Qiu Shuang, Yang Banghua, Chen Xiaogang, Wang Yijun, Xu Minpeng, Lyu Baoliang, Gao Xiaorong, He Huiguang
{"title":"A survey on encoding and decoding technology of non-invasive brain-computer interface","authors":"Qiu Shuang, Yang Banghua, Chen Xiaogang, Wang Yijun, Xu Minpeng, Lyu Baoliang, Gao Xiaorong, He Huiguang","doi":"10.11834/jig.230031","DOIUrl":"https://doi.org/10.11834/jig.230031","url":null,"abstract":"","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81653887","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}
Wang Rongfang, W. Liang, Li-En Chang, Huo Chunlei, Chen Jiawei
{"title":"IIQ-CNN-based cross-domain change detection of SAR images","authors":"Wang Rongfang, W. Liang, Li-En Chang, Huo Chunlei, Chen Jiawei","doi":"10.11834/jig.211159","DOIUrl":"https://doi.org/10.11834/jig.211159","url":null,"abstract":"","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90758890","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}
Y. Xiang, Cheng Gong, Li Ge, Dai Wei, Wenxin Yin, Yingchao Feng, Yao Xiwen, Zhongling Huang, Sun Xian, Junwei Han
{"title":"Progress in small object detection for remote sensing images","authors":"Y. Xiang, Cheng Gong, Li Ge, Dai Wei, Wenxin Yin, Yingchao Feng, Yao Xiwen, Zhongling Huang, Sun Xian, Junwei Han","doi":"10.11834/jig.221202","DOIUrl":"https://doi.org/10.11834/jig.221202","url":null,"abstract":"","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87383497","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}
： Objective Breast cancer - prognostic Ki67 score can be as a key indicator for the proliferation rate of malignant （ invasive ） cells. Negative and positive nuclei detection is an essential part of Ki67 scoring. An automated algorithm for nuclei detection can alleviate the negative impact of intra/inter - observer variation and labor - intensive nuclei counting. In
{"title":"CentroidNet：a light-weight，fast nuclei centroid detection model for breast Ki67 scoring","authors":"Wen Ke, Jin Xu, An Hong, He Jie, Wang Jue","doi":"10.11834/jig.211207","DOIUrl":"https://doi.org/10.11834/jig.211207","url":null,"abstract":"： Objective Breast cancer - prognostic Ki67 score can be as a key indicator for the proliferation rate of malignant （ invasive ） cells. Negative and positive nuclei detection is an essential part of Ki67 scoring. An automated algorithm for nuclei detection can alleviate the negative impact of intra/inter - observer variation and labor - intensive nuclei counting. In","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83946361","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}
Pang Xiaokun, Liu Haomin, Fang Ming, Wang Zheng, Zhang Yong, Zhang Guofeng
{"title":"Dynamic 3D scenario-oriented monocular SLAM based on semantic probability prediction","authors":"Pang Xiaokun, Liu Haomin, Fang Ming, Wang Zheng, Zhang Yong, Zhang Guofeng","doi":"10.11834/jig.210632","DOIUrl":"https://doi.org/10.11834/jig.210632","url":null,"abstract":"","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82513071","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}
{"title":"A survey of medical image captioning technique: encoding, decoding and latest advance","authors":"Zhu Yi, Li Xiu","doi":"10.11834/jig.211021","DOIUrl":"https://doi.org/10.11834/jig.211021","url":null,"abstract":"","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":"531 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72550479","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}
Xiao Zhaolin, Sun Tongxin, Zhang Jingrui, Jin Haiyan
目的 油画区别于其他绘画形式的重要特征之一是其颜料图层存在厚度变化。因此，油画相似度鉴别不仅关注油画的纹理色彩细节，也需考虑其表面颜料厚度的差异。针对颜料厚度的细微变化，提出一种油画相似度的表面光场特征点分布鉴别方法。采用光场成像技术观测由于油画表面厚度起伏导致的不同角度成像的差异，以量化计算油画表面光场的相似性。方法 该方法采用一块微透镜阵列板对油画表面的角度域变化进行光场编码，利用光场相机采集编码后的油画表面光场。在此基础上，选取油画表面光场中角度域差异大的特征点集合，采用K-Means方法对该特征点集合的二维分布进行多边形向量化描述。进而提出计算关键点连接线的欧氏距离与线段夹角，以度量表面光场特征点分布多边形的相似性。结果 采用Illum光场相机拍摄了多组真实油画的表面光场实验数据，实验结果表明本文方法可对存在细微颜料厚度差别的油画相似度进行鉴别。在对油画表面光场的识别区分度以及检测精度方面，本文方法显著优于现有图像特征匹配鉴别方法。结论 实验分析表明，相比于经典交并比及相似度系数，所提出油画相似性度量具有更优的相似性度量精度。通过调整编码板与测试油画表面距离的反复实验，验证所提方法能够有效检测0.5 mm以上厚度变化的表面光场差异。;Objective Painting-specific layer thickness variation of oil painting can be used to reflect the shape,depth,and texture of the painted objects. Since the layer thickness is not be replicated easily,it is beneficial to identify the similarity between two sort of oil paintings. Thanks to its layer thickness,the surface of an oil painting will theoretically appear nonLambertian feature,i. e.,the reflectance light of a targeted point will have obvious variation at a different angle. This kind of non-Lambertian feature is beneficial for oil painting identification. However,since the thickness variation of the oil painting surface is often less than 1mm,this sort of non-Lambertian effect is illustrated as a stronger weakness and it is still challenging to be captured using a traditional camera,even though for its higher spatial resolution there. To identify the weak angular variation better,we develop a compact plenoptic camera-based acquisition system further for capturing the oil painting surface. We facilitate a surface light field of the target oil paintings using a micro-lens array,which amplifies the non-Lambertian feature. It can activate sensitive identification of the oil painting in a non-contactable measurement. Method First,a surface light field is constructed via a micro-lens array in front of the oil painting surface at a distance of one focal length of the elemental micro-lens. In this case,the non-Lambertian effect of an oil painting can be significantly amplified based on the micro-lens array. Then,a compactable light field camera is used to capture a surface light field of the painting at 0. 2 m away. A captured light field is composed of multiple angular samples (i. e.,the sub-aperture images),which can refer to the speciality of the painting surfaces. For surface thickness variation,the angular samples of the captured light field are distributed deliberately,especially for some angular variation sensitive feature points. Therefore,this kind of non-Lambertian feature can be calculated to identify oil painting similarities. A theoretical analysis is used to demonstrate the optical path design of this amplification of angular differences. After capturing the surface light fields rather than applying the feature points matching,we try to measure the similarity of oil painting surfaces using a multiple angular views-derived polygonal similarity computation. The similarity metric is proposed,and the global distribution
目的 油画区别于其他绘画形式的重要特征之一是其颜料图层存在厚度变化。因此，油画相似度鉴别不仅关注油画的纹理色彩细节，也需考虑其表面颜料厚度的差异。针对颜料厚度的细微变化，提出一种油画相似度的表面光场特征点分布鉴别方法。采用光场成像技术观测由于油画表面厚度起伏导致的不同角度成像的差异，以量化计算油画表面光场的相似性。方法 该方法采用一块微透镜阵列板对油画表面的角度域变化进行光场编码，利用光场相机采集编码后的油画表面光场。在此基础上，选取油画表面光场中角度域差异大的特征点集合，采用K-Means方法对该特征点集合的二维分布进行多边形向量化描述。进而提出计算关键点连接线的欧氏距离与线段夹角，以度量表面光场特征点分布多边形的相似性。结果 采用Illum光场相机拍摄了多组真实油画的表面光场实验数据，实验结果表明本文方法可对存在细微颜料厚度差别的油画相似度进行鉴别。在对油画表面光场的识别区分度以及检测精度方面，本文方法显著优于现有图像特征匹配鉴别方法。结论 实验分析表明，相比于经典交并比及相似度系数，所提出油画相似性度量具有更优的相似性度量精度。通过调整编码板与测试油画表面距离的反复实验，验证所提方法能够有效检测0.5 mm以上厚度变化的表面光场差异。;Objective Painting-specific layer thickness variation of oil painting can be used to reflect the shape,depth,and texture of the painted objects. Since the layer thickness is not be replicated easily,it is beneficial to identify the similarity between two sort of oil paintings. Thanks to its layer thickness,the surface of an oil painting will theoretically appear nonLambertian feature,i. e.,the reflectance light of a targeted point will have obvious variation at a different angle. This kind of non-Lambertian feature is beneficial for oil painting identification. However,since the thickness variation of the oil painting surface is often less than 1mm,this sort of non-Lambertian effect is illustrated as a stronger weakness and it is still challenging to be captured using a traditional camera,even though for its higher spatial resolution there. To identify the weak angular variation better,we develop a compact plenoptic camera-based acquisition system further for capturing the oil painting surface. We facilitate a surface light field of the target oil paintings using a micro-lens array,which amplifies the non-Lambertian feature. It can activate sensitive identification of the oil painting in a non-contactable measurement. Method First,a surface light field is constructed via a micro-lens array in front of the oil painting surface at a distance of one focal length of the elemental micro-lens. In this case,the non-Lambertian effect of an oil painting can be significantly amplified based on the micro-lens array. Then,a compactable light field camera is used to capture a surface light field of the painting at 0. 2 m away. A captured light field is composed of multiple angular samples (i. e.,the sub-aperture images),which can refer to the speciality of the painting surfaces. For surface thickness variation,the angular samples of the captured light field are distributed deliberately,especially for some angular variation sensitive feature points. Therefore,this kind of non-Lambertian feature can be calculated to identify oil painting similarities. A theoretical analysis is used to demonstrate the optical path design of this amplification of angular differences. After capturing the surface light fields rather than applying the feature points matching,we try to measure the similarity of oil painting surfaces using a multiple angular views-derived polygonal similarity computation. The similarity metric is proposed,and the global distribution
{"title":"Feature point distribution of the surface light field-measured oil painting similarity identification","authors":"Xiao Zhaolin, Sun Tongxin, Zhang Jingrui, Jin Haiyan","doi":"10.11834/jig.220774","DOIUrl":"https://doi.org/10.11834/jig.220774","url":null,"abstract":"目的 油画区别于其他绘画形式的重要特征之一是其颜料图层存在厚度变化。因此，油画相似度鉴别不仅关注油画的纹理色彩细节，也需考虑其表面颜料厚度的差异。针对颜料厚度的细微变化，提出一种油画相似度的表面光场特征点分布鉴别方法。采用光场成像技术观测由于油画表面厚度起伏导致的不同角度成像的差异，以量化计算油画表面光场的相似性。方法 该方法采用一块微透镜阵列板对油画表面的角度域变化进行光场编码，利用光场相机采集编码后的油画表面光场。在此基础上，选取油画表面光场中角度域差异大的特征点集合，采用K-Means方法对该特征点集合的二维分布进行多边形向量化描述。进而提出计算关键点连接线的欧氏距离与线段夹角，以度量表面光场特征点分布多边形的相似性。结果 采用Illum光场相机拍摄了多组真实油画的表面光场实验数据，实验结果表明本文方法可对存在细微颜料厚度差别的油画相似度进行鉴别。在对油画表面光场的识别区分度以及检测精度方面，本文方法显著优于现有图像特征匹配鉴别方法。结论 实验分析表明，相比于经典交并比及相似度系数，所提出油画相似性度量具有更优的相似性度量精度。通过调整编码板与测试油画表面距离的反复实验，验证所提方法能够有效检测0.5 mm以上厚度变化的表面光场差异。;Objective Painting-specific layer thickness variation of oil painting can be used to reflect the shape,depth,and texture of the painted objects. Since the layer thickness is not be replicated easily,it is beneficial to identify the similarity between two sort of oil paintings. Thanks to its layer thickness,the surface of an oil painting will theoretically appear nonLambertian feature,i. e.,the reflectance light of a targeted point will have obvious variation at a different angle. This kind of non-Lambertian feature is beneficial for oil painting identification. However,since the thickness variation of the oil painting surface is often less than 1mm,this sort of non-Lambertian effect is illustrated as a stronger weakness and it is still challenging to be captured using a traditional camera,even though for its higher spatial resolution there. To identify the weak angular variation better,we develop a compact plenoptic camera-based acquisition system further for capturing the oil painting surface. We facilitate a surface light field of the target oil paintings using a micro-lens array,which amplifies the non-Lambertian feature. It can activate sensitive identification of the oil painting in a non-contactable measurement. Method First,a surface light field is constructed via a micro-lens array in front of the oil painting surface at a distance of one focal length of the elemental micro-lens. In this case,the non-Lambertian effect of an oil painting can be significantly amplified based on the micro-lens array. Then,a compactable light field camera is used to capture a surface light field of the painting at 0. 2 m away. A captured light field is composed of multiple angular samples (i. e.,the sub-aperture images),which can refer to the speciality of the painting surfaces. For surface thickness variation,the angular samples of the captured light field are distributed deliberately,especially for some angular variation sensitive feature points. Therefore,this kind of non-Lambertian feature can be calculated to identify oil painting similarities. A theoretical analysis is used to demonstrate the optical path design of this amplification of angular differences. After capturing the surface light fields rather than applying the feature points matching,we try to measure the similarity of oil painting surfaces using a multiple angular views-derived polygonal similarity computation. The similarity metric is proposed,and the global distribution","PeriodicalId":36336,"journal":{"name":"中国图象图形学报","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135103462","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}
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