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Ceramic Decoration Extraction Method Based on computer vision and Image Processing 2

Ceramic Decoration Extraction Method Based on Computer Visio...

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2nd International conference on Networking, Communications and Information Technology, NetCIT 2022

作者： Li, Li Xi'An Academy of Fine Arts Shaanxi Xi'an10065 China

ISBN: (纸本)9781665492737

China is one of the first countries to invent pottery in the world. Like India, West Asia, Japan and the central Balkans, our ancestors invented pottery in the Neolithic period of primitive society. As a product of computer technology, the development and application of graphics processing technology has been extended to all sectors of society, especially in the field of ceramic decorative pattern design. The speed and accuracy of traditional intelligent manufacturing detection can not meet the current detection requirements. Machine vision detection technology captures the image through the vision system, uses the Prewitt directional derivative approximation operator to detect the edge of the copied image, and completes the template matching and matching value weighting by transforming the image detection space into the parameter space. After non-maximum suppression, all directions of corner response function are processed, and the right corner output and its direction are obtained. After expanding the corner area, the detection area is defined. Zernike moment discretization is used to process the real part and imaginary part of the image, and the image features are extracted. Combining the coordinates of each corner and sub-pixel coordinates of the image, the fake area and the real area are determined, so as to realize the image tampering detection. In this paper, we focus on the body patterns of ceramic artworks, aiming at high-quality reproduction and design re-creation, deeply study the collection and image correction technology of three-dimensional patterns of ceramic artworks, and deeply analyze the algorithm of feature detection and extraction. © 2022 ieee.

关键词： Extraction

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Tracked-Vehicle Retrieval by Natural Language Descriptions With Domain Adaptive Knowledge

Tracked-Vehicle Retrieval by Natural Language Descriptions W...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Huy Dinh-Anh Le Quang Qui-Vinh Nguyen Vuong Ai Nguyen Thong Duy-Minh Nguyen Nhat Minh Chung Tin-Trung Thá i Synh Viet-Uyen Ha School of Computer Science and Engineering International University Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City Vietnam

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

This paper introduces our solution for Track 2 in AI City Challenge 2022. The task is Tracked-Vehicle Retrieval by Natural Language Descriptions with a real-world dataset of various scenarios and cameras. We mainly focus on developing a robust natural language-based vehicle retrieval system to address the domain bias problem due to unseen scenarios and multi-view multi-camera vehicle tracks. Specifically, we apply CLIP [16] to effectively extract both visual and textual representations for contrastive representation learning. Furthermore, for new scenarios in the test set, we pro-pose a novel Domain Adaptive Training method that utilizes information from labeled data and transfers it to the unseen domain by generating pseudo labels. By using this simple and effective strategy, we not only bridge the domain gap between the training set and test set, but also require less computational cost and data compared to previous top performance methods. Finally, we employ a context-sensitive post-processing method to address model’s uncertainty and eliminate the wrong retrieved vehicle track. Taking one step further, we also investigate the impact of different text formats and the number of pseudo labels data for the fine-tuning process. Our proposed method has achieved 3rd place in the AI City Challenge 2022, yielding a competitive performance of 47.73% MRR accuracy on the private test set, which verified the effectiveness of the proposed solution.

关键词： Training Representation learning Visualization Adaptive systems Uncertainty Computational modeling Urban areas

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Classification of handwritten annotations in mixed-media documents 19

Classification of handwritten annotations in mixed-media doc...

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19th conference on Robots and vision, CRV 2022

作者： Dash, Amanda Albu, Alexandra Branzan University of Victoria Electrical and Computer Engineering VictoriaBC Canada

ISBN: (纸本)9781665497749

Handwritten annotations in documents contain valuable information, but they are challenging to detect and identify. This paper addresses this challenge. We propose an al-gorithm for generating a novel mixed-media document dataset, Annotated Docset, that consists of 14 classes of machine-printed and handwritten elements and annotations. We also propose a novel loss function, Dense Loss, which can correctly identify small objects in complex documents when used in fully convolutional networks (e.g. U-NET, DeepLabV3+). Our Dense Loss function is a compound function that uses local region homogeneity to promote contiguous and smooth segmentation predictions while also using an L1-norm loss to reconstruct the dense-labelled ground truth. By using regression instead of a probabilistic approach to pixel classification, we avoid the pitfalls of training on datasets with small or underrepre-sented objects. We show that our loss function outperforms other semantic segmentation loss functions for imbalanced datasets, containing few elements that occupy small areas. Experimental results show that the proposed method achieved a mean Intersection-over-Union (mIoU) score of 0.7163 for all document classes and 0.6290 for handwritten annotations, thus outperforming state-of-the-art loss functions. © 2022 ieee.

关键词： Semantics

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NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results

NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution:...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhang, Kai Gu, Shuhang Timofte, Radu Shang, Taizhang Dai, Qiuju Zhu, Shengchen Yang, Tong Guo, Yandong Jo, Younghyun Yang, Sejong Kim, Seon Joo Zha, Lin Jiang, Jiande Gao, Xinbo Lu, Wen Liu, Jing Yoon, Kwangjin Jeon, Taegyun Akita, Kazutoshi Ooba, Takeru Ukita, Norimichi Luo, Zhipeng Yao, Yuehan Xu, Zhenyu He, Dongliang Wu, Wenhao Ding, Yukang Li, Chao Li, Fu Wen, Shilei Li, Jianwei Yang, Fuzhi Yang, Huan Fu, Jianlong Kim, Byung-Hoon Baek, JaeHyun Ye, Jong Chul Fan, Yuchen Huang, Thomas S. Lee, Junyeop Lee, Bokyeung Min, Jungki Kim, Gwantae Lee, Kanghyu Park, Jaihyun Mykhailych, Mykola Zhong, Haoyu Shi, Yukai Yang, Xiaojun Yang, Zhijing Lin, Liang Zhao, Tongtong Peng, Jinjia Wang, Huibing Jin, Zhi Wu, Jiahao Chen, Yifu Shang, Chenming Zhang, Huanrong Min, Jeongki Hrishikesh, P. S. Puthussery, Densen Jiji, C., V Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland OPPO Res Dongguan Guangdong Peoples R China Yonsei Univ Seoul South Korea Facebook Menlo Pk CA USA Qingdao Hiimage Technol Co Ltd Hisense Visual Technol Co Ltd Qingdao Peoples R China Xidian Univ Xian Peoples R China East China Normal Univ ECNU Multimedia & Comp Vis Lab Shanghai Peoples R China SI Analyt Co Ltd 441 Expo Ro Daejeon 34051 South Korea Toyota Technol Inst TTI Toyota Japan DeepBlue Technol Shanghai Co Ltd Shanghai Peoples R China Baidu Inc Dept Comp Vis Technol VIS Beijing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Peking Univ Beijing Peoples R China Founder Grp State Key Lab Digital Publishing Technol Beijing Peoples R China Shanghai Jiao Tong Univ Shanghai Peoples R China Microsoft Res Beijing Peoples R China Korea Adv Inst Sci & Technol KAIST Daejeon South Korea Amazon Web Serv Seattle WA USA Univ Illinois Champaign IL USA Korea Univ Seoul South Korea Wix Com Ltd Tel Aviv Israel Guangdong Univ Technol Guangzhou Peoples R China Sun Yat Sen Univ Guangzhou Peoples R China Dalian Maritime Univ Dalian Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou Peoples R China Coll Engn Trivandrum Trivandrum Kerala India

ISBN: (纸本)9781728193601

This paper reviews the NTIRE 2020 challenge on perceptual extreme super-resolution with focus on proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor x16 based on a set of prior examples of low and corresponding high resolution images. The goal is to obtain a network design capable to produce high resolution results with the best perceptual quality and similar to the ground truth. The track had 280 registered participants, and 19 teams submitted the final results. They gauge the state-of-the-art in single image super-resolution.

关键词： computer vision

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Unsupervised domain adaptation and super resolution on drone images for autonomous dry herbage biomass estimation

Unsupervised domain adaptation and super resolution on drone...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Paul Albert Mohamed Saadeldin Badri Narayanan Brian Mac Namee Deirdre Hennessy Noel E. O’ Connor Kevin McGuinness School of Electronic Engineering Dublin City University Insight Centre for Data Analytics VistaMilk School of Computer Science University College Dublin Teagasc

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Herbage mass yield and composition estimation is an important tool for dairy farmers to ensure an adequate supply of high quality herbage for grazing and subsequently milk production. By accurately estimating herbage mass and composition, targeted nitrogen fertiliser application strategies can be deployed to improve localised regions in a herbage field, effectively reducing the negative impacts of over-fertilization on biodiversity and the environment. In this context, deep learning algorithms offer a tempting alternative to the usual means of sward composition estimation, which involves the destructive process of cutting a sample from the herbage field and sorting by hand all plant species in the herbage. The process is labour intensive and time consuming and so not utilised by farmers. Deep learning has been successfully applied in this context on images collected by high-resolution cameras on the ground. Moving the deep learning solution to drone imaging, however, has the potential to further improve the herbage mass yield and composition estimation task by extending the ground-level estimation to the large surfaces occupied by fields/paddocks. Drone images come at the cost of lower resolution views of the fields taken from a high altitude and requires further herbage ground-truth collection from the large surfaces covered by drone images. This paper proposes to transfer knowledge learned on ground-level images to raw drone images in an unsupervised manner. To do so, we use unpaired image style translation to enhance the resolution of drone images by a factor of eight and modify them to appear closer to their ground-level counterparts. We then use the enhanced drone images to train a semi-supervised algorithm that uses ground-truthed, ground-level images as the labelled data together with a large amount of unlabeled drone images. We validate our results on a small held-out drone image test set to show the validity of our approach, which opens the way for automate

关键词： Deep learning computer vision Image resolution Costs Estimation Prediction algorithms Cameras

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NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results

NTIRE 2022 Challenge on Super-Resolution and Quality Enhance...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ren Yang Radu Timofte Meisong Zheng Qunliang Xing Minglang Qiao Mai Xu Lai Jiang Huaida Liu Ying Chen Youcheng Ben Xiao Zhou Chen Fu Pei Cheng Gang Yu Junyi Li Renlong Wu Zhilu Zhang Wei Shang Zhengyao Lv Yunjin Chen Mingcai Zhou Dongwei Ren Kai Zhang Wangmeng Zuo Pavel Ostyakov Vyal Dmitry Shakarim Soltanayev Chervontsev Sergey Zhussip Magauiya Xueyi Zou Youliang Yan Pablo Navarrete Michelini Yunhua Lu Diankai Zhang Shaoli Liu Si Gao Biao Wu Chengjian Zheng Xiaofeng Zhang Kaidi Lu Ning Wang Thuong Nguyen Canh Thong Bach Qing Wang Xiaopeng Sun Haoyu Ma Shijie Zhao Junlin Li Liangbin Xie Shuwei Shi Yujiu Yang Xintao Wang Jinjin Gu Chao Dong Xiaodi Shi Chunmei Nian Dong Jiang Jucai Lin Zhihuai Xie Mao Ye Dengyan Luo Liuhan Peng Shengjie Chen Xin Liu Qian Wang Boyang Liang Hang Dong Yuhao Huang Kai Chen Xingbei Guo Yujing Sun Huilei Wu Pengxu Wei Yulin Huang Junying Chen Ik Hyun Lee Sunder Ali Khowaja Jiseok Yoon ETH Z&#x00FC rich rich University of W&#x00FC rzburg

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

This paper reviews the NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video. In this challenge, we proposed the LDV 2.0 dataset, which includes the LDV dataset (240 videos) and 95 additional videos. This challenge includes three tracks. Track 1 aims at enhancing the videos compressed by HEVC at a fixed QP. Track 2 and Track 3 target both the super-resolution and quality enhancement of HEVC compressed video. They require x2 and x4 super-resolution, respectively. The three tracks totally attract more than 600 registrations. In the test phase, 8 teams, 8 teams and 12 teams submitted the final results to Tracks 1, 2 and 3, respectively. The proposed methods and solutions gauge the state-of-the-art of super-resolution and quality enhancement of compressed video. The proposed LDV 2.0 dataset is available at https://***/RenYang-home/LDV_dataset. The homepage of this challenge (including open-sourced codes) is at https://***/RenYang-home/NTIRE22_VEnh_SR.

关键词： computer vision Target tracking Codes conferences Superresolution pattern recognition

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Optic Disc and Exudates Segmentation on Retinal Fundus Images Using Mask R-CNN

Optic Disc and Exudates Segmentation on Retinal Fundus Image...

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2022 International conference on Information Technology Research and Innovation, ICITRI 2022

作者： Dendi Maysanjaya, I Md. Antara Kesiman, Made Windu Diatri, Indradewi G.A.A. Universitas Pendidikan Ganesha Virtual Vision Image and Pattern Research Group Bali Indonesia

ISBN: (数字)9781665461849

ISBN: (纸本)9781665461849

Diabetic retinopathy (DR) is an illness caused by diabetes. In 2019, the Centers for Disease Control and Prevention (CDC) approximated 1.4 million new illnesses of diabetes. To identify the DR severity, the ophthalmologist checked the appearance of clinical features, and one of them is exudates. Exudates arise due to the accumulation of lipids and proteins due to a ruptured retinal vein. Both exudates and optic disc (OD) are almost identical, so it is necessary to conduct the region segmentation to remove OD while want to detect exudates. To obtain new insight into computer-Aided diagnosis (CAD) on DR, this work aims to segment OD and exudates, by implementing the Mask Region-based Convolutional Neural Network (Mask R-CNN) method and the ResNet50 as the backbone network. For the dataset, we used the IDRiD dataset, which contains 54 train images and 27 test images. In this work, we applied Intersection over Union (IoU) as an evaluation parameter. As the result, our proposed scheme gained the IoU value up to 0.8431 and 0.9933 in segmenting OD and exudates on each. It indicates that the applied scheme can support the establishment of CAD for the segmentation of OD and exudates. © 2022 ieee.

关键词： computer aided diagnosis

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Photometric Stereo via Discrete Hypothesis-and-Test Search

Photometric Stereo via Discrete Hypothesis-and-Test Search

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Enomoto, Kenji Waechter, Michael Kutulakos, Kiriakos N. Matsushita, Yasuyuki Osaka Univ Osaka Japan Univ Toronto Toronto ON Canada

ISBN: (纸本)9781728171685

In this paper, we consider the problem of estimating surface normals of a scene with spatially varying, general BRDFs observed by a static camera under varying, known, distant illumination. Unlike previous approaches that are mostly based on continuous local optimization, we cast the problem as a discrete hypothesis-and-test search problem over the discretized space of surface normals. While a naive search requires a significant amount of time, we show that the expensive computation block can be precomputed in a scene-independent manner, resulting in accelerated inference for new scenes. It allows us to perform a MI search over the finely discretized space of surface normals to determine the globally optimal surface normal for each scene point. We show that our method can accurately estimate surface normals of scenes with spatially varying different reflectances in a reasonable amount of time.

关键词： computer vision

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Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection

Generalized Focal Loss V2: Learning Reliable Localization Qu...

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2021 ieee/CVF conference on computer vision and pattern recognition, CVPR 2021

作者： Li, Xiang Wang, Wenhai Hu, Xiaolin Li, Jun Tang, Jinhui Yang, Jian Nanjing University of Science and Technology Momenta Nanjing University Tsinghua University

Localization Quality Estimation (LQE) is crucial and popular in the recent advancement of dense object detectors since it can provide accurate ranking scores that benefit the Non-Maximum Suppression processing and improve detection performance. As a common practice, most existing methods predict LQE scores through vanilla convolutional features shared with object classification or bounding box regression. In this paper, we explore a completely novel and different perspective to perform LQE - based on the learned distributions of the four parameters of the bounding box. The bounding box distributions are inspired and introduced as “General Distribution” in GFLV1, which describes the uncertainty of the predicted bounding boxes well. Such a property makes the distribution statistics of a bounding box highly correlated to its real localization quality. Specifically, a bounding box distribution with a sharp peak usually corresponds to high localization quality, and vice versa. By leveraging the close correlation between distribution statistics and the real localization quality, we develop a considerably lightweight Distribution-Guided Quality Predictor (DGQP) for reliable LQE based on GFLV1, thus producing GFLV2. To our best knowledge, it is the first attempt in object detection to use a highly relevant, statistical representation to facilitate LQE. Extensive experiments demonstrate the effectiveness of our method. Notably, GFLV2 (ResNet-101) achieves 46.2 AP at 14.6 FPS, surpassing the previous state-of-the-art ATSS baseline (43.6 AP at 14.6 FPS) by absolute 2.6 AP on COCO test-dev, without sacrificing the efficiency both in training and inference. © 2021 ieee

关键词： Location awareness Training computer vision Uncertainty Correlation Estimation Object detection

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A Hybrid Approach for Understanding Animal Behavior Using Deep Learning Techniques 7

A Hybrid Approach for Understanding Animal Behavior Using De...

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7th ieee International conference on Emerging Smart Computing and Informatics, ESCI 2025

作者： Vaishnavi, S. VibhavKrishnan, K.S. Manimegalai, R. Shruti, C.S. Kavitha, M. Department of CSE PSG Institute of Technology and Applied Research Coimbatore India Department of CSE eSilicon Labs Chennai India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Animals need health and wellbeing monitoring. It requires constant care, and observation. Manual tracking involves humans visually observing and recording animal movements which makes data management difficult and time-consuming. This automated animal behavior tracking system uses machine learning to accurately observe and comprehend animal behavior. The suggested study uses VGG-16 and ResNet50 for animal identification and LRCN for behavior detection. ResNet50 and VGG-16 process camera tape data. ResNet50 outperforms VGG-16 in cat and dog identification. The LRCN model helps identify animal behavior accurately. The UCF-50 dataset was first created to predict general human behavior, but it did not specifically reflect the actions of cats and dogs, which limited its application to these animals. In order to close this gap and make sure the LRCN model could successfully adapt to cats and dogs, for this study, a customized dataset centered on their distinctive behavioral pattern was exclusively created. The problem of reducing false positives in behavior detection is also addressed by the hybrid strategy that combines LRCN with ResNet50, producing predictions that are more accurate and trustworthy. Proactive health and behavior monitoring is encouraged by this integration, which improves the system's capacity to recognize, detect, and forecast animal behaviors. © 2025 ieee.

关键词： computer vision Deep Learning LRCN Algorithm ResNet50 VGG16

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