Morph-CNN: A Morphological Convolutional Neural Network for Image Classification

Mellouli, Dorra; Hamdani, Tarek M.; Ayed, Mounir Ben; Alimi, Adel M.

doi:10.1007/978-3-319-70096-0_12

Cited by 15 publications

(11 citation statements)

References 14 publications

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“…Therefore, it would be more than natural for researchers to propose works combining the benefits of ConvNets and morphological operations. In fact, several works [20], [25], [26], [28] tried to combine these techniques to create a more robust model. Some works [20], [28] employed morphological operations as a pre-processing step in order to extract the first set of discriminative features.…”

Section: B Related Workmentioning

confidence: 99%

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An Introduction to Deep Morphological Networks

et al. 2021

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Over the past decade, Convolutional Networks (ConvNets) have renewed the perspectives of the research and industrial communities. Although this deep learning technique may be composed of multiple layers, its core operation is the convolution, an important linear filtering process. Easy and fast to implement, convolutions actually play a major role, not only in ConvNets, but in digital image processing and analysis as a whole, being effective for several tasks. However, aside from convolutions, researchers also proposed and developed non-linear filters, such as operators provided by mathematical morphology. Even though these are not so computationally efficient as the linear filters, in general, they are able to capture different patterns and tackle distinct problems when compared to the convolutions. In this paper, we propose a new paradigm for deep networks where convolutions are replaced by non-linear morphological filters. Aside from performing the operation, the proposed Deep Morphological Network (DeepMorphNet) is also able to learn the morphological filters (and consequently the features) based on the input data. While this process raises challenging issues regarding training and actual implementation, the proposed DeepMorphNet proves to be able to extract features and solve problems that traditional architectures with standard convolution filters cannot.

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Section: B Related Workmentioning

confidence: 99%

“…Other works [23]- [26] introduced morphological operations into neural networks, creating a framework in which the structuring elements are optimized. Masci et al [25] proposed a convolutional network that aggregates pseudomorphological operations.…”

Section: B Related Workmentioning

confidence: 99%

An Introduction to Deep Morphological Networks

et al. 2021

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“…Recently, morphological operations have been explored in the context of deep learning frameworks for image denoising and classification [29,31,30]. All three works are based on the approximation of morphological operations, since the minimum and maximum were approximated with the counter-harmonic mean (CHM) [37,38,39].…”

Section: Morphological Operators In Deep Networkmentioning

confidence: 99%

“…Masci et al [29] were the first to propose integrating CHM filters directly into CNNs and showed that the parameters of the CHM filter could be learned via standard back-propagation. We note also [31], which uses these CHM operators to classify digits.Finally, [30] represented as a function, with the intensity at position x be denoted as f (x).…”

Section: Morphological Operators In Deep Networkmentioning

confidence: 99%

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Deep morphological networks

Franchi

Fehri²,

Yao

2020

Pattern Recognition

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Mathematical morphology provides powerful nonlinear operators for a variety of image processing tasks such as filtering, segmentation, and edge detection. In this paper, we propose a way to use these nonlinear operators in an end-toend deep learning framework and illustrate them on different applications. We demonstrate on various examples that new layers making use of the morphological non-linearities are complementary to convolution layers. These new layers can be used to integrate the non-linear operations and pooling into a joint operation. We finally enhance results obtained in boundary detection using this new family of layers with just 0.01% of the parameters of competing state-of-the-art methods.

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Deep Convolutional Neural Networks Based on Image Data Augmentation for Visual Object Recognition

Jayech

2019

Intelligent Data Engineering and Automated Learning – IDEAL 2019

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Morph-CNN: A Morphological Convolutional Neural Network for Image Classification

Cited by 15 publications

References 14 publications

An Introduction to Deep Morphological Networks

An Introduction to Deep Morphological Networks

Deep morphological networks

Deep Convolutional Neural Networks Based on Image Data Augmentation for Visual Object Recognition

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