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检索条件"任意字段=Conference on Statistical and Stochastic Methods for Image Processing"

共 4722 条记录，以下是671-680 订阅

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Paddy Yield Estimation by Identifying Disease Affected Region

Paddy Yield Estimation by Identifying Disease Affected Regio...

引用

Mobile Networks and Wireless Communications (ICMNWC), IEEE International conference on

作者： Sharanamma M. Hugar Mohammed Abdul Waheed Sharnbasva University Kalaburagi India VTU CPGS RO Kalaburagi India

ISBN: (纸本)9781665446075

In the Indian economy, Agriculture is considered one of the strongest pillars. Agriculture sector contributes significantly in our country, and it provides employability to many people live in a rural area. In our work, various methods of image processing are utilized for detecting healthy or unhealthy paddy leaves. This paper aims to carry out image analysis & classification methods to detect paddy leaf diseases and classification. We have built a system using image processing for identifying spots in paddy images by applying segmentation techniques. This system mainly includes four components: pre-processing, segmentation to extract the infected region by adopting FCM and SLIC algorithm, Feature Extraction using statistical Gray-Level Co-Occurrence Matrix (GLCM) features, and color features are extracted using HSV planes. Finally, Classification is made by using an artificial neural network (ANN).

关键词： Wireless communication image segmentation Artificial neural networks Predictive models Feature extraction Prediction algorithms Agriculture

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A Forensic Technique to Detect Copy-Move Forgery Based on image Statistics 10th

A Forensic Technique to Detect Copy-Move Forgery Based on Im...

引用

10th International conference on Security Privacy and Applied Cryptographic Engineering

作者： Nirwal, Ayush Khandelwal, Raghav Patel, Smit Singh, Priyanka Dhirubhai Ambani Inst Informat & Commun Technol Gandhinagar India

ISBN: (纸本)9783030666255;9783030666262

The proliferation of easy multimedia editing tools has ruined the trust in what we see. Forensic techniques are proposed to detect forgeries unnoticeable by naked human eyes. In this paper, we focus on a specific copy-move forgery attack that happens to alter portions within an image. It may be aimed to hide any sensitive information contained in a particular image portion or misguide the facts. Here, we propose to exploit the image's statistical properties, specifically, mean and variance, to detect the forged portions. A block-wise comparison is made based on these properties to localize the forged region called a prediction mask. Post-processing methods have been proposed to reduce false positives and improve the accuracy(F-score) of the prediction mask. This decrease in FPR in the final result is comes from post processing method of overlaying multiple masks with different values of threshold and block size of the sliding window.

关键词： Copy move forgery statistical properties Mean

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Lattice Boltzmann Method for Mathematical Morphology: Application to Porous Media 2

Lattice Boltzmann Method for Mathematical Morphology: Applic...

引用

conference on Multimodal Sensing and Artificial Intelligence - Technologies and Applications II

作者： Noel, Romain Navarro, Laurent Courbebaisse, Guy Univ Gustave Eiffel INRIA COSYS SII I4S F-44344 Bouguenais France Univ Lyon Univ Jean Monnet INSERM U1059 Mines St EtienneCtr Ingn Sante CISSAINBIOSE 158 Cours Fauriel F-42023 St Etienne France Univ Lyon Univ Claude Bernard Lyon 1 INSA Lyon INSERMUJM St EtienneCNRSCREATIS UMR 5220U1294 F-69621 Lyon France

ISBN: (数字)9781510644052

ISBN: (纸本)9781510644052

The LBM (Lattice Boltzmann Method) is often used in CFD (Computational Fluid Dynamics) for efficient fluid flow simulations. Computation of the permeability of a porous media from direct simulations is a common application which benefits from the ability of the LBM (Lattice Boltzmann Method) to embed porosity parameters. The MM (Mathematical Morphology) is widely used in image processing as the theoretical aspects guaranty robust algorithms for geometrical characterization of shapes appearing in images. The MM is commonly used to compute porosity from porous media images. The union of these two methods has been recently done through the LB3M (Lattice Boltzmann Method for Mathematical Morphology). The present work extends the LB3M to the extraction of porosity and pores segmentation from images. In order to benefit from the full capacity of the LB3M, it is necessary to reformulate and adjust the algorithms in a new paradigm. Thus, the underlying concept and algorithms required for computing the different previous information are detailed. Moreover, a comparison is provided between the permeability resulting from the CFD and MM both implemented by using the LBM. To sum up, this work emphasizes the full capacity of the LB3M to obtain complex transformations and operations issued from the MM theory through completely new and innovative algorithms. The herein challenge is to highlight the abilities of the LB3M to match with physical phenomenons. Indeed, the LB3M keeps the advantages from the MM such as a complete theory, fast convergence, scalability, robustness, etc. while adding the power of the LBM: statistical physics origins, partial differential equation solver, intrinsic properties of parallelization, efficiency, etc.

关键词： Lattice Boltzmann Method Mathematical Morphology image processing Porous Media Porosity Permeability

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Auto-Tuning Structured Light by Optical stochastic Gradient Descent

Auto-Tuning Structured Light by Optical Stochastic Gradient ...

引用

IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Chen, Wenzheng Mirdehghan, Parsa Fidler, Sanja Kutulakos, Kiriakos N. Univ Toronto Toronto ON Canada Vector Inst Toronto ON Canada NVIDIA Toronto ON Canada

ISBN: (纸本)9781728171685

We consider the problem of optimizing the performance of an active imaging system by automatically discovering the illuminations it should use, and the way to decode them. Our approach tackles two seemingly incompatible goals: (1) "tuning" the illuminations and decoding algorithm precisely to the devices at hand-to their optical transfer functions, non-linearities, spectral responses, image processing pipelines-and (2) doing so without modeling or calibrating the system;without modeling the scenes of interest;and without prior training data. The key idea is to formulate a stochastic gradient descent (SGD) optimization procedure that puts the actual system in the loop: projecting patterns, capturing images, and calculating the gradient of expected reconstruction error. We apply this idea to structured-light triangulation to "auto-tune" several devices-from smartphones and laser projectors to advanced computational cameras. Our experiments show that despite being model-free and automatic, optical SGD can boost system 3D accuracy substantially over state-of-the-art coding schemes.

关键词： Gradient methods

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A Novel PSO-SGD with Momentum Algorithm for Medical image Classification

A Novel PSO-SGD with Momentum Algorithm for Medical Image Cl...

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IEEE International conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaojuan Liu Ruiqi Feng Shangbo Zhou Ye Yang College of computer science Chongqing University Chongqing China College of computer & information science Southwest University Chongqing China

ISBN: (纸本)9781665429825

In recent years, deep learning has been widely used in the field of medical image processing, such as identification of symptoms, detection of organ. Due to the complexity of medical images, in the model training, there are many parameters when deep learning is used for image classification, and it takes a long time. Therefore, the training process for neural networks needs to be optimized. The stochastic gradient descent with momentum (SGD) is a common optimization algorithm in deep learning, and the particle swarm optimization (PSO) is a classical and effective swarm intelligence optimization algorithm. These two methods have their own advantages and disadvantages. Combining the two algorithms to calculate parameters, this paper proposes a novel particle swarm optimization-stochastic gradient descent with momentum (PSO-SGD) algorithm, which can find the optimal solution of the network more quickly and improve the solution efficiency on the basis of ensuring the classification accuracy. This algorithm is verified on two data sets, namely Blood Cell images Data Set (BCIDS) and COVED-19 Radiography Data Set (COVED19RDS). Experiments prove the effectiveness of the algorithm.

关键词： Deep learning Training Radiography Neural networks stochastic processes Classification algorithms Particle swarm optimization

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stochastic Isogeometric Analysis for Reliability Evaluation of Shell Structures Based on Multi-scale Support Vector Regression 2020

Stochastic Isogeometric Analysis for Reliability Evaluation ...

引用

Proceedings of the 2020 4th International conference on Vision, image and Signal processing

作者： Minglong Yang Jin Cheng Yunkun Xu Zhenyu Liu State Key Laboratory of CAD&CG Zhejiang University Hangzhou P. R. China State Key Laboratory of Fluid Power & Mechatronic Systems Zhejiang University Hangzhou P. R. China

ISBN: (纸本)9781450389532

A new metamodeling approach within isogeometric analysis (IGA) scheme is proposed in this paper to implement the stochastic structural reliability analysis of shell structures. The T-spline model is applied for the low-cost computation and exact geometric representation in analysis. The spatially dependent random material properties are considered and the density-dependent spatial clustering based multi-scale support vector regression (DCMSSVR) is developed for uncertainty quantification. A Cassegrain antenna example is investigated thoroughly, the results of which demonstrate that the proposed DCMSSVR approach have great performance in estimating the statistical characteristics of concerned structural displacement and thus the structural reliability with relatively small number of training samples.

关键词： Reliability evaluation Support vector regression stochastic isogeometric analysis

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Transformer-Based Speech Synthesizer Attribution in an Open Set Scenario

Transformer-Based Speech Synthesizer Attribution in an Open ...

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International conference on Machine Learning and Applications (ICMLA)

作者： Emily R. Bartusiak Edward J. Delp Video and Image Processing Lab School of Electrical and Computer Engineering Purdue University West Lafayette IN

Speech synthesis methods can create realistic-sounding speech, which may be used for fraud, spoofing, and mis-information campaigns. Forensic methods that detect synthesized speech are important for protection against such attacks. Forensic attribution methods provide even more information about the nature of synthesized speech signals because they identify the specific speech synthesis method (i.e., speech synthesizer) used to create a speech signal. Due to the increasing number of realistic-sounding speech synthesizers, we propose a speech attribution method that generalizes to new synthesizers not seen during training. To do so, we investigate speech synthesizer attribution in both a closed set scenario and an open set scenario. In other words, we consider some speech synthesizers to be "known" synthesizers (i.e., part of the closed set) and others to be "unknown" synthesizers (i.e., part of the open set). We represent speech signals as spectrograms and train our proposed method, known as compact attribution transformer (CAT), on the closed set for multi-class classification. Then, we extend our analysis to the open set to attribute synthesized speech signals to both known and unknown synthesizers. We utilize a t-distributed stochastic neighbor embedding (tSNE) on the latent space of the trained CAT to differentiate between each unknown synthesizer. Additionally, we explore poly-1 loss formulations to improve attribution results. Our proposed approach successfully attributes synthesized speech signals to their respective speech synthesizers in both closed and open set scenarios.

关键词： Training Synthesizers Forensics Machine learning Transformers Fraud Speech synthesis

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An Efficient Pipeline for Distant Person Detection and Identification in 4K Video using GPUs

An Efficient Pipeline for Distant Person Detection and Ident...

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International conference on COMmunication Systems and NETworkS (COMSNETS)

作者： Mattela, Govardhan Tripathi, Manmohan Pal, Chandrajit Dhiraj, Rampelli Sai Acharyya, Amit Indian Inst Technol IIT Hyderabad India Redpine Signals Inc San Jose CA USA

ISBN: (纸本)9781728131870

The gradual advent of machine learning has been assisting to shift the field of computer vision from statistical methods to deep neural networks. These networks should be able to process high resolution video streams coming from the HD camera sources in real time. However, due to the fixed network size and to maintain the processing speed, high resolution frames need to be resized and down-sampled before feeding into the networks resulting in loss of feature information, hampering recognition accuracy. This motivated us to propose a methodology which focuses on creating and processing the active region of interests in the foreground image through an active region generator (ARG) module, eliminating the need to traverse the entire frame and down-sample the resolution before feeding it to the neural network. This resulted in saving 25x more image feature information, whilst maintaining a given person detection accuracy of 92 % mAP for longer distance up to 30 similar to 35 metre executing in real time w.r.t it's classical counterpart based on singleshot detector model. Besides, our proposed pipeline architecture utilizing multi-core TESLA GPU increases the execution throughput by a factor of 3X verified in NVIDIA DGX system.

关键词： Active region generator (ARG) module CCTV ReID (reidentification) COTS (component of the shelf) GPU (Graphics processing unit)

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statistical based CNN algorithm for SAR image despeckling

Statistical based CNN algorithm for SAR image despeckling

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EUSAR 2021;13th European conference on Synthetic Aperture Radar

作者： Sergio Vitalea Giampaolo Ferraiolib Vito Pascazio

Despeckling is a key tool for the SAR image understanding and it is the first image pre-processing for other application such as classification, segmentation and detection. The importance of having a filter able to suppress noise without losing spatial details is fundamental. Among different approaches, nowadays several deep learning based algorithm for SAR despeckling are proposed. Most of them rely on simulated datasets based on certain hypothesis for the speckle that usually not fully exploit the characteristics of real SAR images, leading to methods that produce artefacts in areas with different characteristics from those present in the training. The aim of this work is to propose a multi-step despeckling process: in the first step a convolutional neural network trained under the fully developed speckle hypothesis with a statistical loss function is used for despeckling; later, by means of a statistical test and a ratio edge detector, the noise predicted by the network is used for detecting the not fully developed areas where the network will produce artefacts. Once this detection is done, an ad hoc filtering policy can be considered.

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A Review Article on Prediction of Lung Cancer using image processing and Machine Learning Approaches

A Review Article on Prediction of Lung Cancer using Image Pr...

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Advances in Computing, Communication Control and Networking (ICAC3N), International conference on

作者： Kavita Singh D. Gnana Jeba Das Usha Chauhan Lokesh Varshney DEECE Galgotias College of Engineering and Technology Greater Noida Uttar Pradesh India

A wide variety of subtypes of cancer have been discovered. Early cancer research today requires that cancer forms be screened and treated in a timely manner because it aids in the medical treatment of patients. In the fields of biomedicine and bioinformatics, various research has investigated "the use of machine learning and deep learning" to classify cancer patients as having a high or low risk of relapse. Cancer treatment and research have been affected by these methods. It is necessary to employ machine learning techniques when working with enormous datasets in order to determine the most essential properties. "Artificial neural networks (ANN), support vector machines (SVMs), and decision trees can be used to construct cancer cure prediction models (DTs)". This means that while ML techniques can help us better understand how cancer grows, they are not yet ready to be applied in daily clinical practice. "In this study, the ML and DL approaches for cancer progression modeling are explored. Certain ML, input, and data sample supervisions are often used".

关键词： Deep learning Support vector machines Lung cancer stochastic processes Process control Machine learning Artificial neural networks

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