International Conference on Digital Image Processing最新文献_第9页

Optic disc segmentation in retinal fundus images using improved CE-Net 基于改进CE-Net的视网膜眼底图像视盘分割

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2643259

Yingxue Wang, Lin Huang

引用次数: 0

Track initiation algorithm for bearing-only target tracking in complex background 复杂背景下纯方位目标跟踪的航迹起始算法

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2643464

Hao Wang, Weihua Wang

引用次数: 0

Masked facial region recognition using human pose estimation and broad learning system 基于人体姿态估计和广义学习系统的人脸区域识别

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2643023

Hongli Xiao, Bingshu Wang, Jiangbin Zheng, Jin Fang, Zhulin Liu, C. L. P. Chen

引用次数: 0

Identification and localisation of multiple weeds in grassland for removal operation 草地多种杂草的识别和定位，以进行除草作业

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644281

Jinjin Wang, Xiaopeng Yao, B. Nguyen

{"title":"Identification and localisation of multiple weeds in grassland for removal operation","authors":"Jinjin Wang, Xiaopeng Yao, B. Nguyen","doi":"10.1117/12.2644281","DOIUrl":"https://doi.org/10.1117/12.2644281","url":null,"abstract":"Weeds are a common issue in agriculture. Image-based weed identification has regained popularity in recent years as computing power increases. Researchers have successfully applied weed detection in the crop field and have combined the sensor (e.g.camera) and mechanical such as robotic weeders to get the location of the weeds. Meanwhile, many studies also have been conducted on the two classifications between grass and weed. However, there is no excellent and comprehensive weed dataset in reality because weeds are always similar and difficult to obtain by non-specialists. Moreover, it is challenging to identify weeds from grasslands for their similar colors, sizes, and shapes. We investigate three weeds (Bitter Gentian, Hawk's Beard, Pedunculate) relatively common in grasslands. Then, we select the typical grassland dominated by the above weeds for data collection. A natural and effective dataset is built and has generality in the scene of actual grassland. Secondly, we extract image features, including Color, Histogram, and orientation gradient histogram (HOG), and make various combinations to accurately and comprehensively reflect the actual characteristics of weeds. Thirdly, we propose a \"core zone\" algorithm to locate the weeds. The algorithm mainly adopts technology in image processing, such as threshold segmentation and morphological transformations. Experiments show that our binary classifier is more accurate than the comparison method, and the accuracy of the multi-classifier is also high. In addition, the algorithm for weeds location is more efficient than the comparative method.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133912599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Single image 3D scene reconstruction based on ShapeNet models 基于ShapeNet模型的单图像三维场景重建

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2645274

Xue Chen, Yifan Ren, Yaoxu Song

引用次数: 0

Improved video classification method based on non-parametric attention combined with self-supervision 基于非参数关注和自我监督的改进视频分类方法

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2643038

Xuchao Gong, Zongmin Li

引用次数: 0

Weakly supervised deep learning for cervical histopathology images analysis 弱监督深度学习用于宫颈组织病理学图像分析

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644291

Lei Shi, Jing Xu, Yameng Zhang, Guohua Zhao, Yufei Gao

{"title":"Weakly supervised deep learning for cervical histopathology images analysis","authors":"Lei Shi, Jing Xu, Yameng Zhang, Guohua Zhao, Yufei Gao","doi":"10.1117/12.2644291","DOIUrl":"https://doi.org/10.1117/12.2644291","url":null,"abstract":"Cervical cancer is the second most common malignancy in women, while is prevented through diagnosing and treating cervical precancerous lesions. Clinically, histopathological image analysis is recognized as the gold standard for diagnosis. However, the diagnosis of cervical precancerous lesions is challenging due to the massive size of whole slide images and subjective grading without precise quantification criteria. Most existing computer aided diagnosis approaches are patches-based, first learning patch-wise features and then aggregating these local features to infer the final prediction. Cropping pathology images into patches restrains the contextual information available to those networks, causing failing to learn clinically relevant structural representations. To address the above problems, this paper proposes a novel weakly supervised learning method called general attention network (GANet) for grading cervical precancerous lesions. A bag-of-instances pattern is introduced to overcome the limitation of the high resolution of whole slide images. Moreover, based on two transformer blocks, the proposed model is able to encode the dependencies among bags and instances that are beneficial to capture much more informative contexts, and thus produce more discriminative WSI descriptors. Finally, extensive experiments are conducted on a public cervical histology dataset and the results show that GANet achieves the state-of-the-art performance.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130881392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enabling deep reinforcement learning autonomous driving by 3D-LiDAR point clouds 利用3D-LiDAR点云实现深度强化学习自动驾驶

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644369

Yuhan Chen, Rita Tse, Michael Bosello, Davide Aguiari, Su-Kit Tang, Giovanni Pau

{"title":"Enabling deep reinforcement learning autonomous driving by 3D-LiDAR point clouds","authors":"Yuhan Chen, Rita Tse, Michael Bosello, Davide Aguiari, Su-Kit Tang, Giovanni Pau","doi":"10.1117/12.2644369","DOIUrl":"https://doi.org/10.1117/12.2644369","url":null,"abstract":"Autonomous driving holds the promise of revolutionizing our lives and society. Robot drivers will run errands such as commuting, parking cars, or taking kids to school. It is expected that, by the mid-century, humans will drive only for their pleasure. Autonomous vehicles will increase the efficiency and safety of the transportation system by reducing accidents and increasing the overall system capacity. Current autonomous driving systems are based on supervised learning that relies on massive, labeled data. It takes a lot of time, resources, and manpower to produce such data sets. While this approach is achieving remarkable results, the required effort to produce data becomes a limiting factor for general driving scenarios. This research explores Reinforcement Learning to advance autonomous driving models without labeled data. Reinforcement Learning is a learning paradigm that uses the concept of rewards to autonomously discover, through trial & error, how to solve a task. This work uses the LiDAR sensor as a case study to explore the effectiveness of Reinforcement Learning in interpreting complex data. LiDARs provide a dynamic high time-space definition map of the environment and it could be one of the key sensors for autonomous driving.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128184329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

A collaborative spectrum sensing algorithm for cognitive radio based on related vector machine 基于相关向量机的认知无线电协同频谱感知算法

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644619

Baolong Yuan, Yi Ning, F. Kan

引用次数: 0

Texture based adaptive computational resource allocation for fast AVS3 inter coding 基于纹理的快速AVS3编码自适应计算资源分配

International Conference on Digital Image Processing Pub Date : 2022-10-12 DOI: 10.1117/12.2644285

Jianing Chen

{"title":"Texture based adaptive computational resource allocation for fast AVS3 inter coding","authors":"Jianing Chen","doi":"10.1117/12.2644285","DOIUrl":"https://doi.org/10.1117/12.2644285","url":null,"abstract":"The newest Audio Video Coding Standard (AVS3) generation provides better coding efficiency than its predecessor, where two new partitioning structures, i.e., Extend Quad-Tree (EQT) and Binary-Tree (BT), are adopted. Although these split tools bring remarkable coding performance, for the price of increasing of computational coding complexity. For the popular conference video applications, experiments show that the EQT or BT split times in different regions are quite different, which indicates that it is unnecessary to provide all partitioning candidate modes in different area. In this work, an effective partitioning resource allocation method is proposed to reduce computational complexity while guaranteeing the coding performance. Specifically, a Decision Tree (DT) model is trained to determine available partitioning modes for current Coding Unit (CU), where input features are the histogram, sobel texture and average residual difference between current and reference CU, along with the size of CU. The training data are selected from different test sequences of AVS and Joint Video Experts Team Common Test Conditions (JCT) sequences, which are identified by the Structural Similarity (SSIM). The experiments on 720p and Common Intermediate Format (CIF) sequences, implemented on platform of AVS3 reference software HPM-9.1, under Low Delay B (LB) configuration, show the efficiency of the proposed method, which can achieve more than 40.0% computational complexity reduction, and BDBR loss is less than 2.0%.","PeriodicalId":314555,"journal":{"name":"International Conference on Digital Image Processing","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127704892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0