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applications of neural networks And machine learning in image processing ix (Proceedings of S P I E)

Applications of Neural Networks And Machine Learning in Imag...

引用

2005年

作者： Nasser M Nasrabadi Syed A. Rizvi

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Developing a Face Aging Model Using Face Synthesis Techniques

Developing a Face Aging Model Using Face Synthesis Technique...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE conference on

作者： Priyanka Kaushik Zakiya Manzoor Khan Muntaha Manzoor Khan Beenu Mago K. Jeevanadan Shivansh Dwivedi Dept. of CSE (AIML) CSE-APEX Chandigarh University Chandigarh India Dept. of Computer Science Lovely Professional University Punjab India Dept. of Computer Science NIILM University Haryana India Dept. of Computer Science Skyline University College Sharjah UAE Dept. of Civil Engineering Dhanalakshmi Srinivasan College Coimbatore India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Regarding the realm of advancing technology, the subsequent development of face aging applications has garnered substantial interest. This research examines face synthesis techniques, with specific emphasis for the various practical usage of computational adversarial networks (GANs). The key objective of our project is to upgrade both the discriminator and the generator parts of GANs to generate more realistic, age-progressed face images. The project assimilates Python programming, convolutional neural networks (CNNs), and several other machine learning and image processing proceeds, advancing in qualitative data from Kaggle. The goal of our project is to advancement of face-aging technology over the use of this multifaceted method, with the possible uses in amusement, forensic science, and technological process.

关键词： Ethics Technological innovation Forensics Aging Generative adversarial networks Generators Convolutional neural networks Faces Stress Python

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Compararitive Analysis of AI-Powered image Recognition for Dermatological Diagnosis 26th

Compararitive Analysis of AI-Powered Image Recognition for D...

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26th International conference on Artificial Intelligence and applications, ICAI 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

作者： Al-Sinayyid, Ali Sanchez, Alexande Battula, Rohith Reddy Sasidhar, Kadiyala Mannuru, Venkatesh Sanford, Timothy Department of Mathematics and Computer Science West Virginia State University Institute South Charleston United States

ISBN: (纸本)9783031866227

With the increasing incidence of skin cancers such as Basal Cell Carcinoma (BCC), Malignant Melanoma (MM), and Squamous Cell Carcinoma (SCC), the field of dermatology struggles to meet the demand in early diagnosis. Creating a necessity for a portable solution that is both easy to use and cost effective. This study investigates robust Artificial Intelligence (AI)-powered image recognition methods to diagnose skin diseases. Directly comparing different forms of AI such as Convolutional neural networks (CNN), and Deep learning networks (DL). With a special focus on accuracy, sensitivity, and specificity. Aiming to indicate a recognition algorithm that can be implemented reliably. Finding that a DL (IMLT-DL) method would be both effective and easy to implement in an application. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Artificial intelligence Convolutional neural networks Deep learning Dermatologist image recognition machine learning Malignant Melanoma Recurrent neural networks Skin Cancer

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Textured Surface Defect Detection with image-Level Accuracy Optimization via Patch-Level Features and Simulation-Based Samples

Textured Surface Defect Detection with Image-Level Accuracy ...

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Algorithms, Computing and machine learning (CACML), Asia conference on

作者： Fen Li Dongxiao Li Le Huang Hangrui Chen Ming Zhang Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing College of Information Science and Electronic Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798331543143

ISBN: (纸本)9798331543150

Textured surface defect detection plays a critical role in industrial product quality monitoring. However, excessive reliance on pixel-level precision, especially when defect boundaries are unclear, can lead to more complex models, lower efficiency, and increased risk of false positives or missed detections, ultimately compromising system reliability. Additionally, challenges such as the difficulty of collecting defect samples, severe class imbalances, and high pixel-level labeling costs hinder the effectiveness of convolutional neural networks in terms of both inference speed and computational efficiency. To overcome these challenges, we propose SiamSimNet, a dual-channel Siamese network that relies on high-quality training data generated through simulation algorithms, minimizing the need for real defect samples. By incorporating locally aware patch features, the proposed method significantly improves detection accuracy and robustness, while maintaining high inference efficiency. This approach shows strong potential for real-world industrial applications.

关键词： Accuracy Costs Computational modeling Feature extraction Robustness Surface texture Computational efficiency Labeling Monitoring Defect detection

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Finding Identity through Gait

Finding Identity through Gait

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International image processing, applications and Systems conference (IPAS)

作者： Lavanya Srinivasan School of Computing and Engineering University of West London London United Kingdom

ISBN: (数字)9798331506520

ISBN: (纸本)9798331506537

Human gait identification is the recognition of a person from a series of walking images. In contrast to fingerprint or iris-based identification methods, gait identification offers the significant benefit of remote execution. Gait detection is emerging as one of the most promising biometric identification techniques. Traditional methods to identify the identity of gait are Background subtraction, Gait Energy image, Gait Entropy, etc., human gait identification is a potential new tool for identifying individuals beyond traditional methods. The objectives of this study were to develop an automated gait detection system and examine the unique aspects of gait. This study encompassed the following: gait predictions and estimation based on HRNet 17 joint positions (locating body joints of humans in an image/video), better predictions with higher confidence scores by eliminating poor predictions, and the 14 joint coordinates and 17 joint points are fed into Graph Convolutional Network by eliminating low confidence pose to classify labels. Semi-supervised learning on graph-structured data can be accomplished via Graph Convolutional networks. Graph Convolutional networks is a very effective version of convolutional neural networks, which function directly on graphs. The experimental results show that the Graphical Convolutional Network identifies gait subjects with higher accuracy of normal walking and carrying a backpack with 98% and 96% respectively. This work will be extended in the future by fusing face and gait to identify various gait subjects and the gait in forensic identifications.

关键词： Legged locomotion image recognition Graph convolutional networks Forensics Face recognition Pose estimation Time series analysis Semisupervised learning Fingerprint recognition Feature extraction

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image Captioning-A Comprehensive Encoder-Decoder Approach on Flickr8K

Image Captioning-A Comprehensive Encoder-Decoder Approach on...

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Automation and Computation (AUTOCOM), International conference on

作者： Mithilesh Mahajan Saanvi Bhat Madhav Agrawal Gitanjali Shinde Rutuja Rajendra Patil Gagandeep Kaur Dept. of Computer Science Engineering (Artificial Intelligence and Machine Learning) Vishwakarma Institute of Information Technology Pune India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

This paper proposes a new encoder-decoder frame-work based on Convolutional neural networks (CNN) for feature extraction and Long Short-Term Memory (LSTM) networks for image caption generation. image captioning, the process of generating descriptive textual content for images, has diverse applications, including accessibility tools for the visually impaired, virtual assistants, social media automation, and satellite imagery analysis in critical domains like defense and disaster management. The proposed model will leverage the power of CNNs for extracting high-level image features and LSTMs for generating semantically coherent captions. Rigorously evaluated on the Flickr8k dataset, the model achieved competitive BLEU scores, showing its efficacy in generating accurate and meaningful descriptions. The methodology integrates advanced preprocessing techniques and efficient training strategies, ensuring robustness and scalability. This re-search focuses on the need to use state-of-the-art machine learning techniques in the solving of real challenges, and some of the potential routes to improving image captioning systems are suggested to include multilingual capabilities and integration with larger datasets. Contributions include a clear explanation of the encoder-decoder approach, detailed metrics for evaluation, and insights into correcting limitations including dataset size and complex scene representation. This work is a step toward further progress in automated image-to-text systems, integrating precision, relevance, and adaptability.

关键词： Measurement Training Adaptation models Automation Accuracy Social networking (online) Virtual assistants Feature extraction Convolutional neural networks Long short term memory

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A Literature Review of machine learning Techniques for Dance Recognition and Robotic Vision

A Literature Review of Machine Learning Techniques for Dance...

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Robotics and Technologies for Industrial Automation (ROBOTHIA), IEEE International conference on

作者： Lee Wei San Samuel-Soma M. Ajibade Muhammed Basheer Jasser Adefemi Ayodele Babatunde Adedotun Ajayi Mbiatke Anthony Bassey Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Darul Ehsan Malaysia Research Centre for Nanomaterials and Energy Technology (RCNMET) Faculty of Engineering and Technology Sunway University Selangor Malaysia Research Centre for Human-Machine Collaboration (HUMAC) Faculty of Engineering and Technology Sunway University Petaling Jaya Selangor Malaysia University of East London London UK Department of Cyber security Faculty of Computing & Informatics Ladoke Akintola University of Technology Ogbomoso Nigeria Department of Business Administration Universiti Tun Hussein Onn Malaysia Batu Pahat Malaysia

ISBN: (数字)9798350356755

ISBN: (纸本)9798350356762

image recognition, powered by machine learning (ML), has significantly advanced applications in both dance movement recognition and robotic vision. This review examines key ML techniques, including Convolutional neural networks (CNNs), Deep neural networks (DNNs), Self-Organizing Maps (SOMs), and Long Short-Term Memory (LSTM) networks, alongside pose estimation methods like OpenPose and Part Affinity Fields (PAFs). These techniques enhance dance classification, real-time feedback, and motion analysis, with OpenPose + LSTMs and PAFs + LSTMs demonstrating the highest accuracy. Notwithstanding progress, obstacles such as high computational costs, data dependency, and real-time implementation challenges persist. Beyond dance, these methods are critical in robotic vision, intelligent automation, and industrial image processing, enabling autonomous robotic navigation, defect detection in manufacturing, and AI-driven motion tracking. By leveraging human movement analysis for robotics, ML improves human-robot interaction, robotic-assisted rehabilitation, and industrial automation. Despite progress, challenges such as high computational demands, data dependency, and real-time constraints remain. This review explores future directions, including multimodal data fusion, hybrid AI models, and real-time optimization, bridging the gap between AI-driven motion systems and intelligent automation to enhance adaptability and efficiency across domains.

关键词： Intelligent automation Humanities Solid modeling image recognition Accuracy Service robots Robot sensing systems Real-time systems Robots Long short term memory

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Deep learning applications for Malaria Detection and Diagnosis: A Review 1st

Deep Learning Applications for Malaria Detection and Diagnos...

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1st International conference on Artificial Intelligence and its applications, ICAIA 2023

作者： Vandana Khan, Shakir Gupta, Gaurav Mahajan, Shubham School of Bioengineering and Food Technology Shoolini University Himachal Pradesh 173229 India Riyadh11564 Saudi Arabia Computers and Data Science Yogananda School of AI Shoolini University Himachal Pradesh 173229 India Ajeenkya D Y Patil University Maharashtra Pune412105 India

ISBN: (纸本)9783031843969

Malaria is a life-threatening disease that affects millions of people worldwide, particularly in developing countries. Early and accurate detection of malaria is crucial for effective treatment and control of the disease. In recent years, deep learning techniques have shown promising results in various medical imaging tasks, including malaria detection and diagnosis. This paper presents a comprehensive review of deep learning applications for malaria detection and diagnosis. It covers the different stages of the malaria diagnosis pipeline, including image acquisition, pre-processing, parasite detection, and classification. The review discusses various deep learning architectures employed in malaria detection, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants. It also highlights the challenges and limitations of existing approaches and identifies potential areas for future research. The findings of this review demonstrate the potential of deep learning techniques in improving the accuracy and efficiency of malaria detection and diagnosis, ultimately contributing to the efforts in eradicating this global health burden. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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DSnet: Removing scattered light in biomedical images using a Dual Stream neural network

DSnet: Removing scattered light in biomedical images using a...

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Algorithms, Computing and machine learning (CACML), Asia conference on

作者： Yuanhao Gong Qianyan Liu Weichao Lin Electronics and Information Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798331543143

ISBN: (纸本)9798331543150

Light scattering is a common phenomenon in biomedical imaging. However, its removal is a challenging task because of the complex light paths from the scattered light. In this paper, we propose a dual-stream neural network to computationally remove the scattered light in biomedical images. To achieve this task, we first derive a mathematical model from the classical dehazing model for natural images. Then, we propose a dual-stream neural network to solve this model. Different from the dehazing task for natural images that have clear images as ground truth, biomedical images usually do not have the ground truth. Therefore, we use images from two different modalities to train the dual-stream network without knowing the ground truth. Several experiments confirm that our method can significantly reduce the light scattering and improve the image quality. The proposed method can be applied in various applications where removing the scattered light is needed or preferred.

关键词： Training machine learning algorithms Biological system modeling Computational modeling neural networks Light scattering Streaming media Mathematical models X-ray imaging Biomedical imaging

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Optimizing neural Network Effectiveness via Non-monotonicity Refinement

Optimizing Neural Network Effectiveness via Non-monotonicity...

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IEEE Workshop on applications of Computer Vision (WACV)

作者： Koushik Biswas Amit Reza Meghana Karri Debesh Jha Hongyi Pan Nikhil Tomar Aliza Subedi Smriti Regmi Ulas Bagci Machine and Hybrid Intelligence Lab Northwestern University Chicago IL USA Space Research Institute Austrian Academy of Science Graz Austria

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Activation functions play a crucial role in artificial neural networks by introducing non-linearities that enable networks to learn complex patterns in data. An appropriate choice of an activation function plays a crucial role in the training dynamics of a neural network, which can boost network performance significantly. Rectified Linear Unit (ReLU) and its variants, like leaky ReLU and parametric ReLU, have emerged as the most popular activations due to their ability to enable faster training and generalization in deep neural networks despite having some significant issues like vanishing gradient problems. In this paper, we have proposed smooth functions, which we call the AMSU family, which are smooth approximations of the maximum function. We derive three activations from the AMSU family, namely AMSU-1, AMSU-2, & AMSU-3, and show their effectiveness in different deep learning problems. By simply replacing the ReLU function, Top-1 accuracy improves by 5.88%, 5.96%, and 5.32% on the CIFAR100 dataset on the ShuffleNet V2 model. Also, replacing ReLU with AMSU-1, AMSU-2, and AMSU-3, Top-1 accuracy improves by 8.50%, 8.29%, and 7.70% on the CIFAR100 dataset on the ShuffleNet V2 model with FGSM attack. Also, Replacing ReLU with AMSU-1, AMSU-2, and AMSU-3 on imageNet-1K data, we got 3%-5% improvement on ShuffleNet and MobileNet models. The source code is publicly available at https://***/koushik313/AMSU.

关键词： Deep learning Training image segmentation Accuracy Source coding Artificial neural networks Object detection Data models machine translation image classification

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