2023
1.
Aswath, Anusha; Alsahaf, Ahmad; Westenbrink, B. Daan; Giepmans, Ben N. G.; Azzopardi, George
COFI - Coarse-semantic to fine-instance unsupervised mitochondria segmentation in EM Inproceedings
Links | BibTeX | Altmetric | Tags: brain-inspired, contour detection, convnets, deep learning, segmentation
@inproceedings{Anusha2023,
title = {COFI - Coarse-semantic to fine-instance unsupervised mitochondria segmentation in EM},
author = {Anusha Aswath and Ahmad Alsahaf and B. Daan Westenbrink and Ben N. G. Giepmans and George Azzopardi
},
doi = {https://doi.org/10.1007/978-3-031-44240-7_9},
year = {2023},
date = {2023-09-20},
urldate = {2023-07-01},
booktitle = {Computer Analysis of Images and Patterns. CAIP 2023. Lecture Notes in Computer Science},
volume = {14185},
publisher = {Springer},
keywords = {brain-inspired, contour detection, convnets, deep learning, segmentation},
pubstate = {published},
tppubtype = {inproceedings}
}
2.
Prins, Fabian L.; Tomanin, Dario; Kamenz, Julia; Azzopardi, George
Biometric Recognition of African Clawed Frogs Inproceedings
Links | BibTeX | Altmetric | Tags: biometrics, brain-inspired, contour detection
@inproceedings{Prins2023,
title = {Biometric Recognition of African Clawed Frogs},
author = {Fabian L. Prins and Dario Tomanin and Julia Kamenz and George Azzopardi},
doi = {https://doi.org/10.1007/978-3-031-44240-7_15},
year = {2023},
date = {2023-09-20},
urldate = {2023-07-01},
booktitle = {Computer Analysis of Images and Patterns. CAIP 2023. Lecture Notes in Computer Science},
keywords = {biometrics, brain-inspired, contour detection},
pubstate = {published},
tppubtype = {inproceedings}
}
2017
3.
Strisciuglio, Nicola; Azzopardi, George; Petkov, Nicolai
Detection of curved lines with B-COSFIRE filters: A case study on crack delineation Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, contour detection, trainable filters
@inproceedings{strisciuglio2017detection,
title = {Detection of curved lines with B-COSFIRE filters: A case study on crack delineation},
author = {Nicola Strisciuglio and George Azzopardi and Nicolai Petkov},
doi = {https://doi.org/10.1007/978-3-319-64689-3_9},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
booktitle = {International Conference on Computer Analysis of Images and Patterns},
pages = {108--120},
organization = {Springer, Cham},
abstract = {The detection of curvilinear structures is an important step for various computer vision applications, ranging from medical image analysis for segmentation of blood vessels, to remote sensing for the identification of roads and rivers, and to biometrics and robotics, among others. This is a nontrivial task especially for the detection of thin or incomplete curvilinear structures surrounded with noise. We propose a general purpose curvilinear structure detector that uses the brain-inspired trainable B-COSFIRE filters. It consists of four main steps, namely nonlinear filtering with B-COSFIRE, thinning with non-maximum suppression, hysteresis thresholding and morphological closing. We demonstrate its effectiveness on a data set of noisy images with cracked pavements, where we achieve state-of-the-art results (F-measure = 0.865). The proposed method can be employed in any computer vision methodology that requires the delineation of curvilinear and elongated structures.},
keywords = {brain-inspired, contour detection, trainable filters},
pubstate = {published},
tppubtype = {inproceedings}
}
The detection of curvilinear structures is an important step for various computer vision applications, ranging from medical image analysis for segmentation of blood vessels, to remote sensing for the identification of roads and rivers, and to biometrics and robotics, among others. This is a nontrivial task especially for the detection of thin or incomplete curvilinear structures surrounded with noise. We propose a general purpose curvilinear structure detector that uses the brain-inspired trainable B-COSFIRE filters. It consists of four main steps, namely nonlinear filtering with B-COSFIRE, thinning with non-maximum suppression, hysteresis thresholding and morphological closing. We demonstrate its effectiveness on a data set of noisy images with cracked pavements, where we achieve state-of-the-art results (F-measure = 0.865). The proposed method can be employed in any computer vision methodology that requires the delineation of curvilinear and elongated structures.
2012
4.
Azzopardi, George; Petkov, Nicolai
Contour detection by CORF operator Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, contour detection
@inproceedings{azzopardi2012contour,
title = {Contour detection by CORF operator},
author = {George Azzopardi and Nicolai Petkov},
doi = {10.1007/978-3-642-33269-2_50},
year = {2012},
date = {2012-01-01},
urldate = {2012-01-01},
booktitle = {International Conference on Artificial Neural Networks},
pages = {395--402},
organization = {Springer, Berlin, Heidelberg},
abstract = {We propose a contour operator, called CORF, inspired by the properties of simple cells in visual cortex. It combines, by a weighted geometric mean, the blurred responses of difference-of-Gaussian operators that model cells in the lateral geniculate nucleus (LGN). An operator that has gained particular popularity as a computational model of a simple cell is based on a family of Gabor Functions (GFs). However, the GF operator short-cuts the LGN, and its effectiveness in contour detection tasks, which is assumed to be the primary biological role of simple cells, has never been compared with the effectiveness of alternative operators. We compare the performances of the CORF and the GF operators using the RuG and the Berkeley data sets of natural scenes with associated ground truths. The proposed CORF operator outperforms the GF operator (RuG: ?(39)=4.39, ?<10−4 and Berkeley: ?(499)=4.95, ?<10−6).
},
keywords = {brain-inspired, contour detection},
pubstate = {published},
tppubtype = {inproceedings}
}
We propose a contour operator, called CORF, inspired by the properties of simple cells in visual cortex. It combines, by a weighted geometric mean, the blurred responses of difference-of-Gaussian operators that model cells in the lateral geniculate nucleus (LGN). An operator that has gained particular popularity as a computational model of a simple cell is based on a family of Gabor Functions (GFs). However, the GF operator short-cuts the LGN, and its effectiveness in contour detection tasks, which is assumed to be the primary biological role of simple cells, has never been compared with the effectiveness of alternative operators. We compare the performances of the CORF and the GF operators using the RuG and the Berkeley data sets of natural scenes with associated ground truths. The proposed CORF operator outperforms the GF operator (RuG: ?(39)=4.39, ?<10−4 and Berkeley: ?(499)=4.95, ?<10−6).