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6th IFIP TC 12 International Conference on Intelligence science, ICIS 2024

作者： Li, Dingyi PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9783031712524

Perceptual video super-resolution aims at converting low-resolution videos to visually appealing high-resolution ones. It may lead to temporal inconsistency due to the drastically changing outputs. In this paper, we propose cascaded sliding-window-based relativistic GAN (Generative Adversarial Network) fusion for perceptual and consistent video super-resolution (PC-VSR). Firstly, cascaded sliding-window-based relativistic GAN is designed to extract more useful information. It enlarges the temporal receptive field of sliding-window-based model in each step. It is able to enhance perceptual quality and compensate temporal consistency progressively and sufficiently. The trained separate refinement generator networks are fused into a final refinement generator. The final refinement generator can be calculated recursively at the testing stage. With our generator fusion, the parameter number is reduced and good quality is maintained. Extensive experimental results demonstrate that our approach outperforms state-of-the-art super-resolution methods in terms of perceptual quality. Our method also achieves good temporal consistency and per-pixel accuracy, compared with other perceptual approaches. © IFIP International Federation for information Processing 2025.

关键词： Generative adversarial networks

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Learning Inverse Laplacian Pyramid for Progressive Depth Completion

arXiv

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arXiv 2025年

作者： Wang, Kun Yan, Zhiqiang Fan, Junkai Li, Jun Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Depth completion endeavors to reconstruct a dense depth map from sparse depth measurements, leveraging the information provided by a corresponding color image. Existing approaches mostly hinge on single-scale propagation strategies that iteratively ameliorate initial coarse depth estimates through pixel-level message passing. Despite their commendable outcomes, these techniques are frequently hampered by computational inefficiencies and a limited grasp of scene context. To circumvent these challenges, we introduce LP-Net, an innovative framework that implements a multi-scale, progressive prediction paradigm based on Laplacian Pyramid decomposition. Diverging from propagation-based approaches, LP-Net initiates with a rudimentary, low-resolution depth prediction to encapsulate the global scene context, subsequently refining this through successive upsampling and the reinstatement of high-frequency details at incremental scales. We have developed two novel modules to bolster this strategy: 1) the Multi-path Feature Pyramid module, which segregates feature maps into discrete pathways, employing multi-scale transformations to amalgamate comprehensive spatial information, and 2) the Selective Depth Filtering module, which dynamically learns to apply both smoothness and sharpness filters to judiciously mitigate noise while accentuating intricate details. By integrating these advancements, LP-Net not only secures state-of-the-art (SOTA) performance across both outdoor and indoor benchmarks such as KITTI, NYUv2, and TOFDC, but also demonstrates superior computational efficiency. At the time of submission, LP-Net ranks 1st among all peer-reviewed methods on the official KITTI leaderboard. The source code will be made publicly accessible upon paper acceptance. © 2025, CC BY-NC-SA.

关键词： Laplace transforms

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LATEXBLEND: Scaling Multi-concept Customized Generation with Latent Textual Blending

arXiv

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arXiv 2025年

作者： Jin, Jian Yu, Zhenbo Shen, Yang Fu, Zhenyong Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Sci. & Tech. China

Customized text-to-image generation renders user-specified concepts into novel contexts based on textual prompts. Scaling the number of concepts in customized generation meets a broader demand for user creation, whereas existing methods face challenges with generation quality and computational efficiency. In this paper, we propose LATEXBLEND, a novel framework for effectively and efficiently scaling multi-concept customized generation. The core idea of LATEXBLEND is to represent single concepts and blend multiple concepts within a Latent Textual space, which is positioned after the text encoder and a linear projection. LATEXBLEND customizes each concept individually, storing them in a concept bank with a compact representation of latent textual features that captures sufficient concept information to ensure high fidelity. At inference, concepts from the bank can be freely and seamlessly combined in the latent textual space, offering two key merits for multi-concept generation: 1) excellent scalability, and 2) significant reduction of denoising deviation, preserving coherent layouts. Extensive experiments demonstrate that LATEXBLEND can flexibly integrate multiple customized concepts with harmonious structures and high subject fidelity, substantially outperforming baselines in both generation quality and computational efficiency. Project Page. Copyright © 2025, The Authors. All rights reserved.

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SpliceMix: A Cross-scale and Semantic Blending Augmentation Strategy for Multi-label image Classification

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IEEE Transactions on Multimedia 2025年

作者： Wang, Lei Zhan, Yibing Ma, Leilei Tao, Dapeng Ding, Liang Gong, Chen Nanjing University of Science and Technology Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Jiangsu Nanjing210094 China JD Explore Academy Beijing100000 China Anhui University School of Computer Science and Technology Anhui Heifei230601 China Yunnan University FIST LAB School of Information Science and Engineering Yunnan Kunming650091 China

Recently, Mix-style data augmentation methods (e.g., Mixup and CutMix) have shown promising performance in various visual tasks. However, these methods are primarily designed for single-label images, ignoring the considerable discrepancies between single- and multi-label images, i.e., a multi-label image involves multiple co-occurred categories and fickle object scales. On the other hand, previous multi-label image classification (MLIC) methods tend to design elaborate models, bringing expensive computation. In this article, we introduce a simple but effective augmentation strategy for multi-label image classification, namely SpliceMix. The "splice"in our method is two-fold: 1) Each mixed image is a splice of several downsampled images in the form of a grid, where the semantics of images attending to mixing are blended without object deficiencies for alleviating co-occurred bias;2) We splice mixed images and the original mini-batch to form a new SpliceMixed mini-batch, which allows an image with different scales to contribute to training together. Furthermore, such splice in our SpliceMixed mini-batch enables interactions between mixed images and original regular images. We also provide a simple and non-parametric extension based on consistency learning (SpliceMix-CL) to show the potential of extending our SpliceMix. Extensive experiments on various tasks demonstrate that only using SpliceMix with a baseline model (e.g., ResNet) achieves better performance than state-of-the-art methods. Moreover, the generalizability of our SpliceMix is further validated by the improvements in current MLIC methods when married with our SpliceMix. The code is available at https://***/zuiran/SpliceMix. © 1999-2012 IEEE.

关键词： image classification

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Holographic 3D display system using holographic optical element

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Proceedings of SPIE - The International Society for Optical Engineering 1999年 3637卷 78-83页

作者： Yamasaki, Koji Okamoto, Masaaki Ando, Takahisa Kitagawa, Tetuya Shimizu, Eiji Lab Image Information Science and Technology Toyonaka Japan

In recent years, the study of 3D-display is rapid development and many researchers propose many methods. Holography is best methods. But, it is difficult that we developed holographic movie in the future tense. At the present time, stereogram method will make practicable in the near future. These methods can easily make animated 3D image. But this method has one problem; this method is conflict between convergence and accommodation. An observer can't watch 3D-display of this method long time. The authors will solve this problem. The authors proposed the 3D-display system that is used holography and stereogram technology. The proposed system has little conflict between convergence and accommodation. The authors developed this 3D-display system. The developed system has four focuses in horizontal direction. The display parts of developed system are LCD display because the developed system can play 3D movie. Of cause, this display doesn't have special glasses. But, color of this display is single color. It is red. The authors will develop full color 3D-display. The picture size of this display is about 6 inch and the form of this display is very large. The author will develop small size system and show large size picture.

关键词： Liquid crystal displays

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Evaluation of HOE for head mounted display

引用

Proceedings of SPIE - The International Society for Optical Engineering 1999年 3637卷 110-118页

作者： Ando, Takahisa Yamasaki, Koji Okamoto, Masaaki Matsumoto, Toshiaki Shimizu, Eiji Lab of Image Information Science and Technology Osaka Japan

We will discuss the characteristics of the Head Mounted Display(HMD) using Holographic Optical Element(HOE) in this paper. We have already proposed that using the HOE we could realize the see-through HMD, that is to say the binocular stereoscopic display. This time we evaluate the influence on the human vision system regarding the optical characteristics of the HOE. The HMD using HOE we proposed so far is the Maxwellian View which is the direct projection on the human retina. When we see something by Maxwellian View, we don't need the focusing of the crystalline lens (ocular accommodation) because the depth field is extremely wide. Therefore our binocular crystalline lens will focus at the vergence point when the Maxwellian View is used on the binocular retina. And we can solve the dissociation of accommodation and convergence which is the basic problem of the conventional HMD. We have made the prototype of HOE which can provide the Maxwellian View on our retina and we have proved that our HOE could separate the binocular images onto left and right eye. In this report, we will introduce that the Maxwellian View will change the ocular accommodation optionally according to the convergence when we see the real objects and the virtual objects at the same time. We proved that the HOE which provided the Maxwellian View could solve the dissociation of accommodation and convergence.

关键词： Holography

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Mitosis detection and classification for breast cancer diagnosis: What we know and what is next

引用

Computers in Biology and Medicine 2025年 191卷 110057-110057页

作者： Khalil, Rafi Ullah Sajjad, Muhammad Dhahbi, Sami Bourouis, Sami Hijji, Mohammad Muhammad, Khan Digital Image Processing Lab Department of Computer Science Islamia College Peshawar Peshawar25000 Pakistan Applied College of Mahail Aseer King Khalid University Muhayil Aseer62529 Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Faculty of Computers and Information Technology University of Tabuk Tabuk71491 Saudi Arabia Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

Breast cancer is the second most deadly malignancy in women, behind lung cancer. Despite significant improvements in medical research, breast cancer is still accurately diagnosed with histological analysis. During this procedure, pathologists examine a physical sample for the presence of mitotic cells, or dividing cells. However, the high resolution of histopathology images and the difficulty of manually detecting tiny mitotic nuclei make it particularly challenging to differentiate mitotic cells from other types of cells. Numerous studies have addressed the detection and classification of mitosis, owing to increasing capacity and developments in automated approaches. The combination of machine learning and deep learning techniques has greatly revolutionized the process of identifying mitotic cells by offering automated, precise, and efficient solutions. In the last ten years, several pioneering methods have been presented, advancing towards practical applications in clinical settings. Unlike other forms of cancer, breast cancer and gliomas are categorized according to the number of mitotic divisions. Numerous papers have been published on techniques for identifying mitosis due to easy access to datasets and open competitions. Convolutional neural networks and other deep learning architectures can precisely identify mitotic cells, significantly decreasing the amount of labor that pathologists must perform. This article examines the techniques used over the past decade to identify and classify mitotic cells in histologically stained breast cancer hematoxylin and eosin images. Furthermore, we examine the benefits of current research techniques and predict forthcoming developments in the investigation of breast cancer mitosis, specifically highlighting machine learning and deep learning. © 2025 Elsevier Ltd

关键词： Lung cancer

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Unmanned aerial vehicle based multi-person detection via deep neural network models

引用

Frontiers in Neurorobotics 2025年 19卷 1582995页

作者： Alshehri, Mohammed Zahoor, Laiba AlQahtani, Yahya Alshahrani, Abdulmonem AlHammadi, Dina Abdulaziz Jalal, Ahmad Liu, Hui Department of Computer Science King Khalid University Abha Saudi Arabia Faculty of Computer Science Air University Islamabad Pakistan Department of Informatics and Computer Systems King Khalid University Abha Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia Department of Computer Science and Engineering College of Informatics Korea University Seoul Korea Republic of Cognitive Systems Lab University of Bremen Bremen Germany Guodian Nanjing Automation Company Ltd. Nanjing China Jiangsu Key Laboratory of Intelligent Medical Image Computing School of Future Technology Nanjing University of Information Science and Technology Nanjing China

Introduction: Understanding human actions in complex environments is crucial for advancing applications in areas such as surveillance, robotics, and autonomous systems. Identifying actions from UAV-recorded videos becomes more challenging as the task presents unique challenges, including motion blur, dynamic background, lighting variations, and varying viewpoints. The presented work develops a deep learning system that recognizes multi-person behaviors from data gathered by UAVs. The proposed system provides higher recognition accuracy while maintaining robustness along with dynamic environmental adaptability through the integration of different features and neural network models. The study supports the wider development of neural network systems utilized in complicated contexts while creating intelligent UAV applications utilizing neural networks. Method: The proposed study uses deep learning and feature extraction approaches to create a novel method to recognize various actions in UAV-recorded video. The proposed model improves identification capacities and system robustness by addressing motion dynamic problems and intricate environmental constraints, encouraging advancements in UAV-based neural network systems. Results: We proposed a deep learning-based framework with feature extraction approaches that may effectively increase the accuracy and robustness of multi-person action recognition in the challenging scenarios. Compared to the existing approaches, our system achieved 91.50% on MOD20 dataset and 89.71% on Okutama-Action. These results do, in fact, show how useful neural network-based methods are for managing the limitations of UAV-based application. Discussion: Results how that the proposed framework is indeed effective at multi-person action recognition under difficult UAV conditions. Copyright © 2025 Alshehri, Zahoor, AlQahtani, Alshahrani, AlHammadi, Jalal and Liu.

关键词： Drones

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Estimation of the degree of human facial expression

Estimation of the degree of human facial expression

引用

5th IEEE International Workshop on Robot and Human Communication (RO-MAN 96)

作者： Kimura, S Yachida, M Lab of Image Information Science and Technology Osaka Japan

ISBN: (纸本)0780332539

The purpose of this paper is to estimate the degree of human facial expression from expressionless to its maximum. For the purpose of extracting subtle changes in the face, it is necessary to eliminate the individuality appearing in the facial images. Our method is based on the idea that the degree of facial expression can be extracted as a variation from an explessionless face. Using a Potential Net, a face is sampled as a whole patten from a facial edge image which we regard as a potential field. Then applying K-L expansion, Emotion space is achieved with principal components, and estimation is achived by projecting input images on to the eigen space. We have constructed three kind of expression models: happiness, anger, and surprise, and input images evaluated.

关键词： Pattern recognition

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Human motion capture by integrating gyroscopes and accelerometers

Human motion capture by integrating gyroscopes and accelerom...

引用

1996 IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems

作者： Sakaguchi, T Kanamori, T Katayose, H Sato, K Inokuchi, S Lab of Image Information Science and Technology Toyonaka Japan

ISBN: (纸本)078033700X

This paper describes a motion capturing system for human arm motion in real time;directly, simply and precisely by integrating two types of sensor;gyroscopes and accelerometers. A new sensor fusion technique is proposed which is enlarging merits of each sensor and compensating for their demerits. It's potential precision has been verified by experiment and simulation.

关键词： Sensor data fusion

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