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Facial Expression Recognition: A Comparison with Different Classical and Deep Learning Methods 12

Facial Expression Recognition: A Comparison with Different C...

引用

12th Iranian/2nd international conference on Machine vision and image processing, MVIP 2022

作者： Hemmatiyan-Larki, Amir Mohammad Rafiee-Karkevandi, Fatemeh Yazdian-Dehkordi, Mahdi Yazd University Computer-Eng. Department Yazd Iran

ISBN: (纸本)9781665412162

Facial Expression Recognition (FER), also known as Facial Emotion Recognition, is an active topic in computer vision and machine learning fields. This paper analyzes different feature extraction and classification methods to propose an efficient facial expression recognition system. We have studied several feature extraction methods, including Histogram of Oriented Gradients (HOG), face-encoding, and the features extracted by a VGG16 Network. For classification, different classical classifiers, including Support Vector Machines (SVM), Adaptive Boosting (AdaBoost), and Logistic Regression, are evaluated with these features. Besides, we have trained a ResNet50 model from scratch and also tuned a ResNet50 which is pre-trained on VGGFace2 dataset. Finally, a part-based ensemble classifier is also proposed by focusing on different parts of face images. The experimental results provided on FER-2013 Dataset show that the tuned model of ResNet50 with a complete image of face, achieves higher performance than the other methods. © 2022 IEEE.

关键词： Support vector machines

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Mechanism Action Detection Based on YOLO 2

Mechanism Action Detection Based on YOLO

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2nd international conference on image processing, computer vision and Machine Learning, ICICML 2023

作者： Wang, Yunlong Liu, Xiang Yu, Chuang Guo, Zhaowei 713th Research Institute of China State Shipbuilding Corportion Limited Zhengzhou China

ISBN: (纸本)9798350331417

The field of mechanism motion detection has significant research value in the fields of mechanism motion characteristic analysis, information collection, and mechanism fault prevention and diagnosis. The traditional method of detecting the motion of mechanical mechanisms relies on contact sensors to transmit the execution of the motion and reflect the motion status of the mechanism. The traditional contact type mechanism still faces the following challenges in motion detection: (1) under certain operating conditions, the mechanism does not allow the installation of contact type sensors for motion detection;(2) The mechanism action has a measurement dead angle and cannot obtain the execution status of the specified position action;(3) Built in contact sensors often require a pre designed structure to accommodate the sensor, which can make it difficult to temporarily change the measurement method. This article proposes a binocular vision mechanism motion detection technology based on YOLO v5, which utilizes the YOLO v5 algorithm combined with binocular vision technology to achieve motion detection. YOLO v5 obtains feature pixel plane position information by prefabricating feature information. This article utilizes information such as internal parameters and focal length of binocular cameras to obtain spatial three-dimensional coordinates through the similarity triangle method. Ultimately, this method can obtain the motion of the mechanism. The experimental results of YOLO v5 are based on a training method from scratch, without using pre trained weights. This detection method was tested on Nvidia's Jetson Xavier NX system and achieved a detection speed of 30 frames per second. © 2023 IEEE.

关键词： binocular vision detection motion detection YOLO v5

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Watershed Segmentation with Gradient Vista and SOBEL-Crafted Contours for Analyzing EMDS-6 Microbial Environmental Dataset 2

Watershed Segmentation with Gradient Vista and SOBEL-Crafted...

引用

2nd IEEE international conference on Contemporary Computing and Communications, InC4 2024

作者： Mahalakshmi Priya, R. Sumathi, M. Tamilnadu Madurai India Sri Meenakshi Govt. Arts College for Women PG & Research Dept. of Computer Science Tamilnadu Madurai India

ISBN: (纸本)9798350383652

The aim of this research is to present a comprehensive framework for image analysis applied to the EMDS-6 dataset to help identify microorganisms in environmental studies. In order to identify and quantify diverse environmental microorganisms, a reliable methodology is required. Even under the heterogeneous imaging conditions inherent in the EMDS-6 dataset, the proposed methodology proved effective in delineating microorganism boundaries and isolating clustered entities. A framework for image analysis of the EMDS-6 dataset was developed to reliably recognize and quantify a variety of environmental microorganisms. Pre-processing of the images, precision edge detection with SOBEL contours, and advanced segmentation of Graduate Vista Watersheds are among the methods used. Using this combination of techniques, microorganism boundaries can be precisely defined and clustered entities can be isolated across heterogeneous EMDS-6 imaging conditions. According to our methodology, the pipeline classified microorganisms with 93% accuracy, focused edges with 55% Peak Signal-to-Noise Ratio, and segmented objects with 95% Jaccard Index. Compared to baseline approaches, qualitative results indicate considerable improvements in signal localization, feature representation, and region identification. To automate the recognition of species and parsing of complex microbiology images, customized computer vision and stability-driven propagation methods hold great promise. Smart imaging systems will soon be capable of screening environmental samples rapidly, identifying salient organisms, and adapting to detect emerging agents based on this knowledge. © 2024 IEEE.

关键词： Invertebrates

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Enhancing Cartoon Recognition in Real Time: Comparative Analysis of CNN, ResNet50, and VGG16 Deep Learning Models 2

Enhancing Cartoon Recognition in Real Time: Comparative Anal...

引用

2nd international conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Siri, Chinta Sai VIT-AP University School of Computer Science and Engineering Amaravati India

ISBN: (纸本)9798350325799

Cartoon recognition has gained significant importance in recent times due to its applications in the entertainment industry. Cartoon recognition is an important task with various applications in the fields of animation, gaming, and image processing. Cartoon recognition is a challenging task in computer vision due to the diversity and complexity of cartoon images. The study aims to evaluate the performance of these models on a dataset of cartoon images and compare their accuracy, precision, and recall. In this work, A novel approach for cartoon recognition is proposed using the fusion of three deep learning models: CNN, ResNet50, and VGG16. The performance of this approach is compared with other deep learning models such as MobileNet, ResNet101, VGG19, MobileNet V2, DenseNet121, and Inception-GoogLeNet model, using a dataset of 216 images of 4 cartoon characters (daisy duck, goofy, Mickey, pooh). The training and testing of these models took place within a Google Collaboratory environment, utilizing the TensorFlow and Keras software. The comparative analysis considered various parameters to identify the algorithm best suited for the given environment. Additionally, the accuracy of the model was verified through testing on random images, confirming its efficacy. This study offers valuable insights into the effectiveness of deep learning models in cartoon recognition and emphasizes the potential benefits of fusion models in enhancing performance. © 2023 IEEE.

关键词： Software testing

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Anomaly Alert System using CCTV surveillance 2

Anomaly Alert System using CCTV surveillance

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2nd IEEE Mysore Sub Section international conference, MysuruCon 2022

作者： Manu, Y.M. Ravikumar, G.K. Shashikala, S.V. Adichunchanagiri University Bgs Institute of Technology Department of R&d B.G Nagara Mandya India Adichunchanagiri University Bgs Institute of Technology Department of Computer Science and Engineering B.G Nagara Mandya India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

Even with its probable risks and capacity to quickly spread, fire poses a huge danger to life. Fire detection techniques, particularly vision-based mechanisms, identify flames before any damage or devastation occurs. We suggested a fire detection method image processing and analysis approaches in this research, which is appropriate with surveillance systems such as CCTV. We'll make a fire sensor out of a Raspberry Pi and a Raspberry Pi camera, then use image processing to acquire real-time recognition results and evaluate the accurate fire image. The fire image will then be examined with image processing, and the outcome will be sent to the user as a caution (alert) that there is a fire in the area. © 2022 IEEE.

关键词： Fires

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Designing an Improved Deep Learning-Based Classifier for Breast Cancer Identification in Histopathology images 12

Designing an Improved Deep Learning-Based Classifier for Bre...

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12th Iranian/2nd international conference on Machine vision and image processing, MVIP 2022

作者： Babaahmadi, Amirreza Khalafi, Sahar Esfahani, Fatemeh Malekipour University of Tehran School of Mechanical Engineering College of Engineering Tehran Iran Isfahan University of Technology Department of Electrical and Computer Engineering Isfahan Iran Concordia University Department of Mechanical Indistrial and Aerospace Engineering Montreal Canada

ISBN: (纸本)9781665412162

Cancer is a rampant phenomenon caused by uncontrollable cells that grow and spread throughout the body. Invasive Ductal Carcinoma 1 is the most common type of breast cancer, which can be fatal for females if not detected early. As a result, prompt diagnosis is critical to maximizing surveillance rates and, in the meantime, minimizing long-term mortality rates. Nowadays, modern computer vision and deep learning techniques have transformed the medical image analysis arena. computer vision application in medical image analysis has provided us with remarkable results, enhanced accuracy, and reduced costs. The main purpose of designing a new algorithm to detect unusual patches of breast images, was to acquire both high accuracy and low computational cost, simultaneously. Therefore, a novel architecture has been designed by utilizing Xception and *** new algorithm achieves 93.4% balanced accuracy and 94.8% for F1-Score, which outperforms previously published algorithms for identifying IDC histopathology images that use deep learning techniques. © 2022 IEEE.

关键词： Diagnosis

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Improved Induction Network with Hierarchical Self-attention Scheme for Few-Shot Relation Classification 2

Improved Induction Network with Hierarchical Self-attention ...

引用

2nd international conference on image processing, computer vision and Machine Learning, ICICML 2023

作者： Chen, Zhengfei Xi, Xiao Li, Zhiyong Yang, Hang Zhang, Xiaocheng Zhang, Yonggang Powerchina Jilin Electric Power Engineering CO. LTD. Information Center Changchun China Powerchina Jilin Electric Power Engineering CO. LTD. Changchun China Jilin University College of Computer Science and Technology Changchun China

ISBN: (纸本)9798350331417

The vast majority of present techniques for relation classification rely heavily on large-scale labelled datasets, but when the training data is insufficient, these methods are not available. Intuitively, we can regard relation classification as a few-shot learning task when the data is hard to collect. In this paper, we present an advanced induction network with a hierarchical self-attention scheme to effectively tackle the issue of few-shot relation classification. We utilize attention-based dynamic routing algorithm to learn a query-related class representation. Besides, our hierarchical self-attention scheme, including instance-level and task-level attention, can adjust support set and capture high-level information between support set and query set. The results of our extensive experiment on FewRel dataset show that our model is adaptable to task and outperforms the state-of-the-art models. © 2023 IEEE.

关键词： Dynamic routing Few-Shot Learning Induction Network Relation Classification Self-attention

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Underwater Fish image Enhancement Algorithm Based on Dark Channel Prior and Retinex 2

Underwater Fish Image Enhancement Algorithm Based on Dark Ch...

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2nd international conference on image processing, computer vision and Machine Learning, ICICML 2023

作者： Zou, Li Tan, Wenqi Chen, Zhihui Wan, Jin Xiamen Huaxia University College of Information and Smart Electromechanical Engineering Fujian Province Xiamen China

ISBN: (纸本)9798350331417

Due to the intricate underwater conditions and lighting challenges prevalent in marine farms, underwater fish images often suffer from issues like blurriness, low contrast, color distortion, and uneven illumination. In response to these hurdles, we present an underwater fish image enhancement algorithm that leverages the dark channel prior and Retinex. We commence by applying the dark channel prior to dehaze underwater fish images. To discriminate between foreground and background regions within the image, we employ a superpixels segmentation technique based on variations in image brightness. Consequently, we employ a multi-scale Retinex enhancement on each region and perform a secondary enhancement exclusively on the background region. Finally, a Poisson fusion method is utilized to amalgamate the enhanced foreground and background segments, resulting in a unified composite image. We conducted comprehensive experiments employing the Underwater image Enhancement Benchmark Dataset (UIEBD). The experimental findings substantiate the efficacy of the proposed algorithm, with notable improvements observed across three key evaluation metrics: information entropy, average gradient, and standard deviation. These enhancements translate to an average percentage increase of 18.22%, 133.67%, and 59.29%, respectively. Additionally, the algorithm notably enhances underwater fish image clarity and ameliorates issues related to uneven illumination. It demonstrates superior aptitude in preserving image intricacies and elevating contrast when juxtaposed with alternative algorithms, substantiating its superior performance. © 2023 IEEE.

关键词： dark channel prior fish Retinex underwater images uneven illumination

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Real Time Multiple-Object Detection Based on Enhanced SSD 2

Real Time Multiple-Object Detection Based on Enhanced SSD

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2nd international conference on Power, Control and Computing Technologies, ICPC2T 2022

作者： Jaiswal, Tarun Pandey, Manju Tripathi, Priyanka National Institute of Technology Department of Computer Application Raipur India

ISBN: (纸本)9781665458580

Object detection and recognition are widely employed in a variety of computer vision applications, and numerous techniques have been suggested in the literature. Object detection is one of the areas of computer vision that has seen a lot of achievement. In the last decade, various applications of computer vision have been presented. Some of these include autonomous cars, video surveillance, and scene interpretation. Visual recognition systems, including image categorization, localization, and detection, are at the heart of all of these applications and have gathered a lot of research attention. These visual identification algorithms have achieved extraordinary performance because of considerable advancements in neural networks, particularly deep learning (DL). Object detection techniques in real-time systems, on the other hand, necessitate minimal computing time and a thorough performance assessment prior to deployment. In this paper multiple image detection approach is used to identify and locate the object concerning their class label, and an enhanced feature map is used for object detection. All this is possible due to the recent advancement in DL along with image processing. These images, fed to the model, come from the video source. To tackle the problem related to the aspect ratio, the model uses the multi-scale feature map together with a distinct filter for diverse default boxes. © 2022 IEEE.

关键词： computer vision Visualization System performance Power control Object detection Streaming media Feature extraction

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A multi-view stable feature extraction network for SAR target recognition 2

A multi-view stable feature extraction network for SAR targe...

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2nd international conference on image processing, computer vision and Machine Learning, ICICML 2023

作者： Xiao, Ziyi Zhang, Gong Dai, Qijun Wang, Zijun Nanjing University of Aeronautics and Astronautics College of Electronic and InformatioN Engineering Nanjing China

ISBN: (纸本)9798350331417

Since the viewing angle sensitivity of Synthetic Aperture Radar (SAR) image, targets' shape changes dramatically in different viewing angle, the accuracy of SAR target recognition base on deep learning in single view is often affected by this difference between various viewing angle of SAR. So the multi-view approaches have been proposed to solve this limitation by learning the targets' relationships between images from different viewings. In this paper, a multi-view stable feature extraction network (MV-SFN) is proposed which constructs stable feature that doesn't change with viewing angle by using multi-view images. It uses Convolutional Neural Networks (CNN) to gain image feature, then take Gate Recurrent Unit (GRU) to analyze varying regularity of series viewing features. At last a novel features fusion module was designed for fusing different viewing features according to their similarity, and finally going to classify. MSTAR database is used to validate MV-SFN method. © 2023 IEEE.

关键词： deep learning feature fusion multi-view SAR image viewing angle sensitivity

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