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2024 International conference on image processing

作者： Hu, Tingting Fuchikami, Ryuji Nosaka, Shigekiyo Panason Connect Co Ltd Res & Dev DIv Fukuoka 8128531 Japan

ISBN: (纸本)9798350349405;9798350349399

The 1-ms visual feedback system is critical for seamless actuation in robotics, as any delay affects its performance in handling dynamic situations. Specular reflections cause problems in many visual technologies, making specular detection crucial in 1-ms visual feedback systems. However, existing real-time methods, which target Neumann architecture, fail to achieve the 1-ms delay due to spatial memory paths resulting from extensive frame-based processing. This research aims to develop a 1-ms specular detection system from both algorithm and architecture perspectives, proposing 1) temporal clustering and temporal reference based specular detection method, which leverages temporal domain information to address the requirements of frame-based processing;and 2) global-local integrated specular detection architecture, which enables the coexistence of local and global processing within a 1-ms stream-based architecture. The proposed methods are implemented on FPGA. The evaluation shows that the proposed system supports sensing and processing a 1000-fps sequence with a delay of 0.941 ms/frame.

关键词： Clustering based specular detection ultra-low delay visual system real-time processing field programmable gate array (FPGA)

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real time image processing for Autonomous Vehicles

Real Time Image Processing for Autonomous Vehicles

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2024 Asian conference on Intelligent Technologies, ACOIT 2024

作者： Chandrashekhar, Mhamane Sanjeev Bachute, Bhagyashri Amol Gadgoli, Amruta K. Rankhamb, Dinesh Dattatraya Madri, Shrinivas Department of Electronics and Telecommunication Engineering Shree Siddheshwar Women's College of Engineering Maharashtra Solapur413002 India Department of Artificial Intelligence & Data Science VVP Institute of Technology Maharashtra Solapur413008 India

ISBN: (纸本)9798350374933

Autonomous vehicles require real-time image processing to improve their capabilities by allowing them to understand and respond appropriately to their environment. This paper examines the present state of real-time image processing for self-driving vehicles, including the techniques employed, challenges, and advancements. This article investigates methods such as semantic segmentation, object recognition, categorization, and depth estimation, with an emphasis on enhancing vehicle perception, navigation, and decision-making. The article delves into the implementations of significant algorithms on embedded platforms, their computational efficiency, and their deployment in real-world scenarios. In conclusion, the report investigates prospective avenues for additional research to improve the reliability and efficiency of the real-time image processing systems of autonomous automobiles. © 2024 IEEE.

关键词： Semantic Segmentation

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LIGHTWEIGHT UNDERWATER image ENHANCEMENT VIA IMPULSE RESPONSE OF LOW-PASS FILTER BASED ATTENTION NETWORK 31

LIGHTWEIGHT UNDERWATER IMAGE ENHANCEMENT VIA IMPULSE RESPONS...

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2024 International conference on image processing

作者： May Thet Tun Sugiura, Yosuke Shimamura, Tetsuya Saitama Univ Grad Sch Sci & Engn Shimo Okubo 255Sakura Ku Saitama 3388570 Japan

ISBN: (纸本)9798350349405;9798350349399

In this paper, we propose an improved model of Shallow-UWnet for underwater image enhancement. In the proposed method, we enhance the learning process and solve the vanishing gradient problem by a skip connection, which concatenates the raw underwater image and the impulse response of low-pass filter (LPF) into Shallow-UWnet. Additionally, we integrate the simple, parameter-free attention module (SimAM) into each Convolution Block to enhance the visual quality of images. Performance evaluations with state-of-the-art methods show that the proposed method has comparable results on EUVP-Dark, UFO-120, and UIEB datasets. Moreover, the proposed model has fewer trainable parameters and the resulting faster testing time is suitable for real-time processing in underwater image enhancement, which is particularly for resource-constrained underwater robots.

关键词： Underwater image enhancement CNN Shallow-UWnet impulse response of low-pass filter SimAM attention module

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Research on real-time image processing system combining convolutional neural network and edge computing

Research on real-time image processing system combining conv...

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2024 International conference on Physics, Photonics, and Optical Engineering, ICPPOE 2024

作者： Liang, Lu Hebei University of Architecture Hebei Zhangjiakou China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

With the rise of the Internet of Things (IoT) and edge computing technologies, traditional cloud-dependent convolutional neural network (CNN) image processing methods are facing the challenges of latency and bandwidth bottlenecks. In this study, we propose a real-time image processing system that combines CNN and edge computing, which enables the CNN model to run efficiently on resource-constrained edge devices through model pruning and quantisation optimisation. Experimental results show that the system significantly outperforms traditional methods in terms of inference speed, processing frame rate, and energy-efficiency ratio, especially in application scenarios requiring high real-time performance, such as intelligent surveillance, autonomous driving, and industrial inspection. In addition, the system shows good robustness and stability under different network conditions. This study provides an efficient and low-latency solution for image processing in edge computing environments and lays the foundation for future intelligent applications. © 2025 SPIE.

关键词： Convolutional neural networks

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Pothole detection-you only look once: Deformable convolution based road pothole detection

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IET image processing 2025年第1期19卷

作者： Tang, Pei Lv, Mao Ding, Zhenyu Xu, Weikai Jiang, Minnan Yancheng Inst Technol Coll Automot Engn Yancheng Peoples R China Yancheng Inst Technol Jiangsu Coastal New Energy Vehicle Res Inst Yancheng Peoples R China

The detection of road potholes plays a crucial role in ensuring passenger comfort and the structural safety of vehicles. To address the challenges of pothole detection in complex road environments, this paper proposes a model focusing on shape features (pothole detection you only look once, PD-YOLO). The model aims to overcome the limitations of multi-scale feature learning caused by the use of fixed convolutional kernels in the baseline model, by constructing a feature extraction module that better adapts to variations in the shape of potholes. Subsequently, a cross-stage partial network was designed using a one-time aggregation method, simplifying the model while enabling the network to fuse information between feature maps at different stages. Additionally, a dynamic sparse attention mechanism is introduced to select relevant features, reducing redundancy and suppressing background noise. Experiments conducted on the VOC2007 and GRDDC2020_Pothole datasets reveal that compared to the baseline model YOLOv8, PD-YOLO achieves improvements of 3.9% and 2.8% in mean average precision, with a frame rate of approximately 290 frames per second, effectively meeting the accuracy and real-time requirements for pothole detection. The code and dataset for this paper are located at: .

关键词： image capture image classification image sampling

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5th International conference on Computer Vision and image processing, CVIP 2020

5th International Conference on Computer Vision and Image Pr...

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5th International conference on Computer Vision and image processing, CVIP 2020

ISBN: (纸本)9789811611025

The proceedings contain 134 papers. The special focus in this conference is on Computer Vision and image processing. The topics include: Age and Gender Prediction Using Deep CNNs and Transfer Learning;Text Line Segmentation: A FCN Based Approach;precise Recognition of Vision Based Multi-hand Signs Using Deep Single Stage Convolutional Neural Network;human Gait Abnormality Detection Using Low Cost Sensor Technology;Bengali Place Name Recognition - Comparative Analysis Using Different CNN Architectures;action Recognition in Haze Using an Efficient Fusion of Spatial and Temporal Features;face Verification Using Single Sample in Adolescence;evaluation of Deep Learning Networks for Keratoconus Detection Using Corneal Topographic images;deep Facial Emotion Recognition System Under Facial Mask Occlusion;domain Adaptation Based Technique for image Emotion Recognition Using image Captions;gesture Recognition in Sign Language Videos by Tracking the Position and Medial Representation of the Hand Shapes;deepDoT: Deep Framework for Detection of Tables in Document images;Correcting Low Illumination images Using PSO-Based Gamma Correction and image Classifying Method;DeblurRL: image Deblurring with Deep Reinforcement Learning;FGrade: A Large Volume Dataset for Grading Tomato Freshness Quality;enhancement of Region of Interest from a Single Backlit image with Multiple Features;human Action Recognition from 3D Landmark Points of the Performer;real-time Sign Language Interpreter on Embedded Platform;complex Gradient Function Based Descriptor for Iris Biometrics and Action Recognition;on-Device Language Identification of Text in images Using Diacritic Characters;a Pre-processing Assisted Neural Network for Dynamic Bad Pixel Detection in Bayer images;preface;dynamic User Interface Composition.

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5th International conference on Computer Vision and image processing, CVIP 2020

5th International Conference on Computer Vision and Image Pr...

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5th International conference on Computer Vision and image processing, CVIP 2020

ISBN: (纸本)9789811610851

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5th International conference on Computer Vision and image processing, CVIP 2020

5th International Conference on Computer Vision and Image Pr...

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5th International conference on Computer Vision and image processing, CVIP 2020

ISBN: (纸本)9789811610912

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Fast Software-Based real time Panoramic image processing 18

Fast Software-Based Real Time Panoramic Image Processing

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18th International conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Gerlits, Matthew Moh, Melody Moh, Teng-Sheng San Jose State University Department of Computer Science San JoseCA United States

ISBN: (纸本)9798350331011

Panoramic or stitched image processing has wide applications in areas such as medical imaging, topographical mapping, and deep space exploration. Rapid development of high-speed communication and artificial intelligence technologies have enabled real-time panoramic image processing, essential to autonomous driving, robotics, drones, etc., and critical in the advancement of smart cities, smart hospitals, manufacturer automation, and intelligent military warfare. image stitching algorithms are able to join sets of images together and provide a wider field of a vision when compared with an image from a single standard camera. Traditional techniques are able to adequately produce a stitch for a static set of images, but suffer when differing lighting conditions exist between the two images, and from processing times too slow for real time use cases. We propose a solution which resolves these two issues encountered by traditional techniques. First, two advanced blending schemes, including the superpixel approach, have been implemented to resolve the lighting difference. Second, we develop a fast validation scheme to rapidly detect invalid solutions, and package all the system components in a parallel processing architecture to ensure a 0.008 second frame processing time, sufficiently small for most real-time applications. The proposed solution, including its fault-detection system, is implemented in the software level and the need for specialized hardware is completely eliminated. To the best of our knowledge, this is the first software-based solution fast enough for real-time panoramic image processing;it would contribute significantly to the advancement of real-time image processing, especially for its wide applications in the modern smart society. © 2024 IEEE.

关键词： Computer vision

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IMPROVING real-time NEAR-INFRARED FACE ALIGNMENT WITH A PAIRED VIS-NIR DATASET AND DATA AUGMENTATION THROUGH image-TO-image TRANSLATION 31

IMPROVING REAL-TIME NEAR-INFRARED FACE ALIGNMENT WITH A PAIR...

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2024 International conference on image processing

作者： Miao, Langning Kakimoto, Ryo Ohishi, Kaoru Watanabe, Yoshihiro Tokyo Inst Technol Dept Informat & Commun Engn Tokyo Japan KOSE Corp Res Labs Tokyo Japan

ISBN: (纸本)9798350349405;9798350349399

real-time near-infrared (NIR) face alignment holds significant importance across various domains, such as security, healthcare, and augmented reality. However, existing face alignment techniques tailored for visible-light (VIS) encounter a decline in accuracy when applied in NIR settings. This decline stems from the domain discrepancy between VIS and NIR facial domains and the absence of meticulously annotated NIR facial data. To address this issue, we introduce a system and strategy for gathering paired VIS-NIR facial images and meticulously annotating precise landmarks. Our system facilitates streamlined dataset preparation by utilizing automatic annotation transfer from VIS images to their corresponding NIR counterparts. Following our devised approach, we constructed an inaugural dataset comprising high-frame-rate paired VIS-NIR facial images with landmark annotations. Additionally, to enhance the diversity of facial data, we augment our dataset through VIS-NIR image-to-image (img2img) translation using publicly available facial landmark datasets. Through the retraining of face alignment models and subsequent evaluations, our findings demonstrate a noteworthy enhancement in the accuracy of face alignment under NIR conditions using our dataset. Furthermore, the augmented dataset exhibits refined accuracy, particularly notable in the case of different individuals' facial features.

关键词： Face alignment Near-infrared facial dataset image-to-image translation

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