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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VIII"

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Streamlining Crop Segmentation with Multispectral Imaging and Foundation Models: Minimizing Manual Annotation 20

Streamlining Crop Segmentation with Multispectral Imaging an...

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20th IEEE International conference on Intelligent Computer Communication and processing conference, ICCP 2024

作者： Aszkowski, Przemyslaw Kraft, Marek Institute of Robotics and Machine Intelligence Poznań University of Technology Poznań Poland

ISBN: (纸本)9798331539979

Deep learning advancements have significantly enhanced computer vision applications in precision agriculture. While RGB cameras operating in visible light are affordable, they provide limited information compared to multispectral equipment. This research analyses methods to reduce the need for manual annotation when training a model using only RGB images, without compromising the model's accuracy. We propose a semi-supervised approach where a teacher model, trained on multispectral images, generates artificial ground truth data to train a student model that operates solely on RGB images. This strategy has enabled us to achieve nearly a tenfold reduction in the required training data while maintaining similar performance metrics. Additionally, we explore the potential of segmentation foundation models to simplify the manual annotation process, reducing the need for full segmentation masks to just bounding boxes. Our findings also indicate that using multispectral images as input for the Segment Anything Model is more effective than using RGB images. © 2024 IEEE.

关键词： image segmentation

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image processing using Cloud for Surveillance 5

Image Processing using Cloud for Surveillance

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5th International conference on Information Management and machine Intelligence, ICIMMI 2023

作者： Tiwari, Shivam Phukan, Aarhee Vedhavathy, T.R. Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chengalpattu Tamil Nadu Kattankulathur603203 India

ISBN: (纸本)9798400709418

In the era of digitization and big data, the world is inundated with an ever-growing volume of visual content, be it images or videos. As organizations strive to harness the potential of these multimedia data sources, there is an increasing need for advanced image processing techniques that can automate the analysis and extraction of valuable information. Amazon Web Services (AWS) Rekognition emerges as a powerful solution in this landscape, offering a comprehensive system for image and video analysis through the lens of machine learning and computer vision. This paper delves into the realm of image processing using AWS Rekognition, unveiling the transformative capabilities of this cloud-based service and its applications in various domains. As we embark on this journey, we will explore the principles, methodologies, and real-world implications of leveraging AWS Rekognition for image analysis. © 2023 ACM.

关键词： Computer vision

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Movie Recommendation System Based on Emotion Detection Using machine Learning Techniques

Movie Recommendation System Based on Emotion Detection Using...

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2024 IEEE International conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Vaishnavi, S.R. Sreelakshmi, S. Anu Prabha, R.S. Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Applications Amritapuri India

ISBN: (数字)9798350382693

ISBN: (纸本)9798350382693

The face is a critical perspective in predicting human feelings and moods. More frequently than not human senti-ments are extricated with the utilization of the camera. Various applications are being made based on the location of human sentiments. A few applications of feeling revelation are trade notice suggestion, e-learning, mental clutter, sadness disclosure, criminal conduct discovery, etc. This paper presents a novel real-time emotion-based movie recommendation system that combines computer vision, deep learning, and image processing strategies. The system coordinating OpenCV near DeepFace for effective emotion examination utilizing webcam input, giving clients personalized movie recommendations based on their recognized enthusiastic states. The system commences by using OpenCV to capture real-time webcam feeds and utilizes a Haar-Cascade classifier for facial discovery. The recognized faces are analyzed for prevailing feelings utilizing the DeepFace library, empowering exact feeling distinguishing proof. Within the recommendation stage, the system joins content-based filtering by processing a movie dataset utilizing TF-IDF. Genres and plot keywords serve as features for building the TF-IDF matrix. Cosine similarities between the user's emotion vector and relevant movie genres are then calculated, coming about in a list of personalized movie rec-ommendations. Index Terms-FER, OpenCV, DeepFace, Haar-Cascade Algorithm, Content-Based Filtering, TF-IDF © 2024 IEEE.

关键词： Emotion recognition Visualization Webcams Face recognition Motion pictures Real-time systems Libraries

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2024 2nd International conference on Computer, Communication and Control, IC4 2024

2024 2nd International Conference on Computer, Communication...

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2nd International conference on Computer, Communication and Control, IC4 2024

ISBN: (纸本)9798350387933

The proceedings contain 134 papers. The topics discussed include: an adaptive storage switching algorithm for fault-tolerant network attached storage systems;Covid-19 prediction using machine learning algorithms;energy management of hybrid electric vehicles using cascaded fuzzy logic controller;dynamic lane management with IoT for real-time lane configuration and traffic flow;a closer look at sclera: emerging trends in biometric security;cognitive vision companion: an ai-enhanced support system for the visually impaired;advances in medical image processing for liver tumor recognition: a comprehensive survey;a gradient boosting algorithm to predict energy consumption for home applications;and review on text classification using improved deep learning models.

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Review and Enhancement of Discrete Cosine Transform (DCT) for Medical image Fusion 16th

Review and Enhancement of Discrete Cosine Transform (DCT) fo...

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16th International conference on Information Technology and applications, ICITA 2022

作者： Alhatami, Emadalden Bhatti, Uzair Aslam Huang, MengXing Feng, SiLing School of Information and Communication Engineering Hainan University Haikou570100 China

ISBN: (纸本)9789811993305

An image fusion is a kind of single process which combines the necessary or efficient information from a set of different or similar input images into a single output image where the resulting image is more accurate, informative, and complete than any of the input images with a specific algorithm. image enhancement is a process used to improve the quality of an image and increases the application of these input data images, which is helpful in different fields of science such as medical imaging, microscopic imaging, remote sensing, computer vision, robotics, etc. In this paper, we describe the primary function of image fusion to improve an image’s good quality by evaluating the sharpness. Then we attempt to give an overview of multi-modal medical image fusion methods, emphasizing how we can use the DCT method for medical image fusion. This fused image provides more accurate information about the real world, which is helpful for human vision and machine perception or any further image processing tasks in the future. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image fusion

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Omnidirectional Gradient and Its Application in Stylized Edge Extraction of Infrared image

Omnidirectional Gradient and Its Application in Stylized Edg...

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

作者： Wu, Jun Wei, Xingzhan Chongqing Univ Chongqing 400044 Peoples R China Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Chongqing 400714 Peoples R China Univ Chinese Acad Sci Chongqing Sch Chongqing 400714 Peoples R China

ISBN: (纸本)9781665464680

Gradient computing is a low-level technology widely used in image processing. For large gradient magnitude, the pixel value in the field changes a lot, and for small gradient magnitude the pixel in the domain changes little. This is the basis of classical edge extraction algorithms, but it is often necessary to manually set thresholds to differentiate. This paper innovatively brings out the concept of omnidirectional gradient, which uses flexible convolution kernel radius and special law to calculate, and omnidirectional gradient pays more attention to gradient direction and analyzes the relationship and change of the gradient direction with different kernel radius. We present here an algorithm for stylized edge extraction based on omnidirectional gradient, overcoming the drawback of classical edge extraction algorithms that require manual thresholding. Experimental results show that the proposed method outperforms the classical edge extraction methods in terms of adaptive, consistent, and visually friendlier features for infrared imaging. In addition, the algorithm is fast and efficient, its result can be used as real-time input for subsequent applications.

关键词： omnidirectional gradient stylized edge extraction infrared image

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How hard are computer vision datasets? Calibrating dataset difficulty to viewing time 37

How hard are computer vision datasets? Calibrating dataset d...

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37th conference on Neural Information processing Systems (NeurIPS)

作者： Mayo, David Cummings, Jesse Lin, Xinyu Gutfreund, Dan Katz, Boris Barbu, Andrei MIT CSAIL Cambridge MA 02139 USA MIT CBMM Cambridge MA 02139 USA IBM Corp MIT IBM Watson AI Lab Cambridge MA USA

ISBN: (纸本)9781713899921

Humans outperform object recognizers despite the fact that models perform well on current datasets, including those explicitly designed to challenge machines with debiased images or distribution shift. This problem persists, in part, because we have no guidance on the absolute difficulty of an image or dataset making it hard to objectively assess progress toward human-level performance, to cover the range of human abilities, and to increase the challenge posed by a dataset. We develop a dataset difficulty metric MVT, Minimum Viewing Time, that addresses these three problems. Subjects view an image that flashes on screen and then classify the object in the image. images that require brief flashes to recognize are easy, those which require seconds of viewing are hard. We compute the imageNet and ObjectNet image difficulty distribution, which we find significantly undersamples hard images. Nearly 90% of current benchmark performance is derived from images that are easy for humans. Rather than hoping that we will make harder datasets, we can for the first time objectively guide dataset difficulty during development. We can also subset recognition performance as a function of difficulty: model performance drops precipitously while human performance remains stable. Difficulty provides a new lens through which to view model performance, one which uncovers new scaling laws: vision-language models stand out as being the most robust and human-like while all other techniques scale poorly. We release tools to automatically compute MVT, along with image sets which are tagged by difficulty. Objective image difficulty has practical applications - one can measure how hard a test set is before deploying a real-world system - and scientific applications such as discovering the neural correlates of image difficulty and enabling new object recognition techniques that eliminate the benchmark-vsreal-world performance gap.

关键词： Object recognition

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2023 Somaiya International conference on Technology and Information Management, SICTIM 2023

2023 Somaiya International Conference on Technology and Info...

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9th Somaiya International conference on Technology and Information Management, SICTIM 2023

ISBN: (纸本)9798350333299

The proceedings contain 22 papers. The topics discussed include: biometric-based unique identification for bovine animals — comparative study of various machine and deep learning computer vision methods;review of groundwater potential storage and recharge zone map delineation using statistics based hydrological and machine learning based artificial intelligent models;significance of cyber security of IoT devices in the healthcare sector;cryptocurrency with blockchain technology — a literature review;federated learning to preserve the privacy of user data;machine learning techniques for heart disease prediction;Alzheimer prediction using machine learning algorithm;design and control of omnidirectional conveyor model using image processing;and leveraging IoT technologies in retail industry to improve customer experience: current applications and future potential.

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Research on Defect Detection Algorithm for Cigarette Case Appearance Based on machine vision

Research on Defect Detection Algorithm for Cigarette Case Ap...

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International conference on Wireless Communications, Networking and applications (WCNA 2022)

作者： Huang, Chunhui Lu, Haihua Huang, Xiaoping Chen, Sixiao Shen, Miaojie Xiamen Tobacco Industry Co. Ltd. FJ Xiamen361022 China Ningbo Cigarette Factory China Tobacco Zhejiang Industrial Co. Ltd. Ningbo315504 China

ISBN: (纸本)9789819939503

In view of the demand for cigarette case appearance quality detection in the production process of cigarette enterprises, a machine vision-based method for detecting cigarette case appearance defects is proposed, and an appearance packaging quality line detection platform applicable to ultra-high-speed cigarette equipment is established. Haar wavelet de-noising is used for image pre-processing, and the decomposed low-frequency signal is reconstructed to obtain a compressed image. The double thresholding calculation in Canny operator is improved based on the Otsu algorithm, and the defect segmentation of the image is better achieved. Moreover, using Hu moments for defect feature extraction and training SVM classifier by sample features to realize the recognition and classification of cigarette case appearance defects. The ZB48 ultra-high-speed packaging machine was used as the test object to build a detection system, and the experimental results show that the algorithm can effectively achieve the online detection of cigarette case appearance defects. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Defects

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A Survey on Few-Shot Techniques in the Context of Computer vision applications Based on Deep Learning 21st

A Survey on Few-Shot Techniques in the Context of Computer V...

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21st International conference on image Analysis and processing (ICIAP)

作者： San-Emeterio, Miguel G. Atos Res & Innovat Madrid 28037 Spain

ISBN: (纸本)9783031133244;9783031133237

This review article about Few-Shot Learning techniques is focused on Computer vision applications based on Deep Convolutional Neural Networks. A general discussion about Few-Shot Learning is given, featuring a context-constrained description, a short list of applications, a description of a couple of commonly used techniques and a discussion of the most used benchmarks for FSL computer vision applications. In addition, the paper features a few examples of recent publications in which FSL techniques are used for training models in the context of Human Behaviour Analysis and Smart City Environment Safety. These examples give some insight about the performance of state-of-the-art FSL algorithms, what metrics do they achieve, and how many samples are needed for accomplishing that.

关键词： Few-Shot Learning Deep Learning Computer vision Human Behaviour Analysis Smart City Environment Safety

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