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

共 15616 条记录，以下是511-520 订阅

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A ResNet-101 Based Recommendation System for E-Commerce 11

A ResNet-101 Based Recommendation System for E-Commerce

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11th International conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Sharma, Puneet Kumar Bhardwaj, Chetan Mishra, Rishabh Sharma, Dolly Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9798350350357

In today's connected world, where users have millions of choices on online platforms, recommendation systems play an important role in personalizing experiences. Traditionally, recommendation systems are created by utilizing collaborative or content-based filtering;however, both do not utilize visual aspects of items, which playa very important role in helping users to make decisions. With advances in deep learning techniques, image processing and extracting valuable information from images using convolutional neural networks are becoming easier and efficient. Therefore, images of items can provide very accurate and important auxiliary information for recommendation systems to utilize and make more accurate recommendations. This paper proposes a ResNet-lOl based recommendation system which considers the visual aspects as well as the metadata of items to increase the rating prediction performance of already existing recommendation algorithms. This paper also proposes a way through which recommendations could be generated by utilizing predicted ratings. © 2024 IEEE.

关键词： Recommender systems

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Efficient Equivariant Transfer Learning from Pretrained Models 37

Efficient Equivariant Transfer Learning from Pretrained Mode...

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37th conference on Neural Information processing systems (NeurIPS)

作者： Basu, Sourya Katdare, Pulkit Sattigeri, Prasanna Chenthamarakshan, vijil Driggs-Campbell, Katherine Das, Payel Varshney, Lav R. Univ Illinois Champaign IL 61820 USA IBM Res Yorktown Hts NY USA

ISBN: (纸本)9781713899921

Efficient transfer learning algorithms are key to the success of foundation models on diverse downstream tasks even with limited data. Recent works of Basu et al. (2023) and Kaba et al. (2022) propose group averaging (equitune) and optimization-based methods, respectively, over features from group-transformed inputs to obtain equivariant outputs from non-equivariant neural networks. While Kaba et al. (2022) are only concerned with training from scratch, we find that equitune performs poorly on equivariant zero-shot tasks despite good finetuning results. We hypothesize that this is because pretrained models provide better quality features for certain transformations than others and simply averaging them is deleterious. Hence, we propose lambda-equitune that averages the features using importance weights, lambda s. These weights are learned directly from the data using a small neural network, leading to excellent zero-shot and finetuned results that outperform equitune. Further, we prove that lambda-equitune is equivariant and a universal approximator of equivariant functions. Additionally, we show that the method of Kaba et al. (2022) used with appropriate loss functions, which we call equizero, also gives excellent zero-shot and finetuned performance. Both equitune and equizero are special cases of lambda-equitune. To show the simplicity and generality of our method, we validate on a wide range of diverse applications and models such as 1) image classification using CLIP, 2) deep Q-learning, 3) fairness in natural language generation (NLG), 4) compositional generalization in languages, and 5) image classification using pretrained CNNs such as Resnet and Alexnet.

关键词： image classification

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visual Navigation and Shape Reconstruction for Asteroid Approaching Phase 3rd

Visual Navigation and Shape Reconstruction for Asteroid Ap...

引用

3rd International conference on Autonomous Unmanned systems, ICAUS 2023

作者： Hu, Ronghai Jiao, Zhuang Hu, Handong Intelligent Science and Technology Academy Limited of CASIC Beijing100043 China Key Lab of Aerospace Defense Intelligent Systems and Technology Beijing100043 China

ISBN: (纸本)9789819710867

visual navigation and shape reconstruction are of paramount importance for the safety and success of asteroid missions. This paper proposes a new method using silhouette and pixel area measurements to simultaneously estimate the position of probe and the shape of target during approaching an asteroid. First, the silhouette of target projected on the image plane is extracted using segmentation algorithms, and its pixel area is also calculated for subsequent processing. Second, the relative position of the probe concerning the target asteroid is normalized and estimated based on the pixel area on the hypothesis of uniform linear motion. Then, a parametric shape model of the target is established with a one-dimensional vector and is estimated and fused by minimizing the reprojection error of the silhouette. Finally, synthetic image sequences are generated and Monte Carlo simulations are conducted to verify the proposed method, and the numerical results demonstrate its advances in accuracy. © Beijing HIWING Scientific and Technological Information Institute 2024.

关键词： Asteroids

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AI Mediated Surface Waste Auto-Detection on Water Bodies Using Cutting Edge Technology

AI Mediated Surface Waste Auto-Detection on Water Bodies Usi...

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2024 IEEE International conference on Cognitive Computing and Complex Data

作者： Choudhary, vijaya Guha, Paramita Archana Dey, Sharmistha IILM Univ Sch Comp Sci & Engn Plot 16-18Knowledge Pk 2 Greater Noida Uttar Pradesh India Acad Sci & Innovat Res AcSIR CSIR Natl Phys Lab Dr KS Kushnan Marg New Delhi India OP Jindal Global Univ Sonipat India Galgotias Univ Sch Comp Sci & Applicat Greater Noida India

ISBN: (纸本)9798350352900;9798350352894

The proliferation of surface waste in water bodies poses significant environmental and ecological challenges. Traditional methods of waste detection are often labor-intensive and limited in scope. This paper presents a novel approach to surface waste detection using artificial intelligence (AI) and advanced imaging technologies. Leveraging cutting-edge techniques such as deep learning algorithms, high-resolution satellite imagery, and real-time data processing, our system offers an automated solution for identifying and monitoring waste in water bodies. We developed a robust AI model trained on diverse datasets, including satellite and drone-captured images, to detect various types of surface waste with high accuracy. The system integrates real-time processing capabilities to provide timely alerts and actionable insights for environmental management. Evaluation results demonstrate that our approach significantly improves detection accuracy and operational efficiency compared to conventional methods. This research contributes to the advancement of smart environmental monitoring systems and offers a scalable solution for mitigating the impact of surface waste on aquatic ecosystems.

关键词： Artificial Intelligence Garbage detections Convolutional neural network river research image processing computer vision

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A PROPER ORTHOGONAL DECOMPOSITION APPROACH FOR PARAMETERS REDUCTION OF SINGLE SHOT DETECTOR NETWORKS 29

A PROPER ORTHOGONAL DECOMPOSITION APPROACH FOR PARAMETERS RE...

引用

IEEE International conference on image processing (ICIP)

作者： Meneghetti, Laura Demo, Nicola Rozza, Gianluigi SISSA Math Area MathLab Via Bonomea 265 I-34136 Trieste Italy

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

As a major breakthrough in artificial intelligence and deep learning, Convolutional Neural Networks have achieved an impressive success in solving many problems in several fields including computer vision and image processing. Real-time performance, robustness of algorithms and fast training processes remain open problems in these contexts. In addition object recognition and detection are challenging tasks for resource-constrained embedded systems, commonly used in the industrial sector. To overcome these issues, we propose a dimensionality reduction framework based on Proper Orthogonal Decomposition, a classical model order reduction technique, in order to gain a reduction in the number of hyperparameters of the net. We have applied such framework to SSD300 architecture using PASCAL VOC dataset, demonstrating a reduction of the network dimension and a remarkable speedup in the fine-tuning of the network in a transfer learning context.

关键词： image processing Object Detection Dimensionality Reduction Convolutional Neural Network

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Improving image Clarity with Artificial Intelligence-Powered Super-Resolution Methods 1

引用

3rd International conference on Mechanical and Energy Technologies, ICMET 2023

作者： Malathy, V. Poornima, M. Varun, V.L. Venugopal, Padmaja Acharjee, Purnendu Bikash Krishnaveni, S. School of Engineering SR University Telangana Warangal India Department of Mathematics SJB Institute of Technology Bengaluru India Computer Science CHRIST University Pune Lavasa Campus Lavasa India Department of Management Studies Adithya Institute of Technology Coimbatore Kovilpalayam India

ISBN: (数字)9789819727162

ISBN: (纸本)9789819727155

Super-resolution has advanced significantly in the last 20 years, particularly with the application of deep learning methods. One of the most important image processing methods for boosting an image's resolution in computer vision is image super-resolution besides providing an extensive overview of the most recent developments in artificial intelligence and deep learning for single-image super-resolution. This study delves into the subject of image enhancement by investigating sophisticated AI-based super-resolution techniques. High-quality photographs have become more and more in demand in a variety of industries recently, including medical imaging, satellite imaging, entertainment, and surveillance. Pixilation reduction and detail preservation are two areas where traditional image enhancing techniques fall short. Artificial intelligence has demonstrated amazing promise in addressing these issues, especially with regard to Deep Learning models. The applications, benefits, and difficulties of modern super-resolution techniques are thoroughly examined in this work. We also suggest new approaches and push the limits of image enhancement by experimenting with state-of-the-art artificial intelligence algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image enhancement

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Fired:An image-Based Fire Detector for Smart Homes using Machine Learning algorithms

Fired:An Image-Based Fire Detector for Smart Homes using Mac...

引用

2024 International conference on Knowledge Engineering and Communication systems, ICKECS 2024

作者： Srinath, G.M. Yashaswini, K.M. Sahana Shree, N. Supraja, B. Varshitha, V. Arfa Thareen, K. Visvesvaraya Technological University Department of Computer Science and Engineering Chickballapur India

ISBN: (纸本)9798350359688

Smart homes are to be protected from fire hazards which is a crucial safety concern. Existing ways of detecting fire is time consuming, hence causing maximum injuries and financial loss, so we have come up with an effective solution for detecting fire in smart homes using image processing and Machine Learning techniques. FireD extracts the features from photos and captured video frames using Convolutional Neural Network (CNN) and in turn sends them to Neural Network which implements clustering algorithm known as Yolov5 to ensure the ability to classify images as Fire and Non-Fire. Once the fire is detected, model will send the images to Heroku cloud which serves as container based cloud Platform as a Service (PaaS) to access, deploy and manage the images. On the detection of fire Notification is sent to Telegram through telegram bot. Overall performance data from model indicates that FireD is likely to perform well in real-world scenarios. Therefore, FireD is more reliable than other existing systems since FireD application is pushed to the docker container with all the required libraries and files. The application can be accessed and implemented easily. © 2024 IEEE.

关键词： Platform as a Service (PaaS)

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Refining Boundary Sample Space for Differentiable Rendering 16

Refining Boundary Sample Space for Differentiable Rendering

引用

2024 16th International conference on Graphics and image processing, ICGIP 2024

作者： Gao, Ruicheng Qi, Yue State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Jingdezhen Research Institute Beihang University Jingdezhen333000 China Qingdao Research Institute Beihang University Qingdao266104 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

The objective of physics-based differentiable rendering (PBDR) is to propagate gradients between scene parameters and the intensities of image pixels in a manner that is physically correct. The gradients obtained can be applied in optimization algorithms for the reconstruction of 3D geometry or materials, or they can be further propagated into neural network to learn neural representations of the scene. However, applying automatic differentiation techniques directly to the primary rendering process will result in biased gradients, as the rendering integral contains moving high-dimensional discontinuities. Based on how these discontinuities are managed-either implicitly or explicitly-existing PBDR methods can be categorized into two groups: reparameterization methods and boundary sampling methods. Boundary sampling methods need to construct paths that have one segment tangent to the geometry being differentiated in order to estimate a boundary integral to address the discontinuities explicitly. Such paths are usually constructed by sampling the tangent segment first and then extending it to complete the paths for subsequent processing. Fortunately, the number of dimensions in the space composed of such tangent segments is only three. In scenes comprised solely of triangle meshes, the first dimension is used to parameterize all the edges on the mesh, which determines a point on the tangent segment. The remaining two dimensions are used to parameterize the direction of the tangent segment. However, state-of-the-art boundary sampling methods parameterize the first dimension uniformly, which is inefficient because only a small portion of the edges contributes to the boundary integral, resulting in wasted parameter space. In this paper, we parameterize the first dimension by considering both edge length and contributions, thereby allocating more parameter space to important edges. Experiments demonstrate that our methods achieve lower variance gradients in the forward dif

关键词： Mesh generation

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Thorax Disease Classification Based on the Convolutional Network SqueezeNet 12

Thorax Disease Classification Based on the Convolutional Net...

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12th IEEE International conference on Intelligent Data Acquisition and Advanced Computing systems: Technology and Applications, IDAACS 2023

作者： Avanzato, Roberta Beritelli, Francesco University of Catania Dieei Catania95125 Italy

ISBN: (纸本)9798350358056

Chest radiography allows a detailed inspection of a patient's thorax via an imaging modality, but requires specialized training for proper interpretation. With the advent of high performance general purpose image analysis and computer vision algorithms, the accurate automated analysis of chest radiographs is becoming increasingly of interest to researchers. In this paper, we propose a classification algorithm distinguishing between chest radiographs performed on people with or without lung disease. The dataset used as the input to the SqueezeNet neural network is Chest X-ray. The only pre-processing phase applied to the dataset is the resize to 224×224 and the conversion to grayscale. The accuracy percentage obtained during the network testing phase is 94%. © 2023 IEEE.

关键词： computer vision Convolutional neural network deep learning healthcare image processing

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Deep Learning-Based image processing for Defect Detection in Electronic Components Manufacturing

Deep Learning-Based Image Processing for Defect Detection in...

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2024 International conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Mathivanan, B. Maruthupandi, J. Lakshmi, Namala vijaya Suvitha, S. Karthik, K. Swaroop, Anand Sri Ramakrishna Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore641022 India New Horizon College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru560103 India Kkr & Ksr Institute of Technology and Sciences Department of Computer Science and Engineering Andhra Pradesh 522017 India S.A. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai600077 India Velammal College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Madurai625009 India Institute of Engineering and Technology Department of Computer Science and Engineering Uttar Pradesh Lucknow226021 India

ISBN: (纸本)9798331543617

In electronic factors manufacturing, icing product quality through disfigurement discovery is pivotal for maintaining trustability and performance norms. This exploration investigates the operation of deep literacy ways in image processing for disfigurement discovery. Deep literacy algorithms, particularly convolutional neural networks(CNNs), have shown remarkable success in automated image analysis tasks. This study explores their effectiveness in relating blights similar to face abnormalities, cracks, or indecorous assembly in electronic factors. By using annotated datasets and advanced neural network infrastructures, the proposed approach aims to enhance delicacy and effectiveness in disfigurement discovery compared to traditional styles. The methodology involves preprocessing raw images, training CNN models on labeled data, and validating their performance through rigorous testing and evaluation. Results demonstrate the eventuality of deep literacy to revise disfigurement discovery processes in manufacturing, offering more dependable examinations that contribute to bettered product quality and reduced product costs. This exploration contributes precious perceptivity to the integration of AI technologies for enhancing quality control in artificial settings. © 2024 IEEE.

关键词： Smart manufacturing

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