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

共 2642 条记录，以下是2341-2350 订阅

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Transformation Parameters Estimation for Medical image Registration Using CEL-DNN

Transformation Parameters Estimation for Medical Image Regis...

引用

Advancement in Renewable Energy and Intelligent Systems (AREIS), International conference on

作者： M. Priya S Perumal Sankar TocH Institute of Science and Technology Ernakulum Kerala India Department of ECE TocH Institute of Science and Technology Ernakulum Kerala India

ISBN: (数字)9798350387230

ISBN: (纸本)9798350387247

image registration has become a major medical image computing technology over the past ten years, with applications ranging from computer-assisted therapy and surgery to computer-assisted diagnosis. A medical image registration model based on a Cross-Entropy-based Deep neural Network (CEL-DNN) is presented in this paper. First, pre-processing is done on fixed and moving images, removing noise and enhancing contrast. Next, the features of both images are retrieved and then matched using the Mahalanobis Distance-based Brute-Force Matcher (MD-BFM) approach. FIS is used to estimate the transformation parameters based on the matched features. The final aligned image is obtained by applying the parameters using the CEL-DNN model over the moving image. To show the proposed model's superior performance, it is finally benchmarked against existing models.

关键词： image registration Renewable energy sources Sensitivity Deformation Noise Surgery artificial neural networks Feature extraction Robustness Medical diagnostic imaging

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Household Waste Classification Algorithms Based on Various Convolutional neural networks: A Review of Performance Optimization

Household Waste Classification Algorithms Based on Various C...

引用

International conference on Wavelet Active Media Technology and Information processing (ICWAMTIP)

作者： Huang Renxiang Chen Jia Li Jianping School of Artificial Intelligence and Big Data Sichuan University of Arts and Science Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

This paper reviews the research progress of deep learning-based household waste classification algorithms. It first introduces the importance of household waste classification and the application value of deep learning in this field. Next, it outlines the theoretical foundations of deep learning technology in waste classification, including the principles of convolutional neural networks and their applications in image recognition. The article provides a detailed analysis of the application of different deep learning models in waste classification, such as MobileNetV2, ShuffleNetV1, EfficientNetV1, and YOLOV5, as well as their optimizations and innovations in practical applications. Furthermore, it discusses optimization and innovation methods for deep learning models, including learning rate adjustment, optimizer selection, attention mechanisms, data augmentation, and lightweight network design. Finally, it summarizes the current research shortcomings and challenges, and looks forward to future research directions. This review provides a systematic perspective for a deeper understanding of the application of deep learning in waste classification and offers directions for future research.

关键词： Deep learning Analytical models Technological innovation Attention mechanisms Reviews Data augmentation Data models Classification algorithms Convolutional neural networks Optimization

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Staistical features extraction in wavelet domain for texture classification

Staistical features extraction in wavelet domain for texture...

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International conference on image and Signal processing and their applications (ISPA)

作者： Soraya ZEHANI Abdeldjalil OUAHABI Malika MIMI Abdelmalik TALEB-AHMED Electrical Engineering department Biskra University Biskra Algeria Polytech Tours Imaging and Brain University of Tours Tours France Electrical Engineering department Mostaganem University Mostaganem Algeria IEMN DOAE UMR CNRS 8520 UPHF Valenciennes France

ISBN: (纸本)9781728131573

This paper presents a new approach for texture classification generalizing a well-known statistical features combining the fractal analysis by means of fractal dimension (FD) with the selection first and second order statistics features in the spatial and wavelet domain. The objective of our paper is to propose the features extraction using statistical parameters in the spatial domain and in wavelet domain with different wavelets, with and without preprocessing stage for the texture classification using neural networks for pattern recognition and studying the effect of the preprocessing and wavelets in classification accuracy. The extracted features are used as the input of the ANN classifier. The performance of the proposed methods are evaluated by using two classes of Brodatz database textures. Finally, classification assessment measures such as the confusion matrix, ROC curves and accuracy are applied to the proposed methods.

关键词： artificial neurals network (ANN) Discrete wavelet transform (DWT) Fractal dimension (FD) Preprocessing Statistical features extraction Texture analysis Feature extraction Wavelet domain texture analysis Pretreatment Taxonomy wavelets texture textures fractal analysis spatial domain

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Co-parent selection for fast region merging in pyramidal image segmentation

Co-parent selection for fast region merging in pyramidal ima...

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Workshops on image processing Theory, Tools and applications, IPTA

作者： Milos Stojmenovic Andres Solis-Montero Amiya Nayak University of Ottawa Canada

The goal of image segmentation is to partition an image into regions that are internally homogeneous and heterogeneous with respect to other neighbouring regions. We build on the pyramid image segmentation work by making a more efficient method by which children chose parents within the pyramid structure. Instead of considering only four immediate parents each child node considers the neighbours of its candidate parent, and the candidate parents of its neighbouring nodes in the same level. In this paper, we also introduce the concept of a co-parent node for possible region merging at the end of each iteration. The new parents of the former children are co-parent candidates as if they are similar. The co-parent is chosen to be the one with the largest receptive field among candidate co-parents. Each child then additionally considers one more candidate, the co-parent of the previous parent. Other steps in the algorithm, and its overall layout, were also improved. The new algorithm is tested on a set of images. Our algorithm is fast (produces segmentations within seconds), results in the correct segmentation of elongated and large regions, very simple compared to plethora of existing algorithms, and appears competitive in segmentation quality with the best publicly available implementations. The major improvement over is that it produces visually appealing results at earlier levels of pyramid segmentation, and not only at the top one.

关键词： image segmentation artificial neural networks Pixel

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A Study on Various Classifier Techniques Used in image processing

A Study on Various Classifier Techniques Used in Image Proce...

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International conference on ent Computing and Control Systems (ICICCS)

作者： C. Arun Akshatha Prabhu Mohammed Zeeshan N. Shobha Rani Amrita School of Arts and Sciences Mysuru India

ISBN: (数字)9781728148762

ISBN: (纸本)9781728148779

Handling information, item, and picture grouping is a very significant and important task in image processing. Solving real-world problems using computer vision is an emerging trend and fruit classification is one such area. This classification using machine techniques are essential in supermarkets essentially at the point of sale(POS). Fruit classification has always posed inter and intraclass variations and appropriate data acquisition and feature representations are important. Various works have been carried out in classification methods and in recent year's classifiers such as support vector machine, K nearest neighbor, artificial and convolution neural networks have been trending. This paper presents the state of art methods in fruit classification proposed by various researchers that also hold for applications such as fruit quality assessment and harvesting.

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Application of the artificial neural Network for blood pressure evaluation with smartphones

Application of the Artificial Neural Network for blood press...

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IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and applications

作者： Francesco Lamonaca Kurt Barbe Yuriy Kurylyak Domenico Grimaldi Wendy Van Moer Angelo Furfaro Vitaliano Spagnuolo Department of Computer Science Universiy of Calabria. Rende (Cs) Ttaly Department of Fundamental Electricity and Instrumentation (M2ESA) Vrije Universiteit Brussel Brussels Belgium Clinical and Therapeutic Unit of Arterial Hypertension - Unit of Internal Medicine-AO of Cosenza Italy

The smartphone is proposed to evaluate the Blood Pressure (BP) anywhere and anytime. The tasks performed by smartphone are (i) extraction of the PhotoPlethysmoGram (PPG) signal from a frame sequence acquired by the integrated camera, and (ii) processing it by artificial neural Network for the evaluation of the BP. The PPG signal is evaluated by analyzing the volumetric blood variation of the fingertip on the frame sequence. Successively, parameters characterizing the pulses of the PPG signal are sent to the Fit Forward neural Network for the simultaneously evaluation of the systolic and the diastolic BP. The validation of the results is performed by comparing them with the ones obtained by the Ambulatory Blood Pressure monitor ABP Spacelabs 90207. Preliminary experimental results show useful information to address the future research devoted to reduce the maximum error.

关键词： Smart phones Biomedical monitoring artificial neural networks Blood pressure Cameras Monitoring Training

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A Framework for neural Network Inference on FPGA-Centric SmartNICs

A Framework for Neural Network Inference on FPGA-Centric Sma...

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International conference on Field Programmable Logic and applications

作者： Anqi Guo Tong Geng Yongan Zhang Pouya Haghi Chunshu Wu Cheng Tan Yingyan Lin Ang Li Martin Herbordt ECE Department Boston University Boston MA ECE Department University of Rochester Rochester NY Pacific Northwest National Laboratory Richland WA Rice University Houston TX Microsoft Redmond WA

FPGA-based SmartNICs offer great potential to significantly improve the performance of high-performance computing and warehouse data processing by tightly coupling support for reconfigurable data-intensive computation with cross-node communication thereby mitigating the von Neumann bottleneck. Existing work however has generally been limited in that it assumes an accelerator model where kernels are offloaded to SmartNICs with most control tasks left to the CPUs. This leads to frequent waiting reduced performance and scaling challenges. In this work we propose a new distributive data-centric computing framework named FCsN for reconfigurable SmartNIC-based systems. Through a lightweight task circulation execution model and its implementation architecture FCsN allows the complete detaching of NN kernel execution control logic system scheduling and network communication to the SmartNICs. This boosts performance by (i) avoiding control dependency with CPUs and (ii) supporting streaming NN kernel execution and network communication at line rate and in a very fine-grained manner. We demonstrate the efficiency and flexibility of FCsN using various types of neural network kernels and applications including deep neural networks (DNN) and graph neural networks (GNN) as these last are both irregular and data intensive they offer an especially robust demonstration. Evaluations using commonly-used neural network models and graph datasets show that a system with FCsN can achieve 10 × speedups over the MPI-based standard CPU baselines

关键词： Runtime Processor scheduling Computational modeling High performance computing neural networks artificial neural networks Programming

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Moving object tracking in the sequence of images acquired from non-stationary camera

Moving object tracking in the sequence of images acquired fr...

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IEEE International conference on image processing

作者： J. Stepan M. Jirina Faculty of Electrical Engineering Department of Cybernetics Czech Technical University Prague Czech Republic

This paper discusses the possibility of moving object tracking in the sequence of images acquired from a non-stationary (even shaky) camera that is held in the hand. In the process of image acquisition the effort is to keep the moving object in the central area of the image. In this way we manually prompt the evaluation system to recognize and consequently track the object concerned. This task is very complex because the camera is held in the hand and the object is moving far from the camera. In addition to the shaking camera, we do not have any a priori information about the scene and the objects detected. This leads to a very shaky sequence of images. Such a problem has not been studied systematically. The proposed method is based on the weighted gradient object edge detection and object size and color tracking. To speed up the evaluation process for real-time applications no previous image stabilization has been processed. The method is demonstrated on a sequence of images acquired from a real situation.

关键词： Cameras Object detection Layout Target tracking Intelligent sensors Motion estimation Robustness artificial neural networks Vehicle dynamics Vehicle detection

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Exploring the Interplay of Interpretability and Robustness in Deep neural networks: A Saliency-Guided Approach

Exploring the Interplay of Interpretability and Robustness i...

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image processing Challenges (ICIPC), IEEE International conference on

作者： Amira Guesmi Nishant Suresh Aswani Muhammad Shafique eBrain Lab Division of Engineering New York University Abu Dhabi Abu Dhabi UAE Department of Computer Science and Engineering New York University Tandon USA

ISBN: (数字)9798331515942

ISBN: (纸本)9798331515959

Adversarial attacks pose a significant challenge to deploying deep learning models in safety-critical applications. Maintaining model robustness while ensuring interpretability is vital for fostering trust and comprehension in these models. This study investigates the impact of Saliency-guided Training (SGT) on model robustness, a technique aimed at improving the clarity of saliency maps to deepen understanding of the model’s decision-making process. Experiments were conducted on standard benchmark datasets using various deep learning architectures trained with and without SGT. Findings demonstrate that SGT enhances both model robustness and interpretability. Additionally, we propose a novel approach combining SGT with standard adversarial training to achieve even greater robustness while preserving saliency map quality. Our strategy is grounded in the assumption that preserving salient features crucial for correctly classifying adversarial examples enhances model robustness, while masking non-relevant features improves interpretability. Our technique yields significant gains, achieving a 35% and 20% improvement in robustness against PGD attack with noise magnitudes of 0.2 and 0.02 for the MNIST and CIFAR-10 datasets, respectively, while producing high-quality saliency maps.

关键词： Training Deep learning image processing Noise Focusing artificial neural networks Robustness Standards Resilience Glass box

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Real-Time Tracing Of A Weld Line Using artificial neural networks

Real-Time Tracing Of A Weld Line Using Artificial Neural Net...

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International conference on Computer and Information Science (ACIS)

作者： Srinath Hanumantha Rao V. Kalaichelvi R. Karthikeyan Department of Electrical and Electronics Engineering BITS Pilani Dubai Campus Dubai UAE Department of Mechanical Engineering BITS Pilani Dubai Campus Dubai UAE

Robotic Manipulators are becoming increasingly popular nowadays with applications in almost every industry and production line. It is difficult but essential to create a common algorithm for the different types of manipulators present in todays market so that automation can be achieved at a faster rate. This paper aims to present a real time implementation of a method to control a Tal Brabo! Robotic manipulator to move along a given weld line in order to be utilized in factories for increasing production capacity and decreasing production time. The controller used here is provided by Trio, whose ActiveX component is interfaced to MATLAB. images were captured to identify weld lines in every possible alignment to find points of interest and the neural network was trained in order to follow a given weld line once the work-piece was placed on the work-table.

关键词： Welding Manipulators Biological neural networks image processing Matlab

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