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

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Human Detection using ROI of an image

Human Detection using ROI of an Image

引用

2024 International conference on Smart systems for Electrical, Electronics, Communication and Computer Engineering, ICSSEEC 2024

作者： Kartik, R. Megadarshini, V. Kavisri, S. Manimegalai, R. PSG Institute of Technology and Applied Research Department of EEE Tamilnadu India

ISBN: (纸本)9798350378177

Efficient human presence detection within specified Regions of Interest (ROIs) is essential to many real-world applications, including as resource allocation, security surveillance, and crowd monitoring. In this paper, we present a robust human pres-ence detection system leveraging advanced techniques, notably YOLOv8, for accurate and real-time object detection. Our system addresses the challenges associated with traditional methods by focusing on multi-scale detection windows and efficient merging of nearby detections to refine results. We introduce a streamlined approach for ROI definition and utilize transfer learning to adapt YOLOv8 to specific human presence detection tasks. Extensive experiments on diverse datasets demonstrate the effectiveness and robustness of our system, showcasing superior performance in terms of accuracy, real-time processing, and adaptability to chal-lenging scenarios. Additionally, we implemented four algorithms, including Histogram of Oriented Gradients (HOG) [1], Roboflow 3.0 object detection algorithm, YOLOv5, and YOLOv8. After thorough examination, we determined that YOLOv8 showed the most favorable outcomes in terms of both precision and speed. Hence, we opted for YOLOv8 as the primary algorithm for our system aimed at detecting human presence. This decision was informed by its superior performance across diverse datasets and its capability to address the challenges associated with traditional methods. © 2024 IEEE.

关键词： Object detection

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Synthetic Aperture Radar Signal processing Algorithm Implementation in Python 5

Synthetic Aperture Radar Signal Processing Algorithm Impleme...

引用

5th IEEE Asia-Pacific conference on Geoscience, Electronics and Remote Sensing Technology, AGERS 2022

作者： Setiadi, Bambang Arisal, Andria Firmansyah, Iman Subekti, Agus Chasanah, Nurul Hamonangan, Jefri Abner Kurniawan, Farohaji Research Center for Data and Information Sciences National Research and Innovation Agendy Bandung Indonesia Research Center for Telecommunication National Research and Innovation Agency Bandung Indonesia Research Center for Aeronautics Technology National Research and Innovation Agency Bogor Indonesia

ISBN: (纸本)9798350398212

The implementation of signal processing algorithms is crucial in the development of Synthetic Aperture Radar (SAR) systems. However, many references do not provide source code-level explanations, making it difficult for researchers and students to understand and implement these algorithms. This paper presents the implementation of the Omega-K algorithm (WKA) for SAR signal processing using Python. By using a freely accessible programming language, we aim to provide a low-cost, simple, and portable way to implement SAR signal processing algorithms compared to using commercial software packages. We describe the implementation details of two WKA variants at the source code level and demonstrate their use in processing Radarsat data. The execution results for various image sizes are tested, and the image results are compared with other reference implementations. Our results indicate that Python has considerable potential for SAR signal processing tasks. © 2022 IEEE.

关键词： Python

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Quilt-1M: One Million image-Text Pairs for Histopathology 37

Quilt-1M: One Million Image-Text Pairs for Histopathology

引用

37th conference on Neural Information processing systems, NeurIPS 2023

作者： Ikezogwo, Wisdom O. Seyfioglu, Mehmet S. Ghezloo, Fatemeh Geva, Dylan Mohammed, Fatwir S. Anand, Pavan K. Krishna, Ranjay Shapiro, Linda G. University of Washington United States

ISBN: (纸本)9781713899921

Recent accelerations in multi-modal applications have been made possible with the plethora of image and text data available online. However, the scarcity of analogous data in the medical field, specifically in histopathology, has slowed comparable progress. To enable similar representation learning for histopathology, we turn to YouTube, an untapped resource of videos, offering 1, 087 hours of valuable educational histopathology videos from expert clinicians. From YouTube, we curate QUILT: a large-scale vision-language dataset consisting of 802, 144 image and text pairs. QUILT was automatically curated using a mixture of models, including large language models, handcrafted algorithms, human knowledge databases, and automatic speech recognition. In comparison, the most comprehensive datasets curated for histopathology amass only around 200K samples. We combine QUILT with datasets from other sources, including Twitter, research papers, and the internet in general, to create an even larger dataset: QUILT-1M, with 1M paired image-text samples, marking it as the largest vision-language histopathology dataset to date. We demonstrate the value of QUILT-1M by fine-tuning a pre-trained CLIP model. Our model outperforms state-of-the-art models on both zero-shot and linear probing tasks for classifying new histopathology images across 13 diverse patch-level datasets of 8 different sub-pathologies and cross-modal retrieval tasks. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Large datasets

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Utilizing Sensitive Features for image Tampering Detection 5

Utilizing Sensitive Features for Image Tampering Detection

引用

5th IEEE International conference on Information systems and Computer Aided Education, ICISCAE 2022

作者： Deng, Yuchao School of Software Northwestern Poly Technical University Xi'an China

ISBN: (数字)9781665481229

ISBN: (纸本)9781665481229

image tampering has brought a great negative impact on society. People who do not know the truth are easy to be misled and used by people with intentions. Its impact on society has attracted extensive attention of Chinese and foreign researchers. With the development of image editing technology, image tampering detection has made remarkable achievements from the initial naked eye recognition to machine learning recognition of tampered images, and then to the excellent characteristics of deep learning algorithms in processing images in complex image tampering detection in recent years. In view of the outstanding performance of Faster RCNN network in the field of target detection technology, this paper studies an image algorithm and detection method based on Faster R-CNN network, and adds position sensitive candidate area pooling on this basis. In each rectangular cell, the pixel values of a fixed number of sampling points at a fixed position are calculated for average pooling. The input tampered image is feature extracted through the feature extraction network. After the extracted feature map passes through the RPN layer, the tampered area candidate frame is automatically generated for subsequent classification and regression. Experimental results show that this method greatly improves the effect of image tampering detection. © 2022 IEEE.

关键词： Learning algorithms

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Convolution Neural Networks: Intersection of Deep Learning and image processing in Computational Art 10

Convolution Neural Networks: Intersection of Deep Learning a...

引用

10th International conference on Digital and Interactive Arts (ARTECH)

作者： Shabbar, Aya Tishreen Univ Mechatron Engn Latakia Syria

ISBN: (纸本)9781450384209

Deep Learning and image processing is a key concept in today's world of computational art, where artists employed AI algorithms to generate visuals. This paper explores AI-generated images, using Convolutional Neural Networks software as a paradigm of symbolic AI creative systems, and contextualizes the use of modern image processing technologies to create visual artworks. It discusses the methodologies and strategies used to make art using AI algorithms, manipulating them with processing software tool. The discussion focuses on CNN (Convolutional Neural Network) and processing software (Java) as the main technologies used in distinct fields to generate images. My conception of technical images provides a conceptual framework for examining the qualities and attributes of AI-generated images.

关键词： Deep learning image processing computational art convolutional neural networks

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Sound and Complete Verification of Polynomial Networks 36

Sound and Complete Verification of Polynomial Networks

引用

36th conference on Neural Information processing systems (NeurIPS)

作者： Rocamora, Elias Abad Sahin, Mehmet Fatih Liu, Fanghui Chrysos, Grigorios G. Cevher, Volkan Ecole Polytech Fed Lausanne LIONS Lausanne Switzerland

ISBN: (纸本)9781713871088

Polynomial Networks (PNs) have demonstrated promising performance on face and image recognition recently. However, robustness of PNs is unclear and thus obtaining certificates becomes imperative for enabling their adoption in real-world applications. Existing verification algorithms on ReLU neural networks (NNs) based on classical branch and bound (BaB) techniques cannot be trivially applied to PN verification. In this work, we devise a new bounding method, equipped with BaB for global convergence guarantees, called Verification of Polynomial Networks or VPN for short. One key insight is that we obtain much tighter bounds than the interval bound propagation (IBP) and DeepT-Fast [Bonaert et al., 2021] baselines. This enables sound and complete PN verification with empirical validation on MNIST, CIFAR10 and STL10 datasets. We believe our method has its own interest to NN verification. The source code is publicly available at https://***/megaelius/PNVerification.

关键词： image recognition

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Solar Powered Auto Irrigation Monitoring System with Plant Health Indication Using image processing

Solar Powered Auto Irrigation Monitoring System with Plant H...

引用

International conference on Open Source systems and Technologies (ICOSST)

作者： Maida Saleem Khadija Nawaz Shahroz Ali Hira Ali Jamal Dept. of Electrical Engineering University of Engineering and Technology Lahore RCET Campus Gujranwala Pakistan

ISBN: (数字)9798331508692

ISBN: (纸本)9798331508708

This project aims to develop a solar powered auto irrigation system that incorporates image processing techniques to monitor and maintain the health of the plants. The system utilizes solar energy to power the irrigation process, making it environmentally friendly and cost-effective. image processing algorithms are employed to analyze the visual characteristics of the plants and detect any signs of distress or disease. This information is then used to automatically adjust the irrigation schedule and provide necessary nutrients to ensure optimal plant health. Furthermore, the system integrates image processing techniques to analyze the health of the plants. images of the plants are captured using a camera module and processed to detect any signs of diseases or abnormalities. The system then provides real-time feedback on the plant health status. Through the implementation of this project, the efficiency and effectiveness of irrigation systems can be improved, leading to better crop yields and reduced water usage.

关键词： Temperature sensors Training Irrigation Visualization image processing Valves Sensor systems Real-time systems Sensors Monitoring

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image Compressive Sensing With Adaptive Sampling by Median Filtering

Image Compressive Sensing With Adaptive Sampling by Median F...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yanfeng Wu Chen Hui Ronghua Liao Shaohui Liu Debin Zhao Department of Computing Harbin Institute of Technology Harbin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep unfolding compressive sensing (CS) has experienced remarkable advancements. However, there still exist two challenges: (1) Many algorithms either use uniform block-based sampling, which ignore the fact that the content of different blocks is different, or allocate the sampling rate referring to complete signal before CS sampling, which is not always feasible in real-world scenarios. (2) Traditional CNN is difficult to capture broader contextual priors during iterative recovery. In this paper, we propose a novel network ASMFNet to solve the above two issues. Specifically, to address the first issue, we introduce a dual-branch network featuring a basic sampling branch to acquire reference image and an adaptive sampling branch by median filtering for allocating remaining sampling rate adaptively. For the second problem, we use Swin Transformer and feature fusion block to increase the feature interactions. Experimental results demonstrate that our proposed method outperforms existing methods.

关键词： Adaptive systems Filtering Signal processing algorithms Signal processing Transformers Iterative algorithms Sensors Speech processing Compressed sensing image reconstruction

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A Review on Designing of Memory Computing Architecture for image Enhancement in AI Applications 1

引用

16th International conference on Multi-disciplinary Trends in Artificial Intelligence, MIWAI 2023

作者： Radhika, C. Ganesh, G.V. Babu, P. Ashok Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation AP Vaddeswaram India Department of ECE Institute of Aeronautical Engineering Telangana Dundigal Hyderabad India

ISBN: (数字)9783031364020

ISBN: (纸本)9783031364013

This paper presents the random spray retinex (RSR) algorithm, which is an efficient image enhancement technique with the potential to improve image quality. However, the computational complexity of the algorithm, as well as the hardware and memory units it requires, may limit its applicability in contexts with constrained resources, such as Internet of Things systems. image augmentation is increasingly important for improving the performance of many modern applications, including artificial intelligence (AI). Our research confirms that RSR can enhance segmentation accuracy. To overcome the limitations of the RSR algorithm, we propose a memristor-based in-memory computing architecture, utilizing Resistive RAM (RRAM) technology, which provides low-power solutions at a low cost. We evaluate the performance of various RSR algorithms in terms of processing time on this architecture. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Memristors

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image Target Detection Algorithm Based on Computer Vision Technology

Image Target Detection Algorithm Based on Computer Vision Te...

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2022 International conference on 3D Immersion, Interaction and Multi-Sensory Experiences, ICDIIME 2022

作者： Yuan, Haidi Anhui Sanlian College Basic Experiment Teaching Center Anhui Hefei China

ISBN: (数字)9781665490092

ISBN: (纸本)9781665490092

With the rapid development of intelligent systems and the advent of the era of big data, the continuous development of computers is being promoted. Exporting and tracking moving targets in video images is one of the most important research contents of computer vision. It combines many advanced technologies in the field of computing, such as image processing, pattern recognition, automatic control and artificial intelligence, and is widely used in intelligent surveillance. In various fields such as traffic control, machine intelligence and medical diagnosis, visual effects are obtained through image or image processing. Record videos from the computer and perform specific mechanical tasks. In terms of intelligent tracking, as the demand for applications in various complex environments continues to grow, how to improve the robustness and accuracy of moving target tracking and tracking algorithms has become the focus of ongoing target tracking research. This paper studies the image target detection algorithm based on computer vision technology. Firstly, the literature research method is used to summarize the existing problems of image target detection based on computer vision technology and the existing algorithms. The experiment is used to analyze the image target based on computer vision technology. The detection algorithm is verified, and the error rate of image target detection of the algorithm proposed in this paper is compared. According to the experimental results, it can be seen from Figure 1 that in experiment 1, the target detection of the GMM-STMRF algorithm is more accurate than other methods based on the calculation of the false detection rate. The maximum false detection rate is only 2.3%, and the other algorithms have 5.4%-11.1% false detection rate The GMM-STMRF algorithm increases the multi-frame calculation in the time dimension, so the calculation time has increased. algorithms such as GMM and MeanShift need to estimate the multi-frame parameters, an

关键词： Target tracking

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