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Scalable image Coding for Humans and machines

IEEE TRANSACTIONS ON image processing 2022年 31卷 2739-2754页

作者： Choi, Hyomin Bajic, Ivan, V Simon Fraser Univ Sch Engn Sci Burnaby BC V5A 1S6 Canada

At present, and increasingly so in the future, much of the captured visual content will not be seen by humans. Instead, it will be used for automated machine vision analytics and may require occasional human viewing. Examples of such applications include traffic monitoring, visual surveillance, autonomous navigation, and industrial machine vision. To address such requirements, we develop an end-to-end learned image codec whose latent space is designed to support scalability from simpler to more complicated tasks. The simplest task is assigned to a subset of the latent space (the base layer), while more complicated tasks make use of additional subsets of the latent space, i.e., both the base and enhancement layer(s). For the experiments, we establish a 2-layer and a 3-layer model, each of which offers input reconstruction for human vision, plus machine vision task(s), and compare them with relevant benchmarks. The experiments show that our scalable codecs offer 37%-80% bitrate savings on machine vision tasks compared to best alternatives, while being comparable to state-of-the-art image codecs in terms of input reconstruction.

关键词： Task analysis image reconstruction image coding Scalability Object detection Multitasking Transforms image compression deep neural network multitask network scalable coding latent-space scalability

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Computational Imaging and Artificial Intelligence: The Next Revolution of Mobile vision

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PROCEEDINGS OF THE IEEE 2023年第12期111卷 1607-1639页

作者： Suo, Jinli Zhang, Weihang Gong, Jin Yuan, Xin Brady, David J. Dai, Qionghai Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Tsinghua Univ Inst Brain & Cognit Sci Beijing 100084 Peoples R China Shanghai Artificial Intelligence Lab Shanghai 200232 Peoples R China Westlake Univ Res Ctr Ind Future Hangzhou 310030 Zhejiang Peoples R China Westlake Univ Sch Engn Hangzhou 310030 Zhejiang Peoples R China Univ Arizona Wyant Coll Opt Sci Tucson AZ 85721 USA

Signal capture is at the forefront of perceiving and understanding the environment;thus, imaging plays a pivotal role in mobile vision. Recent unprecedented progress in artificial intelligence (AI) has shown great potential in the development of advanced mobile platforms with new imaging devices. Traditional imaging systems based on the "capturing images first and processing afterward" mechanism cannot meet this explosive demand. On the other hand, computational imaging (CI) systems are designed to capture high-dimensional data in an encoded manner to provide more information for mobile vision systems. Thanks to AI, CI can now be used in real-life systems by integrating deep learning algorithms into the mobile vision platform to achieve a closed loop of intelligent acquisition, processing, and decision-making, thus leading to the next revolution of mobile vision. Starting from the history of mobile vision using digital cameras, this work first introduces the advancement of CI in diverse applications and then conducts a comprehensive review of current research topics combining CI and AI. Although new-generation mobile platforms, represented by smart mobile phones, have deeply integrated CI and AI for better image acquisition and processing, most mobile vision platforms, such as self-driving cars and drones only loosely connect CI and AI, and are calling for a closer integration. Motivated by this fact, at the end of this work, we propose some potential technologies and disciplines that aid the deep integration of CI and AI and shed light on new directions in the future generation of mobile vision platforms.

关键词： Artificial intelligence (AI) autonomous driving brain science cameras cloud computing computational imag-ing (CI) deep learning edge cloud edge computing machine learning machine vision mobile vision neural networks optics self-driving

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Application of computer vision techniques to fermented foods: An overview

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TRENDS IN FOOD SCIENCE & TECHNOLOGY 2025年 160卷

作者： Song, Zheli Li, Yuanbo Zhao, Hongyuan Liu, Xiaogang Ding, Hailong Ding, Qiansu Ma, Dongna Liu, Shuangping Mao, Jian Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Natl Engn Res Ctr Cereal Fermentat & Food Biomfg Sch Food Sci & Technol State Key Lab Food Sci & Technol Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Shaoxing Key Lab Tradit Fermentat Food & Human Hlt Shaoxing Ind Technol Res Inst Shaoxing 312000 Zhejiang Peoples R China Luzhou Laojiao Grp Co Ltd Luzhou 646000 Sichuan Peoples R China Univ Minnesota Twin Cities Dept Agron & Plant Genet St Paul MN USA

Background: Fermented foods are products processed through microbial fermentation and are widely appreciated by consumers around the world for their unique flavors. With advancements in industrial technology and increasing consumer demand, modern techniques are being progressively integrated into the production and quality control of fermented foods to enhance production efficiency and product quality. Among these innovations, computer vision technology stands out as particularly impactful. Scope and approach: This paper provides an overview of the applications of computer vision in the field of fermented foods, focusing on its technical algorithms and applications within the food industry. It outlines the specific uses of computer vision technology across different types of fermented foods and discusses the relevant techniques employed. Finally, this review highlights the transformative potential of adaptive learning and multimodal fusion in addressing current limitations of computer vision for fermented food monitoring. Key findings and conclusions: The adoption of computer vision technology has significantly improved both the efficiency and accuracy of quality control processes in fermented food production. Through non-contact real-time monitoring, researchers can quickly identify the dynamic changes in microorganisms and related parameter indicators during fermentation and evaluate their impact on food quality. These technologies have not only boosted the efficiency of fermented food production but have also enhanced control over product flavor and safety assessments. Despite ongoing challenges in technology implementation and data analysis, the continuous advancements in deep learning and image processing technologies are expected to increase the impact of computer vision in the field of fermented foods, driving sustainable industry development.

关键词： Computer vision Fermented foods Food quality evaluation Nondestructive testing machine learning

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Raspberry Pi-powered door lock with facial recognition

Raspberry Pi-powered door lock with facial recognition

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2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2024

作者： Jha, Anjali Bulbule, Rajesh Nagrale, Nilesh Belambe, Tanavi Dit E&tc Dept. Pune India

ISBN: (纸本)9798350348460

Facial recognition is a widely-used process that aims to detect and verify an individual's identity. This technique is employed in various applications, such as image and video analysis, surveillance, and security systems. OpenCV is a computer vision technique that enables users to comprehend the storage, modification and feature extraction of images and videos. The advancement of artificial intelligence is heavily reliant on computer vision techniques, particularly in self-driving cars, robotics, and image-editing software. The OpenCV Library is a valuable tool for processing and displaying camera images. This research utilizes the Haar Cascade Classifier, a machine learning-based algorithm, to process a user's face image and detect faces accurately. © 2024 IEEE.

关键词： computer vision Facial recognition Haar Cascade Classifier image processing machine learning OpenCV robotics security systems self-driving cars

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Deep learning-based automated steel surface defect segmentation: a comparative experimental study

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MULTIMEDIA TOOLS AND applications 2023年第1期83卷 2995-3018页

作者： Sime, Dejene M. Wang, Guotai Zeng, Zhi Peng, Bei Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu 611731 Sichuan Peoples R China

The use of machine vision and deep learning for intelligent industrial inspection has become increasingly important in automating the production processes. Despite the fact that machine vision approaches are used for industrial inspection, deep learning-based defect segmentation has not been widely studied. While state-of-the-art segmentation methods are often tuned for a specific purpose, extending them to unknown sets or other datasets, such as defect segmentation datasets, require further analysis. In addition, recent contributions and improvements in image segmentation methods have not been extensively investigated for defect segmentation. To address these problems, we conducted a comparative experimental study on several recent state-of-the-art deep learning-based segmentation methods for steel surface defect segmentation and evaluated them on the basis of segmentation performance, processing time, and computational complexity using two public datasets, NEU-Seg and Severstal Steel Defect Detection (SSDD). In addition we proposed and trained a hybrid transformer-based encoder with CNN-based decoder head and achieved state-of-the-art results, a Dice score of 95.22% (NEU-Seg) and 95.55% (SSDD).

关键词： machine vision Automated inspection Deep learning Defect segmentation

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Make a Long image Short: Adaptive Token Length for vision Transformers

Make a Long Image Short: Adaptive Token Length for Vision Tr...

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5th International Workshop on Learning with Imbalanced Domains - Theory and applications / European Conference on machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)

作者： Zhou, Qiqi Zhu, Yichen Shanghai Univ Elect Power Shanghai Peoples R China Midea Grp Shanghai Peoples R China

ISBN: (纸本)9783031434143;9783031434150

The vision transformer is a model that breaks down each image into a sequence of tokens with a fixed length and processes them similarly to words in natural language processing. Although increasing the number of tokens typically results in better performance, it also leads to a considerable increase in computational cost. Motivated by the saying "A picture is worth a thousand words," we propose an innovative approach to accelerate the ViT model by shortening long images. Specifically, we introduce a method for adaptively assigning token length for each image at test time to accelerate inference speed. First, we train a Resizable-ViT (ReViT) model capable of processing input with diverse token lengths. Next, we extract token-length labels from ReViT that indicate the minimum number of tokens required to achieve accurate predictions. We then use these labels to train a lightweight Token-Length Assigner (TLA) that allocates the optimal token length for each image during inference. The TLA enables ReViT to process images with the minimum sufficient number of tokens, reducing token numbers in the ViT model and improving inference speed. Our approach is general and compatible with modern vision transformer architectures, significantly reducing computational costs. We verified the effectiveness of our methods on multiple representative ViT models on image classification and action recognition.

关键词： vision transformer token compression

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Generalized Authoring Tool for Computer vision machine Learning Application Deployments 2nd

Generalized Authoring Tool for Computer Vision Machine Learn...

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2nd European Symposium on Artificial Intelligence in Manufacturing-ESAIM

作者： Stein, Peter Antony, Jibinraj Bergweiler, Simon Schorr, Christian Univ Appl Sci Kaiserslautern Amerikastr 1 D-66482 Kaiserslautern Germany German Res Ctr Artificial Intelligence DFKI Trippstadter Str 122 D-67663 Kaiserslautern Germany German Res Ctr Artificial Intelligence DFKI Campus 32 D-66123 Saarbrucken Germany

ISBN: (纸本)9783031864919;9783031864896;9783031864889

Automated authoring enables simplified deployment of applications and services for complex use cases, especially in the field of machine learning. This paper presents the development and implementation of a specialized authoring tool that can be used for computer vision applications, enabling automated creation of machine learning services. The proposed authoring tool realizes a microservices architecture to facilitate the conversion and deployment of machine learning inference services, especially in image classification and object detection use cases. The authoring process addresses the interoperability issues commonly faced in machine learning frameworks, leveraging the Open Neural Network Exchange (ONNX) for model conversion into a standardized format. By encapsulating machine learning tools in containerized applications, this authoring tool offers a modular solution that can be easily adapted to various industrial applications. The developed authoring tool integrates the common machine learning frameworks PyTorch and TensorFlow, coupling DevOps methodologies such as CI/CD, ensuring a robust, maintainable, and user-friendly system that meets the growing needs of machine learning use cases in manufacturing.

关键词： machine Learning Computer vision Containerization Microservices ONNX PyTorch TensorFlow ML Authoring ML Life cycle

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Remote Sensing Scene Classification with Masked image Modeling 2

Remote Sensing Scene Classification with Masked Image Modeli...

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Conference on Microwave Remote Sensing - Data processing and applications ii

作者： Wang, Liya Tien, Alex MITRE Corp Mclean VA 22102 USA

ISBN: (纸本)9781510666931;9781510666948

Remote sensing scene classification has been extensively studied for its critical roles in geological survey, oil exploration, traffic management, earthquake prediction, wildfire monitoring, and intelligence monitoring. In the past, the machine Learning (ML) methods for performing the task mainly used the backbones pretrained in the manner of supervised learning (SL). As Masked image Modeling (MIM), a self-supervised learning (SSL) technique, has been shown as a better way for learning visual feature representation, it presents a new opportunity for improving ML performance on the scene classification task. This research aims to explore the potential of MIM pretrained backbones on four well-known classification datasets: Merced, AID, NWPU-RESISC45, and Optimal-31. Compared to the published benchmarks, we show that the MIM pretrained vision Transformer (ViTs) backbones outperform other alternatives (up to 18% on top 1 accuracy) and that the MIM technique can learn better feature representation than the supervised learning counterparts (up to 5% on top 1 accuracy). Moreover, we show that the general-purpose MIM-pretrained ViTs can achieve competitive performance as the specially designed yet complicated Transformer for Remote Sensing (TRS) framework. Our experiment results also provide a performance baseline for future studies.

关键词： Remote sensing classification self-supervised learning (SSL) Masked image Modeling (MIM) vision Transformer (ViTs)

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Shape Preservation in image Style Transfer for Gaze Estimation 18

Shape Preservation in Image Style Transfer for Gaze Estimati...

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18th International Conference on machine vision and applications (MVA)

作者： Mushiake, Daiki Otomo, Kentaro Nakatani, Chihiro Ukita, Norimichi Toyota Technol Inst Toyota Japan

ISBN: (纸本)9784885523434

This paper proposes image style transfer with shape preservation for gaze estimation. While several shape preservation constraints are proposed, we present additional shape preservation constraints using (i) dense pixelwise correspondences between the original and its transferred images and (ii) task-driven learning using gaze estimation error for directly improving gaze direction estimation. A variety of experiments with other SOTA methods, publicly-available datasets, and ablation studies validate the effectiveness of our method.

关键词： image enhancement

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Advanced machine vision techniques for real-time quality detection and grading of navel oranges using high-resolution imaging and deep learning algorithms

Advanced machine vision techniques for real-time quality det...

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International Conference on Computer vision, Robotics, and Automation Engineering, CRAE 2024

作者： Zhang, Xiaoyan Beijing Institute of Technology Zhuhai Zhuhai519000 China

ISBN: (纸本)9781510682283

In recent years, the rapid development of computer vision and artificial intelligence has significantly advanced agricultural applications, particularly in the quality detection and grading of navel oranges. This review explores the latest advancements in machine vision technologies for navel orange quality assessment, focusing on image processing, deep learning algorithms, and multispectral imaging. image processing techniques, fundamental to machine vision systems, enable the extraction of visual features such as color, shape, and texture, enhancing detection accuracy. Recent studies have demonstrated the efficacy of deep learning algorithms, particularly convolutional neural networks (CNNs), in achieving high-precision grading and real-time performance. Furthermore, multispectral and hyperspectral imaging technologies offer rich spectral information, facilitating more accurate quality detection and maturity assessment. Despite significant progress, challenges such as complex natural environments, lighting conditions, and the high cost of imaging equipment persist. Future research directions include integrating multi-source data fusion, developing efficient deep learning algorithms, and promoting cost-effective imaging technologies. Our proposed advanced algorithm employs the DAHENG machine vision experimental platform and MER-132-30UC camera for high-resolution image acquisition, with HALCON software for sophisticated image processing and real-time classification. This system enhances accuracy and efficiency in navel orange quality detection and grading, addressing key challenges in current agricultural practices. © 2024 SPIE.

关键词： Citrus fruits

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