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8th International visual Informatics Conference, IVIC 2023

作者： Baharin, Hanif Harun, Afdallyna Fathiyah Mohd Norowi, Noris Institute of Visual Informatics Universiti Kebangsaan Malaysia UKM Selangor Bangi43600 Malaysia School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Selangor Shah Alam40450 Malaysia Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Serdang43400 Malaysia

ISBN: (纸本)9789819973385

This paper describes a pilot ethnomethodology study of visiting a 3D virtual gallery that exhibits 3D models of physical artworks and of viewing photographs of artworks in a 2D online artist portfolio. Artwork, with its intrinsic information and layers of meaning, is a form of data which narrates a compelling and evocative story to engage audience. The digital landscape is a compelling medium to deliver this storytelling. However, such approach demands a delicate balance between technology incorporation and artistic vision. In an era of converging data and art, this paper explores the aesthetics of data storytelling and immersive online art experiences. Amid CoVID-19 lockdowns, physical art exhibitions migrated online, prompting a study of 3D virtual gallery visits and 2D online artist portfolios. Participants engaged in think-aloud protocols while navigating both formats, revealing insights into rationalizing actions and interpreting artworks online. Challenges emerged, including disrupted responses due to the inability to discern texture in 3D models or artwork photos. Navigational control, viewing distance, detailed artwork descriptions, and stable internet connections further influenced the online viewing experience. This prompts a further exploration on means to curate online exhibitions thoughtfully, where data aesthetics and artistic storytelling is harmonized for realistic interpretation within the digital expanse. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Textures

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Cross-Angle Facial Individual Verification of Giant Panda

Cross-Angle Facial Individual Verification of Giant Panda

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Wu, Jiawei Su, Han Min, Peng He, Mengnan Wu, Pengcheng Luo, Gai Hou, Rong Chen, Peng College Computer Science Of Sichuan Normal University Chengdu China Visual Computing and Visual Reality Key Laboratory of Sichuan Province Chengdu China College of Sichuan Normal University Chengdu China Chengdu Research Base of Giant Panda China Chengdu Research Base of Giant Panda Breeding China Sichuan Normal University Chengdu Research Base of Giant Panda Breeding Chengdu China

ISBN: (纸本)9781665410205

Enhancing the protection of giant pandas necessitates a precise means of verifying their individual information. With the development of deep learning technology, some individual recognition methods for giant pandas have emerged. However, these methods often neglect cross-angle facial recognition, a common occurrence in natural settings, and predominantly focus on identification rather than verification, rendering them unsuitable for wild populations of unknown individuals. Cross-angle facial verification poses novel challenges, notably feature misalignment and geometric deformation induced by varying angles, particularly in the verification process reliant on comparing feature discrepancies. To address this issue, we developed the CrossPandaFace model. This model employs a Pixel Drift Unit (PDU) to adjust feature pixels and utilizes template features generated by the Template Generation Module (TGM) as a reference to align giant pandas from different angles onto a unified template. Furthermore, Multi-scale Feature Supplement (MSFS) compensates for the potential risk of losing local features of aligned features. Experimental results show state-of-the-art performance, thus affirming the efficacy of our model for cross-angle giant panda verification. © 2024 IEEE.

关键词： Deep learning

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A Smart Farming System for Rubber Nursery Management in Monitoring Plant Growth Performance Using IoT Technology 1

A Smart Farming System for Rubber Nursery Management in Moni...

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1st Engineering and science International Conference 2024, ESIC 2024

作者： Sadikan, S.F.N. Aziz, M.L.H.A. Umor, M.A.S. Mahzan, S. Marjudi, S. Salamat, M.A. Dunne, R.S. Faculty of Plantation and Agrotechnology Universiti Teknologi MARA Merlimau Melaka77300 Malaysia College of Computing Informatics and Media Universiti Teknologi MARA Merlimau Melaka77300 Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Batu Pahat86400 Malaysia Faculty of Communication Visual art and Computing Universiti Selangor Kuala Selangor45600 Malaysia

Rubber plantations are an integral part of economy in Malaysia. However, inadequate soil moisture poses a significant problem for rubber trees as it can lead to water stress, ultimately hindering their growth, leaf development, and latex production. Water stress occurs when the trees are unable to obtain sufficient water from the soil, which resulting in stunted growth, leaf development, leading to fewer and smaller leaves. There is needed an effective water management strategy to ensure optimal growth of rubber trees for latex production. Thus, this project is aim to design and build an active soil monitoring system based on the Internet of Things (IoT). This system is made up of two major components: soil moisture and light intensity. The first component is a soil moisture sensor, which determines the soil's moisture content;light intensity sensor, which detects the intensity of the light and ANSELF Brushless DC Pump connected to an Arduino. This system provides sustainable solutions to increase crop output, reduce waste, and develop a greener plant cultivation. This smart monitoring plant growth system prototype is important in agriculture and plant production. It has potential to promote healthy plant growth and increase agricultural production. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Soil moisture

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Mahalanobis Distance-based Multi-view Optimal Transport for Multi-view Crowd Localization

arXiv

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arXiv 2024年

作者： Zhang, Qi Zhang, Kaiyi Chan, Antoni B. Huang, Hui Visual Computing Research Center College of Computer Science and Software Engineering Shenzhen University Shenzhen China China Department of Computer Science City University of Hong Kong Hong Kong

Multi-view crowd localization predicts the ground locations of all people in the scene. Typical methods usually estimate the crowd density maps on the ground plane first, and then obtain the crowd locations. However, the performance of existing methods is limited by the ambiguity of the density maps in crowded areas, where local peaks can be smoothed away. To mitigate the weakness of density map supervision, optimal transport-based point supervision methods have been proposed in the single-image crowd localization tasks, but have not been explored for multi-view crowd localization yet. Thus, in this paper, we propose a novel Mahalanobis distance-based multi-view optimal transport (M-MVOT) loss specifically designed for multi-view crowd localization. First, we replace the Euclidean-based transport cost with the Mahalanobis distance, which defines elliptical iso-contours in the cost function whose long-axis and short-axis directions are guided by the view ray direction. Second, the object-to-camera distance in each view is used to adjust the optimal transport cost of each location further, where the wrong predictions far away from the camera are more heavily penalized. Finally, we propose a strategy to consider all the input camera views in the model loss (M-MVOT) by computing the optimal transport cost for each ground-truth point based on its closest camera. Experiments demonstrate the advantage of the proposed method over density map-based or common Euclidean distance-based optimal transport loss on several multi-view crowd localization datasets. Project page: MVOT Project. © 2024, CC BY.

关键词： Cost functions

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Curricular Object Manipulation in LiDAR-based Object Detection

Curricular Object Manipulation in LiDAR-based Object Detecti...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Ziyue Zhu Qiang Meng Xiao Wang Ke Wang Liujiang Yan Jian Yang Tianjin Key Laboratory of Visual Computing and Intelligent Perception College of Computer Science Nankai University Didi Chuxing

This paper explores the potential of curriculum learning in LiDAR-based 3D object detection by proposing a curricular object manipulation (COM) framework. The framework embeds the curricular training strategy into both the loss design and the augmentation process. For the loss design, we propose the COMLoss to dynamically predict object-level difficulties and emphasize objects of different difficulties based on training stages. On top of the widely-used augmentation technique called GT-Aug in Li-DAR detection tasks, we propose a novel COMAug strategy which first clusters objects in ground-truth database based on well-designed heuristics. Group-level difficulties rather than individual ones are then predicted and updated during training for stable results. Model performance and generalization capabilities can be improved by sampling and augmenting progressively more difficult objects into the training samples. Extensive experiments and ablation studies reveal the superior and generality of the proposed framework. The code is available at https://***/ZZY816/COM.

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Learning to Rank Patches for Unbiased Image Redundancy Reduction

Learning to Rank Patches for Unbiased Image Redundancy Reduc...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yang Luo Zhineng Chen Peng Zhou Zuxuan Wu Xieping Gao Yu-Gang Jiang Shanghai Collaborative Innovation Center of Intelligent Visual Computing School of Computer Science Fudan University University of Maryland College of Information Science and Engineering Hunan Normal University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Images suffer from heavy spatial redundancy because pixels in neighboring regions are spatially correlated. Existing approaches strive to overcome this limitation by reducing less meaningful image regions. However, current leading methods rely on supervisory signals. They may compel models to preserve content that aligns with labeled categories and discard content belonging to unlabeled categories. This categorical inductive bias makes these methods less effective in real-world scenarios. To address this issue, we propose a self-supervised framework for image redundancy reduction called Learning to Rank Patches (LTRP). We observe that image reconstruction of masked image modeling models is sensitive to the removal of visible patches when the masking ratio is high (e.g., 90%). Building upon it, we implement LTRP via two steps: inferring the semantic density score of each patch by quantifying variation between reconstructions with and without this patch, and learning to rank the patches with the pseudo score. The entire process is self-supervised, thus getting out of the dilemma of categorical inductive bias. We design extensive experiments on different datasets and tasks. The results demonstrate that LTRP outperforms both supervised and other self-supervised methods due to the fair assessment of image content. Code is available at https://***/irsLu/1trp.

关键词： visualization Accuracy Semantic segmentation Redundancy Semantics Self-supervised learning Object detection

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Dynamic Semi-structured Data Clustering Based on Frequently Changing Structure

Dynamic Semi-structured Data Clustering Based on Frequently ...

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Cloud computing, Big Data Applications and Software Engineering (CBASE), International Conference on

作者： Junren Yang Wei Li College of Computer Science Sichuan Normal University Chengdu China Visual Computing and Visual Reality Key Laboratory of Sichuan Sichuan Normal University Chengdu China

In order to cluster dynamic semi-structured data documents, a dynamic semi-structured data clustering algorithm based on frequently changing structure is proposed. The algorithm uses dynamic model to store the historical change information of dynamic semi-structured data, builds frequent structure path tree based on the spatial frequent changing structure, uses frequent structure path tree to calculate document similarity, and finally uses DBSCAN algorithm to cluster. Experimental results show that the Jaccard coefficient and Rand index of the proposed method are above 0.99, and the performance consumption and clustering effect are better than the traditional static clustering algorithm.

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Curricular Object Manipulation in LiDAR-based Object Detection

arXiv

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arXiv 2023年

作者： Zhu, Ziyue Meng, Qiang Wang, Xiao Wang, Ke Yan, Liujiang Yang, Jian Tianjin Key Laboratory of Visual Computing and Intelligent Perception College of Computer Science Nankai University China Didi Chuxing China

This paper explores the potential of curriculum learning in LiDAR-based 3D object detection by proposing a curricular object manipulation (COM) framework. The framework embeds the curricular training strategy into both the loss design and the augmentation process. For the loss design, we propose the COMLoss to dynamically predict object-level difficulties and emphasize objects of different difficulties based on training stages. On top of the widely-used augmentation technique called GT-Aug in LiDAR detection tasks, we propose a novel COMAug strategy which first clusters objects in ground-truth database based on well-designed heuristics. Group-level difficulties rather than individual ones are then predicted and updated during training for stable results. Model performance and generalization capabilities can be improved by sampling and augmenting progressively more difficult objects into the training samples. Extensive experiments and ablation studies reveal the superior and generality of the proposed framework. The code is available at https://***/ZZY816/COM. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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Utilizing Multipath Effects for Mobile Charging

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IEEE Transactions on Mobile computing 2025年

作者： Gao, Jing Wu, Die Zhang, Linglin Li, Jingwen Yang, Jin Peng, Jian Liu, Tang Sichuan Normal University College of Computer Science Sichuan Chengdu610101 China Sichuan University School of Cyber Science and Engineering Chengdu610065 China Sichuan University College of Computer Science Chengdu610065 China Sichuan Normal University Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Chengdu610068 China

Recently, Wireless Rechargeable Sensor Networks (WRSNs) have emerged as a promising solution to address the energy limitations of wireless sensor networks. In practical applications of WRSNs, environmental objects are ubiquitous, reflecting radio waves and causing them to reach sensors via multiple paths. These multipath effects significantly impact the power intensity received by sensors. In this paper, we study a fundamental issue of charGing schEduling with mulTipath effectS (GETS), that is, how to schedule a mobile charger by comprehensively considering the multipath effects to maximize the overall charging utility. To this end, we first establish a charging model with environmental objects to investigate the impact of multipath effects on power distribution. Then, we propose a charging scheduling scheme that not only selects a series of sojourn locations for the MC (Mobile Charger) to maximize the total power received by nearby sensors but also construct a charging path that avoids environmental objects. We conduct extensive simulations as well as indoor and outdoor field experiments to evaluate the performance of our scheme. The results demonstrate that, on average, our scheme outperforms baseline algorithms by 48.87%. © 2002-2012 IEEE.

关键词： Charging stations

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A Novel Multi-Source Contrastive Learning Approach for Robust Cross-Subject Emotion Recognition in Eeg Data

SSRN

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SSRN 2024年

作者： Deng, Xin Li, Chenhui Hong, Xinyi Huo, Huaxiang Qin, Hongxing The Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and Telecommunications Chongqing400065 China College of Computer Science Chongqing University Chongqing400044 China

Emotion Brain computer Interface (BCI) based on Electroencephalography (EEG) is a significant branch in the field of affective computing. However, the variability in emotional feedback among subjects facing the same emotional stimuli and the potential presence of noisy labels in the data significantly limit the effectiveness and generalizability of EEG-based emotion recognition models. In this paper, we propose the Multi-Source Contrastive Learning Transfer Learning Model (MSCL) to address the emotion recognition in cross-subject scenarios. The MSCL model consists of three components: Firstly, the unsupervised and supervised contrastive learning are utilized to learn the differences and commonalities among different individuals. Secondly, domain adaptation methods and feature learning in multiple domains enhance the interaction of information between source and target domains, thereby improving the model's generalization across different individuals. Thirdly, in order to mitigate the detrimental effects of noisy labels on the model, this research dynamically allocate the weights to source domains similar to the target domain based on behavioral characteristics within the target domain and employ the corresponding noise learning methods. In our experiments, MSCL achieves excellent cross-subject emotion recognition performance on the CEED Dataset and the SEED dataset. Our research highlights the significant capability of MSCL model in addressing the issues associated with inter-subject variability and label noise within EEG-based emotion BCI systems. This advancement sets the stage for the development of more refined and broadly applicable affective computing applications. © 2024, The Authors. All rights reserved.

关键词： Emotion Recognition

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