... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging最新文献

Color constancy using AlexNet convolutional neural network 色彩恒常性使用AlexNet卷积神经网络

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2021-08-06 DOI: 10.1117/12.2604686

Mengyao Yang, K. Xie, Tong Li, Yonghua Ye, Zepeng Yang

引用次数: 2

Detection of Wagyu beef sources with image classification using convolutional neural network 基于卷积神经网络的和牛源图像分类检测

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2021-08-06 DOI: 10.1117/12.2604971

Nattakorn Kointarangkul, Y. Limpiyakorn

{"title":"Detection of Wagyu beef sources with image classification using convolutional neural network","authors":"Nattakorn Kointarangkul, Y. Limpiyakorn","doi":"10.1117/12.2604971","DOIUrl":"https://doi.org/10.1117/12.2604971","url":null,"abstract":"Wagyu beef originated in Japan. There are many types of Wagyu beef in the market around the globe, though. Primary sources may include Australia, the United States of America, Canada, and the United Kingdom. The authentic Japanese Wagyu is well known for its intense marbling, juicy rich flavor, and tenderness. And there are differences in flavor, texture, and quality between the different types of Wagyu. Nowadays, there is a growing interest in deep learning as a remarkable solution for several domain problems such as computer vision and image classification. In this study, we thus present an AI-based approach to identifying Wagyu beef sources with image classification. A deep neural network, CNN, was constructed to detect the marbled fat patterns of two sources, Japanese Wagyu and Australian Wagyu. The images were collected from reliable sources on the internet and augmented with DCGAN. The prediction of Wagyu sources achieved high accuracy of 95%. The learning model of Convolutional Neural Networks was found to be a promising method for the rapid characterization of the unique patterns of marbled fat layers. The classifier would benefit the customers for buying what they expect from the products in terms of quality and taste.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"23 1","pages":"119130J - 119130J-5"},"PeriodicalIF":0.0,"publicationDate":"2021-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76719291","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

The generalized covariance union fusion approach for distributed sensors with different fields of view 不同视场分布式传感器的广义协方差联合融合方法

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2021-08-06 DOI: 10.1117/12.2604692

Feng Ma, Huan-zhang Lu, Luping Zhang, Xinglin Sheng

引用次数: 0

Single frame shadow segmentation based on image enhancement for video SAR 基于图像增强的视频SAR单帧阴影分割

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2021-08-06 DOI: 10.1117/12.2604767

Yiming Xu, Dongsheng Li, Jushi Tang

{"title":"Single frame shadow segmentation based on image enhancement for video SAR","authors":"Yiming Xu, Dongsheng Li, Jushi Tang","doi":"10.1117/12.2604767","DOIUrl":"https://doi.org/10.1117/12.2604767","url":null,"abstract":"As Video synthetic aperture radar (SAR) technology has been developing rapidly in recent years, moving target detection and tracking has gradually become a research hotspot in the field of SAR. Since moving targets in Video SAR produce relatively clear shadows at their real locations, the shadow-based approach provides a new method for ground moving target detection. In this paper, a new approach based on image fusion enhancement is proposed to improve the extraction effect of target shadow in single frame Video SAR image, and the process of shadow segmentation is studied accordingly. First, we use Median Filter to denoise the image, and then use a variety of image enhancement methods to improve the contrast between shadows and background, including piecewise linear stretching, histogram specification, and S-curve enhancement, then use adaptive threshold segmentation algorithm to realize the separation of background and target shadow, finally use morphological processing method to further highlight the target shadow. The effectiveness of the proposed approach is verified on the Video SAR dataset published by Sandia Lab.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"38 1","pages":"1191306 - 1191306-8"},"PeriodicalIF":0.0,"publicationDate":"2021-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81229078","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

Research on infrared and visible image registration of substation equipment based on multi-scale Retinex and ASIFT features 基于多尺度Retinex和ASIFT特征的变电站设备红外和可见光图像配准研究

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2021-08-06 DOI: 10.1117/12.2605000

Ning Yang, Yang Yang, Peng Li, Fei Gao

{"title":"Research on infrared and visible image registration of substation equipment based on multi-scale Retinex and ASIFT features","authors":"Ning Yang, Yang Yang, Peng Li, Fei Gao","doi":"10.1117/12.2605000","DOIUrl":"https://doi.org/10.1117/12.2605000","url":null,"abstract":"Infrared and visible image registration of substation equipment is of great significance for power equipment detection and fault diagnosis. The scene of substation is complex, and the background of equipment image is usually messy, and the feature points of visible image are easy to fall on the background. The metal has good thermal conductivity, and its temperature is close to the ambient temperature. The metal part in the infrared image with metal tower as the background can not be clearly displayed, which is easy to cause the image mismatch or even unable to match. The existing registration methods such as SIFT, SURF and ASIFT are difficult to effectively solve this kind of image registration problem of substation equipment with complex background. To solve this problem, this paper proposes an infrared and visible image registration algorithm based on Multi-scale Retinex and ASIFT features. Firstly, the Multi-scale Retinex algorithm is used to separate the components representing the properties of the object in the visible image, so as to weaken the influence of the clutter background. Then, the ASIFT algorithm is used to do affine transformation to simulate the affine deformation under all parallax, and the feature points are roughly matched Finally, the random sampling consistent algorithm is added to eliminate the mismatching points. Experimental results show that the algorithm can increase the number of matching points by at least 4 times, the average matching accuracy is improved by 13%, and the average matching time is shortened by 183ms.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"1 1","pages":"1191303 - 1191303-8"},"PeriodicalIF":0.0,"publicationDate":"2021-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87190004","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

Nonnegative matrix factorization of DCE-MRI for prostate cancer classification DCE-MRI非阴性基质因子分解在前列腺癌分类中的应用

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2021-08-06 DOI: 10.1117/12.2604770

Aijie Hou, Yahui Peng, Xinchun Li

引用次数: 0

Quality perception and discrimination thresholds in quantised triangle meshes 量化三角形网格的质量感知和判别阈值

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2021-08-06 DOI: 10.1117/12.2604720

Aeshah Almutairi, T. Saarela, I. Ivrissimtzis

引用次数: 0

Image classification based on self-attention convolutional neural network 基于自注意卷积神经网络的图像分类

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2021-08-06 DOI: 10.1117/12.2604788

Xiaohong Cai, Ming Li, H. Cao, Jin-gang Ma, Xiaoyan Wang, Xuqiang Zhuang

引用次数: 0

Mobile geo-tagging and cloud-based underwater garbage identification using convolutional neural network 基于卷积神经网络的移动地理标记和云水下垃圾识别

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2021-08-06 DOI: 10.1117/12.2605058

Jessie R. Balbin, Marianne M. Sejera, Ziad N. Al-Sagheer, Jann Amiel Nidehn B. Castañeda, Von Andrine V. Francisco

{"title":"Mobile geo-tagging and cloud-based underwater garbage identification using convolutional neural network","authors":"Jessie R. Balbin, Marianne M. Sejera, Ziad N. Al-Sagheer, Jann Amiel Nidehn B. Castañeda, Von Andrine V. Francisco","doi":"10.1117/12.2605058","DOIUrl":"https://doi.org/10.1117/12.2605058","url":null,"abstract":"Water is the essence of life, and water pollution is a major threat to all living things on this planet. To provide solutions to help combat water pollution, we have created a device that would help locate and identify the different garbage types underwater. This paper focused on the detection and identification of cans, plastics, polystyrenes, and glass underwater using object detection and object identification by Convolutional Neural Network and Geotagging. The system set-up comprises the following: a webcam, power bank, Raspberry Pi, GPS module, and an improvise floater. The GUI will display the camera's captured video, the number of garbage identified, and its location in coordinates. The testing was done in two ways: different water visibility and different water levels. The identification accuracy of our program is 94.33% for plastics, 97.34% for glass, 96.89% for polystyrenes, 98.22% for cans, and 96.88% for random garbage, reliability for identification is 100% for plastics, 91.67% for glass, 91.67% for polystyrenes, 95.83% for cans, and 91.67% for random garbage. The mean, median, and mode for the visibility levels are 96.375, 98, and 99, and the depth level is 96.385, 98, and 99.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"6 1","pages":"119130N - 119130N-5"},"PeriodicalIF":0.0,"publicationDate":"2021-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89936207","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: 1

Learning human-object interactions by attention aggregation 通过注意力聚合学习人与物体的交互

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2021-08-06 DOI: 10.1117/12.2604708

Dongzhou Gu, Shuang Cai, Shiwei Ma

{"title":"Learning human-object interactions by attention aggregation","authors":"Dongzhou Gu, Shuang Cai, Shiwei Ma","doi":"10.1117/12.2604708","DOIUrl":"https://doi.org/10.1117/12.2604708","url":null,"abstract":"Recent years, deep neural networks have achieved impressive progress in object detection. However, detecting the interactions between objects is still challenging. Many researchers pay attention to human-object interaction (HOI) detection as a basic task in detailed scene understanding. Most conventional HOI detectors are in a two-stage manner and usually slow in inference. One-stage methods for direct parallel detection of HOI triples breaks through the limitation of object detection, but the extracted features are still insufficient. To overcome these drawbacks above, we propose an improved one-stage HOI detection approach, in which attention aggregation module and dynamic point matching strategy play key roles. The attention aggregation enhances the semantic expression ability of interaction points explicitly by aggregating contextually important information, while the matching strategy can filter the negative HOI pairs effectively in the inference stage. Extensive experiments on two challenging HOI detection benchmarks: VCOCO and HICO-DET show that our method achieves considerable performance compared to state-of-the-art performance without any additional human pose and language features.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"68 1","pages":"119130H - 119130H-5"},"PeriodicalIF":0.0,"publicationDate":"2021-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74934336","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