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检索条件"任意字段=Conference on Real-Time Image and Video Processing 2010"

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Segmentation of kidney stones in endoscopic video feeds

Segmentation of kidney stones in endoscopic video feeds

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conference on Medical Imaging - image processing

作者： Stoebner, Zachary A. Lu, Daiwei Hong, Seok Hee Kavoussi, Nicholas L. Oguz, Ipek Vanderbilt Univ Nashville TN 37235 USA Vanderbilt Univ Med Ctr Nashville TN USA

ISBN: (数字)9781510649408

ISBN: (纸本)9781510649408;9781510649392

image segmentation has been increasingly applied in medical settings as recent developments have skyrocketed the potential applications of deep learning. Urology, specifically, is one field of medicine that is primed for the adoption of a real-time image segmentation system with the long-term aim of automating endoscopic stone treatment. In this project, we explored supervised deep learning models to annotate kidney stones in surgical endoscopic video feeds. In this paper, we describe how we built a dataset from the raw videos and how we developed a pipeline to automate as much of the process as possible. For the segmentation task, we adapted and analyzed three baseline deep learning models - U-Net, U-Net++, and DenseNet - to predict annotations on the frames of the endoscopic videos with the highest accuracy above 90%. To show clinical potential for real-time use, we also confirmed that our best trained model can accurately annotate new videos at 30 frames per second. Our results demonstrate that the proposed method justifies continued development and study of image segmentation to annotate ureteroscopic video feeds.

关键词： segmentation deep learning endoscopy computer vision

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Multimodal Sentiment Analysis: Techniques, Implementations and Challenges across Diverse Modalities 18

Multimodal Sentiment Analysis: Techniques, Implementations a...

引用

18th INDIAcom;11th International conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Shah, Pooja Ambekar, Dhruti Bodat, Harshil Kumari, Sudesh Pandit Deendayal Energy University Computer Science and Engineering Gandhinagar382007 India Bps Mahila Vishwavidalaya Department of Electronics and Communication Engineering Sonipat Khanpur Kalan India

ISBN: (纸本)9789380544519

Multimodal Sentiment Analysis has gained significant attention in Machine Learning. It's popular because it not only provides better results and a deeper understanding of context but also offers a valuable alternative to analyzing just one type of data. In recent years, there has been a growing focus on this area, with the emergence of new datasets and advanced models designed to handle the complexities of analyzing emotions from different types of data. This paper presents an innovative approach for real-time emotion analysis using diverse inputs: text, images, videos, and audio. We begin by establishing baseline models and curating datasets for thorough evaluation. Our research introduces three cutting-edge deep learning techniques - Convolutional Neural Networks (CNN), Visual Geometry Group Networks (VGG), and Recurrent Neural Networks (RNN) - to enhance sentiment analysis accuracy. Additionally, we integrate YOLOv5 for sophisticated image processing, Mel Frequency Cepstral Coefficients (MFCC)-based frameworks for audio analysis, and advanced Natural Language processing (NLP) models for text interpretation. Uniquely, we transform audio into text, allowing for dual-mode evaluation - as audio and text - using our NLP models. Our study critically examines factors often neglected in emotion analysis, like the impact of varying data sources and the system's performance in different scenarios. The results of our research significantly deviate from existing methodologies by offering a more holistic and versatile framework. Our framework not only sets a standard for future research but also emphasizes the essential considerations needed for effective real-time emotion analysis across various modes. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Audio Benchmark Dataset Division Deep Learning Architectures Emotion Analysis Evaluation Framework Generalizability image Innovation MFCC-based Models Modality Significance Multimodal Sentiment Analysis NLP Models real-time Text video YOLOv5

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real-time traffic flow detection system based on video image processing

Real-time traffic flow detection system based on video image...

引用

2022 IEEE Asia-Pacific conference on image processing, Electronics and Computers, IPEC 2022

作者： Shuyun, Ding Tianjin University of Technology Tianjin China

ISBN: (纸本)9781665409025

With the development of urbanization, traffic problems are becoming more and more serious. Relevant literature shows that traffic information is a direct reflection of road conditions, and timely mastering of traffic information has important research significance for rational allocation of traffic resources and alleviation of congestion. In order to obtain the main traffic information of the road in real time, this paper designed a detection system of vehicle flow and vehicle queue length based on FPGA. Through analyzing the system requirements, and conducting experiments on the development system and simulation tools used, relevant data were obtained. In addition, this paper studies image preprocessing, vehicle presence detection algorithm, vehicle flow statistics algorithm and vehicle queuing length to get relevant conclusions. It provides practical research significance for road traffic resource optimization and traffic light signal lamp. © 2022 IEEE.

关键词： image processing

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STREAMING-CAPABLE HIGH-PERFORMANCE ARCHITECTURE OF LEARNED image COMPRESSION CODECS 29

STREAMING-CAPABLE HIGH-PERFORMANCE ARCHITECTURE OF LEARNED I...

引用

IEEE International conference on image processing (ICIP)

作者： Lin, Fangzheng Sun, Heming Katto, Jiro Waseda Univ Sch Fundamental Sci & Engn Tokyo Japan Waseda Univ Waseda Res Inst Sci & Engn Tokyo Japan PRESTO JST 4-1-8 Honcho Saitama Japan

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Learned image compression allows achieving state-of-the-art accuracy and compression ratios, but their relatively slow runtime performance limits their usage. While previous attempts on optimizing learned image codecs focused more on the neural model and entropy coding, we present an alternative method to improving the runtime performance of various learned image compression models. We introduce multi-threaded pipelining and an optimized memory model to enable GPU and CPU workloads' asynchronous execution, fully taking advantage of computational resources. Our architecture alone already produces excellent performance without any change to the neural model itself. We also demonstrate that combining our architecture with previous tweaks to the neural models can further improve runtime performance. We show that our implementations excel in throughput and latency compared to the baseline and demonstrate the performance of our implementations by creating a real-time video streaming encoder-decoder sample application, with the encoder running on an embedded device.

关键词： learned image compression real-time streaming high-performance pipelining

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FPGA-based image processing algorithm design 24

FPGA-based image processing algorithm design

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Proceedings of the 2024 International conference on image processing, Multimedia Technology and Maching Learning

作者： Yi Hu Benyuan Chen Hubei University of Technology Wuhan Hubei China

ISBN: (纸本)9798400713880

As one of the most important data sources in machine vision system, camera is facing increasingly strict requirements in video image acquisition, processing and transmission. Through the in-depth analysis of the development trend of the camera, it can be clear that its main pursuit of three goals: improve video image processing performance, reduce costs and enhance flexibility. However, most of the existing video processing platforms have problems such as low data processing efficiency and lack of real-time performance, which is difficult to meet the high performance requirements of modern cameras for video image processing. Because of its high real-time performance, powerful computing power, high integration and flexibility, embedded system has become a promising direction to realize advanced video image processing algorithms. With the embedded platform, the camera system can realize more efficient real-time data processing and higher quality video image output. In the current market, three high-performance embedded processors stand out, namely DSP, ASIC and FPGA. Among them, FPGA chip has become the ideal hardware platform for camera video image processing with its parallel computing capability, rich interface resources and field programmable characteristics. Based on Anlogic EG4S20 FPGA platform, this paper realizes image processing functions, including Bayer format to RGB conversion, gray world algorithm and perfect reflection algorithm. This research makes full use of the powerful computing power of FPGAs to demonstrate image processing and enhancement techniques to address key performance challenges in camera systems.

关键词： FPGA

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PositIV:A Configurable Posit Processor Architecture for image and video processing 25

PositIV:A Configurable Posit Processor Architecture for Imag...

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25th Euromicro conference on Digital System Design (DSD)

作者： Ramachandran, Akshat Gustafson, John Roy, Anusua Ansari, Rizwan Ahmed Daruwala, Rohin Veermata Jijabai Technol Inst Mumbai Maharashtra India Vq Res Inc Mumbai Maharashtra India

ISBN: (纸本)9781665474047

image processing is essential for applications such as robot vision, remote sensing, computational photography, augmented reality etc. In the design of dedicated hardware for such applications, IEEE Std 754T floating point (float) arithmetic units have been widely used. While float-based architectures have achieved favorable results, their hardware is complicated and requires a large silicon footprint. In this paper we propose a Posit-based image and video processor (PositIV), a completely pipelined, configurable, image processor using posit arithmetic that guarantees lower power use and smaller silicon footprint than floats. PositIV is able to effectively overlap computation with memory access and supports multidimensional addressing, virtual border handling, prefetching and buffering. It is successfully able to integrate configurability, flexibility, and ease of development with real-time performance characteristics. The performance of PositIV is validated on several image processing algorithms for different configurations and compared against state-of-the-art implementations. Additionally, we empirically demonstrate the superiority of posits in processing images for several conventional algorithms, achieving at least 35-40% improvement in image quality over standard floats.

关键词： posits vision processor image processing computer vision edge detection HDR enhancement

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Context-Aware Recognition of Elevator Buttons Using a Sequential Training Methodology 24

Context-Aware Recognition of Elevator Buttons Using a Sequen...

引用

24th International conference on Control, Automation and Systems, ICCAS 2024

作者： Ghosh, Arpan Joo, Kyeong-Jin Giraldo, Gilberto Galvis Kuc, Tae-Yong Sungkyunkwan University Department of Electrical and Computer Engineering Suwon16419 Korea Republic of Sungkyunkwan University Department of Electronic and Electrical Engineering Suwon16419 Korea Republic of

ISBN: (纸本)9788993215380

In this paper, we present a sequential training methodology aimed at improving the recognition of elevator buttons using the YOLOv5 object detection model. The methodology is structured into three distinct phases. In the first phase, we generate a synthetic dataset where elevator buttons, cropped from their original context, are placed on random image backgrounds. This phase is designed to help the model learn to identify buttons independently of their surroundings, ensuring a foundational understanding of button features without contextual distractions. In the second phase, we augment the cropped button dataset by applying various transformations such as random flips, rotations, and scaling. These augmentations increase the diversity and robustness of the training data, allowing the model to generalize better to variations in button appearances. The final phase involves training the model on images of full elevator panels. This step is crucial for helping the model understand the contextual placement and spatial relationships of the buttons within the panel, which is essential for accurate detection in real-world scenarios. Additionally, we enhance the real-time video input exposure to improve visibility under varying lighting conditions, addressing common challenges faced in practical applications. For post-processing, we integrate a Channel and Spatial Reliability Tracker (CSRT) to maintain button-tracking consistency in video sequences. This tracker helps ensure that once a button is detected, its position is reliably followed across frames, improving the overall accuracy and reliability of the system. This comprehensive approach, which combines the use of synthetic data, extensive data augmentation techniques, and contextual training on full panel images, aims to better simulate real-world scenarios. As a result, the proposed methodology significantly enhances the robustness and reliability of the YOLOv5 model in recognizing elevator buttons under diverse condi

关键词： image enhancement

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Faster image Deblurring for Unmanned Aerial Vehicles 2

Faster Image Deblurring for Unmanned Aerial Vehicles

引用

2nd International conference on Unmanned Vehicle Systems (UVS-Oman)

作者： Sineglazov, Viktor Lesohorskyi, Kyrylo Chumachenko, Olena Natl Aviat Univ Dept Aeronavigat Elect & Telecommun Kiev Ukraine Natl Tech Univ Ukraine Dept Artificial Intelligence IASA Igor Sikorsky Kyiv Polytech Inst Kiev Ukraine

ISBN: (纸本)9798350372557

This work is devoted to the development of a novel deep learning encoder-decoder algorithm for real-time noise and blur elimination in video frames, received from UAV. This work improves on existing algorithms by providing a more flexible blind deblurring solution than existing kernel-based methods. The proposed method can be applied to both improve the drone operator's capabilities and to improve the performance of autonomous image processing tasks, such as object identification and visual navigation systems. Different types of blur as well as possible types of noise are presented. A brief overview of existing methods is provided. The problem of frame alignment due to the object's movement and associated noise is considered. Existing deblurring and image restoration methods are reviewed, including state-of-the-art. Their limitations are highlighted. To solve the limitations a method based on a fully convolutional encoder-decoder network with residual connections is presented. Dataset generation and training procedures are discussed. The approach is then compared to existing state-of-the-art deep learning methods. The proposed method enables up to 9 times faster blind image restoration with comparable quality in comparison to existing state-of-the-art image restoration methods.

关键词： convolutional neural networks decoder-encoder blind deblurring unmanned aerial vehicles blur kernels

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Ultra-fast Seam Carving for real time Object Detection and Removal 22

Ultra-fast Seam Carving for Real Time Object Detection and R...

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Annual ACM Southeast conference ACMSE

作者： Kosoris, Nicole Georgia Tech Res Inst Atlanta GA 30332 USA

ISBN: (纸本)9781450386975

This paper outlines a method used to successfully modify the Seam Carving algorithm so that it can be run to remove objects from videos in real time. The successful approach was a combination of multiprocessing speedups and what we called the "Spiral Model". Resultant removal took about 35 milliseconds. The performant object recognition technique was not novel, but resulted in a total processing delay of less than.1 seconds and reasonably continuous video streaming while detecting and removing an object of interest. This paper will discuss the methods used and display the visual results of the approach.

关键词： image processing

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A Survey on Crime Detection using CCTV Systems 3

A Survey on Crime Detection using CCTV Systems

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3rd International conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Sheela, A Jeba Balaji, S. Balaji, B. Hemanth Kumar, U. Easwari Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350343632

In order to improve security measures across numerous domains, such as public safety, transportation, and critical infrastructure protection, the use of closed-circuit television (CCTV) systems for threat analysis has become increasingly important. The technical abstract gives a summary of the most recent algorithms and techniques used in threat analysis when using CCTV. This study emphasizes the importance of spatial and temporal reasoning, machine learning models like Support Vector Machines (SVM), Haar-Cascade, ViF (Violent Flows), OViF (Oriented Violent Flows), RINOC (real-time image and video Object Classification), and LRT (Live-Streaming real-time). Insights into the diverse methodologies that can be applied to enhance the effectiveness of CCTV-based threat analysis, the efficiency of CCTV systems and their impact on detecting and reducing criminal activities are discussed. The comparative outcomes of different detection approaches and their ensembles have been empirically discussed. © 2023 IEEE.

关键词： CCTV (Closed Circuit Television) CNN Deep learning Haar-Cascades image Classification image processing SVM (Support Vector Machine)

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