A Fast Gabor Filter Approach for Multi-Channel Texture Feature Discrimination

Sobieranski, Antonio Carlos; Linhares, Rodrigo T. F.; Comunello, Eros; Wangenheim, Aldo von

doi:10.1007/978-3-319-12568-8_17

Cited by 3 publications

(1 citation statement)

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“…Gabor filters have been shown to help in performing texture-based image segmentation through integrated color-texture descriptors [33]. Gabor filter-based segmentation approaches have also been shown to be easy to parallelize, implement in GPUs and use to perform fast color-texture-based image segmentation [34]. The Gabor filter can be expressed by the equation 4, where u 0 and φ respectively correspond to frequency, and the phase offset (in degrees).…”

Section: Gabor Filtersmentioning

confidence: 99%

Comparison of Classical Computer Vision vs. Convolutional Neural Networks for Weed Mapping in Aerial Images

Júnior

Monteiro

Ribeiro

et al. 2020

RITA

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In this paper, we present a comparison between convolutional neural networks and classicalcomputer vision approaches, for the specific precision agriculture problem of weed mapping on sugarcane fields aerial images. A systematic literature review was conducted to find which computer vision methods are being used on this specific problem. The most cited methods were implemented, as well as four models of convolutional neural networks. All implemented approaches were tested using the same dataset, and their results were quantitatively and qualitatively analyzed. The obtained results were compared to a human expert made ground truth, for validation. The results indicate that the convolutional neural networks present better precision and generalize better than the classical models

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Section: Gabor Filtersmentioning

confidence: 99%