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Integrating Autoencoder-Based Hybrid Models into Cervical Carcinoma Prediction from Liquid-Based Cytology

Integrating Autoencoder-Based Hybrid Models into Cervical Ca...

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12th International conference on Data Science, Technology and applications, DATA 2023

作者： Idlahcen, Ferdaous Idri, Ali Zerouaoui, Hasnae Al Khwarizmi College of Computing Mohammed VI Polytechnic University Ben Guerir43150 Morocco Software Project Management Research Team ENSIAS Mohammed V University Rabat10000 Morocco

ISBN: (纸本)9789897586644

Artificial intelligence (AI)-assisted cervical cytology is poised to enhance sensitivity whilst lessening bias, labor, and time expenses. It typically involves image processing and deep learning to automatically recognize pre-cancerous lesions on a given whole-slide image (WSI) prior to lethal invasive cancer development. Here, we introduce autoencoder (AE)-based hybrid models for cervical carcinoma prediction on the Mendeleyliquid-based cytology dataset. This is built on fourteen combinations of AE, DenseNet-201, and six state-ofthe-art classifiers: adaptive boosting (AdaBoost), support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), k-nearest neighbors (k-NN), and random forest (RF). As empirical evaluations, four performance metrics, Scott-Knott (SK), and Borda count voting scheme, were performed. The AE-based hybrid models integrating AdaBoost, MLP, and RF as classifiers are among the top-ranked architectures, with respective accuracy values of 99.30, 99.20, and 98.48%. Yet, DenseNet-201 remains a solid option when adopting an end-to-end training strategy. © 2023 by SCITEPRESS–Science and Technology Publications, Lda.

关键词： Support vector machines

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Research on Unmarked Motion Action Recognition Technology Based on Computer vision

Research on Unmarked Motion Action Recognition Technology Ba...

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image processing, Electronics and Computers (IPEC), Asia-Pacific conference on

作者： Lun Zhao Shandong Vocational College of Science and Technology Weifang China

ISBN: (数字)9798350374407

ISBN: (纸本)9798350374414

The main purpose of this study is to explore the issues of real-time, accurate, and unmarked recognition of sports movements in recent years. By reviewing the relevant research on machine learning or deep learning for specific sports or target actions based on computer vision image data input, the aim is to provide references for the application of unmarked motion capture technology in the field of sports motion recognition. The research employed a literature review methodology, conducting searches in six databases, namely Web of Science, PubMed, Scopus, Google Scholar, IEEE Xplore, and China National Knowledge Infrastructure (CNKI), covering publications from January 2000 to June 2020. Through boolean logic operations on the retrieved literature, key information such as first author/publication year, types/targets of motion, participant information, camera parameters, image feature extraction techniques, action recognition algorithms, evaluation methods for action recognition quality, training and validation methods for image data, and performance metrics for action recognition were extracted. After screening, a total of 23 articles were included in the study. The findings revealed that $39 \%$ of the studies utilized machine learning algorithms based on support vector machines, while $35 \%$ employed deep learning algorithms based on convolutional neural networks. Commonly used evaluation metrics for action recognition quality included classification accuracy, confusion matrix, and displacement error. The development of computer vision motion capture, models, and algorithms demonstrated promising applications in areas such as action technique recognition and sports performance analysis. Traditional machine learning algorithms like support vector machines and principal component analysis remain dominant in action recognition technology; however, in certain scenarios, the performance of deep learning algorithms surpassed that of traditional machine learning methods.

关键词： Deep learning Support vector machines Computer vision machine learning algorithms image recognition Cameras Feature extraction Motion capture Real-time systems Sports

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An Improved Depth image Clustering Algorithm Based on Spatial Information

An Improved Depth Image Clustering Algorithm Based on Spatia...

引用

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

作者： Li Yan Shan Su Xiao Hu Yuquan Zhou Changjun Chen School of Geodesy and Geomatics Wuhan University Wuhan China Hubei Luojia Laboratory Wuhan China

Depth image spatial clustering is an important task in the fields of computer vision and machine learning, aiming to group pixels or point cloud data of depth images into clusters with similar features. This is crucial for tasks such as object recognition, scene segmentation, 3D reconstruction, and other applications. The K-means algorithm is a commonly used clustering method that divides data points into K clusters, ensuring that each data point belongs to the cluster whose center is closest to it. However, traditional K-means algorithms face several challenges in depth image spatial clustering, such as sensitivity to the choice of K and the influence of cluster center initialization on the results. In this study, we propose an improved depth image clustering method based on spatial information. The method includes depth image edge extraction, intra-edge region growing, unclassified point re-clustering, and connected region merging. Through experiments conducted on datasets from TUM, Bonn, and our collected data, our algorithm demonstrates superior performance in depth image clustering tasks, higher clustering accuracy, fewer unclassified points, and faster convergence speed.

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Computer vision-Based Hand Recognition and Gesture Control for Dino Games

Computer Vision-Based Hand Recognition and Gesture Control f...

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Emerging Smart Computing and Informatics (ESCI), conference on

作者： Balumuri Dinesh C V Naveeth Reddy N. Senthamilarasi Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Hand Recognition and Gesture Control For Dino Game Using Computer vision to control the popular Chrome Dino game using hand recognition and gesture control through computer vision techniques. The system leverages real-time image processing and machine learning algorithms to detect and interpret hand gestures, allowing for an intuitive and interactive gaming experience. The proposed method utilizes a webcam to capture live video feed, from which hand landmarks are extracted using a pre-trained neural network model. Various hand gestures, such as swipe and hold, are then mapped to corresponding in-game actions such as jumping and ducking. This gesture-based control mechanism not only enhances user engagement but also demonstrates the potential of computer vision in creating touchless interfaces for gaming applications.

关键词： Hands Computer vision machine learning algorithms Sensitivity Webcams Scalability Lighting Games Streaming media Real-time systems

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Smart Soft Sensor for Froth flotation quality monitoring using pre-trained feature extractor and vision Transformer viT

Smart Soft Sensor for Froth flotation quality monitoring usi...

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International IEEE conference on Signal-image Technologies and Internet-Based System

作者： Oussama Hasidi Ahmed Bendaouia El Hassan Abdelwahed El Alaoui-Chrifi Moulay Abdellah Abdelmalek Boussetta Intissar Benzakour François Bourzeix Kaotar Oubouhss LISI Laboratory Computer Science Departement UCA Analytics Lab Mohammed VI Polytechnic University Marrakech Morocco LISI Laboratory Computer Science Departement Cadi Ayyad University Marrakech Morocco R&D and Engineering center Reminex Managem Group Marrakech Morocco Analytics Lab Mohammed VI Polytechnic University Rabat Morocco Department of Electrical and Industrial Engineering National School of Applied Science UAE Tangier Morocco

Smart sensor technologies have gained prominence in the field of machine vision-based applications. Within this context, the utilization of machine vision systems for monitoring and controlling flotation plants has become increasingly prevalent. This paper addresses the critical need for accurate froth image classification in the development of an online machine vision-based control system. This paper explores the recent advancements in machine learning, with a focus on deep learning techniques. Specifically, it introduces the application of a vision Transformer (viT) model as an alternative to the traditional Convolutional Neural Network (CNN) for classifying froth images collected from an industrial lead flotation circuit. The results of this research demonstrate the remarkable performance of the viT model, achieving an impressive overall accuracy rate of 96% when combined with pre-trained feature extractor models. This achievement surpasses the capabilities of conventional artificial neural network (CNN) classifiers of the state of art in terms of both accuracy and computational efficiency. These findings underscore the promising potential of deep learning neural networks in froth image analysis, highlighting their significance in the advancement of smart sensor-based machine vision systems within the field of flotation plant control.

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Comparing Bag of features Techniques for image Retrieval 6

Comparing Bag of features Techniques for Image Retrieval

引用

6th International conference on Intelligent Computing and Control Systems, ICICCS 2022

作者： Ali, Roohi Maheshwari, Manish Makhanlal Chaturvedi University of Journalism and Communication Department of Computer Science and Applications Bhopal India

ISBN: (纸本)9781665410359

Bag of Feature (BOF) is a customizable technique that provides a free hand on the type of feature selection for creating visual-word vocabulary that fits better for the various image retrieval applications and this is the main advantage of using this technique to create a customizable feature mix of local and global features. One of the two prominent techniques for creating Bag of features in image processing and computer vision is through grid formation and custom extractor. Therefore, selection of type of feature used for Bag creation is very basic and critical one as whole process of Retrieval depends upon it. Feature Color is the well known one to be used most. In this paper Lab color space is used for creating one of the feature Bag as Lab Colorbag. While two other KAZEbag and SURFbag are created using different descriptor methods namely SURF and Kaze. For unfolding different hidden patters out of collected visual words some machine learning intelligence is adopted like support Vector machine is used here for clustering and creating cluster centers. MATLAB R2021a is used for all the implementation. Along with the Dataset of Natural images with ten categories is used for evaluation. All the three created Bags are compared on the different parameters such as number of features extracted, strongest feature metric, word frequency and average precision. © 2022 IEEE.

关键词： image retrieval

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Convolutional Neural Network for image Classification Research - Based on VGG16

Convolutional Neural Network for Image Classification Resear...

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

作者： Ruoxi Yang College of Big Data Zhuhai College of Science and Technology Zhuhai China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

In recent years, convolutional neural networks (CNNs) have increasingly become the predominant approach for image classification, demonstrating remarkable performance in this domain. Notably, VGG16 has gained widespread adoption due to its deep architecture and robust feature extraction capabilities. Consequently, this paper investigates the efficacy of employing the VGG16 convolutional neural network model for image classification on the CIFAR-10 dataset. The architecture of the VGG16 model and its distinctive application characteristics are first introduced, followed by an exploration of key steps including data preprocessing, model training, and hyperparameter tuning. 99.4% accuracy on the training set and 90.6% accuracy on the test set were attained after the training procedure was optimized. To improve the model's classification performance even more, this paper identifies limitations inherent in the current framework and proposes optimization strategies such as incorporating attention mechanisms and transfer learning. These strategies provide valuable insights for future applications of deep learning techniques on small-scale image datasets.

关键词： Training Accuracy Attention mechanisms Computational modeling Transfer learning Data models Convolutional neural networks Tuning Optimization image classification

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Comparison of Large Language And vision Models on Representative Downstream Tasks

Comparison of Large Language And Vision Models on Representa...

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

作者： Huitong Chen International College Beijing University of Posts and Telecommunications Beijing China

The success of large model pre-training in the fields of natural language processing and computer vision has been remarkable. By pre-training on large-scale data, these models can learn richer semantic and visual representations to achieve better performance on various tasks. performance. In order to enable more researchers and practitioners to fully understand the advantages and applicable fields of different models, this paper summarizes the current large model pre-training methods, including vision Transformer (viT), Pre-trained Transformer (GPT), Text-to-Text Transfer Transformer (T5), Bidirectional Encoder Representations from Transformers (BERT) and Contrastive Language-image Pretraining (CLIP) are all included, and discusses their principles and advantages are presented. At the same time, compare their results are compared on different datasets, and compare the results of combining them with different other techniques. The final research results show that viT has advantages over other models in image classification, GPT performs best in generative tasks compared to other models, and T5 has achieved significant results in various text tasks compared to other models, while BERT performs best in various discriminative tasks compared to other models. CLIP is suitable for image and text-related tasks, including image classification, text retrieval, visual question answering, etc. Finally, applications of various models are summarized, providing direction for researchers and practitioners.

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Interpreting and understanding the image using deep learning and imprecise information from the monitoring system

Interpreting and understanding the image using deep learning...

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2023 IEEE International conference on Big Data, BigData 2023

作者： Kubanek, Mariusz Bobulski, Janusz Karbowiak, Lukasz Czestochowa University of Technology Department of Computer Science Czestochowa Poland

ISBN: (纸本)9798350324457

The interpretation and understanding of images using deep learning algorithms in monitoring systems is an important area of research in computer vision. In this paper, we propose a novel approach for interpreting and understanding images of human behaviour captured from cameras and drones in monitoring systems. In the work, we presented a pedestrian detection method. This is the first element of a larger system of interpreting the behaviour of traffic participants. We begin by pre-processing the images to remove noise and unwanted artefacts. We then use labelled and unlabelled data to train a deep learning algorithm to accurately classify and predict human behaviour. To account for imprecise information, we incorporate probabilistic models into the algorithm to allow it to make predictions with uncertainty. To evaluate the performance of our proposed approach, we use standard metrics, such as precision, recall, and F1 score, and compare our results to existing methods. We also identify the limitations of existing approaches and propose new methods for interpreting and understanding images that can improve their accuracy and reliability. Our proposed approach has the potential to provide a more accurate and robust method for interpreting and understanding images of human behaviour in monitoring systems. It can be used in various applications like traffic monitoring, security systems, and surveillance. Our results demonstrate the importance of incorporating imprecise information into deep learning algorithms to improve their performance and highlight the potential for future research in this area. © 2023 IEEE.

关键词： autonomous car computer vision pedestrian safety smart city

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Closed-loop active object recognition with constrained illumination power

Closed-loop active object recognition with constrained illum...

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conference on Real-Time image processing and Deep Learning

作者： Noom, Jacques Soloviev, Oleg Smith, Carlas Verhaegen, Michel Delft Univ Technol Delft Ctr Syst & Control Mekelweg 2 NL-2628 CD Delft Netherlands Flexible Opt BV Polakweg 10-11 NL-2288 GG Rijswijk Netherlands

ISBN: (纸本)9781510650817;9781510650800

Some applications require high level of image-based classification certainty while keeping the total illumination energy as low as possible. Examples are minimally invasive visual inspection in Industry 4.0, and medical imaging systems such as computed tomography, in which the radiation dose should be kept "as low as is reasonably achievable". We introduce a sequential object recognition scheme aimed at minimizing phototoxicity or bleaching while achieving a predefined level of decision accuracy. The novel online procedure relies on approximate weighted Bhattacharyya coefficients for determination of future inputs. Simulation results on the MNIST handwritten digit database show how the total illumination energy is decreased with respect to a detection scheme using constant illumination.

关键词： Active fault diagnosis Auxiliary signal design machine vision Computational Tomography Medical imaging Industry 4.0

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