16 research outputs found
Learning Deep Morphological Networks with Neural Architecture Search
Deep Neural Networks (DNNs) are generated by sequentially performing linear
and non-linear processes. Using a combination of linear and non-linear
procedures is critical for generating a sufficiently deep feature space. The
majority of non-linear operators are derivations of activation functions or
pooling functions. Mathematical morphology is a branch of mathematics that
provides non-linear operators for a variety of image processing problems. We
investigate the utility of integrating these operations in an end-to-end deep
learning framework in this paper. DNNs are designed to acquire a realistic
representation for a particular job. Morphological operators give topological
descriptors that convey salient information about the shapes of objects
depicted in images. We propose a method based on meta-learning to incorporate
morphological operators into DNNs. The learned architecture demonstrates how
our novel morphological operations significantly increase DNN performance on
various tasks, including picture classification and edge detection.Comment: 19 page
An Introduction to Deep Morphological Networks
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
Logarithmic Morphological Neural Nets robust to lighting variations
Morphological neural networks allow to learn the weights of a structuring
function knowing the desired output image. However, those networks are not
intrinsically robust to lighting variations in images with an optical cause,
such as a change of light intensity. In this paper, we introduce a
morphological neural network which possesses such a robustness to lighting
variations. It is based on the recent framework of Logarithmic Mathematical
Morphology (LMM), i.e. Mathematical Morphology defined with the Logarithmic
Image Processing (LIP) model. This model has a LIP additive law which simulates
in images a variation of the light intensity. We especially learn the
structuring function of a LMM operator robust to those variations, namely : the
map of LIP-additive Asplund distances. Results in images show that our neural
network verifies the required property.Comment: Submitted to DGMM 2022 - Second International Conference on Discrete
Geometry and Mathematical Morpholog
Geometric Back-Propagation in Morphological Neural Networks
This paper provides a definition of back-propagation through geometric correspondences for morphological neural networks. In addition, dilation layers are shown to learn probe geometry by erosion of layer inputs and outputs. A proof-of-principle is provided, in which predictions and convergence of morphological networks significantly outperform convolutional networks
Postprocessing for skin detection
Skin detectors play a crucial role in many applications: face localization, person tracking, objectionable content screening, etc. Skin detection is a complicated process that involves not only the development of apposite classifiers but also many ancillary methods, including techniques for data preprocessing and postprocessing. In this paper, a new postprocessing method is described that learns to select whether an image needs the application of various morphological sequences or a homogeneity function. The type of postprocessing method selected is learned based on categorizing the image into one of eleven predetermined classes. The novel postprocessing method presented here is evaluated on ten datasets recommended for fair comparisons that represent many skin detection applications. The results show that the new approach enhances the performance of the base classifiers and previous works based only on learning the most appropriate morphological sequences
Image Processing Using Morphology on Support Vector Machine Classification Model for Waste Image
Sorting waste has always been an important part of managing waste. The primary issue with the waste sorting process has been the discomfort caused by prolonged contact with waste odor. A machinelearning method for identifying waste types was created to address this issue. The study’s goal was to create machine learning to solve waste management challenges by applying the most accurate categorization model available. The research approach was the quantitative analysis of the classification model accuracy. The Kaggle dataset was used to collect and curate data, which was subsequently preprocessed using the morphology approach. Based on picture sources, the data was trained and used to classify waste. The Support Vector Machine model was used in this investigation and feature extraction via the Convolutional Neural Network. The results showed that the system categorized waste successfully, with an accuracy of 99.30% and a loss of 2.47% across all categories. According to the findings of this study, SVM combined with morphological image processing functioned as a strong classification model, with a remarkable accuracy rate of 99.30%. This study’s outcomes contributed to waste management by giving an efficient and dependable waste classification solution compared to many previous studies
MorphPool: Efficient Non-linear Pooling & Unpooling in CNNs
Pooling is essentially an operation from the field of Mathematical
Morphology, with max pooling as a limited special case. The more general
setting of MorphPooling greatly extends the tool set for building neural
networks. In addition to pooling operations, encoder-decoder networks used for
pixel-level predictions also require unpooling. It is common to combine
unpooling with convolution or deconvolution for up-sampling. However, using its
morphological properties, unpooling can be generalised and improved. Extensive
experimentation on two tasks and three large-scale datasets shows that
morphological pooling and unpooling lead to improved predictive performance at
much reduced parameter counts.Comment: Accepted paper at the British Machine Vision Conference (BMVC) 202