Pub Date : 2021-04-06DOI: 10.1186/s13640-021-00551-9
Alper Emlek, Murat Peker
Depth is essential information for autonomous robotics applications that need environmental depth values. The depth could be acquired by finding the matching pixels between stereo image pairs. Depth information is an inference from a matching cost volume that is composed of the distances between the possible pixel points on the pre-aligned horizontal axis of stereo images. Most approaches use matching costs to identify matches between stereo images and obtain depth information. Recently, researchers have been using convolutional neural network-based solutions to handle this matching problem. In this paper, a novel method has been proposed for the refinement of matching costs by using recurrent neural networks. Our motivation is to enhance the depth values obtained from matching costs. For this purpose, to attain an enhanced disparity map by utilizing the sequential information of matching costs in the horizontal space, recurrent neural networks are used. Exploiting this sequential information, we aimed to determine the position of the correct matching point by using recurrent neural networks, as in the case of speech processing problems. We used existing stereo algorithms to obtain the initial matching costs and then improved the results by utilizing recurrent neural networks. The results are evaluated on the KITTI 2012 and KITTI 2015 datasets. The results show that the matching cost three-pixel error is decreased by an average of 14.5% in both datasets.
{"title":"Refinement of matching costs for stereo disparities using recurrent neural networks","authors":"Alper Emlek, Murat Peker","doi":"10.1186/s13640-021-00551-9","DOIUrl":"https://doi.org/10.1186/s13640-021-00551-9","url":null,"abstract":"<p>Depth is essential information for autonomous robotics applications that need environmental depth values. The depth could be acquired by finding the matching pixels between stereo image pairs. Depth information is an inference from a matching cost volume that is composed of the distances between the possible pixel points on the pre-aligned horizontal axis of stereo images. Most approaches use matching costs to identify matches between stereo images and obtain depth information. Recently, researchers have been using convolutional neural network-based solutions to handle this matching problem. In this paper, a novel method has been proposed for the refinement of matching costs by using recurrent neural networks. Our motivation is to enhance the depth values obtained from matching costs. For this purpose, to attain an enhanced disparity map by utilizing the sequential information of matching costs in the horizontal space, recurrent neural networks are used. Exploiting this sequential information, we aimed to determine the position of the correct matching point by using recurrent neural networks, as in the case of speech processing problems. We used existing stereo algorithms to obtain the initial matching costs and then improved the results by utilizing recurrent neural networks. The results are evaluated on the KITTI 2012 and KITTI 2015 datasets. The results show that the matching cost three-pixel error is decreased by an average of 14.5% in both datasets.</p>","PeriodicalId":49322,"journal":{"name":"Eurasip Journal on Image and Video Processing","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2021-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138518808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-29DOI: 10.1186/s13640-021-00549-3
Xiang Li, Jianzheng Liu, Jessica R. Baron, Khoa Luu, Eric Patterson
{"title":"Evaluating effects of focal length and viewing angle in a comparison of recent face landmark and alignment methods","authors":"Xiang Li, Jianzheng Liu, Jessica R. Baron, Khoa Luu, Eric Patterson","doi":"10.1186/s13640-021-00549-3","DOIUrl":"https://doi.org/10.1186/s13640-021-00549-3","url":null,"abstract":"","PeriodicalId":49322,"journal":{"name":"Eurasip Journal on Image and Video Processing","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2021-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13640-021-00549-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45706600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-06DOI: 10.21203/RS.3.RS-277475/V1
B. Świderski, S. Osowski, Grzegorz Gwardys, J. Kurek, M. Słowińska, I. Lugowska
The paper presents a novel approach for designing the CNN structure of improved generalization capability in the presence of a small population of learning data. Unlike the classical methods for building CNN, we propose to introduce some randomness in the choice of layers with a different type of nonlinear activation function. The image processing in these layers is performed using either the ReLU or the softplus function. This choice is random. The randomness introduced in the network structure can be interpreted as a special form of regularization. Experiments performed on the detection of images belonging to either melanoma or non-melanoma cases have shown a significant improvement in average quality measures such as accuracy, sensitivity, precision, and area under the ROC curve.
{"title":"Random CNN structure: tool to increase generalization ability in deep learning","authors":"B. Świderski, S. Osowski, Grzegorz Gwardys, J. Kurek, M. Słowińska, I. Lugowska","doi":"10.21203/RS.3.RS-277475/V1","DOIUrl":"https://doi.org/10.21203/RS.3.RS-277475/V1","url":null,"abstract":"The paper presents a novel approach for designing the CNN structure of improved generalization capability in the presence of a small population of learning data. Unlike the classical methods for building CNN, we propose to introduce some randomness in the choice of layers with a different type of nonlinear activation function. The image processing in these layers is performed using either the ReLU or the softplus function. This choice is random. The randomness introduced in the network structure can be interpreted as a special form of regularization. Experiments performed on the detection of images belonging to either melanoma or non-melanoma cases have shown a significant improvement in average quality measures such as accuracy, sensitivity, precision, and area under the ROC curve.","PeriodicalId":49322,"journal":{"name":"Eurasip Journal on Image and Video Processing","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2021-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49436404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-02-24DOI: 10.1186/s13640-021-00548-4
Jannis Priesnitz, C. Rathgeb, Nicolas Buchmann, C. Busch, Marian Margraf
{"title":"An overview of touchless 2D fingerprint recognition","authors":"Jannis Priesnitz, C. Rathgeb, Nicolas Buchmann, C. Busch, Marian Margraf","doi":"10.1186/s13640-021-00548-4","DOIUrl":"https://doi.org/10.1186/s13640-021-00548-4","url":null,"abstract":"","PeriodicalId":49322,"journal":{"name":"Eurasip Journal on Image and Video Processing","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2021-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13640-021-00548-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65718604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Steganography algorithm based on modified EMD-coded PU partition modes for HEVC videos","authors":"Zhenzhen Zhang, Zhaohong Li, Jindou Liu, Huanma Yan, Lifang Yu","doi":"10.1186/s13640-021-00547-5","DOIUrl":"https://doi.org/10.1186/s13640-021-00547-5","url":null,"abstract":"","PeriodicalId":49322,"journal":{"name":"Eurasip Journal on Image and Video Processing","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2021-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13640-021-00547-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48053052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-02-08DOI: 10.21203/RS.3.RS-174352/V1
Kuo-Ming Hung, Li-Ming Chen, Ting-Wen Chen
Trademarks are common graphic signs in human society. People used this kind of graphic sign to distinguish the signs of representative significance such as individuals, organizations, countries, and groups. Under effective use, these graphic signs can bring maintenance and development resources and profits to the owner. In addition to maintenance and development, organizations that have obtained resources can further promote national and social progress. However, the benefits of these resources have also attracted the attention of unfair competitors. By imitating counterfeit trademarks that appear, unfair competitors can steal the resources of the original trademark. In order to prevent such acts of unfair competitors, the state has formulated laws to protect trademarks. In the past, there have also been researches on similar trademark searches to assist in trademark protection. Although the original trademark is protected by national laws, unfair competitors have recently used psychological methods to counterfeit the original trademark and steal its resources. Trademarks counterfeited through psychology have the characteristics of confuse consumers and do not constitute infringement under the law. Under the influence of such counterfeit trademarks, the original trademark is still not well protected. In order to effectively prevent such trademark counterfeiting through psychology, this article proposes new features based on trademark design and Gestalt psychology to assist legal judgments. These features correspond to a part of the process that is not fully understood in the human visual system and quantify them. In the experimental results, we used past cases to analyze the proposed assistance system. Discussions based on past judgments proved that the quantitative results of the proposed system are similar to the plaintiff or the judgment to determine the reasons for plagiarism. This result shows that the assistance system proposed in this article can provide visually effective quantitative data, assist the law to prevent malicious plagiarism on images by unfair competitors, and reduce the plagiarism caused by the similar design concepts of late trademark designers.
{"title":"Trademark infringement recognition assistance system based on human visual Gestalt psychology and trademark design","authors":"Kuo-Ming Hung, Li-Ming Chen, Ting-Wen Chen","doi":"10.21203/RS.3.RS-174352/V1","DOIUrl":"https://doi.org/10.21203/RS.3.RS-174352/V1","url":null,"abstract":"Trademarks are common graphic signs in human society. People used this kind of graphic sign to distinguish the signs of representative significance such as individuals, organizations, countries, and groups. Under effective use, these graphic signs can bring maintenance and development resources and profits to the owner. In addition to maintenance and development, organizations that have obtained resources can further promote national and social progress. However, the benefits of these resources have also attracted the attention of unfair competitors. By imitating counterfeit trademarks that appear, unfair competitors can steal the resources of the original trademark. In order to prevent such acts of unfair competitors, the state has formulated laws to protect trademarks. In the past, there have also been researches on similar trademark searches to assist in trademark protection. Although the original trademark is protected by national laws, unfair competitors have recently used psychological methods to counterfeit the original trademark and steal its resources. Trademarks counterfeited through psychology have the characteristics of confuse consumers and do not constitute infringement under the law. Under the influence of such counterfeit trademarks, the original trademark is still not well protected. In order to effectively prevent such trademark counterfeiting through psychology, this article proposes new features based on trademark design and Gestalt psychology to assist legal judgments. These features correspond to a part of the process that is not fully understood in the human visual system and quantify them. In the experimental results, we used past cases to analyze the proposed assistance system. Discussions based on past judgments proved that the quantitative results of the proposed system are similar to the plaintiff or the judgment to determine the reasons for plagiarism. This result shows that the assistance system proposed in this article can provide visually effective quantitative data, assist the law to prevent malicious plagiarism on images by unfair competitors, and reduce the plagiarism caused by the similar design concepts of late trademark designers.","PeriodicalId":49322,"journal":{"name":"Eurasip Journal on Image and Video Processing","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2021-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42760039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-28DOI: 10.1186/s13640-021-00546-6
Yang Yu, Hongqing Zhu
{"title":"Retinal vessel segmentation with constrained-based nonnegative matrix factorization and 3D modified attention U-Net","authors":"Yang Yu, Hongqing Zhu","doi":"10.1186/s13640-021-00546-6","DOIUrl":"https://doi.org/10.1186/s13640-021-00546-6","url":null,"abstract":"","PeriodicalId":49322,"journal":{"name":"Eurasip Journal on Image and Video Processing","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2021-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13640-021-00546-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65718581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-15DOI: 10.1186/s13640-020-00543-1
Yanyang Guo, Hanzhou Wu, Xinpeng Zhang
{"title":"Steganographic visual story with mutual-perceived joint attention","authors":"Yanyang Guo, Hanzhou Wu, Xinpeng Zhang","doi":"10.1186/s13640-020-00543-1","DOIUrl":"https://doi.org/10.1186/s13640-020-00543-1","url":null,"abstract":"","PeriodicalId":49322,"journal":{"name":"Eurasip Journal on Image and Video Processing","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2021-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13640-020-00543-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65718480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A classification method for social information of sellers on social network","authors":"Haoliang Cui, Shuai Shao, Shaozhang Niu, Chengjie Shi, Lingyu Zhou","doi":"10.1186/s13640-020-00545-z","DOIUrl":"https://doi.org/10.1186/s13640-020-00545-z","url":null,"abstract":"","PeriodicalId":49322,"journal":{"name":"Eurasip Journal on Image and Video Processing","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2021-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s13640-020-00545-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65718511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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