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A Review on machine Learning Styles in Computer vision-Techniques and Future Directions

IEEE ACCESS 2022年 10卷 107293-107329页

作者： Mahadevkar, Supriya, v Khemani, Bharti Patil, Shruti Kotecha, Ketan vora, Deepali R. Abraham, Ajith Gabralla, Lubna Abdelkareim Symbiosis Int Deemed Univ Symbiosis Inst Technol Pune 412115 Maharashtra India Symbiosis Int Deemed Univ Symbiosis Ctr Appl Artificial Intelligence Symbiosis Inst Technol Pune 412115 Maharashtra India Machine Intelligence Res Labs MIR Labs Auburn WA 98071 USA Princess Nourah Bint Abdulrahman Univ Coll Appl Dept Comp Sci & Informat Technol Riyadh 11671 Saudi Arabia

Computer applications have considerably shifted from single data processing to machine learning in recent years due to the accessibility and availability of massive volumes of data obtained through the internet and various sources. machine learning is automating human assistance by training an algorithm on relevant data. Supervised, Unsupervised, and Reinforcement Learning are the three fundamental categories of machine learning techniques. In this paper, we have discussed the different learning styles used in the field of Computer vision, Deep Learning, Neural networks, and machine learning. Some of the most recent applications of machine learning in computer vision include object identification, object classification, and extracting usable information from images, graphic documents, and videos. Some machine learning techniques frequently include zero-shot learning, active learning, contrastive learning, self-supervised learning, life-long learning, semi-supervised learning, ensemble learning, sequential learning, and multi-view learning used in computer vision until now. There is a lack of systematic reviews about all learning styles. This paper presents literature analysis of how different machine learning styles evolved in the field of Artificial Intelligence (AI) for computer vision. This research examines and evaluates machine learning applications in computer vision and future forecasting. This paper will be helpful for researchers working with learning styles as it gives a deep insight into future directions.

关键词： machine learning Computer vision Object detection Artificial intelligence machine learning algorithms image segmentation Feature extraction machine learning techniques computer vision supervised learning multi-task learning object detection artificial intelligence image categorization zero-shot learning

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Transformer-Based visual Segmentation: A Survey

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND machine INTELLIGENCE 2024年第12期46卷 10138-10163页

作者： Li, Xiangtai Ding, Henghui Yuan, Haobo Zhang, Wenwei Pang, Jiangmiao Cheng, Guangliang Chen, Kai Liu, Ziwei Loy, Chen Change Nanyang Technol Univ S Lab Singapore 639798 Singapore Fudan Univ Inst Big Data Shanghai 200437 Peoples R China Shanghai AI Lab Shanghai 200240 Peoples R China Univ Liverpool Liverpool L69 7ZX Merseyside England

visual segmentation seeks to partition images, video frames, or point clouds into multiple segments or groups. This technique has numerous real-world applications, such as autonomous driving, image editing, robot sensing, and medical analysis. Over the past decade, deep learning-based methods have made remarkable strides in this area. Recently, transformers, a type of neural network based on self-attention originally designed for natural language processing, have considerably surpassed previous convolutional or recurrent approaches in various vision processing tasks. Specifically, vision transformers offer robust, unified, and even simpler solutions for various segmentation tasks. This survey provides a thorough overview of transformer-based visual segmentation, summarizing recent advancements. We first review the background, encompassing problem definitions, datasets, and prior convolutional methods. Next, we summarize a meta-architecture that unifies all recent transformer-based approaches. Based on this meta-architecture, we examine various method designs, including modifications to the meta-architecture and associated applications. We also present several specific subfields, including 3D point cloud segmentation, foundation model tuning, domain-aware segmentation, efficient segmentation, and medical segmentation. Additionally, we compile and re-evaluate the reviewed methods on several well-established datasets. Finally, we identify open challenges in this field and propose directions for future research.

关键词： image segmentation Transformers Surveys Task analysis Measurement Object detection visualization vision transformer review dense prediction image segmentation video segmentation scene understanding

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vision-guided robot application for metal surface edge grinding

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SN APPLIED SCIENCES 2023年第9期5卷 236页

作者： Li, Chunlei Dun, Xiaofeng Li, Liang Nan, Rui Baoji Univ Arts & Sci Sch Mech Engn Baoji 721016 Peoples R China Shaanxi Key Lab Adv Mfg & Evaluat Robot Key Compon Baoji 721016 Peoples R China Nanjing ESTUN Automat Co Ltd Applicat Proc Res Dept Nanjing 211102 Peoples R China

The combination of machine vision and grinding robots can be visualized as a collaboration between human eyes and limbs to achieve a deep integration between external perception and execution actions. This combination will give the grinding robot more operability and flexibility, which will enable it to better realize the purpose of replacing humans with machines. In response to the demand for flexible grinding of titanium surface edges proposed by a titanium manufacturer, this paper conducts an in-depth study on the prototype system of vision-guided grinding robots and related applications. Firstly, this study analyzes the shortcomings of the existing robotic regrinding process and achieves the improvement of the regrinding process by introducing machine vision technology. Subsequently, this study further utilizes machine vision and image processing algorithms to achieve high-quality recognition and high-precision positioning of metal surface edges. Then, the D-H parameter model of the regrinding robot is established, and the planning and simulation of the regrinding trajectory is carried out using the position information of the identified regrinding edges. Finally, the simulation-validated grinding trajectory is introduced into the grinding robot, and the effectiveness of the proposed scheme is verified by actual grinding experiments.

关键词： Grinding robot machine vision image processing Grinding trajectory planning Simulation modeling

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Electron Density Specification in the Inner Magnetosphere From the Narrow Band Receiver Onboard DSX

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RADIO SCIENCE 2024年第2期59卷 1-20页

作者： Su, Yi-Jiun Carilli, John A. Parham, J. Brent Chu, Xiangning Galkin, Ivan A. Ginet, Gregory P. AF Res Lab Space Vehicles Directorate Kirtland AFB NM 87117 USA MIT Lincoln Lab Cambridge MA USA Univ Colorado Boulder Lab Atmospher & Space Phys Boulder CO USA Univ Massachusetts Lowell Lowell MA USA

Electron density plays an important role in the study of wave propagation and is known to be associated with the index of refraction and radiation belt diffusion coefficients. The primary objective of our investigation is to explore the possibility of implementing an onboard signal processing algorithm to automatically obtain electron densities from the upper hybrid resonance traces of wave spectrograms for future missions. U-Net, developed for biomedical image segmentation, has been adapted as our deep learning architecture with results being compared with those extracted from a more traditional semi-automated method. As a product, electron densities and cyclotron frequencies for the entire DSX mission between 2019 and 2021 are acquired for further analysis and applications. Due to limited space measurements, a synthetic image generator based on data statistics and randomization is proposed as an initial step toward the development of a generative adversarial network in hopes of providing unlimited realistic data sources for advanced machine learning. Plain Language Summary Electron density is the most important fundamental plasma parameter, however, it is very difficult to directly measure in situ due to spacecraft potential. A convolutional neural network (CNN), developed to recognize features from biomedical images, has been adapted to pull out the resonance traces from space wave receivers automatically specifying densities along satellite orbits. The comparison between computer vision based on a CNN and human vision based on a semi-automated extraction is demonstrated in this paper. With additional development and refinement, our proof-of-concept study may be matured to a level suitable for incorporation into onboard signal processing units to reduce human labor and human-in-the-loop induced operational errors during future space missions.

关键词： deep machine learning electron density satellite wave receiver plasmasphere image processing space instrument software development

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A fast, lightweight deep learning vision pipeline for autonomous UAv landing support with added robustness

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ENGINEERING applications OF ARTIFICIAL INTELLIGENCE 2024年 131卷

作者： Pieczynski, Dominik Ptak, Bartosz Kraft, Marek Piechocki, Mateusz Aszkowski, Przemyslaw Poznan Univ Tech Inst Robot & Machine Intelligence Piotrowo 3A PL-60965 Poznan Poland

Despite massive development in aerial robotics, precise and autonomous landing in various conditions is still challenging. This process is affected by many factors, such as terrain shape, weather conditions, and the presence of obstacles. This paper describes a deep learning-accelerated image processing pipeline for accurate detection and relative pose estimation of the UAv with respect to the landing pad. Moreover, the system provides increased safety and robustness by implementing human presence detection and error estimation for both landing target detection and pose computation. Human presence and landing pad location are performed by estimating the presence probability via segmentation. This is followed by the landing pad keypoints' location regression algorithm, which, in addition to coordinates, provides the uncertainty of presence for each defined landing pad landmark. To perform the aforementioned tasks, a set of lightweight neural network models was selected and evaluated. The resulting measurements of the system's performance and accuracy are presented for each component individually and for the whole processing pipeline. The measurements are performed using onboard embedded UAv hardware and confirm that the method can provide accurate, low-latency feedback information for safe landing support.

关键词： Unmanned aerial vehicle Landing support image processing Deep learning On-board processing

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Guest Editorial Introduction to the Special Section on Transformer Models in vision

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND machine INTELLIGENCE 2023年第11期45卷 12721-12725页

作者： Khan, Salman Khan, Fahad Shahbaz vaswani, Ashish Parmar, Niki Yang, Ming-Hsuan Shah, Mubarak Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates Australian Natl Univ Canberra 2601 Australia Linkoping Univ S-58183 Linkoping Sweden Stealth Mt View WY USA Univ Calif Merced Merced CA 95343 USA Univ Cent Florida Orlando FL 32816 USA

Transformer models have achieved outstanding results on a variety of language tasks, such as text classification, ma- chine translation, and question answering. This success in the field of Natural Language processing (NLP) has sparked interest in the computer vision community to apply these models to vision and multi-modal learning tasks. However, visual data has a unique structure, requiring the need to rethink network designs and training methods. As a result, Transformer models and their variations have been suc- cessfully used for image recognition, object detection, seg- mentation, image super-resolution, video understanding, image generation, text-image synthesis, and visual question answering, among other applications.

关键词： Special issues and sections Transformers Text categorization machine translation Natural language processing

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Temporal compressive edge imaging enabled by a lensless diffuser camera

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OPTICS LETTERS 2024年第11期49卷 3058-3061页

作者： Zheng, Ze Liu, Baolei Song, Jiaqi Ding, Lei Zhong, Xiaolan Chang, Lingqian Wu, Xiaojun McGloin, David Wang, Fan Beihang Univ Sch Phys Beijing 100191 Peoples R China Univ Technol Sydney Fac Engn & IT Sch Biomed Engn Sydney NSW 2007 Australia Beihang Univ Beijing Adv Innovat Ctr Biomed Engn Sch Biol Sci & Med Engn Beijing 100191 Peoples R China Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Univ Aberdeen Kings Coll Sch Nat & Comp Sci Aberdeen AB24 3FX Scotland

Lensless imagers based on diffusers or encoding masks enable high -dimensional imaging from a single-shot measurement and have been applied in various applications. However, to further extract image information such as edge detection, conventional post -processing filtering operations are needed after the reconstruction of the original object images in the diffuser imaging systems. Here, we present the concept of a temporal compressive edge detection method based on a lensless diffuser camera, which can directly recover a time sequence of edge images of a moving object from a single-shot measurement, without further post -processing steps. Our approach provides higher image quality during edge detection, compared with the "conventional post -processing method." We demonstrate the effectiveness of this approach by both numerical simulation and experiments. The proof-of-concept approach can be further developed with other image post -processing operations or versatile computer vision assignments toward task-oriented intelligent lensless imaging systems.

关键词： Computational imaging Digital image processing Edge detection Imaging systems machine vision Numerical simulation

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Systematic literature review of AI algorithms applied to unmanned aerial vehicle images

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INTERNATIONAL JOURNAL OF image AND DATA FUSION 2025年第1期16卷

作者： El Khadir, Kenza Ait Fadil, Abdelhamid El Brirchi, El Hassan Hassania Sch Publ Works LaGes Lab Km 7 Jadida RdBP 8108 Casablanca Morocco

Artificial Intelligence (AI) combined with image processing has shown significant improvements through new techniques such as machine Learning (ML) models. This paper introduces the key methods and algorithms used for Drone image processing. We discuss the benefits and limitations of using ML models instead of classical techniques. Our goal is to classify, categorize and describe the methods that are used in realistic settings of diverse domains of applications. We conducted a systematic literature review where systems presented in the papers were analysed based on their domain, task, technology, and efficiency. By extensively reviewing the existing literature, we successfully identified key themes and trends that emerged across the various research questions. The overall findings of the research emphasise the potential of AI and drone imagery in numerous fields. However, the review also uncovered several challenges that necessitate attention, such as issues related to data quality and the requirement for more advanced AI algorithms. The paper outlines significant innovations in the field and offers recommendations for future research directions. By highlighting cross-disciplinary insights, it delves into methodological approaches, exploring commonalities in AI algorithms and UAvs technologies.

关键词： Artificial intelligence ML image processing computer vision UAvs (unmanned aerial vehicles) drones image recognition fault detection object detection inspection agriculture environmental pollution CNNs (convolutional neural networks) infrastructure

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Ultrabroadband Detection and Self-Powered Functionality in Quasi-One-Dimensional Nb3Se12I Nanowire Photodetectors for Bionic vision applications

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ACS APPLIED MATERIALS & INTERFACES 2025年第14期17卷 21448-21458页

作者： Zhang, Jianbin Zhao, Yi Liu, Ge Wang, Guangyi Chen, Liangqiang Shang, Conghui Li, Jiaxuan Zhou, Nan Xu, Hua Yang, Rusen Li, Xiaobo Xidian Univ Sch Adv Mat & Nanotechnol Shaanxi Joint Key Lab Graphene Shaanxi Key Lab High Orbits Electron Mat & Protect Xian 710126 Peoples R China Shaanxi Normal Univ Shaanxi Engn Lab Adv Energy Technol Key Lab Appl Surface & Colloid Chem Shaanxi Key Lab Adv Energy DevicesMinist EducSch Xian 710119 Peoples R China

The burgeoning fields of the Internet of things (IoT) and artificial intelligence (AI) have escalated the demands for image sensing technologies, necessitating advancements in sensor efficiency and functionality. Traditional image sensors, structured on von Neumann architectures with discrete processing units, face challenges, such as high power consumption, latency, and escalated hardware costs. In this work, we introduced a unique approach through the development of a quasi-one-dimensional nanowire Nb3Se12I-based double-ended photosensor. The advanced sensor not only replicated the adaptive behavior of biological vision systems but also effectively managed the decreased sensitivity triggered by intense light stimuli. The integration of the photothermoelectric and bolometric effects allows the device to operate in a self-powered mode, offering broadband detectivity ranging from visible (405 nm) to midwave infrared (4060 nm). Additionally, the quasi-one-dimensional structure enables an angle-dependent response to polarized light with a polarization ratio of 1.83. Our findings suggest that the biomimetic vision adaptive sensor based on Nb3Se12I could effectively enhance the capabilities of smart optical sensors and machine vision systems.

关键词： photothermoelectric self-powered Nb3Se12I ultrabroadband detection bionicvisual adaptation

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Retinal fundus image enhancement using an ensemble framework for accurate glaucoma detection

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Neural Computing and applications 2024年 1-19页

作者： Lenka, Satyabrata Mayaluri, Zefree Lazarus Panda, Ganapati Department of Electrical Engineering C.V. Raman Global University Odisha Bhubaneswar India C.V. Raman Global University Odisha Bhubaneswar India

Retinal fundus imaging plays a crucial role in the diagnosis of ophthalmic diseases such as glaucoma, a significant cause of vision loss worldwide. Accurate detection of glaucoma using image processing, machine learning, and deep learning approaches depends on the effectiveness with which the retinal fundus images are captured. Poor-quality images with artifacts, including uneven illumination, blur, and color distortion, can lead to incorrect diagnoses. In this work, we propose an end-to-end glaucoma detection model based on the ensemble of image enhancement networks, segmentation networks, and image classification networks. The proposed approach consists of an improved version of generative adversarial network (GAN) called the cycle consistency GAN (cycle-GAN) for image quality enhancement, U-Net for optic cup and optic disc segmentation, and support vector machine for image classification. The cycle-GAN model uses autoencoders as generators and a deep convolutional neural network (CNN) as discriminators to generate high-quality fundus images. The cup-to-disc ratio, a popular feature, is utilized to categorize fundus images as either glaucomatous or non-glaucomatous. We use six imbalanced datasets for experimental analysis of the proposed ensemble model, including ORIGA, ACRIMA, DRISTI-GS, REFUGE, Messidor, and Mendeley. The experimental findings demonstrate that the proposed ensemble model works better than individual models such as GAN, Autoencoder, deep CNN, and also from existing methods. The proposed method not only reduces the artifacts from fundus images but also solves the problem of imbalanced datasets for accurate glaucoma detection. The experimental results show maximum accuracy, precision, recall, and F-measure values of 0.968, 0.821, 0.974, and 0.891, respectively. © The Author(s), under exclusive licence to Springer-verlag London Ltd., part of Springer Nature 2024.

关键词： Convolutional neural networks

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