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检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

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Kernel-Guided Texture-Aware Blind Super-Resolution via Uncertainty Learning

Kernel-Guided Texture-Aware Blind Super-Resolution via Uncer...

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ieee international Symposium on Information (IT) in Medicine and Education, ITME

作者： Tingting Zhao Zuoyong Li Li Quan Kun Zeng Shenghua Teng College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou China College of Computer Science and Big Data Fuzhou University Fuzhou China

The accuracy of kernel estimation is critical to the performance of blind super-resolution (SR). Traditional kernel estimation methods usually use L1 or L2 loss function to minimize the difference between the estimated kernel and the ground-truth kernel. This tends to make the estimated kernel converge towards the average of all possible kernels, deviating from the ground-truth kernel. To improve the performance of kernel estimation, this paper proposes an uncertainty loss for training a kernel estimation network, focusing on regions with high uncertainty (variance) in the kernel. In addition, a texture-aware SR network is proposed that utilizes the Gumbel Softmax trick to pay more attention to the complex regions of the image texture, thus improving the SR performance. Extensive experiments on synthetic datasets show that our approach achieves promising performance.

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Color Retention Assessment of Duhat Dye to Cotton Fabric Using Color Fastness Test and Digital image processing

Color Retention Assessment of Duhat Dye to Cotton Fabric Usi...

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Computing (ICOCO), ieee international conference on

作者： Jilbert Novelero Rennel Mallari Jennifer Dela Cruz Donna Gomez Ricardo Banal School of Electrical Electronics and Computer Engineering Mapua University Metro Manila Philippines College of Engineering and Architecture Angeles University Foundation Angeles City Philippines School of EECE and School of Graduate Studies Mapua University Manila Philippines ECE Department Systems Plus College Foundation College of Engineering Angeles City Philippines

Synthetic dyes have changed perceptions of natural dyes, but their chemical origins pose environmental risks. Seeking an eco-friendly alternative, the indigenous fruit duhat from the Philippines shows promise as a potential dye for cotton fabrics. This study aimed to explore duhat's dyeing capability using a cost-effective and non-invasive testing technique. The evaluation began by creating a duhat filtrate and dyeing white cotton fabrics following standard procedures from the AATCC 61 1A handbook for color fastness testing through washing. The results and statistical analysis showed that when 150g of duhat skin was mixed with 50 mL of ethyl alcohol, it produced a filtrate with commendable color retention. An image enhancement technique converted RGB images to grayscale, aiding visual grading of color changes in cotton fabrics. The duhat skin dye scored a grade of 4 for noticeable color change and a grade of 3 for moderate staining on adjacent undyed fabric, according to the results from the gray scale for color change and staining. Furthermore, a paired t-test on the numerical data indicated no significant color change after the color fastness test compared to the fabric's original color. In conclusion, the experimentation suggests that duhat could serve as a potential natural dye source for cotton fabrics. Embracing duhat's coloring properties may reduce our reliance on synthetic dyes and help protect the environment.

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Real-Time 6-DoF Object Pose Estimation Network Based on 2D-3D Coordinate Correspondence 3rd

Real-Time 6-DoF Object Pose Estimation Network Based on 2D-3...

引用

3rd international conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2023

作者： Li, Shuai Chen, Jinlong Yang, Minghao Su, Jianhua Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guangxi Guilin541004 China Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guangxi Guilin541004 China Beijing100190 China HuaiRou Beijing101400 China Beijing100190 China

ISBN: (纸本)9789819721993

Object 6D pose estimation is essential for a variety of applications, including autonomous driving, robotic manipulation, and automated harvesting. Due to the influence of different lighting conditions and occlusions, objects with different poses may exhibit significant variations in appearance in different views. This makes the estimation of the 6D pose of objects from a single RGB image a challenging task. This paper proposes a deep learning network capable of simultaneously detecting and estimating the 6D pose (3D rotation + 3D translation) of target objects. As establishing a large-scale dataset using real data is extremely challenging, this paper introduces an automatic collection scheme based on virtual data generated from real images. We conducted experiments on the generated astronaut dataset using our data collection scheme for 6D pose estimation. The experimental results show that the accuracy of 6D pose estimation based on the ADD metric is 96%. Therefore, this method has significant potential and can be extended to other downstream tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Is one label all you need? Single positive multi-label training in medical image analysis

Is one label all you need? Single positive multi-label train...

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ieee international conference on Big Data

作者： Helen Schneider Priya Priya David Biesner Rebecca Kador Yannik C. Layer Maike Theis Sebastian Nowak Alois M. Sprinkart Ulrike I. Attenberger Rafet Sifa Department Media Engineering Fraunhofer IAIS Department of Computer Science University of Bonn Department of Diagnostic and Interventional Radiology University Hospital Bonn

Deep Learning is proving its immense potential in medical image processing. However, noisy label data can weaken the generalization ability of the model and cause significant performance degradation. This issue can be more prevalent in multi-label classification tasks, since their annotation is more challenging. With too many labels, human annotators may have difficulties mentioning all possible classes, which leads to an increased number of false negative labels. Single Positive Multi-Label (SPML) training deals with the most severe version of this problem, in which for each sample only one positive label is available. All other labels are not observed, i.e. not confirmed as positive or negative. While SPML has already achieved good results for the multi-label detection of objects, its impact on the extremely pertinent medical imaging use case has not yet been explored. In this work, we therefore investigate the performance of state-of-the-art SPML loss functions in the analysis of chest X-rays, both on the public CheXpert and on an in-house data set of the University Hospital Bonn. In addition, we propose our new SPML loss functions, the Generalized Assume Negative and Implicit Weighting Assume Negative loss, which increase the mean average precision by up to 4.6% compared to the popular binary cross-entropy loss.

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Efficient Multi-Scale Attention Residual Network for Breast Cancer Histopathology image Classification 23

Efficient Multi-Scale Attention Residual Network for Breast ...

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Proceedings of the 2023 international conference on Advances in Artificial Intelligence and applications

作者： Lu Cao Ke Pan School of Computer Science and Engineering Dalian Minzu University China

ISBN: (纸本)9798400708268

In recent years, convolutional neural networks have achieved significant success in the field of breast cancer histopathological image classification. This study reexamines the application of scale analysis and cross-spatial learning, introducing EMAResNet, a novel breast cancer classification approach based on multi-scale attention mechanisms. EMAResNet integrates feature grouping and cross-spatial learning, efficiently processing and distributing spatial semantic features while significantly reducing computational complexity. By parallel processing of multi-scale features and efficient feature fusion, the model effectively captures and utilizes crucial information in image data. This approach has been extensively evaluated on the BreakHis breast cancer pathology image dataset, demonstrating superior classification performance.

关键词： Breast Cancer Convolutional Neural Network Efficient Multi-Scale Attention Histopathological image Classification

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Detection and Recognition of Vehicles Using Indian Driving Datasets

Detection and Recognition of Vehicles Using Indian Driving D...

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ieee international conference on Cloud Computing in Emerging Markets (CCEM)

作者： Yathish Padukote H. S Suresha R Manohar N Tian Jipeng Department of Computer Science School of Computing Mysuru Amrita Vishwa Vidyapeetham India Computer Science college Zhongyuan University of Technology Zhengzhou Henan Province China

ISBN: (数字)9798350360059

ISBN: (纸本)9798350360066

Vehicle identification and recognition are essential computer vision tasks with important applications in autonomous driving, traffic management, and surveillance systems. The Indian Driving Dataset (IDD) dataset used in this work was compiled from a variety of driving scenarios in the Indian environment, and it includes a thorough investigation on vehicle detection and recognition. The dataset is meticulously chosen to reflect the difficulties brought on by the state of Indian roads, traffic patterns, and vehicle kinds. In this study, a Faster RCNN method for precise and effective vehicle detection and recognition deep learning approaches by enhancing images using power-law transformation. The collection consists of annotated IDD dataset images taken in a range of lighting situations, urban-rural locales, and camera angles. The testing results demonstrate the effectiveness of the recommended approach, which achieves competitive performance with 79.5% detection accuracy and 76% recognition accuracy. Additionally, the performance of the model is impacted by elements like various lighting situations and various vehicle models. This research offers insightful information about the model's ability to generalise in realistic situations.

关键词： Deep learning computer vision Accuracy image recognition Vehicle detection Surveillance Lighting Traffic control Road traffic Testing

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Capped L2p-Norm Based 2DPCA with Adaptive Capped Threshold Learning for image processing

Capped L2p-Norm Based 2DPCA with Adaptive Capped Threshold L...

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international conference on Communication Technology (ICCT)

作者： Meiling Liu Wei Feng Chengquan Pei Qianqian Wang Quanxue Gao School of Telecommunications Engineering Xidian University Xi'an China School of Computer Science and Technology Xi'an Jiaotong University Xi'an China School of Artificial Intelligence Xidian University Xi'an China

Two-dimensional principal component analysis (2DPCA) is an effective technique to extract low-dimensional representations for face clustering and recognition. However, it suffers from extreme sensitivity to outliers and noise. To improve the robustness of 2DPCA, we design a novel optimal-mean 2DPCA with adaptive capped L2p-norm minimization, called Adaptive Capped L2p-2DPCA, which adopts capped L2p-norm in the objective function to measure the reconstruction error. Simultaneously, Adaptive Capped L2p-2DPCA integrates the adaptive capped threshold learning during the optimization process, such that the capped threshold can be obtained without human intervention. Furthermore, we develop a non-greedy iterative algorithm to solve Adaptive Capped L2p-2DPCA. The proposed algorithm has a closed-form solution in each iteration and is proven to achieve good convergence. We conduct experiments on four face image datasets, and the recognition rates on the four datasets Extended Yale B, AR, CMU PIE, and ORL are 5.02%, 1.86%, 0.47%, and 5.5% higher than the second best method, respectively, which indicates the effectiveness and superiority of our method.

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Gender Recognition Based on Face image Using Deep Learning Method

Gender Recognition Based on Face Image Using Deep Learning M...

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international conference on Cyber and IT Service Management (CITSM)

作者： Mochamad Wahyudi Waeisul Bismi Firmansyah Mugi Raharjo Untung Rahardja Lise Pujiastuti Departement of Computer Science Universitas Bina Sarana Informatika Jakarta Indonesia Departement of Informatics University Nusa Mandiri Jakarta Indonesia Departement of Digital Business University Raharja Tangerang Indonesia Departement of Information System STMIK Antar Bangsa Tangerang Indonesia

Gender recognition based on facial images is one of the interesting applications in the field of image processing and artificial intelligence. Deep Learning methods, particularly artificial neural networks, have emerged as an effective tool for extracting complex facial features and classifying gender with high accuracy. This research outlines a study that aims to develop an employee gender recognition system based on facial images using Deep Learning methods. The method used in this study proposes the inceptionV3 model as well as the OpenCV and matplotlib libraries in Python in involving the collection of facial image datasets covering different genders with a total of 1024 facial images, each with 40 attributes. This dataset was used to train a deep artificial neural network to recognize patterns and features that are unique to male and female genders and validated. Furthermore, the artificial neural network was tested with a dataset of never-before-seen facial images to evaluate the performance of the system. The results show that the InceptionV3 Deep Learning model has great potential in gender recognition based on facial images. The developed system was able to achieve a high level of accuracy in classifying the gender of individuals from facial images with a total accuracy of 94.8% against the test data, even in complex situations such as variations in facial expressions and lighting.

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EyeRis: Visual image Recognition using Machine Learning for the Visually-Impaired

EyeRis: Visual Image Recognition using Machine Learning for ...

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international conference on Electronics, Information and Communications (ICEIC)

作者： Alliah May Eugenio Marian Jowie Patulot Lykha Jane Seguiro Angelica Nicole Tuazon Shekinah Lor Huyo-a Mideth Abisado Gabriel Avelino Sampedro Department of Computer Science Technological University of the Philippines Manila Philippines Philippine Coding Camp Research and Development Center Manila Philippines College of Computing and Information Technologies National University Manila Philippines Faculty of Information and Communication Studies University of the Philippines Open University Laguna Philippines

Visually impaired people struggle daily and have difficulty recognizing and distinguishing objects around them. Thus, they mainly depend on supervision from other people to assist them. Since smartphones have become a necessity in this modern world, the researchers formulated a solution to help the visually impaired through a machine learning-based mobile application for object recognition. Nowadays, software applications can provide accurate findings in picture classification and processing processes thanks to machine learning techniques and algorithms. In this study, the researchers use a convolutional neural network (CNN)-based system on TensorFlow Lite to create a mobile version of a visual information system employing a machine learning strategy and deep learning framework. The main objectives of the smartphone application, EyeRis, is to recognize and categorize items in real-time and to separate photographs from the user-selected scenarios. Results were analyzed and contrasted based on the app’s obtained recognition accuracy data. It demonstrated the utility of CNN as a model for image recognition algorithms.

关键词： Visualization image recognition Machine learning algorithms Software algorithms Neural networks Software Real-time systems

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MedPipe: End-to-End Joint Search of Data Augmentation and Neural Architecture for 3D Medical image Classification

MedPipe: End-to-End Joint Search of Data Augmentation and Ne...

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Medical Artificial Intelligence (MedAI), ieee international conference on

作者： Xin He Xiaowen Chu Dept. Computer Science Hong Kong Baptist University Hong Kong China Data Science and Analytics Thrust The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China

Data augmentation plays a crucial role in deep learning-based medical imaging analysis, but manually designing tailored data augmentation strategies for each dataset is impractical. Although automatic data augmentation (ADA) techniques have been explored, they often focus solely on data augmentation without considering the importance of neural architecture. Similarly, neural architecture search (NAS) methods mainly concentrate on optimizing the neural architecture while overlooking the impact of data augmentation. However, both data augmentation and neural architecture are interrelated and should be considered together. The joint optimization of data augmentation and neural architecture can lead to improved model performance by harnessing the complementary effects of customized data augmentation strategies and compatible neural architectures. Despite this, the seamless integration of data augmentation and neural architecture search remains under-explored. To address this research gap, we propose MedPipe, an approach that enables end-to-end joint search of data augmentation and neural architecture. We introduce a compact data augmentation search space and unify data augmentation and neural architecture into a cohesive network. This allows simultaneous exploration, optimizing their synergy for enhanced performance. Experimental evaluation on nine medical datasets highlights the necessity of the joint search for data augmentation and neural architecture, demonstrating the superior performance of our approach. Our work opens up possibilities for future applications in diverse medical domains.

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