2007
DOI: 10.1117/12.719776
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Visual detection, recognition, and classification of surface-buried UXO based on soft-computing decision fusion

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Cited by 6 publications
(9 citation statements)
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“…One of the earliest works dealt with UXO detection from images that were captured by a hypothetical robotic system. To discriminate the UXOs from background clutter, foreground segmentation via adaptive thresholding was performed followed by color, texture and shape-based classification [2,29]. To further reduce the false positive rate a decision-level fusion schema was proposed.…”
Section: Related Work 21 Uxo Recognition From Visual Contentmentioning
confidence: 99%
“…One of the earliest works dealt with UXO detection from images that were captured by a hypothetical robotic system. To discriminate the UXOs from background clutter, foreground segmentation via adaptive thresholding was performed followed by color, texture and shape-based classification [2,29]. To further reduce the false positive rate a decision-level fusion schema was proposed.…”
Section: Related Work 21 Uxo Recognition From Visual Contentmentioning
confidence: 99%
“…This problem can be solved by estimating the background over a long image sequence, and then thresholding the absolute difference between the incoming frames and the estimated background [9]. Other recent work on image sequence restoration uses a variational approach to estimate the background in a set of consecutive frames and segment the non-background regions [10]. This system requires that the entire sequence be processed before performing segmentation, and thus does not perform in real-time.…”
Section: Scene Segmentationmentioning
confidence: 99%
“…Neural Networks also known as Artificial Neural Networks (ANN) are paradigms that mimic the biological nervous system to perform information processing [17]. This project exploits the supervised Hamming Network that is trained using the color, shape and texture by adding a maximum likelihood classifier to the frond end.…”
Section: Neural Networkmentioning
confidence: 99%
“…For the difficulty of data acquisition for Mars terrain images, many studies tested terrain classification methods with roves’ fully operational duplicates in Earth conditions and then applied those methods to the actual rover. The image features that are often used for terrain classification in those studies include color features based on the RGB space [ 8 , 9 , 10 , 11 , 12 ], HSV [ 6 , 7 , 13 , 14 , 15 ], and Lab [ 16 ] spaces; Gabor features [ 12 , 17 , 18 ]; the contrast [ 10 , 11 , 12 ], correlation [ 12 ], energy [ 11 , 12 , 13 ], and consistency [ 12 ] of gray-level co-occurrence matrix (GLCM); SURF features [ 19 , 20 ]; Daisy features [ 19 , 20 ]; local binary patterns (LBP) [ 19 , 20 , 21 ]; local ternary patterns (LTP) [ 19 , 20 , 21 ]; local adaptive ternary patterns (LATP) [ 19 , 20 ]; contrast context histogram (CCH) [ 20 ]; and the mean [ 2 , 9 , 13 , 14 , 15 , 22 ], entropy [ 8 , 9 , 22 ], contrast [ 8 , 23 ], correlation [ 23 ], energy [ 8 ,…”
Section: Introductionmentioning
confidence: 99%
“…It will reduce the classification accuracy for terrain classification. In these studies, the classifiers used include random forests (RFs) [ 2 , 12 , 18 , 19 , 20 , 21 ], SVMs [ 6 , 7 , 8 , 9 , 16 , 17 , 19 , 20 ], multilayer perceptron [ 13 , 14 , 15 , 19 , 20 ], LIBLINEAR [ 19 , 20 ], decision tree [ 19 , 20 ], naïve Bayes classifier [ 19 , 20 ], K-nearest neighbor (KNN) [ 13 , 14 , 15 , 17 , 19 , 20 ], extreme learning machine [ 17 , 24 ], batch-incremental regression tree model [ 22 ], probabilistic neural network [ 23 ], and multilayer feed forward neural network learning algorithm [ 10 ].…”
Section: Introductionmentioning
confidence: 99%