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检索条件"任意字段=Conference on Applications of Neural Networks and Machine Learning in Image Processing IX"

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Using Cropped Datasets for training Deep neural networks for face recognition 31

Using Cropped Datasets for training Deep Neural Networks for...

引用

31st International conference on Systems, Signals and image processing (IWSSIP)

作者： Quintella, Felipe Ferreira Ganacim, Francisco Hurtado, Jan Raposo, Alberto Pontif Catholic Univ Rio De Janeiro Dept Informat & Tecgraf Inst Rio De Janeiro Brazil

ISBN: (纸本)9798350391893;9798350391886

Face recognition is a fundamental problem in numerous real-world applications, and it can be tackled using classification deep learning models. This kind of solution requires extensive datasets to represent the target subjects, where maintaining their privacy is essential. Further, there are challenging scenarios where the face of the subject can be partially occluded, e.g. a subject wearing a mask, making it difficult the recognition process. In this paper, extensive experiments are presented for deep learning-based face recognition, considering different dataset settings. More precisely, cropping operations are proposed to partially represent the face focusing on the eyes and nose region for model training, minimizing privacy issues, storage space, and allowing the trained model to process partially occluded faces. Two publicly available datasets named VGG-Face and DigiFace-1M are adapted for evaluation, and five convolutional neural network models are used for comparison, including ResNet, VGG-16, AlexNet, DenseNet-169, and EfficientNetV2-M. The results suggest that, although this cropping operation impacts the accuracy of the model when compared to the processing of full faces, it can be viable solution to minimize the pointed issues while preserving acceptable performance.

关键词： face recognition deep learning classification crop

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Deep learning applications in Thermal IR image processing 1

引用

International conference on Data Analytics and Management, ICDAM 2023

作者： Hadi, Hayder Sabeeh Sabah, Hawraa Ali Obaid, Ahmed J. Zearah, Sajad Ali College of Nursing University of Kufa Najaf Iraq National University of Science and Technology Thi-Qar Iraq Faculty of Computer Science and Mathematics University of Kufa Kufa Iraq Scientific Research Center Al-Ayen University Thi-Qar Iraq

ISBN: (数字)9789819965472

ISBN: (纸本)9789819965465

IR image recognition has been a promising field for the past few years. However, it is difficult to identify facial emotions when it is dark, the lighting is poor, or there are other elements present. Thermal pictures are therefore recommended as a fix for these issues and for a number of additional advantages. Additionally, focusing on important areas of a face rather than the complete face is sufficient to reduce processing while also enhancing accuracy. This study provides brand-new infrared thermal image-based methods for identifying the images. The face’s whole image is first divided into four parts. Then, to create training and testing datasets, we only allowed four active regions (ARs). An approach to machine learning is called active neural network (ANN). We also used a parallelism strategy to speed up processing of training and testing datasets. We have observed a 46% reduction in processing time as a result. Finally, to increase the recognition accomplishes a recognition accuracy of 92.38%. The achieved accuracy validates the stability of our suggested strategy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： image recognition

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Multi-Modal learning with Joint image-Text Embeddings and Decoder networks 7

Multi-Modal Learning with Joint Image-Text Embeddings and De...

引用

IEEE 7th International conference on Industrial Cyber-Physical Systems (ICPS)

作者： Chemmanam, Ajai John Jose, Bijoy A. Moopan, Asif Cochin Univ Sci & Technol CPS Lab Dept Elect Cochin Kerala India Vuelogix Technol Pvt Ltd Kochi Kerala India

ISBN: (纸本)9798350363029;9798350363012

Advances in machine learning and neural networks have transformed natural language processing (NLP) and computer vision (CV) applications. Recent research efforts have begun to bridge the gap between the two domains. In this work, we propose a semi supervised Multi-Modal Encoder Decoder Network (MMEDN) to capture the relationship between images and textual descriptions, allowing us to generate meaningful descriptions of images and retrieve images from a database using cross-modality search. The semi-supervised training approach, which combines ground truth text descriptions and pseudotext generated by the text decoder within the model, requires far fewer image-text pairs in the training data and can directly add new raw images without manual text labelling for training. This approach is particularly useful for active learning environments, where labels are expensive and hard to obtain. We show that our model performs well with qualitative evaluations. We applied our model for finding images of a person from large databases and generating descriptions of people involved in an event for adding to an automatically generated report. The model was able to retrieve relevant images and generate accurate descriptions, demonstrating its applicability to more practical use cases.

关键词： Multi-modal learning Cross-modal retrieval Encoder-decoder architectures Computer Vision Natural Language processing

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Exploring Advanced Deep learning Techniques for Multi-Class Melanoma Cancer Classification 6

Exploring Advanced Deep Learning Techniques for Multi-Class ...

引用

6th IEEE International conference on image processing, applications and Systems, IPAS 2025

作者： Alhasson, Haifa F. Qassim University College of Computer Department of Information Technology Buraydah52571 Saudi Arabia

ISBN: (纸本)9798331506520

Melanoma represents one of the most lethal forms of skin cancer, underscoring the importance of early detection for effective treatment and improved survival rates. Traditional diagnostic methods, which predominantly rely on visual inspection and biopsies, are often time-consuming and susceptible to human error. Timely diagnosis significantly enhances the likelihood of recovery and can reduce healthcare costs by minimizing the necessity for surgical, radiographic, or chemical treatments. Recently, deep learning techniques have demonstrated considerable promise in automating and improving the accuracy of medical diagnoses, including melanoma classification. In this study, we evaluate the performance of several state-of-the-art deep learning models - DenseNet, ResNet, VGG-16, VGG-19, Inception v3, and AlexNet - for multi-class melanoma cancer classification. Our objective is to identify the model that offers the best performance in terms of accuracy, sensitivity, and specificity. We conduct a comprehensive comparison using publicly available datasets, such as HAM10000 ("Human Against machine with 10000 training images"), to ensure robust and generalizable results. This evaluation aims to advance the field of melanoma diagnosis by identifying the most effective deep learning approach, thereby facilitating early and accurate detection of this life-threatening disease. © 2025 IEEE.

关键词： Convolutional neural networks

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Histopathology image Classification for Breast Cancer Using Convolutional neural Network 5

Histopathology Image Classification for Breast Cancer Using ...

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5th International conference on Advances in Electrical Engineering and Computer applications, AEECA 2024

作者： Gu, Xinfei Shenzhen University Shenzhen China

ISBN: (纸本)9798350355253

Breast cancer detection presents considerable challenges in terms of diagnostic accuracy and efficiency, particularly when relying on traditional manual examination techniques. As medical imaging data grows in scale and complexity, traditional machine learning methods struggle due to their dependence on manual feature extraction and limitations in modeling nonlinear relationships. Computer vision, a key area of artificial intelligence, plays a crucial role in automating image recognition tasks, and its application in medical imaging is becoming increasingly prominent. In this study, we propose a deep learning framework based on convolutional neural networks (CNN) for breast cancer histopathology image classification, a critical task within computer vision. By automatically learning hierarchical features from raw image data, our model bypasses the need for manual feature engineering, achieving an impressive classification accuracy of 94.26% and an F1 score of 0.97. The robustness and generalization ability of the model are especially evident in its performance on complex patterns found in invasive ductal carcinoma (IDC) images. Specifically, the CNNbased approach effectively captures distinct visual features such as edge details, complex textures, and differential staining, which are essential for precise classification in medical image recognition. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on image Recognition Technology Based on Multimodal Deep learning 2

Research on Image Recognition Technology Based on Multimodal...

引用

2nd IEEE International conference on image processing and Computer applications, ICIPCA 2024

作者： Wang, Jinyin Li, Xingchen Jin, Yixuan Zhong, Yihao Zhang, Keke Zhou, Chang Stony Brook University New York United States University of Southern California Los Angeles United States New York United States New York University New York United States Northeastern University MA United States Columbia University New York United States

ISBN: (纸本)9798350360240

This research explores a human multi-modal behavior identification algorithm using deep neural networks. The algorithm leverages various deep neural networks tailored to different types of modal information to process diverse video data. By integrating these neural networks, the algorithm effectively identifies behaviors across multiple modalities. Data collection for this study utilized multiple cameras developed by Microsoft Kinect, capturing skeletal point data in addition to conventional images. This dual data collection method enables the extraction of motion features from the images. The synthesized behavioral characteristics from these data sources facilitate precise behavior identification and categorization. The proposed algorithm's effectiveness was validated using the MSR3D dataset. Experimental results demonstrate that behavior recognition accuracy remains consistent across various scenarios, indicating the robustness of the recognition process. The findings reveal that the algorithm substantially enhances the accuracy of pedestrian behavior recognition in video footage. © 2024 IEEE.

关键词： Action Recognition Deep learning Multimodal Recurrent Loop neural Network

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A Comprehensive Liver Tumor Detection and Stages Classification Using Deep learning and image processing Techniques

A Comprehensive Liver Tumor Detection and Stages Classificat...

引用

2024 International conference on Smart Systems for applications in Electrical Sciences, ICSSES 2024

作者： Prakash, P. Bhagavan, K. Apsiya, P. Chandana, C. Madanapalle Institute of Technology & Science Dept. of ECE India

ISBN: (纸本)9798350364040

This research presents a deep learning framework designed to automatically detect and classify liver tumors in CT images, leveraging Convolutional neural networks (CNNs). The suggested method includes a sequence of pre-processing steps, such as resizing images and enhancing contrast through histogram equalization. Additionally, a bilateral filter is applied for noise removal, followed by K-means image-based segmentation for improved localization. The CNN is then employed for binary classification, distinguishing between benign and malignant tumors with an accuracy of 98.88%. If the CNN identifies a tumor as malignant, a secondary CNN-based classification system is employed to further categorize the malignant tumors into different stages: Early Stage, Intermediate Stage, and Metastatic Stage with an accuracy of 98.72%. This multi-step approach not only automates tumor detection but also provides a finer-grained analysis of malignant cases, offering valuable insights into the progression of liver tumors. The methodology combines advanced image processing techniques with deep learning classification, showcasing a comprehensive framework for efficient and detailed liver tumor analysis in medical imaging. © 2024 IEEE.

关键词： Convolutional neural networks

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Design of English Teaching Quality Evaluation System Based on Deep learning and Data Mining 2

Design of English Teaching Quality Evaluation System Based o...

引用

2nd IEEE International conference on image processing and Computer applications, ICIPCA 2024

作者： Chen, Yang Guangzhou Huashang Vocational College Guangzhou511300 China

ISBN: (纸本)9798350360240

This research delves into the prospects of using deep learning and data mining to design an English teaching and quality system model. The paper first conducts a research and analysis on the relevant literature that examine the evaluation systems designed through data mining and deep learning algorithms. As presented in the findings, deep learning and data mining technologies that include the use of neural networks play a critical role in the development of English language assessment and evaluation systems. The findings equally establish that data mining algorithms and techniques significantly decrease the delay times caused in the traditional English teaching and evaluation system designed through the use of deep learning and data mining techniques. © 2024 IEEE.

关键词： Contrastive learning

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Multilingual image Caption Generator 15

Multilingual Image Caption Generator

引用

15th International conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Rane, Milind Waghulde, Piyush Yadav, Srushti Vemula, Pranamya Wagh, Om Waikar, Abhijeet Vishwakarma Institute of Technology Department of Multidisciplinary Engineering Pune India

ISBN: (纸本)9798331300579

The rise of social media and global communication requires multilingual image captioning for diverse participants, enabling cross-cultural understanding and engagement. By automatically generating captions in multiple languages, this technology enables seamless communication and enriches the user experience in an increasingly interconnected world. Existing image captioning models leverage alternative methodologies, such as transformer-based models, attention mechanisms, and pre-trained language models, to generate descriptive captions for images without relying on the CNN-LSTM architecture. These approaches provide effective solutions for image captioning, showcasing advancements in natural language processing and machine learning. However, the integration of CNN-LSTM architecture with the Flickr8k dataset and translation enables more accurate and contextually relevant multilingual image captions, enhancing versatility and adaptability for real-world applications. This approach combines deep learning techniques, diverse training data, and machine translation capabilities for superior multilingual image caption generation. The model's accuracy is assessed using accepted evaluation metrics and the model generates accurate captions in multiple languages for the images provided. © Grenze Scientific Society, 2024.

关键词： Convolutional neural networks

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APL: Adaptive Parameter learning for image Dehazing 23

APL: Adaptive Parameter Learning for Image Dehazing

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23rd IEEE International conference on machine learning and applications, ICMLA 2024

作者： Martinho, Laura A. Cavalcanti, João M. B. Pio, José L. S. Oliveira, Felipe G. Manaus Brazil Itacoatiara Brazil

ISBN: (纸本)9798350374889

Foggy or hazy images result from light scattering and absorption by atmospheric particles. Intensity transformation techniques offer solutions to solve this problem, but param-eters selection significantly impacts the quality of the final image. In this paper we propose the APL method, a parameter learning approach for enhancing hazy images using Convolutional neural networks (CNNs), resulting in a new ISP learning-based pipeline. A set of intensity transformation techniques is applied, combined with image quality metrics, to define parameters for hazy image enhancement. A CNN regression model is employed to learn about the problem and estimate parameters for the transfor-mation stage. The best dehazing parameters are determined and utilized to enhance the quality of degraded images. Experiments are conducted on three datasets of hazy images, including two datasets available in the literature and one proposed dataset of real-world foggy images. Results are evaluated comparing to other dehazing methods using full-reference (PSNR and SSIM) and non-reference (NIQE and BRISQUE) metrics, demonstrating a high accuracy in image dehazing achieved by our method. The proposed source code and dataset are available here. © 2024 IEEE.

关键词： Convolutional neural networks

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