&lt;title&gt;Automated training of 3D morphology algorithm for object recognition&lt;/title&gt;

Bullock, Michael E.; Wang, David L.; Fairchild, Scott R.; Patterson, Tim J.

doi:10.1117/12.181022

Cited by 4 publications

(4 citation statements)

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“…Among the various applications of morphological filters in SAR ATR, the more popular have been for speckle noise suppression, edge detection, and object detection/recognition 71 Target Location Figure 3: A basic outline of the morphology based target detection technique [10,11,12]. A unique adaptation of morphology has been to apply erosion and dilation operations in three dimensions for object recognition [12].…”

Section: Cfar Based Detection Algorithmmentioning

confidence: 99%

“…A unique adaptation of morphology has been to apply erosion and dilation operations in three dimensions for object recognition [12]. This technique is novel in its approach, but too computationally intensive to be implemented in a real-time SAR ATR system.…”

Section: Cfar Based Detection Algorithmmentioning

confidence: 99%

“…CFAR algorithm [1,2,3,4], adaptive matched filter [5]). Computational morphology [12], wavelet transforms [6], and neural networks [7] are based on emerging techniques and are not considered to be classical in nature.. In this paper, we report on modifications to both CFAR and morphology based approaches for prescreening in SAR imagery.…”

Section: Introductionmentioning

confidence: 99%

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<title>Multistage algorithm for detection of targets in SAR image data</title>

1997

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A popular approach to SAR ATR is to use a hierarchical arrangement of stages, aimed at narrowing the field of interest and concentrating detection resources in those areas with the highest probability of containing a target. CFAR detectors have played a prominent role in such systems. So have morphological processing techniques, but to a lesser extent. The work reported in this paper follows the traditional hierarchical approach and employs CFAR and morphological principles. With the goal of improving detection accuracy and reducing the false alarm rate, we have explored and developed some new variations on the theme.The baseline CFAR and morphology algorithms have been significantly modified in proposed techniques to better match SAR target signatures. In addition, the modified algorithms have the ability to locate targets in a fast and efficient manner, allowing for the possibility of real-time implementation. Experimental evaluations indicate that both approaches perform well, with further performance gains noted when the algorithms are merged. The advantages and drawbacks of each algorithm are presented and experimental results are shown for a variety of test cases.

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Section: Cfar Based Detection Algorithmmentioning

confidence: 99%

Section: Cfar Based Detection Algorithmmentioning

confidence: 99%

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<title>Multistage algorithm for detection of targets in SAR image data</title>

1997

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“…Decision trees are widely used in image detection in a variety areas. They can be used for recognizing three dimensional objects (Bullock et al, 1994;Spirkovska, 1993) and for high level vision (Kodratoff et al, 1994). Sometimes decision trees are the only method of analysis, but often they are used in conjunction with other statistical tools.…”

Section: Application Of Decision Treesmentioning

confidence: 99%

Prediction of Infertility Treatment Outcomes Using Classification Trees

Milewska

Jankowska

Cwalina

et al. 2016

Studies in Logic, Grammar and Rhetoric

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Infertility is currently a common problem with causes that are often unexplained, which complicates treatment. In many cases, the use of ART methods provides the only possibility of getting pregnant. Analysis of this type of data is very complex. More and more often, data mining methods or artificial intelligence techniques are appropriate for solving such problems. In this study, classification trees were used for analysis. This resulted in obtaining a group of patients characterized most likely to get pregnant while using in vitro fertilization.

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Semi-Automatic Probabilistic Morphological Detection

Coetzee¹,

Ramesh²

Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.

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<title>Automated training of 3D morphology algorithm for object recognition</title>

Cited by 4 publications

References 0 publications

<title>Multistage algorithm for detection of targets in SAR image data</title>

<title>Multistage algorithm for detection of targets in SAR image data</title>

Prediction of Infertility Treatment Outcomes Using Classification Trees

Semi-Automatic Probabilistic Morphological Detection

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