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

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Comparative image processing and Object Detection in Aerial Search and Rescue

Comparative Image Processing and Object Detection in Aerial ...

引用

Cognitive Robotics and Intelligent systems (ICC - ROBINS), International conference on

作者： Sushil Rawat Abhay Singh Rawat vikas Singh Mewad Himadri Vaidya Department of Computer Science and Engineering Graphic Era Hill University Dehradun India

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372755

Aerial search and response plays an important role in finding and rescuing persons in need. Unmanned Aerial Vehicle (UAV) -acquired aerial images provide an intensive profile search area and facilitate identification of potential targets. To improve the effectiveness of aircraft Search and Rescue missions, this study compares many object detection algorithms and image processing methods. The investigated object detection methods are Mask R-CNN, SSD, and YOLOv8, and image processing techniques are contrast improvement and image brightness control. The effectiveness of these strategies has been evaluated by using real aerial images that include different targets such as humans, vehicles, and fire. The results show how well the object detection algorithms work to accurately distinguish targets, and how an attractive image processing approach improves the image performance of the best object detection model, as well as how the performance of the best object detection model with image processing can improve the performance of model. This research shows that combining image processing (contrast and brightness) with an optimal object detection algorithm (Mask R-CNN) significantly improves target detection in aerial search and rescue. While only Mask R-CNN performs best, adding image processing increases its accuracy to 0.92, mAP to 0.89, and makes it more reliable in finding people in need. This shows the potential of this joint approach to save people in air rescue missions.

关键词： image quality image processing Brightness Object detection Search problems Autonomous aerial vehicles Real-time systems

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Benchmarking and Analyzing 3D-aware image Synthesis with a Modularized Codebase 37

Benchmarking and Analyzing 3D-aware Image Synthesis with a M...

引用

37th conference on Neural Information processing systems, NeurIPS 2023

作者： Wang, Qiuyu Shi, Zifan Zheng, Kecheng Xu, Yinghao Peng, Sida Shen, Yujun Ant Group China HKUST Hong Kong CUHK Hong Kong ZJU China

ISBN: (纸本)9781713899921

Despite the rapid advance of 3D-aware image synthesis, existing studies usually adopt a mixture of techniques and tricks, leaving it unclear how each part contributes to the final performance in terms of generality. Following the most popular and effective paradigm in this field, which incorporates a neural radiance field (NeRF) into the generator of a generative adversarial network (GAN), we build a well-structured codebase, dubbed Carver, through modularizing the generation process. Such a design allows researchers to develop and replace each module independently, and hence offers an opportunity to fairly compare various approaches and recognize their contributions from the module perspective. The reproduction of a range of cutting-edge algorithms demonstrates the availability of our modularized codebase. We also perform a variety of in-depth analyses, such as the comparison across different types of point feature, the necessity of the tailing upsampler in the generator, the reliance on the camera pose prior, etc., which deepen our understanding of existing methods and point out some further directions of the research work. We release code and models here to facilitate the development and evaluation of this field. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Generative adversarial networks

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An Efficient Hybrid Technique for Automatic License Plate Recognitions 1

An Efficient Hybrid Technique for Automatic License Plate Re...

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1st International conference on Recent Developments in Electronics and Communication systems, RDECS 2022

作者： Devi, Terli V. Satyanarayana, V. Singh, Mahesh K. Department of ECE Aditya Engineering College Surampalem India Department of ECE Aditya Engineering College A.P. Surampalem India

ISBN: (纸本)9781643683607

Automatic License Plate Recognition (ALPR) is an embedded real-time technology that automatically recognizes a vehicle's license plate. There are numerous uses, ranging from complex security to shared spaces, parking to acceleration. The ALPR is used to control the traffic and to take hold for those who violate the traffic rules. Automatic recognition of vehicle license plate numbers has become critical in our daily lives due to the rapid expansion of autos and transportation systems, which makes it difficult for the general public to keep up with and monitor. A license plate identification system uses image processing theory and soft computing technology to identify cars based on their number plates. Using fewer and simpler algorithms can reduce both calculation time and execution time needed by most number plate localization approaches. This is because most of these techniques require several operations. License plate recognition is the most used approach in traffic-related applications. Various approaches, tactics, and algorithms have been developed for the identification and recognition of license plates. In the context of Indian license plate identification, only a few research have looked at the topic. Character segmentation and character recognition are two of the three key steps in identifying a license plate. © 2023 The authors and IOS Press.

关键词： image segmentation

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Compact 3D Time-of-Flight Sensor with Real-Time Multi-Path Separation 7

Compact 3D Time-of-Flight Sensor with Real-Time Multi-Path S...

引用

7th Iberian Robotics conference

作者： Ludwig, Matthias Gubler, Roman Schueller, Samuel Loeliger, Teddy ZHAW Zurich Univ Appl Sci Inst Signal Proc & Wireless Commun Winterthur Switzerland

ISBN: (纸本)9798350376371;9798350376364

The acquisition of 3D data using indirect 3D Time-of-Flight (3D ToF) sensors is a well-established technology. However, Multi-Path Interference (MPI) is a key issue and causes significant errors in the distance measurements. This paper presents a novel compact 3D ToF sensor with real-time multi-path separation. The sensor combines multiple measurements at different modulation frequencies and employs Particle Swarm Optimization (PSO) in combination with Sequential Quadratic Programming (SQP) to separate the different paths. The sensor is used to demonstrate the separation of two different paths in each pixel of the entire image in real-time. Evaluation in various laboratory and real-world scenarios reveals a significant improvement in distance accuracy compared to a standard 3D ToF sensor. Furthermore, the proposed algorithm exhibits processing speeds that are orders of magnitude faster than previously reported separation algorithms with comparable accuracy.

关键词： 3D Time-of-Flight (3D ToF) Multi-Path Interference (MPI) Multi-Layer ToF Particle Swarm Optimization (PSO) Sequential Quadratic Programming (SQP) Real-time Embedded systems Optimization Methods

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Unveiling the Depths: A Comprehensive Analysis of Natural Language processing and Generative Adversarial Neural Networks for Text Generation Models in Deep Learning 1

Unveiling the Depths: A Comprehensive Analysis of Natural La...

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1st International conference on Circuits, Power, and Intelligent systems, CCPIS 2023

作者： Agarwal, Rashi Agarwal, Himanshu Pandey, Senam Gehu India

ISBN: (纸本)9798350315769

Deep Learning comes under Machine Learning that accomplishes more power and flexibility by learning to present different concepts or relations of real world to simpler concepts. We use Deep learning fundaments in this paper because it has massive amount of data that helps in innovations. We include these neural networks of deep learning because it comes with a high accuracy rate with lower computations. Natural processing Language (NLP) and Generative Adversarial Network (GAN) are the methods that individually contribute to the text generation method. Although these are two different technologies giving the output for some common motive where Text generation plays a very important role in smart translations and dialogue systems. This review paper presents a model centered around text generation. This is done because combinedly we want to present what can be different approaches to look at a model like this. To solve the problem of unnecessarily used large texts, unsatisfactory feedback, NLP is used for text generation, GANN is used for text generation model, image generation etc. Finally, this is done to reduce time complexities, speed, efficiency in process because this is noticed that learning for a problem plays a vital role in education to enhance features. © 2023 IEEE.

关键词： Learning algorithms

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Optimal Target Interception using Foldable Quadrotor with Rotating Arms

Optimal Target Interception using Foldable Quadrotor with Ro...

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

作者： Zakaria Mehal Yasser Bouzid Mohammed Guiatni Saddam Hocine Derrouaoui Complex Systems Control and Simulators (CSCS) Laboratory Ecole Militaire Polytechnique Bordj el Bahri Algeria Ecole Superieure Ali Chabati Algires Algeria

ISBN: (数字)9798350309249

ISBN: (纸本)9798350309256

This article addresses a critical problem in the field of target interception using a new foldable quadrotor with rotating arms. The primary challenge is maximizing linear acceleration to enhance the quadrotor’s ability to intercept targets effectively. To address this challenge, we introduce a novel optimization framework. Our study employs two distinct optimization algorithms, namely the genetic algorithms and the whales optimization algorithm, to ascertain the maximum attainable linear acceleration for the foldable quadrotor. The results obtained are confirmed through the use of our innovative foldable quadrotor.

关键词： Heuristic algorithms Signal processing algorithms Life estimation Signal processing Whale optimization algorithms Optimization Robots

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C-ESRGAN: Synthesis of super-resolution images by image classification 5

C-ESRGAN: Synthesis of super-resolution images by image clas...

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5th IEEE International conference on image processing Applications and systems (IPAS)

作者： Liu, Jingan Chandrasiri, Naiwala P. Kogakuin Univ Intelligent Media Lab Tokyo Japan

ISBN: (纸本)9781665462198

With the development of deep learning, super-resolution image synthesis techniques for enhancing low-resolution images have advanced remarkably. However, mainstream algorithms focus on improving the quality of the entire image on average and this may result in blurring. In this paper, we propose three key components for synthesizing super-resolution images that can reflect the fine details of an image. We synthesize super-resolution images by image classification. First, the neural network weights learned using the images in the same image category were utilized in synthesizing super-resolution images. For this purpose, image classification was performed using a transfer-trained ResNet. Second, SENet was applied to the generators in our proposed method to obtain detailed information about the images. Finally, the feature extraction network was changed from VGG to ResNet in order to get more important features. As a result, we achieved better image evaluation values (PSNR, NIQE) for the super-resolution images of dogs and cats compared to the previous studies. Furthermore, the images were generated more naturally on the benchmark dataset.

关键词： Super-Resolution Generative Adversarial Networks (GANs) SENet image Classification ResNet

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Building Safe and Reliable AI systems for Safety Critical Tasks with vision-Language processing 45th

Building Safe and Reliable AI Systems for Safety Critical Ta...

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45th European conference on Information Retrieval (ECIR)

作者： Ao, Shuang Open Univ Walton Hall Milton Keynes MK7 6AA Bucks England

ISBN: (纸本)9783031282409;9783031282416

Although AI systems have been applied in various fields and achieved impressive performance, their safety and reliability are still a big concern. This is especially important for safety-critical tasks. One shared characteristic of these critical tasks is their risk sensitivity, where small mistakes can cause big consequences and even endanger life. There are several factors that could be guidelines for the successful deployment of AI systems in sensitive tasks: (i) failure detection and out-ofdistribution (OOD) detection;(ii) overfitting identification;(iii) uncertainty quantification for predictions;(iv) robustness to data perturbations. These factors are also challenges of current AI systems, which are major blocks for building safe and reliable AI. Specifically, the current AI algorithms are unable to identify common causes for failure detection. Furthermore, additional techniques are required to quantify the quality of predictions. All these contribute to inaccurate uncertainty quantification, which lowers trust in predictions. Hence obtaining accurate model uncertainty quantification and its further improvement are challenging. To address these issues, many techniques have been proposed, such as regularization methods and learning strategies. As vision and language are the most typical data type and have many open source benchmark datasets, this thesis will focus on vision-language data processing for tasks like classification, image captioning, and vision question answering. In this thesis, we aim to build a safeguard by further developing current techniques to ensure the accurate model uncertainty for safety-critical tasks.

关键词： Deep learning Model calibration Uncertainty

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Prior Case Retrieval for the Court of Cassation of Turkey 16

Prior Case Retrieval for the Court of Cassation of Turkey

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16th International conference on Signal-image Technology and Internet-Based systems (SITIS)

作者： Ozturk, Ceyhun E. Koksal, Omer Ozcelik, S. Baris Koc, Aykut Bilkent Univ Dept Elect & Elect Engn Ankara Turkey ASELSAN Res Ctr Ankara Turkey Bilkent Univ Fac Law Ankara Turkey Natl Magnet Resonance Res Ctr UMRAM Ankara Turkey

ISBN: (纸本)9781665464956

This work introduces the first prior case retrieval models for Turkish courts. We investigated the rulings of the Court of Cassation of Turkey. Since law professionals have to find texts they are interested in from enormous legal databases and computers can process large amounts of text swiftly, information retrieval algorithms are helpful for law professionals. The information retrieval algorithms utilized in this work are recurrent neural network autoencoders, and the combinations of recurrent neural network autoencoders with BM25 algorithms. The combination of the long-short term memory autoencoder with ATIRE BM25 achieves the best scores on our dataset.

关键词： natural language processing law deep learning information retrieval NLP in law AI in law prior legal case retrieval

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Hybrid image processing Device as Wearable Aide for visually Impaired 8

Hybrid Image Processing Device as Wearable Aide for Visually...

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8th International conference on Advanced Computing and Communication systems, ICACCS 2022

作者： Papanai, Ashish Kaushik, Harsh Maharaja Agrasen Institute of Technology Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665408165

With the advent of technology and algorithms in image processing, many wearable aides are available in the market, and researchers across the globe are developing new solutions. The existing solutions and products fail to provide an economically viable and single solution to the problem of visually impaired people. This study introduces AIde, a novel system amalgamating optimized computer vision algorithms and wearable IoT devices to convert the information extracted from the camera mounted on the user to audio-based signals for assisted mobility. The proposed system has three user modes: indoor, outdoor, and hybrid, which can be modified using the buttons provided on the wearable device. The indoor mode can be used for object detection and classification to increase the accessibility of the objects inside a confined space, and the outdoor mode will provide the user with enhanced mobility and directional sense based on the highlighted path captured by the mounted camera. The hybrid model combines both the features of indoor and outdoor modes, which will detect objects and improve mobility simultaneously. The proposed model is optimized to work in a computationally efficient environment with an at par efficiency and accuracy as the state-of-art and costly market alternatives. © 2022 IEEE.

关键词： Edge computing

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