An Analysis of Gabor Detection

Kong, Adams Wai-Kin

doi:10.1007/978-3-642-02611-9_7

Cited by 15 publications

(6 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Especially, the use of Gabor filters in texture analysis was motivated due to the studies of Daugman on visual modeling of simple cells. He found out that the experimental findings on orientation selectivity of visual cortical neurons were previously observed by Hubel and Wiesel in human beings and cats [10], [11], [12]. Gabor filters represent timevarying signals in terms of functions that are localized in both time and frequency domains.…”

Section: Introductionmentioning

confidence: 88%

Texture Image Retrieval Based on Log-Gabor Features

Nava

Escalante-Ramı́rez

Cristóbal

2012

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

View full text Add to dashboard Cite

Since Daugman found out that the properties of Gabor filters match the early psychophysical features of simple receptive fields of the Human Visual System (HVS), they have been widely used to extract texture information from images for retrieval of image data. However, Gabor filters have not zero mean, which produces a non-uniform coverage of the Fourier domain. This distortion causes fairly poor pattern retrieval accuracy. To address this issue, we propose a simple yet efficient image retrieval approach based on a novel log-Gabor filter scheme. We make emphasis on the filter design to preserve the relationship with receptive fields and take advantage of their strong orientation selectivity. We provide an experimental evaluation of both Gabor and log-Gabor features using two metrics, the Kullback-Leibler (DKL) and the Jensen-Shannon divergence (DJS). The experiments with the USC-SIPI database confirm that our proposal shows better retrieval performance than the classic Gabor features.

show abstract

Section: Introductionmentioning

confidence: 88%

Texture Image Retrieval Based on Log-Gabor Features

Nava

Escalante-Ramı́rez

Cristóbal

2012

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

View full text Add to dashboard Cite

show abstract

“…Ref. [28] suggested a new counting-based leaf detection method. The Swedish, Flavia, and ICL databases were used as leaf databases.…”

Section: B Textural Analysis Methodsmentioning

confidence: 99%

Textural Analysis for Medicinal Plants Identification Using Log Gabor Filters

et al. 2022

View full text Add to dashboard Cite

Texture plays a crucial role in computer vision, providing valuable information about image regions. Log Gabor filters that mimic the human eye's visual cortex are used as feature extractors to identify medicinal plants based on the leaf textural features. This method was tested on a dataset developed from the Centre of Plant Medicine Research, Ghana, consisting of forty-nine (49) plant species as well as the Flavia and Swedish Leaf datasets, which are benchmark datasets. The Log Gabor filter outperformed the Gabor filters, which have been extensively used in this area when tested on nine supervised classifiers (K-Nearest Neighbour, Support Vector Machine, Naïve Bayes, Logistic Regression, Decision tree, Random Forest, Multilayer Perceptron, Gradient Boosting and Stochastic Gradient Descent) with 10-fold cross-validation. The Support Vector Machine and Multilayer Perceptron were the best performing classifiers for both Log Gabor filter and Gabor filter in terms of accuracy, precision, true positive rate, F1 score and false positive rate. The Log Gabor filter's highest accuracy was 79% for Mydatastet, 97% for Flavia, and 98% for the Swedish Leaf dataset whiles the Gabor filter's highest accuracy was 66% for Mydatastet, 92% for Flavia and 96% for the Swedish Leaf dataset.

show abstract

“…These functions shown by the product of a sinusoid and Gaussian function constitute a single family of linear filters that behave optimally in the sense that their simultaneous resolution in both domains is maximal (Rai and Rivas, 2020). Specifically, using a Gabor filter in texture analysis was motivated due to the studies of Daugman on visual modeling of simple cells based on the experimental findings on the orientation selectivity of visual cortical neurons previously observed by Hubel and Wiesel in human beings and cats (Hubel and Wiesel, 2012;Kong, 2009).…”

Section: Gabor Filtermentioning

confidence: 99%

Medicinal Plant Identification using Gabor Filters and Deep Learning Techniques: A Paper Review

Oppong¹,

Twum²,

Hayfron-Acquah³

et al. 2021

Journal of Computer Science

View full text Add to dashboard Cite

Computer-aided identification of plants is a branch of machine learning that has become more recognized recently and proves itself as a vital tool in numerous sectors including pharmacological science, forestry and agriculture. This has essentially generated a zeal in creating automated systems for the identification of diverse species of plants. This study reviewed plant species classification relying on leaf textural features using Gabor filters and revealed that Gabor filters perform better when combined with other feature extraction methods. Therefore, this study proposes using Log-Gabor filter in the field of plant identification to improve accuracy since they overcome the drawbacks of Gabor filters which are; the maximum bandwidth of a Gabor filter is limited to approximately one octave and Gabor filters are not optimal if one is seeking broad spectral information with maximal spatial localization.

show abstract

An Analysis of Gabor Detection

Cited by 15 publications

References 18 publications

Texture Image Retrieval Based on Log-Gabor Features

Texture Image Retrieval Based on Log-Gabor Features

Textural Analysis for Medicinal Plants Identification Using Log Gabor Filters

Medicinal Plant Identification using Gabor Filters and Deep Learning Techniques: A Paper Review

Contact Info

Product

Resources

About