&lt;title&gt;Matching using object models generated from photometric stereo images&lt;/title&gt;

Bellaire, Gunter; Schluens, Karsten; Oppermann, Katrin; Schimke, W.

doi:10.1117/12.232253

Cited by 3 publications

(2 citation statements)

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“…Several procedures are formulated that can helpful to identify breast cancer in mammogram images. Various algorithms were suggested for identifying masses in mammogram images ( [1][2][3][4][5]). These algorithms accomplished sensitivity ranging from 77% to 88%, and false positive is 0.43 to 5.1 per an image.…”

Section: Literature Surveymentioning

confidence: 99%

Mammogram Images Detection Using Support Vector Machines

Mummalaneni

Sandhya²

2017

ijarcs

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Breast cancer begins when an abnormal growth of cells takes place in the breast. We formulated a procedure which explains identification of cancer cells in breast cancer X-ray images. This study is useful for doctors to discover abnormal tissues in given set of X-ray images. The initial phase of this procedure intended to enhance the mammogram image sequence. Initial phase is data cleaning phase in which noise is removed and emphasizing the inner structure of the mammogram image. In the second phase CNNs are used to segment the regions which consists of cancer cells. These regions may have various shapes like circular density, eccentricity, density, circularity and circular disproportion. Shape descriptors are used to asses the shapes of the regions of interest. Textures are analyzed with the help of geostatistic functions like Geary's index and Moran's index. SVMs are used to categorize the brain image into two regions such as non-masses and masses, with 0.3 false negative value per mammogram image, 0.86 false positive value per mammogram image, sensitivity 79% and ROC value 90%.

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Section: Literature Surveymentioning

confidence: 99%

Mammogram Images Detection Using Support Vector Machines

Mummalaneni

Sandhya²

2017

ijarcs

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“…Multi layered model used attributes related semantic properties to interpret crop images [2]. Several methods like model based procedures, statistical based procedures and texture based algorithms are used to interpret forest density, geology, oceanography and forest density [3,4,5]. Statistical properties like mean, variance, covariance and correlation coefficient are used to analyze cashew plantation, coconut plantation and harvested field [6,7].…”

Section: Related Workmentioning

confidence: 99%

Classification of Synthetic Aperture Radar Images using Fuzzy SVMs

Sekar¹

2017

IJRASET

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In this study, the classification of synthetic aperture radar images are analyses using fuzzy support vector machines. The issues in the classification of SAR images are not addressed properly and remains an unsolved problem in few instances. This study provides one of the best methodology to classify SAR based image databases with fuzzy support vector machines. All the concepts which are derived and applied are based machine learning algorithms. The designed and developed methodology has got many applications in the area like weather forecasting, flood impact monitoring, designing crop calenders, early detection of drought and many more. All the algorithms applied in this study are pixel oriented. This study is carried out on the SAR images collected from the publicly available AXA EORC data set. We designed a model based mathematics and statistics which is developed in the python programming language. Several algorithms are designed to analyze SAR images in which fuzzy support methodology is exhibited high performance. Reports discussed in this study shown that the efficiency of fuzzy support vector machines to classify synthetic aperture radar image is 95.3% without any heuristic information.

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3-D object recognition by matching the total view information

Bellaire

Schlüns

1996

Proceedings of 13th International Conference on Pattern Recognition

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<title>Matching using object models generated from photometric stereo images</title>

Cited by 3 publications

References 12 publications

Mammogram Images Detection Using Support Vector Machines

Mammogram Images Detection Using Support Vector Machines

Classification of Synthetic Aperture Radar Images using Fuzzy SVMs

3-D object recognition by matching the total view information

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