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2022 International conference on image processing, Computer vision and machine Learning, ICICML 2022

ISBN: (纸本)9781665464680

The proceedings contain 120 papers. The topics discussed include: mobile device fingerprinting recognition using insensitive information;garbage image classification based on improved residual neural networks;object detection in visible and infrared missile borne fusion image;augmented reality calibration with stereo image registration for surgical navigation;momentum contrast learning for aerial image segmentation and precision agriculture analysis;transformer with convolution for irregular image inpainting;image recognition of marine organisms based on convolutional neural networks;multiple recurrent attention convolutional neural network for fine-grained image recognition;oracle bone inscriptions detection based on standard evaluation metric;the application of square module elements in digital images from the sense of order;and image and lidar fusion mapping method based on joint adjustment.

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IPMV 2022 - 2022 4th International conference on image processing and machine vision

IPMV 2022 - 2022 4th International Conference on Image Proce...

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4th International conference on image processing and machine vision, IPMV 2022

ISBN: (纸本)9781450395823

The proceedings contain 19 papers. The topics discussed include: infrared dim and small target detection based on total variation and multiple noise constraints modeling;infrared small target detection algorithm with complex background based on YOLO-NWD;masked face recognition with 3D facial geometric attributes;group sparse-based discriminative feature learning for face recognition;CLAMOT: 3D detection and tracking via multi-modal feature aggregation;research on the application of three-dimensional digital model in the protection and inheritance of traditional architecture: take the example of the ma tau wall of Huizhou architecture;hierarchical iris image super resolution based on wavelet transform;an improved dark channel prior defogging algorithm based on transmissivity image segmentation;image processing based scoring system for small arms firing in the military domain;and a quantitative comparison of automated cleaning techniques for web scraped image data of ‘smart cities’.

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iranian wheat varieties classification by using a fusion of texture features 32

Iranian wheat varieties classification by using a fusion of ...

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32nd European Signal processing conference (EUSIPCO)

作者： Backes, Andre Ricardo Khojastehnazhand, Mostafa Univ Fed Sao Carlos Dept Comp Sao Carlos SP Brazil Univ Bonab Dept Mech Engn Fac Engn Bonab Iran

ISBN: (纸本)9789464593617;9798331519773

Wheat is one of the important nutritional products in agriculture. Planting a specific variety in each region depends on the climatic conditions of that region and farm efficiency. Therefore the classification of different varieties is one of the most important challenges for producers. For this purpose, various methods of image texture extraction have been presented, and each method has a specific accuracy. In order to use all the extracted features and modeling based on them, in this research, the Particle Swarm Optimization (PSO) method was used. For this purpose, using 34 algorithms for extracting texture features of 7 varieties of iranian wheat, 3519 features were extracted and modeled with Linear Discriminate Analysis (LDA), Support Vector machine (SVM), and K-Nearest Neighbor (KNN) modeling methods. In the following, using PSO method, the amount of accuracy improvement of each modeling method was extracted and compared. The results of the research showed that the PSO method can increase the accuracy of different modeling methods up to 24% and improve the performance of the classifier.

关键词： image processing Wheat Texture Feature Classification machine vision PSO

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PARALLEL TASK-PROMPTS ICM: A VERSATILE FEATURE CODEC FOR machine vision 31

PARALLEL TASK-PROMPTS ICM: A VERSATILE FEATURE CODEC FOR MAC...

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2024 International conference on image processing

作者： Shen, Tianma Liu, Ying Santa Clara Univ Dept Comp Sci & Engn Santa Clara CA 95053 USA

ISBN: (纸本)9798350349405;9798350349399

image Coding for machines (ICM) is developed to compress images with a focus on machine vision tasks rather than human perception. For ICM, It is very important to develop a universal codec adaptable to different machine tasks. In this paper, we propose novel parallel task-prompts that can be easily adapted to various machine vision tasks without necessitating new networks or scratch training. Besides, Our parallel prompts are compatible with mainstream backbones such as transformers and convolutional neural networks, making them widely applicable across different model architectures. In order to fine-tune our task-prompts, we leverage a machine task network as the teacher net, guiding our student ICM network to efficiently compress feature maps for downstream machine tasks. Through extensive experimentation on object detection and segmentation, we demonstrate that our proposed method surpasses traditional image compression techniques and state-of-the-art learning-based feature compression techniques in terms of rate-accuracy performance.

关键词： entropy model image coding for machines object detection segmentation task-prompts transformer

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image Stitching Techniques Applied to Plane or 3-D Models: A Review

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IEEE SENSORS JOURNAL 2023年第8期23卷 8060-8079页

作者： Fu, Mengyin Liang, Hao Zhu, Chunhui Dong, Zhipeng Sun, Rundong Yue, Yufeng Yang, Yi Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China Nanjing Univ Sci & Technol Sch Automat Nanjing 210014 Peoples R China Beijing Inst Technol Integrated Nav & Intelligent Nav Lab Beijing 100081 Peoples R China Beijing Inst Technol Natl Key Lab Autonomous Intelligent Unmanned Syst Beijing 100081 Peoples R China

image stitching is a technique in which multiple overlapping images of the scene are stitched together to generate an image with a wide view and high resolution. image stitching methods can be broadly classified into feature-based and deep learning methods. Feature-based methods use manually designed features to establish transformation relationships between multiple images. This technology has played an important role in medical, industrial, military, and other fields. With the rise of deep learning in computer vision, it has also become the mainstream method in the field of image stitching. This article provides a systematic literature review of image stitching techniques applied on the plane and 3-D models for both feature-based and deep learning methods. We divide the stitching methods into two categories, namely, mosaic stitching methods for generating stitched plane images and panoramic stitching methods for generating stitched panoramic images. Based on the camera type, it is further divided into pinhole camera plane stitching methods, pinhole camera panoramic stitching methods, fisheye camera panoramic stitching methods, and light field camera plane stitching methods. An extensive search was conducted in International conference on image processing (ICIP), IEEE Transactions on image processing (TIP), International conference on Computer vision (ICCV), European conference on Computer vision (ECCV), IEEE conference on Computer vision and Pattern Recognition (CVPR), British machine vision conference (BMVC), International conference on Pattern Recognition (ICPR), International Journal of Computer vision (IJCV), IEEE/ASME International conference on Advanced Intelligent Mechatronics (AIM), IEEE Transactions on Intelligent Transportation Systems (ITS), IEEE Transactions on Pattern Analysis and machine Intelligence (TPAMI), and ACM SIGGRAPH Computer Graphics (SIGGRAFH) to summarize related image stitching techniques;89 articles are selected for systematic literatur

关键词： Cameras image stitching Pipelines Sensors Light fields Bibliographies Systematics Geometric correction image models image stitching reconstruction registration

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image CODING FOR machine VIA ANALYTICS-DRIVEN APPEARANCE REDUNDANCY REDUCTION 31

IMAGE CODING FOR MACHINE VIA ANALYTICS-DRIVEN APPEARANCE RED...

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2024 International conference on image processing

作者： Shen, Xuelin Ou, Haoqiao Yang, Wenhan Guangdong Lab Artificial Intelligence & Digital E Shenzhen Guangdong Peoples R China PengCheng Lab Shenzhen Guangdong Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (纸本)9798350349405;9798350349399

Among various technical approaches in machine vision coding, image Coding for machine (ICM) stands out for its capability to simultaneously fulfill both human perception and machine vision needs. However, it is often criticized for its lack of efficiency regarding rate-analytics performance. In this paper, we propose an Appearance Redundancy Reduction (ARR) module, designed to function as a plug-in for existing ICM frameworks, aiming to further enhance the coding efficiency regarding rate-analytics without any changes to the ICM itself. To be specific, our work pays additional attention to the intrinsic correlation between the low-level image structure and high-level vision analytics, and subsequently proposes a novel colour quantization mechanism to squeeze out the analytics-free redundant appearance information. Moreover, a differentiable soften quantization operation is derived to enable end-to-end training within the ICM framework. Extensive experimental results have shown that integrating the proposed ARR module yields substantial improvements regarding rate-analytic performance, even surpassing the performance of the feature coding paradigm, while maintaining the generalizability across different tasks and acceptable perceptual representation.

关键词： image coding for machine colour distillation machine vision image compression

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FEATURE STRUCTURE SIMILARITY INDEX FOR HYBRID HUMAN AND machine vision 30

FEATURE STRUCTURE SIMILARITY INDEX FOR HYBRID HUMAN AND MACH...

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30th IEEE International conference on image processing (ICIP)

作者： Lin, Yongbing Wan, Lei Ma, Sha Zhang, Peike Huawei Technol Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9781728198354

More and more images/videos will be consumed by both human and machine in many fields. Optimization of image processing algorithm for hybrid human and machine becomes a challenging task. To address this problem, feature structure similarity index (FSSIM) is proposed in this paper as an objective metric for image quality assessment (IQA), by defining structure similarity in low-level feature domain. Features extracted by the first convolutional layer of pretrained resnet50 network are treated as common feature domain for both human and machine vision. Moreover, multi-scale structure similarity with weighting matrix is used as distance measure in the feature domain. FSSIM is capable of fully decoupling image processing and its downstream machine tasks, enabling image processing algorithm optimization for hybrid human and machine vision. Experimental results show FSSIM-optimized image processing algorithms achieve significant performance improvement over existing metrics in context of machine vision tasks including object detection and semantic segmentation. Meanwhile reconstructed images of FSSIM-optimized algorithms are better friendly to human vision.

关键词： Feature structure similarity multi-task machine vision machine perception image processing algorithm optimization image quality assessment

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image CODING FOR ANALYTICS VIA ADVERSARIALLY AUGMENTED ADAPTATION 49

IMAGE CODING FOR ANALYTICS VIA ADVERSARIALLY AUGMENTED ADAPT...

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49th IEEE International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Shen, Xuelin Yin, Kangsheng Wang, Xu He, Yulin Wang, Shiqi Yang, Wenhan Guangdong Lab Artificial Intelligence & Digital E Shenzhen Guangdong Peoples R China Shenzhen Univ Shenzhen Peoples R China City Univ Hong Kong Hong Kong Peoples R China Peng Cheng Lab Shenzhen Peoples R China

ISBN: (纸本)9798350344868;9798350344851

image Coding for machine (ICM) aims to compress an image so that the reconstructed one can meet the requirements of both human vision and machine vision. Existing methods apply the constraint from the downstream models to improve machine analytics performance while compromising the visual quality. This paper proposes a novel adversarially augmented adaptation route that achieves a better trade-off between the utility of the human and machine perspectives by making slight changes to the image manifold. In detail, a targeted adversarial attack is employed to generate subtle image perturbations that are nearly imperceptible to humans but significantly improve machine analytic performance. These perturbed images would be subsequently employed as ground truth to guide training/fine-tuning of an end-to-end image compression network. Note that, our method is a plug-and-play framework that does not rely on any change in existing architecture or loss functions. Extensive experimental results demonstrate the superiority of the proposed scheme over conventional ICM frameworks and the effectiveness of our design.

关键词： image coding for machine machine vision Targeted adversarial attack machine vision coding

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Application of image processing Technology based on machine vision in Traffic Sign Recognition 1

Application of Image Processing Technology based on Machine ...

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1st International conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Qin, Peng Hu, Jiajun Hohhot Minzu College College of Computer and Information Technology Inner Mongolia Hohhot China

ISBN: (纸本)9798331529246

The accuracy and real-time performance of existing traffic sign recognition methods in complex environments need to be improved. This study aims to propose an efficient traffic sign recognition solution based on machine vision image processing technology. First, a high-definition camera is used to collect road scene images in real time and preprocess them, including converting the image into a grayscale image, using Gaussian filtering to remove noise, and using the Canny edge detection algorithm to extract edge information. Next, morphological operations such as dilation and erosion are used to further enhance the features of traffic signs. The recognition rate of this method on the test set reached 98.9%, and the processing time of 120 codes was 50 milliseconds, which met the requirements of real-time recognition. The application of machine vision-based image processing technology in traffic sign recognition effectively improves the recognition accuracy. © 2025 IEEE.

关键词： machine vision

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A machine vision Based Method for Extracting Visual Features of Froth in Copper Floatation Process 12

A Machine Vision Based Method for Extracting Visual Features...

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12th iranian/2nd International conference on machine vision and image processing, MVIP 2022

作者： Barhoun, Abbas Khiavi, Abdolhamid Moallemi Sorkhabi, Alireza Sokhandan Aghdasi, Hadi S. Kargari, Behzad University of Tabriz Faculty of Electrical and Computer Engineering Tabriz Iran National Iranian Copper Industries Co. Tabriz Iran

ISBN: (纸本)9781665412162

Froth flotation is one of the most important and widespread methods of separation of minerals and waste materials and at the same time one of the most accurate methods of refining low-grade metal minerals. This paper presents a method for visual feature extraction of froth bubbles including the size, color, shape, and mobility based on machine vision and image processing techniques. The proposed method is capable of identifying bubbles properties as well as estimating their velocity and direction of movement. The performance of the proposed method is evaluated using real videos captured from the copper floatation process. The method description, as well as simulation results, are presented. © 2022 IEEE.

关键词： Computer vision

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