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5th International Conference on Advances in Computing and Data Sciences (ICACDS)

作者： Chakrabarty, Navoneel Int Inst Informat Technol IIIT Bangalore Bengaluru India

ISBN: (纸本)9783030814625;9783030814618

In light of the recent advancements in Artificial Intelligence (AI), the application of machine Learning in the domains of Natural Language processing and Computer vision is increasing by leaps and bounds. Deployment of machine Learning Models in applications (apps) have been rampant with the aim of achieving automation, mainly involving textual and image data. Textual data indulges the subject of Text Analytics (also known as NLP) into action and image data indulges the subject of Computer vision into play. But, the performance of machine Learning in the domains of Text Analytics or vision needs to be judged before deployment. Now, performance analysis of ML Models are done with the help of performance metrics, most importantly AUC Score in Classification Problems, but justification by means of numerical scores only, can't establish the relevance of the Model Performance with Domain Knowledge. In this paper, 1 standard NLP use-case and 4 Computer vision use-cases are considered for ML Model Interpretability Enhancement that can throw light on the relevance with the concerned Domain Knowledge, the use-case deals with.

关键词： Model Interpretability NLP Computer vision Wordcloud Feature Map Visualization

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Enhancing Industrial Automation Flexibility Through Neural Network-Empowered machine vision applications 1

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1st International Conference on Artificial Intelligence and its applications, ICAIA 2023

作者： George, Navin M. Rosero, Evelyn Mayakannan, S. Shameem, A. Divya, S. Arul, N. Electronics and Communication Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore641114 India Riobamba060155 Ecuador Department of Mechanical Engineering Vidya Vikas College of Engineering and Technology Namakkal Tamilnadu Tiruchengode India AMET Business School AMET University Tamilnadu Chennai India Erode Tamil Nadu Perundurai638057 India Department of Mechatronics Engineering Sona College of Technology Tamilnadu Salem India

ISBN: (数字)9783031843976

ISBN: (纸本)9783031843969

vision-based systems provide non-contact sensory input for processing and feedback, expanding the capabilities of industrial automation applications. Prediction using ANNs aids such conformities in navigating nonlinear computational landscapes. They map the plethora of potential outcomes or zones of uncertainty introduced by the system’s constituents onto a set of feasible options for action. A measure of intelligence is transferred to robotic systems through the use of trained networks. Two uses of machine vision are presented here. The 3 DOF robotic assembly enables precision cutting of fragile materials with the help of visual guiding and pixel elimination. The 6-degrees-of-freedom robot uses a combination of visual feedback from an onboard camera and supervision from an external camera to achieve its goals. Pick-and-place operations are executed with a switching control method. Both systems improved in terms of computing time and convergence once ANN was used to make the strategies intelligent. The retrieved scene image features are put to use by the networks in a variety of ways. The simulation and experimental results that back up the suggested approaches demonstrates the value of artificial neural network in machine vision applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Robotic assembly

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Need for Speed: A Comprehensive Benchmark of JPEG Decoders in Python

arXiv

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arXiv 2025年

作者： Iglovikov, Vladimir Ternaus Inc.

image loading represents a critical bottleneck in modern machine learning pipelines, particularly in computer vision tasks where JPEG remains the dominant format. This study presents a systematic performance analysis of nine popular Python JPEG decoding libraries on different computing architectures. We benchmark traditional image processing libraries (Pillow, OpenCV), machine learning frameworks (TensorFlow, PyTorch), and specialized decoders (jpeg4py, kornia-rs) on both ARM64 (Apple M4 Max) and x86 64 (AMD Threadripper) platforms. Our findings reveal that modern implementations using libjpeg-turbo achieve up to 1.5x faster decoding speeds compared to traditional approaches. We provide evidence-based recommendations for choosing optimal JPEG decoders across different scenarios, from high-throughput training pipelines to real-time applications. This comprehensive analysis helps practitioners make informed decisions about image loading infrastructure, potentially reducing training times and improving system efficiency. © 2025, CC BY.

关键词： image enhancement

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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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Analysis of Medical images using image Registration Feature-based Segmentation Techniques 2

Analysis of Medical Images using Image Registration Feature-...

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2nd International Conference on Technological Advancements in Computational Sciences, ICTACS 2022

作者： Sindhu Madhuri, G. Shashikala, H.K. Faculty of Engineering & Technology Department of Computer Science & Engineering Bangalore India

ISBN: (数字)9781665476577

ISBN: (纸本)9781665476577

image Segmentation is one of the very important optimistic and emerging fields in all image processing applications. It has a wide range of applications like machine vision, fingerprint recognition, digital forensics, medical imaging, and face recognition and so on. Based on specific application, various image segmentation techniques like thresholding, region growing, watershed, clustering algorithms, fuzzy algorithms etc., are used to segment or partition the input images, labels each pixel in the images, locate the points, edges, boundaries and objects to identify various problems in the medical images. Also the identification of important parameters, detection of fractures and diseases, to decrease the death rate of patients suffering from various health problems is challenging research work in medical images. In this paper, author carryout the analysis for the automatic detection of bone fracture in early stage by taking two input x-ray medical images that are captured at different timings. This process is carried out and registered in 4 stages: In first stage-acquire input images and perform pre-processing by using geometrical transformation and register the input images, in second stage- the registered image is segmented using adaptive k-means clustering method, in third stage- automatic detection of the important features in x-ray image is extracted using image registration feature-based method. Automatic feature extraction is carried out for the observation of bone fracture in initial phase to increase the complexity of geometrical alignments of input images. Finally in the fourth stage, the performance of the results is analyzed with respect to accuracy and error rate. © 2022 IEEE.

关键词： Medical imaging

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Generating Physically-Consistent Satellite imagery for Climate Visualizations

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷

作者： Lutjens, Bjorn Leshchinskiy, Brandon Boulais, Oceane Chishtie, Farrukh Diaz-Rodriguez, Natalia Masson-Forsythe, Margaux Mata-Payerro, Ana Requena-Mesa, Christian Sankaranarayanan, Aruna Pina, Aaron Gal, Yarin Raissi, Chedy Lavin, Alexander Newman, Dava Massachusetts Inst Technol MIT Dept Aeronaut & Astronaut Cambridge MA 02139 USA Scripps Inst Oceanog La Jolla CA USA Univ British Columbia Vancouver BC Canada Peaceful Soc Sci & Innovat Fdn Petaluma CA 94953 USA Univ Granada Dept Comp Sci & AI Granada Spain Grad Sch Washington Washington DC USA Max Planck Inst Biogeochem D-07745 Jena Germany MIT Comp Sci & Artificial Intelligence Lab Cambridge MA USA US Forest Serv Washington DC 20024 USA Univ Oxford Dept Comp Sci Oxford OX1 2JD England INRIA Villers Les Nancy France Bur Labs New York NY USA

Deep generative vision models are now able to synthesize realistic-looking satellite imagery. However, the possibility of hallucinations prevents their adoption of risk-sensitive applications, such as generating materials for communicating climate change. To demonstrate this issue, we train a generative adversarial network (GAN, pix2pixHD) to create synthetic satellite imagery of future flooding and reforestation events. We find that a pure deep learning-based model can generate photorealistic flood visualizations but hallucinate floods at locations that are not susceptible to flooding. To address this issue, we propose to condition and evaluate generative vision models on segmentation maps of physics-based flood models. We show that our physics-conditioned model outperforms the pure deep learning-based model and a handcrafted baseline. We evaluate the generalization capability of our method to different remote sensing data and different climate-related events (reforestation). We publish our code and dataset which includes the data for a third case study of melting Arctic sea ice and >30 000 labeled HD image triplets-or the equivalent of 5.5 million images at 128 x 128 pixels-for segmentation guided image-to-image (im2im) translation in Earth observation. Code and data are available at ***/blutjens/eie-earth-public.

关键词： Climate change Artificial intelligence machine learning Generative adversarial networks image processing Reforestation Visualization Satellite images Remote sensing deep generative vision models flooding generative adversarial networks (GANs) generative AI image-to-image (im2im) translation physics-informed machine learning (ML) reforestation remote sensing synthetic satellite imagery visualization

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Redefining Night vision: The Power of MSR-Driven Neural ISP

Redefining Night Vision: The Power of MSR-Driven Neural ISP

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jingchao Hou Guanghui He Shanghai Jiao Tong University

This paper addresses two key limitations in existing image Signal processing (ISP) approaches: the suboptimal performance in low-light conditions and the lack of trainability in traditional ISP methods. To tackle these issues, we propose a novel, trainable ISP framework that incorporates both the strengths of traditional ISP techniques and advanced Multi-Scale Retinex (MSR) algorithms for night-time enhancement. Our method consists of three primary components: an ISP-based Luminance Harmonization layer to initially optimize luminance levels in RAW data, a deep learning-based MSR layer for nuanced decomposition of image components, and a specialized enhancement layer for both precise, region-specific luminance enhancement and color denoising. The proposed approach is validated through rigorous experiments on machine vision benchmarks and objective visual quality indicators. Our results demonstrate not only a significant improvement over existing methods but also robust adaptability under diverse lighting conditions. This work offers a versatile ISP framework with promising applications beyond its immediate scope.

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An optimized YOLO-based object detection model for crop harvesting system

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IET image processing 2021年第9期15卷 2112-2125页

作者： Junos, Mohamad Haniff Mohd Khairuddin, Anis Salwa Thannirmalai, Subbiah Dahari, Mahidzal Univ Malaya Fac Engn Dept Elect Engn Kuala Lumpur Malaysia Sime Darby Technol Ctr Sdn Bhd Adv Technol & Robot Serdang Selangor Malaysia

The adoption of automated crop harvesting system based on machine vision may improve productivity and optimize the operational cost. The scope of this study is to obtain visual information at the plantation which is crucial in developing an intelligent automated crop harvesting system. This paper aims to develop an automatic detection system with high accuracy performance, low computational cost and lightweight model. Considering the advantages of YOLOv3 tiny, an optimized YOLOv3 tiny network namely YOLO-P is proposed to detect and localize three objects at palm oil plantation which include fresh fruit bunch, grabber and palm tree under various environment conditions. The proposed YOLO-P model incorporated lightweight backbone based on densely connected neural network, multi-scale detection architecture and optimized anchor box size. The experimental results demonstrated that the proposed YOLO-P model achieved good mean average precision and F1 score of 98.68% and 0.97 respectively. Besides, the proposed model performed faster training process and generated lightweight model of 76 MB. The proposed model was also tested to identify fresh fruit bunch of various maturities with accuracy of 98.91%. The comprehensive experimental results show that the proposed YOLO-P model can effectively perform robust and accurate detection at the palm oil plantation.

关键词： Optical, image and video signal processing Optimisation techniques Computer vision and image processing techniques Optimisation Information technology applications Agriculture Optimisation techniques Agriculture, forestry and fisheries computing Neural nets

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High-precision velocity measurement method based on high frame rate image processing

High-precision velocity measurement method based on high fra...

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2022 International Conference on machine vision, Automatic Identification and Detection, MVAID 2022

作者： Zhou, Saiao Nie, Yuman Fan, Fan Tang, Yuanyang Wang, Zhou Wang, Lingfei Wang, Yaoxiong Cao, Pingguo Hefei University of Technology Anhui Hefei China Hefei Institutes of Physical Science Chinese Academy of Sciences Anhui Hefei China Hefei Shuangling Electronic Technology Co. Ltd. Anhui Hefei China

The traditional drop-weight impact velocity measurement is affected by the measurement distance of the sensor and the measurement environment, and the accuracy is difficult to guarantee. Aiming at this problem, this paper proposes a high-precision velocity measurement method based on high frame rate image. Firstly, a measurement scheme based on high frame rate image is designed, the position information is obtained by processing the image of the marked point, and the target impact velocity is obtained according to the time difference between frames;Then an error model is established, and the sources of errors and solutions are analyzed;Then by improving the subpixel edge and other methods to improve the feature extraction accuracy of the marking point image, thereby improving the velocity measurement accuracy. Finally, the measurement accuracy is verified by the actual drop-weight free-fall experiment. The experimental results show that the method proposed in this paper is better than 0.5% in the measurement of the impact velocity of the drop weight, which meets the needs of practical applications, and the measuring process is visualized and traceable, which has high application value. © Published under licence by IOP Publishing Ltd.

关键词： Velocity measurement

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Combining SIFT and BRISK Descriptors to Improve image Matching Accuracy

Combining SIFT and BRISK Descriptors to Improve Image Matchi...

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IEEE Pacific Rim Conference on Communications, Computers and Signal processing

作者： Sina Ghaffari Kin Fun Li David W. Capson Department of Electrical and Computer Engineering University of Victoria Victoria Canada

ISBN: (数字)9798350362312

ISBN: (纸本)9798350362329

Local descriptor algorithms are foundational in computer vision applications such as image matching and image retrieval. Some local descriptor algorithms extract features containing similar information from images while others extract complementary information. In this work, we investigate the advantages of combining a binary and a non-binary local descriptor algorithm. We propose and compare three methods to combine SIFT and BRISK descriptor algorithms selected because they produce complementary descriptor vectors. First, we propose to combine SIFT and BRISK descriptors using a weighted summation of their individual descriptor distances with learned weights. Our second method converts SIFT into a binary descriptor and concatenates the binary SIFT vector with the BRISK descriptor. The third method is to scale the binary BRISK descriptor vector and concatenate it with the SIFT descriptor. Parameters for combining the descriptors are learned based on the HPatches data set for each of the three methods. Our proposed methods increase the mean Average Precision in the range of 3% to 15.8% over the original BRISK and in the range of 5.9% to 21.8% over the original SIFT algorithm in various evaluation conditions.

关键词： machine learning algorithms image matching Neural networks image retrieval Signal processing algorithms machine learning Signal processing Feature extraction Vectors Data mining

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