&lt;title&gt;Detection of minelike targets using grayscale morphological image reconstruction&lt;/title&gt;

Banerji, A.; Goutsias, John

doi:10.1117/12.211377

Cited by 7 publications

(4 citation statements)

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“…In summary, the algorithm mainly uses morphological operations to perform lineto-plane conversion on the skeleton structure image, and then calculates and analyzes the morphological features of the connected domain, and finally identifies the vehicle, as shown in Figure 16. The pseudo codes of the vehicle recognition Algorithm 2 for steps (1)-( 4) are as follows [27]. shown in Figure 16.…”

Section: Morphological Detectionmentioning

confidence: 99%

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Vehicle Recognition from Unmanned Aerial Vehicle Videos Based on Fusion of Target Pre-Detection and Deep Learning

et al. 2022

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For accurate and effective automatic vehicle identification, morphological detection and deep convolutional networks were combined to propose a method for locating and identifying vehicle models from unmanned aerial vehicle (UAV) videos. First, the region of interest of the video frame image was sketched and grey-scale processing was performed; sub-pixel-level skeleton images were generated based on the Canny edge detection results of the region of interest; then, the image skeletons were decomposed and reconstructed. Second, a combination of morphological operations and connected domain morphological features were applied for vehicle target recognition, and a deep learning image benchmark library containing 244,520 UAV video vehicle samples was constructed. Third, we improved the AlexNet model by adding convolutional layers, pooling layers, and adjusting network parameters, which we named AlexNet*. Finally, a vehicle recognition method was established based on a candidate target extraction algorithm with AlexNet*. The validation analysis revealed that AlexNet* achieved a mean F1 of 85.51% for image classification, outperforming AlexNet (82.54%), LeNet (63.88%), CaffeNet (46.64%), VGG16 (16.67%), and GoogLeNet (14.38%). The mean values of Pcor, Pre, and Pmiss for cars and buses reached 94.63%, 6.87%, and 4.40%, respectively, proving that this method can effectively identify UAV video targets.

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Section: Morphological Detectionmentioning

confidence: 99%

“…shown in Figure 16. The pseudo codes of the vehicle recognition Algorithm 2 for steps (1)-( 4) are as follows [27].…”

Section: Morphological Detectionmentioning

confidence: 99%

Vehicle Recognition from Unmanned Aerial Vehicle Videos Based on Fusion of Target Pre-Detection and Deep Learning

et al. 2022

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“…Improvements in mine detection results could also be achieved by improving the image processing algorithm itself. The MM-MNF algorithm (Braga-Neto and Goutsias 1998;Banerji and Goutsias 1995) has been successfully applied to COBRA images. The false alarm rate in their processed images is much lower than in the point processes we considered, which were obtained with a widely used algorithm.…”

Section: Discussionmentioning

confidence: 99%

Detecting Mines in Minefields With Linear Characteristics

Walsh

Raftery

2002

Technometrics

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We consider the problem of detecting minefields using aerial images. A first stage of image processing has reduced the image to a set of points, each one representing a possible mine. Our task is to decide which ones are actual mines.We assume that the minefield consists of approximately parallel rows of mines laid out according to a probability distribution that encourages evenly spaced, linear patterns. The noise points are assumed to be distributed as a Poisson process. We construct a Markov chain Monte Carlo algorithm to estimate the model and obtain posterior probabilities for each point being a mine. The algorithm performs well on several real minefield datasets.

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“…The first operation is carried out by means of a morphological opening, which has been modified to avoid the creation of isolated regions. To achieve this, a morphological reconstruction method has been used, similar to that described in 8 . The second operation is performed using a classical morphological closing.…”

Section: Silhouette Preprocessingmentioning

confidence: 99%

A decision support system for ship identification based on the curvature scale space representation

Luna

Miravet

Otaduy

et al. 2005

Electro-Optical Remote Sensing

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In this paper, a decision support system for ship identification is presented. The system receives as input a silhouette of the vessel to be identified, previously extracted from a side view of the object. This view could have been acquired with imaging sensors operating at different spectral ranges (CCD, FLIR, image intensifier). The input silhouette is preprocessed and compared to those stored in a database, retrieving a small number of potential matches ranked by their similarity to the target silhouette. This set of potential matches is presented to the system operator, who makes the final ship identification. This system makes use of an evolved version of the Curvature Scale Space (CSS) representation. In the proposed approach, it is curvature extrema, instead of zero crossings, that are tracked during silhouette evolution, hence improving robustness and enabling to cope successfully with cases where the standard CCS representation is found to be unstable. Also, the use of local curvature was replaced with the more robust concept of lobe concavity, with significant additional gains in performance. Experimental results on actual operational imagery prove the excellent performance and robustness of the developed method.

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<title>Detection of minelike targets using grayscale morphological image reconstruction</title>

Cited by 7 publications

References 0 publications

Vehicle Recognition from Unmanned Aerial Vehicle Videos Based on Fusion of Target Pre-Detection and Deep Learning

Vehicle Recognition from Unmanned Aerial Vehicle Videos Based on Fusion of Target Pre-Detection and Deep Learning

Detecting Mines in Minefields With Linear Characteristics

A decision support system for ship identification based on the curvature scale space representation

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