesi Turkey Müh. Fak. Elektronik Müh. Bölümü Kayseri Jeodezi ve Fotagrametri Müh. Böl. Kayseri Sivil Havacılık Yüksek Okulu Kayseri

A new operator for the restoration of digital images corrupted by impulse noise is presented. The proposed operator is a simple recursive switching median filter controlled by a neuro-fuzzy network functioning as an impulse detector. The internal parameters of the neuro-fuzzy impulse detector are adaptively determined by training. The training is easily accomplished using simple artificial images that can be generated in a computer. The most distinctive feature of the proposed operator over other operators is that it offers excellent detail and texture preservation performance while at the same time effectively removing noise from the input image. Experiments show that the proposed operator may be used for efficient restoration of digital images corrupted by impulse noise without distorting the useful information in the image.

关键词： image restoration Filters Fuzzy neural networks Digital images Detectors

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Design of a neural Network for a Concentrated Acid Hydrolysis of Biomass in Bioethanol Production as a Clean Energy

Design of a Neural Network for a Concentrated Acid Hydrolysi...

引用

International conference on artificial Intelligence and image processing(ICAiiP’2012);International conference on Innovations in Electrical and Electronics Engineering(ICIEE’2012);International conference on Mechanical,Automobile and Biodiesel Engineering(ICMABE’2012);International conference on History,Literature and Management(ICHLM’2012);International conference on Agriculture,Chemical and Environmental Sciences(ICACES’2012);International conference on Biological and Medical applications(ICBMA’2012)

作者： Ali Arasteh Nodeh Department of Engineering Quchan BranchIslamic Azad University

Application of neural network for modeling the saccharification of biomass by concentrated acid hydrolysis has been *** to a more complex and unknown kinetics of the investigated reaction,the proposed approach based on application of neural networks is an efficient and accurate tool to solve modeling *** Influence of the main pretreatment variables including temperature,processing time,solid contents,and acid concentration on fermentable sugar generation was *** for pretreatment temperature (65,80,90oC),process time (120,180,240 minute),solid content (5,10,15%) and concentration of sulfuric acid (20,40,60%) were *** were analyzed by HPLC and modeled by two layers neural network with different *** layers neural network with different neurons was *** using simple data and a simple neural network,high accuracy was *** trained MLP that has one hidden layer with 11 Log-sig neurons has a lower average relative error for test ***,it is selected as the most suitable network to simulate the concentrated acid hydrolysis process.

关键词： biomass,glucose,Acid sulfuric,concentrated acid hydrolysis,neural network

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Comparison of remote sensing approach for mangrove mapping over Penang Island

Comparison of remote sensing approach for mangrove mapping o...

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International conference on Computer and Communication Engineering, ICCCE

作者： Beh Boon Chun Mohd. Zubir Mat Jafri Lim Hwee San School of Physics Universiti Sains Malaysia Minden Penang Malaysia

Mangrove mapping is crucial for the policy maker to have more proper planning of land use of a country or nation while helping to preserve the mangrove area. The unique mangrove ecosystem need to be conserved as mangrove trees has many applications for human being not only in their forestry products such as timber and charcoal but also serve as a strong barrier from the attack of wave, erosion and tsunami to inland area near the seashore or coastal region. The aim of this paper is to compare the accuracy of the mangrove map produced from different remote sensing techniques. Thailand Earth Observation System (THEOS) satellite data of Penang Island with date 29 January 2010 was utilized for the image processing analysis. All the pre-processing, classification, validation and post-classification analysis were done by using Geomatica version 10.3.2 software package. The results obtained show that artificial neural Network (ANN) with classification accuracy of 93.5% can increase the overall accuracy by 2.0% as compare to Maximum Likelihood Classification method (91.5%). This study indicates that ANN approach which has the highest accuracy and kappa coefficient is more reliable used for mangrove mapping at generic level.

关键词： Accuracy Remote sensing artificial neural networks Vegetation mapping Satellites Earth

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A Methodological Study of Document Layout Analysis

A Methodological Study of Document Layout Analysis

引用

Virtual Reality, Human-Computer Interaction and artificial Intelligence (VRHCIAI), International conference on

作者： Chunhu Zhang Mayire Ibrayim Askar Hamdulla Xinjiang Key Laboratory of Signal Detection and Processing Urumqi China School of Information Science and Engineering Xinjiang University Xinjiang China

Document layout analysis is an important part of document information processing systems, which is essential for many applications such as optical character recognition (OCR) systems, machine translation, information retrieval, and document structured data extraction, as well as for digitizing paper documents and classifying and identifying document image regions. Document-like images contain a wealth of information, and in order to automatically extract and classify regions of interest in document images, the document images are programmed to analyze the layout content for subsequent OCR and automatic transcription. However, the proposed algorithms still have more limitations due to various document layouts and variations of block positions, inter-class and within-class variations, and background noise. This paper first summarizes the traditional learning algorithms based on tour smoothing and segmentation projection, deep learning algorithms using recurrent convolutional neural networks and twin networks, and algorithms combining traditional learning and deep learning proposed in recent years. The current mainstream algorithms and common datasets in experiments for deep learning and their access are highlighted. As well as the comparison of some algorithms on benchmark datasets, and some experimental results with good robustness are given. Finally, the future research areas are prospected for further development.

关键词： Deep learning Text analysis Smoothing methods Layout Optical character recognition Virtual reality Information processing

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A Novel Method to Authenticate Texture of Cotton and Silk Saree Using image processing & RCNN Machine Learning Techniques

A Novel Method to Authenticate Texture of Cotton and Silk Sa...

引用

artificial Intelligence, Computational Electronics and Communication System (AICECS), International conference on

作者： Yogesh B S Praveen N Annapurna P Patil Nisarga K Information Science & Engineering Dayananda Sagar college of Engineering Bengaluru Associate Software Engineer Iron Mountain Bengaluru

ISBN: (数字)9798350391244

ISBN: (纸本)9798350391251

The popularity of sarees, as Indian traditional garment, is spreading all over the world, and because of their variety and different type of materials used in making traditional garment need to automate and classification methods are required to identify best among the existing. This work offers a novel method for classifying and detecting saree types using computer vision techniques. The visual features of sarees are classified by using image processing with machine learning models as preprocessing to improve the classification model. With the use of region based CNNs texture feature will be extracted and identification of different types of material used (Silk/Cotton/Shiffon). The provided material and its category plays a vital role in differentiating Kanchivaram Silk Sarees. The model ensures stability across datasets and training conditions, as demonstrated by experimental findings that show its improved accuracy and speed over using traditional deep learning techniques. This research increases computer vision in apparel recognition and categorization with applications in e-commerce and fashion recommendation.

关键词： Training Deep learning Computer vision Accuracy image color analysis Computational modeling Clothing Feature extraction Convolutional neural networks Cotton

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Efficient Embedded Deep neural-Network-based Object Detection Via Joint Quantization and Tiling

Efficient Embedded Deep Neural-Network-based Object Detectio...

引用

IEEE International conference on artificial Intelligence Circuits and Systems (AICAS)

作者： George Plastiras Shahid Siddiqui Christos Kyrkou Theocharis Theocharides KIOS Research and Innovation Center of Excellence

ISBN: (数字)9781728149226

ISBN: (纸本)9781728149233

Embedded visual AI is a growing trend in applications requiring low latency, real-time decision support, increased robustness and security. Visual object detection, a key task in visual data analytics, has enjoyed significant improvements in terms of capabilities and accuracy due to the emergence of Convolutional neural networks (CNNs). However, such complex paradigms require heavy computational resources that prevent their deployment on resource-constrained devices, and in particular, impose significant constraints in possible hardware accelerators geared towards such applications. In this work therefore, we investigate how a combination of techniques can lead to efficient visual AI pipelines for resource-constrained object detection. In particular we leverage an efficient search strategy based on a combination of pre-processing mechanisms, that reduce the processing demands of deep network as a counter measure for potential accuracy reduction caused by quantization. The proposed approach enables the detection of objects in higher resolution frames using quantized models, while maintaining the accuracy of full-precision CNN-based object detectors. We illustrate the impact on the accuracy and average processing time using quantization techniques and different tiling approaches on efficient object detection architectures; as a case study, we focus on Unmanned-Aerial- Vehicles (UAVs). Through the proposed methodology, hardware accelerator demands are thereby reduced, leading to both performance benefits and associated power savings.

关键词： Quantization (signal) Detectors Object detection Visualization image resolution Real-time systems Pipelines

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A Synergetic Attack against neural Network Classifiers combining Backdoor and Adversarial Examples

A Synergetic Attack against Neural Network Classifiers combi...

引用

IEEE International conference on Big Data

作者： Guanxiong Liu Issa Khalil Abdallah Khreishah NhatHai Phan New Jersey Institute of Technology Newark USA Qatar Computing Research Institute Doha Qatar

ISBN: (纸本)9781665445993

The pervasiveness of neural networks (NNs) in critical computer vision and image processing applications makes them very attractive for adversarial manipulation. A large body of existing research thoroughly investigates two broad categories of attacks targeting the integrity of NN models. The first category of attacks, commonly called Adversarial Examples, perturbs the model’s inference by carefully adding noise into input examples. In the second category of attacks, adversaries try to manipulate the model during the training process by implanting Trojan backdoors. Researchers show that such attacks pose severe threats to the growing applications of NNs and propose several defenses against each attack type individually. However, such one-sided defense approaches leave potentially unknown risks in real-world scenarios when an adversary can unify different attacks to create new and more lethal ones bypassing existing *** this work, we show how to jointly exploit adversarial perturbation and model poisoning vulnerabilities to practically launch a new stealthy attack, dubbed AdvTrojan. AdvTrojan is stealthy because it can be activated only when: 1) a carefully crafted adversarial perturbation is injected into the input examples during inference, and 2) a Trojan backdoor is implanted during the training process of the model. We leverage adversarial noise in the input space to move Trojan-infected examples across the model decision boundary, making it difficult to detect. The stealthiness behavior of AdvTrojan fools the users into accidentally trusting the infected model as a robust classifier against adversarial examples. AdvTrojan can be implemented by only poisoning the training data similar to conventional Trojan backdoor attacks. Our thorough analysis and extensive experiments on several benchmark datasets show that AdvTrojan can bypass existing defenses with a success rate close to 100% in most of our experimental scenarios and can be extended to attack fede

关键词： Training Toxicology Perturbation methods Computational modeling image processing Training data artificial neural networks

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Face recognition classifier based on dimension reduction in deep learning properties

Face recognition classifier based on dimension reduction in ...

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IEEE Signal processing and Communications applications (SIU)

作者： Ahmet Bılgıç Onur Can Kurban Tülay Yildirim Bilişim Teknolojileri Enstitüsü Tübitak BİLGEM Kocaeli Türkiye Elektronik ve Haberleşme Mühendisliği Bölümü Yıldız Teknik Üniversitesi İstanbul Türkiye

Nowadays, with the increasing use of biometric data, it is expected that systems can give successful results against difficult situations and work robustly. Especially, in face recognition systems, variables such as direction of light, facial expression and reflection are making difficult to identify. Thus, in recent years, Convolutional neural Network (CNN) models, which are deep learning models as an alternative to traditional feature extraction and artificial neural network methods, have begun to be developed. In this work, for face recognition, VGG Face deep learning model is compared with our proposed model which uses Multi Layer Perceptron (MLP) classifier and reduced deep features by principal component analysis. The Kinect RGB image dataset belonging to 40 people with different facial expressions and lighting conditions has been tested with 4-fold cross validation method. While 97.18% classification ratio was achieved with the first model, 100% recognition accuracy has been obtained by the second model. The results show that deep learning achieves a high performance in face recognition under different light and expression conditions, however, the proposed classification method based on dimension reduction in deep learning properties achieves better performance.

关键词： Face Face recognition Machine learning neural networks conferences Computer vision

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