In this paper, we present a fast non-parametric adaptive scheme for detecting corner points on a boundary curve. The accuracy of the proposed method steps from the use of an asymmetric region of support that automatically adopts to the scale of the corner point. The speed of the method is achieved based on a dynamic update of the covariance matrix used to determine the region of support. Several experiments have been conducted to revel the qualities of the proposed method and also to establish its superiority over several other existing methods.
{"title":"Efficient Non-Parametric Corner Detection: An Approach Based on Small Eigenvalue","authors":"R. Dinesh, D. S. Guru","doi":"10.1109/CRV.2007.25","DOIUrl":"https://doi.org/10.1109/CRV.2007.25","url":null,"abstract":"In this paper, we present a fast non-parametric adaptive scheme for detecting corner points on a boundary curve. The accuracy of the proposed method steps from the use of an asymmetric region of support that automatically adopts to the scale of the corner point. The speed of the method is achieved based on a dynamic update of the covariance matrix used to determine the region of support. Several experiments have been conducted to revel the qualities of the proposed method and also to establish its superiority over several other existing methods.","PeriodicalId":304254,"journal":{"name":"Fourth Canadian Conference on Computer and Robot Vision (CRV '07)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125599034","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}
Characterization of mitosis is important for understanding the mechanisms of development in early stage embryos. In studies of cancer, another situation in which mitosis is of interest, the tissue is stained with contrast agents before mitosis characterization; an intervention that could lead to atypical development in live embryos. A new image processing algorithm that does not rely on the use of contrast agents was developed to detect mitosis in embryonic tissue. Unlike previous approaches that uses still images, the algorithm presented here uses temporal information from time-lapse images to track the deformation of the embryonic tissue and then uses changes in intensity at tracked regions to identify the locations of mitosis. On a one hundred minute image sequence, consisting of twenty images, the algorithm successfully detected eighty-one out of the ninety-five mitosis. The performance of the algorithm is calculated using the geometric mean measure as 82%. Since no other method to count mitoses in live tissues is known, comparisons with the present results could not be made.
{"title":"Automated Detection of Mitosis in Embryonic Tissues","authors":"P. Siva, G. Brodland, David A Clausi","doi":"10.1109/CRV.2007.11","DOIUrl":"https://doi.org/10.1109/CRV.2007.11","url":null,"abstract":"Characterization of mitosis is important for understanding the mechanisms of development in early stage embryos. In studies of cancer, another situation in which mitosis is of interest, the tissue is stained with contrast agents before mitosis characterization; an intervention that could lead to atypical development in live embryos. A new image processing algorithm that does not rely on the use of contrast agents was developed to detect mitosis in embryonic tissue. Unlike previous approaches that uses still images, the algorithm presented here uses temporal information from time-lapse images to track the deformation of the embryonic tissue and then uses changes in intensity at tracked regions to identify the locations of mitosis. On a one hundred minute image sequence, consisting of twenty images, the algorithm successfully detected eighty-one out of the ninety-five mitosis. The performance of the algorithm is calculated using the geometric mean measure as 82%. Since no other method to count mitoses in live tissues is known, comparisons with the present results could not be made.","PeriodicalId":304254,"journal":{"name":"Fourth Canadian Conference on Computer and Robot Vision (CRV '07)","volume":"153 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115550522","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}
A novel image restoration algorithm is presented in this paper. The restoration problem is formulated under the energy minimization framework and is solved using a dynamic programming-based approach. Through applying dynamic programming iteratively along both horizontal and vertical scanlines, the new algorithm can quickly converge to a near-global-optimal solution, without suffering the so-called streak artifacts. Experiments on both grayscale and color images demonstrate that the presented algorithm can effectively remove Gaussian noise and impulse noise from corrupted images, as well as to restore images with missing intensity values.
{"title":"Images Restoration Using an Iterative Dynamic Programming Approach","authors":"Minglun Gong","doi":"10.1109/CRV.2007.40","DOIUrl":"https://doi.org/10.1109/CRV.2007.40","url":null,"abstract":"A novel image restoration algorithm is presented in this paper. The restoration problem is formulated under the energy minimization framework and is solved using a dynamic programming-based approach. Through applying dynamic programming iteratively along both horizontal and vertical scanlines, the new algorithm can quickly converge to a near-global-optimal solution, without suffering the so-called streak artifacts. Experiments on both grayscale and color images demonstrate that the presented algorithm can effectively remove Gaussian noise and impulse noise from corrupted images, as well as to restore images with missing intensity values.","PeriodicalId":304254,"journal":{"name":"Fourth Canadian Conference on Computer and Robot Vision (CRV '07)","volume":"415 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124167839","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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