2021
1.
Bennabhaktula, Guru Swaroop; Antonisse, Joey; Azzopardi, George
Abstract | Links | BibTeX | Altmetric | Tags: adversarial attacks, brain-inspired, convnets, deep learning, image classification, noise suppression
@inproceedings{bennabhaktula2021improving,
title = {On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator},
author = {Guru Swaroop Bennabhaktula and Joey Antonisse and George Azzopardi},
doi = {10.1007/978-3-030-89128-2_42},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {International Conference on Computer Analysis of Images and Patterns},
pages = {434--444},
organization = {Springer},
abstract = {Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise.},
keywords = {adversarial attacks, brain-inspired, convnets, deep learning, image classification, noise suppression},
pubstate = {published},
tppubtype = {inproceedings}
}
Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise.
2018
2.
Strisciuglio, Nicola; Azzopardi, George; Petkov, Nicolai
Brain-inspired robust delineation operator Inproceedings
Abstract | Links | BibTeX | Altmetric | Tags: brain-inspired, noise suppression, pattern recognition
@inproceedings{strisciuglio2018brain,
title = {Brain-inspired robust delineation operator},
author = {Nicola Strisciuglio and George Azzopardi and Nicolai Petkov},
doi = {https://doi.org/10.1007/978-3-030-11015-4_41},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV) Workshops},
pages = {555--565},
publisher = {Springer},
abstract = {In this paper we present a novel filter, based on the existing COSFIRE filter, for the delineation of patterns of interest. It includes a mechanism of push-pull inhibition that improves robustness to noise in terms of spurious texture. Push-pull inhibition is a phenomenon that is observed in neurons in area V1 of the visual cortex, which suppresses the response of certain simple cells for stimuli of preferred orientation but of non-preferred contrast. This type of inhibition allows for sharper detection of the patterns of interest and improves the quality of delineation especially in images with spurious texture.
We performed experiments on images from different applications, namely the detection of rose stems for automatic gardening, the delineation of cracks in pavements and road surfaces, and the segmentation of blood vessels in retinal images. Push-pull inhibition helped to improve results considerably in all applications.},
keywords = {brain-inspired, noise suppression, pattern recognition},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we present a novel filter, based on the existing COSFIRE filter, for the delineation of patterns of interest. It includes a mechanism of push-pull inhibition that improves robustness to noise in terms of spurious texture. Push-pull inhibition is a phenomenon that is observed in neurons in area V1 of the visual cortex, which suppresses the response of certain simple cells for stimuli of preferred orientation but of non-preferred contrast. This type of inhibition allows for sharper detection of the patterns of interest and improves the quality of delineation especially in images with spurious texture.
We performed experiments on images from different applications, namely the detection of rose stems for automatic gardening, the delineation of cracks in pavements and road surfaces, and the segmentation of blood vessels in retinal images. Push-pull inhibition helped to improve results considerably in all applications.
We performed experiments on images from different applications, namely the detection of rose stems for automatic gardening, the delineation of cracks in pavements and road surfaces, and the segmentation of blood vessels in retinal images. Push-pull inhibition helped to improve results considerably in all applications.