0000000000131835

AUTHOR

Amadou T. Sanda Mahama

showing 3 related works from this author

A deep semantic segmentation-based algorithm to segment crops and weeds in agronomic color images

2022

Abstract In precision agriculture, the accurate segmentation of crops and weeds in agronomic images has always been the center of attention. Many methods have been proposed but still the clean and sharp segmentation of crops and weeds is a challenging issue for the images with a high presence of weeds. This work proposes a segmentation method based on the combination of semantic segmentation and K-means algorithms for the segmentation of crops and weeds in color images. Agronomic images of two different databases were used for the segmentation algorithms. Using the thresholding technique, everything except plants was removed from the images. Afterward, semantic segmentation was applied usin…

Subtractive colorComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONConfusion matrixForestryAquatic ScienceThresholdingAccurate segmentationComputer Science ApplicationsClassification rateAnimal Science and ZoologySegmentationPrecision agricultureCluster analysisAgronomy and Crop ScienceAlgorithmInformation Processing in Agriculture
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Proposition of Convolutional Neural Network Based System for Skin Cancer Detection

2019

Skin cancer automated diagnosis tools play a vital role in timely screening, helping dermatologists focus on melanoma cases. Best arts on automated melanoma screening use deep learning-based approaches, especially deep convolutional neural networks (CNN) to improve performances. Because of the large number of parameters that could be involved during training in CNN many training samples are needed to avoid overfitting problem. Gabor filtering can efficiently extract spatial information including edges and textures, which may reduce the features extraction burden to CNN. In this paper, we proposed a Gabor Convolutional Network (GCN) model to improve the performance of automated diagnosis of …

business.industryComputer scienceDeep learningFeature extractionPattern recognition02 engineering and technologyFilter (signal processing)OverfittingConvolutional neural network030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicineGabor filter0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingArtificial intelligencebusinessFocus (optics)Spatial analysis2019 15th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)
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Extension of luminance component based demosaicking algorithm to 4- and 5-band multispectral images

2021

Abstract Multispectral imaging systems are currently expanding with a variety of multispectral demosaicking algorithms. But these algorithms have limitations due to the remarkable presence of artifacts in the reconstructed image. In this paper, we propose a powerful multispectral image demosaicking method that focuses on the G band and luminance component. We've first identified a relevant 4-and 5-band multispectral filter array (MSFA) with the dominant G band and then proposed an algorithm that consistently estimates the missing G values and other missing components using a convolution operator and a weighted bilinear interpolation algorithm based on the luminance component. Using the cons…

Computer engineering. Computer hardwareDemosaicingDemosaicking algorithmComputer scienceMultispectral imageBilinear interpolationQA75.5-76.95General MedicineExtension (predicate logic)Filter (signal processing)Multispectral filter arrayLuminanceConvolutionTK7885-7895G bandElectronic computers. Computer scienceComponent (UML)Weighted bilinear interpolationLuminance componentAlgorithmArray
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