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

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Software to Assist Visually Impaired People During the Craps Game Using machine Learning on Python Platform 2nd

Software to Assist Visually Impaired People During the Craps...

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2nd International conference on Smart Technologies, Systems and applications (SmartTech-IC)

作者： Hernandez Diaz, Nicolas Penaloza, Yersica C. Yuliana Rios, Y. Magre Colorado, Luz A. Univ Tecnol Bolivar Parque Ind & Tecnol Carlos Velez Pombo Cartagena De Indias Colombia Univ Pamplona Km 1 Via Bucaramanga Pamplona Colombia

ISBN: (纸本)9783030991708;9783030991692

Pattern recognition is a prominent area of research in computer vision, where different methods have been proposed in the last 50 years. This work presents the development of a Python API to identify the result of two six-sided dice used in the game called "Craps" as a no-controlled environment to help visually impaired people. The software is structured in four stages. The first one is capturing images through a device with a digital camera connected to the web via IP address. The second stage corresponds to the captured image processing;it is necessary to establish a standard image size and resize and equalize the digitized image. The third stage seeks to segment the object of study by artificial vision techniques to identify the result of the dice after being thrown. Finally, the fourth stage is to interpret the result and play it through a speaker. The expected possible result is a system that integrates the four stages mentioned above through an intuitive and accessible low-cost Python API, mainly aimed at visually impaired people.

关键词： Craps game Visually impaired people Non controlled environment Python API Artificial vision techniques image processing

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ComAI: Enabling Lightweight, Collaborative Intelligence by Retrofitting vision DNNs 41

ComAI: Enabling Lightweight, Collaborative Intelligence by R...

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41st IEEE conference on Computer Communications (IEEE INFOCOM)

作者： Jayarajah, Kasthuri Wanniarachchige, Dhanuja Abdelzaher, Tarek Misra, Archan Univ Maryland Baltimore Cty Baltimore MD 21228 USA Singapore Management Univ Singapore Singapore Univ Illinois Champaign IL USA

ISBN: (纸本)9781665458221

While Deep Neural Network (DNN) models have transformed machine vision capabilities, their extremely high computational complexity and model sizes present a formidable deployment roadblock for AIoT applications. We show that the complexity-vs-accuracy-vs-communication tradeoffs for such DNN models can be significantly addressed via a novel, lightweight form of "collaborative machine intelligence" that requires only runtime changes to the inference process. In our proposed approach, called ComAI, the DNN pipelines of different vision sensors share intermediate processing state with one another, effectively providing hints about objects located within their mutually-overlapping Field-of-Views (FoVs). CoMAI uses two novel techniques: (a) a secondary shallow ML model that uses features from early layers of a peer DNN to predict object confidence values in the image, and (b) a pipelined sharing of such confidence values, by collaborators, that is then used to bias a reference DNN's outputs. We demonstrate that CoMAI (a) can boost accuracy (recall) of DNN inference by 20-50%, (b) works across heterogeneous DNN models and deployments, and (c) incurs negligible processing, bandwidth and processing overheads compared to non-collaborative baselines.

关键词： Deep learning Runtime Computational modeling machine vision conferences Pipelines Neural networks

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Enhancing Insect image Recognition with Sand Cat Swarm Optimization with Deep Feature Extraction 2

Enhancing Insect Image Recognition with Sand Cat Swarm Optim...

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2nd IEEE International conference on Advances in Information Technology, ICAIT 2024

作者： Yogaraja, C.A. Priyanka, C. Priyadharshini, S.P. Babu, J. Jagan Rajagopal, S. Poorani, S. Ramco Institute of Technology Rajapalayam India School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore Campus India St. Joseph's College of Engineering OMR Chennai119 India Department of ECE R.M.D Engineering College Kavaraipettai India Department of IT National Engineering College Kovilppati India Kongu Engineering College Perundurai Erode India

ISBN: (纸本)9798350383867

Insect image recognition (IIR) is a specified field in machine learning (ML) and computer vision that efforts to automatically recognise and detection of insect species utilizing visual data attained from images. Leveraging deep learning (DL) techniques, convolutional neural network (CNN), and image processing enables exact and effective classification of a huge range of insect species. IIR has many applications that range from biodiversity observation and pest control in farming in order to entomological study and disease vector identification. The unique procedure of insect detection, it permits experts, entomologists, and ecologists to attain valuable insights, make knowledgeable decisions, and update challenges that contain insect detection which contribute to more real conservation efforts, ecological research, and agriculture management. This work contains a framework of Insect image Recognition with Sand Cat Swarm Optimization with Deep Feature Extraction (IIR-SCSODFR) model. The developed IIR-SCSODFR method incorporates a complete procedure that starts with Gaussian filtering-based image pre-processing, improving image quality and decreasing noise in order to offer a clear basis for precise analysis. Deep feature extraction is carried out to capture intricate visual designs essential to numerous insect species, using advanced models for inclusive insect characterization. For exact insect detection, Long Short-Term Memory (LSTM) systems are employed, proficient in demonstrating time-based needs in image sequences. Besides, model leverages SCSO technique for parameter tuning, adjusting model's performance to adjust unique features of a dataset. The proposed IIR-SCSODFR model signifies an important leap forward in IIR, and provides a forceful and precise solution for automated identification of various insect species. © 2024 IEEE.

关键词： Convolutional neural networks

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Hand Gesture-Based AI System for Accessing Windows applications 4th

Hand Gesture-Based AI System for Accessing Windows Applicati...

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4th International conference on image processing and Capsule Networks, ICIPCN 2023

作者： Palivela, Lakshmi Harika Premanand, V. Begum, Afruza School of Computer Science Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819970926

Actions speak more than words. In the context of the above statement, the importance of gestures and using them to control a system has become popular. The hand gesture recognition system for opening applications in Windows is a computer vision application that uses a webcam and machine learning models to recognize hand gestures and open specific applications. This system allows users to control their computers using simple hand gestures instead of relying on traditional input devices such as keyboards or mouse. The system uses the Tensor Flow and Media-Pipe libraries for training and detecting hand gestures, respectively. The trained machine learning model recognizes the gestures, and specific applications are opened based on the recognized gesture. The system has potential applications in accessibility, gaming, and virtual reality. In this work, we will discuss potential applications of the hand gesture recognition system for opening applications in Windows, and the second proposed system, which may also act as the control system of PowerPoint presentations, can be made easy and convenient using these gestures-based control. Thus, the proposed model improves the interaction between the human and the computer. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Palmprint recognition

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Cat-CNN: Human Eye Cataract Detection from Color Fundus Photograph with Deep CNN with Optimized Cascaded Network

Cat-CNN: Human Eye Cataract Detection from Color Fundus Phot...

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2024 IEEE International conference on Signal processing, Information, Communication and Systems, SPICSCON 2024

作者： Islam, Md Sariful Bappy, Md Tusher Ahmad Shawon, Jubayer Ahmed Hasan, Mehedi Rahman, Wahidur Akter, Lija Azad, Mir Mohammad Dept. of Computer Science and Engineering Uttara University Uttara Dhaka Bangladesh Dept. of ICE Bangladesh Army University of Engineering and Technology Natore Bangladesh Dept. of Computer Science and Engineering Inpendent Researcher Dhaka Bangladesh

ISBN: (纸本)9798331510213

Cataracts are clouding of the lens in the eye, leading to loss of vision that can progress to blindness if not treated. This paper proposed a new method for automatic cataract detection using color fundus images and deep learning methods. A dataset of 1,105 color fundus images labeled by expert ophthalmologists was used in this process. We used seven pre-trained CNNs (DenseNet121, EfficientNetB0, MobileNetV2, InceptionV3, Xception, ResNet50, VGG16, and VGG19) for feature extraction before reducing the extracted features using PCA. We used the following combination of machine learning classifiers: SVC, RF, Decision Tree, Gaussian Naive Bayes, XGBoost, K-Nearest Neighbors, and Logistic Regression. For evaluating the models’ performance, we used accuracy, precision, recall, F1-score, and computational efficiency. For all metrics, MobileNetV2 with Random Forest achieved perfect scores: 100% accuracy, precision, recall, and F1-score, with an average processing time of 669 ms ± 28.8 ms. Thus, it can be applied in real-time applications. EfficientNetB0 with SVC gave an average accuracy of 87.33%, with the rest of the precision and recall metrics above 86%. Then, ResNet50, VGG16, and VGG19 followed with high accuracies between the range of 89.64% to 90.50%. It systemizes the proper choice of architectures of CNNs and classifiers, making the system both accurate and computationally efficient. Future work will include augmentation of the dataset, real-time support in the clinical setting, and advanced techniques for image preprocessing, generative adversarial networks. In addition, the development of an automated annotation tool, improvement of explainable AI, will further improve the deployment of robust AI systems in early diagnosis of cataracts, enhancing the outcome for patients. © 2024 IEEE.

关键词： Cataract detection CNN color fundus photography deep learning feature extraction image classification machine learning ophthalmology PCA

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A machine vision Measurement Method for Large Plates Based on Reference Point Assistance 5

A Machine Vision Measurement Method for Large Plates Based o...

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5th International conference on Electronics and Electrical Engineering Technology (EEET)

作者： Ji, Tianyi Zhao, Zhiwei Zhao, Ning Southeast Univ Coll Elect Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9798350320428

In this work, a coordinate positioning method based on machine vision with reference point assistance is proposed to apply to the size measurement for large rectangular plate parts. This method first obtains the coordinate information of the reference point through circular detection, and uses the line detection and mean-shift clustering algorithm to fit the plate vertex information. Then the pixel coordinate relationship between the reference point and the plate vertex is used to solve the world coordinate of the plate vertex. Finally, the world coordinate of the plate vertex is used to calculate the length and width dimensions of the plate. Instead of other methods which unify coordinate system by moving cameras, this method uses reference point assistance system on measuring plate to unify the coordinate system, simplifying the difficulty of the unification of coordinate system and ensuring accuracy. Effectiveness of such method is verified by measuring rectangular plates with scales from centimeters to meters. The results showed that the measurement error could be controlled within 5 millimeters. In practical applications, the measurement accuracy has been much higher than the processing accuracy during the plate manufacturing, meeting the needs of size measurement.

关键词： reference points size measurement machine vision image processing

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Picture processing Optimization Technology Based on Mask R-CNN Algorithm 3

Picture Processing Optimization Technology Based on Mask R-C...

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3rd International conference on machine Learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

作者： Tan, Guihua Liu, Yiran College of Information Engineering Loudi Xiaoxiang Vocational College Hunan Loudi417000 China

Picture processing is applied in all kind of fields, such as space science research, medical imaging, photography art. Because the human vision system is a complex nonlinear dynamic system, the traditional image enhancement methods often can not meet the requirements of real-time in practical applications. In the face of a large number of images, it is of great practical significance to obtain practical information from these data. With the rapid development of computer CnTech, image compression coding CnTech has also made great progress. And through the Mask R-CNN algorithm for effective segmentation and image recognition, can help people face massive image information, to their own needs as the goal, to achieve efficient retrieval, analysis, induction. The traditional methods of image classification by manually selecting features or manually extracting template matching have some defects. In this paper, a Mask R-CNN image optimization processing CnTech is proposed, which can accurately identify images and has higher accuracy for image feature extraction. It is meaningful to achieve automatic and accurate segmentation of microscopic images. Through training on COCO data set, it is verified through experiments that there are problems in image segmentation and recognition. In Mask R-CNN model, resource consumption is reduced and the efficiency of image segmentation and recognition is improved. Compared with the current cutting-edge algorithms, the image object detection on the strength of Mask R-CNN model has significant advantages. © 2023 The Authors. Published by Elsevier B.V.

关键词： image retrieval

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Multilevel Crop image Segmentation Using Firefly Algorithm and Recursive Minimum Cross Entropy 4th

Multilevel Crop Image Segmentation Using Firefly Algorithm a...

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4th International conference on machine Intelligence and Signal processing, MISP 2022

作者： Kumar, Arun Kumar, A. Vishwakarma, Amit PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur India

ISBN: (纸本)9789819900466

image segmentation plays an important role in computer vision technology and agriculture is one of their applications. The crop images present near the vicinity are complex and dense. Hence, multilevel thresholding of such crop images is a tedious task. In this paper, we propose multilevel thresholding of crop images using recursive minimum cross entropy and firefly algorithm. The firefly is based on the social behavior of the swarm of fireflies and bioluminescent information-sharing phenomena. It is a swarm-based algorithm, which offers a better search mechanism to find the optimum threshold value. The performance of the proposed method is observed over ten complex background crop images and compared with the wind-driven optimization algorithm. The better fidelity parameters evidence the superiority of the proposed method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image segmentation

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Enhancing Human-Computer Interaction: Hand Detection for Air Writing Utilizing NumPy and OpenCV 3

Enhancing Human-Computer Interaction: Hand Detection for Air...

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3rd International conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Shukla, Pranjal Das, Prasenjit Chandigarh University Department of Cse Punjab Mohali India

ISBN: (纸本)9798350342338

In recent years, there has been a remarkable increase in interest and challenges in image processing and pattern recognition, specifically in the context of air writing. This exciting research area has significant potential to advance automation processes and improve human-machine interfaces in various applications. The emergence of faster computers, affordable high-performance video cameras, and the need for automated analysis of videos has led to an increase in the popularity of object tracking, a critical task in computer vision. The process of video analysis typically encompasses object detection, tracking, and behavior analysis. Object tracking involves four main aspects choosing the suitable object representation, selecting features for tracking, detecting the object, and tracking the object. Object tracking algorithms find applications in different domains, including vehicle navigation, video indexing, and surveillance that are automated. The objective of the paper is to create a software application for smart wearable devices that utilizes computer vision to track finger gestures in the air, functioning as a motion-to-text converter for air-writing. The technology will facilitate communication for people by enabling them to generate text for multiple purposes, like sending emails and messages, through intermittent gestures. This is a productive means of communication that curbs the usage of laptops and mobiles, making it particularly beneficial for individuals who are deaf. © 2023 IEEE.

关键词： Air Writing Computer vision Digital sketching Gesture Recognition Hand Detection Hand Tracking Interactive whiteboard

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Rapid detection of imperfect wheat grains based on deep learning technique 2

Rapid detection of imperfect wheat grains based on deep lear...

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2nd International conference on image processing, Computer vision and machine Learning, ICICML 2023

作者： Hu, Kui Zhang, Hongming Lyu, Bo Shen, Yongcai Fu, Jia Wang, Fudi Lin, Zichao Anhui University Institute of Physical Science and Information Technology Hefei China Chinese Academy of Sciences Institute of Plasma Physics HFIPS Hefei China University of Science and Technology of China Science Island Branch Graduate School Hefei China Hefei Normal University Hefei China

ISBN: (纸本)9798350331417

The detection and identification of imperfect wheat grains are of great significance in evaluating their quality. Manual inspection and separation of imperfect grains in wheat are time-consuming and expensive. Therefore, there is a need for a fast, automated, and accurate method to detect imperfect grains in wheat. In this study, we created an image acquisition platform to capture images of wheat grains. Each image was labeled as either spotted, sprouted, moldy, broken, or perfect grains. To balance calculation efficiency and prediction accuracy, it is necessary to identify a suitable model. Thus, we applied several mainstream deep-learning algorithms, including ResNet50, ResNet50 with an attention module, MobileNetv1, MobileNetv2, MobileNetv3-small, and MobileNetv3-large, to construct a model suitable for practical application. It was found that the MobileNetV3-Small model achieved a good balance between computational efficiency and prediction accuracy. The model based on the MobileNetV3-Small architecture achieved high accuracy, recall rate, and F1-score, all exceeding 96%. Although the MobileNetV3-Small model's accuracy is slightly lower than that of the ResNet50 model with an added attention mechanism, it significantly reduces computational costs, improves detection speed, and cuts prediction time by 50%. Compared with other models, the MobileNetv3-small-based model is more suitable for practical applications due to its advantages of high speed, high precision, and stable prediction performance. This study can provide technical guidance for intelligent recognition and detection of wheat grains in the future. © 2023 IEEE.

关键词： deep learning image pattern recognition MobileNetV3-small Wheat imperfect grains

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