Morphological Neural Networks Research Articles

DARTS-based morphological neural network design and application in bearing fault diagnosis

Morphological filters are widely used in data pre-processing, whereas the existing morphological filters are not optimal in terms of performance, since they are designed by combining the basic morphological operators together artificially. Hence, we proposed an automatic morphological neural network design method based on differentiable architecture search (DARTS), which is a neural architecture search (NAS) method. Firstly, the mechanism is revealed that the morphological dilation with flat structural elements behaves the same as Maxpooling without down-sampling. Secondly, morphological dilation and erosion neural network layers were defined. Thirdly, using DARTS and the search space with morphological neural network layers, end-to-end morphological neural network was implemented. Finally, the morphological neural network was successfully applied in bearing fault diagnosis. Experiments on the three datasets show that the proposed method improved the feature extraction capabilities of neural networks, then achieved end-to-end bearing fault diagnosis, and the diagnosis accuracy was improved by more than 2.0%.

Max-C and Min-D Projection Auto-Associative Fuzzy Morphological Memories: Theory and an Application for Face Recognition

Max-C and min-D projection auto-associative fuzzy morphological memories (max-C and min-D PAFMMs) are two-layer feedforward fuzzy morphological neural networks designed to store and retrieve finite fuzzy sets. This paper addresses the main features of these auto-associative memories: unlimited absolute storage capacity, fast retrieval of stored items, few spurious memories, and excellent tolerance to either dilative or erosive noise. Particular attention is given to the so-called Zadeh’ PAFMM, which exhibits the most significant noise tolerance among the max-C and min-D PAFMMs besides performing no floating-point arithmetic operations. Computational experiments reveal that Zadeh’s max-C PFAMM, combined with a noise masking strategy, yields a fast and robust classifier with a strong potential for face recognition tasks.

Layer-by-Layer Knowledge Distillation for Training Simplified Bipolar Morphological Neural Networks

Layer-by-Layer Knowledge Distillation for Training Simplified Bipolar Morphological Neural Networks

Highly Optimized Hardware Morphological Neural Network Through Stochastic Computing and Tropical Pruning

This work aimed to enhance a previous neural network hardware implementation based on an efficient combination of Stochastic Computing (SC) and Morphological Neural Networks (MNN). This enhancement focused on exploiting the natural ease of morphological neurons to be pruned in order to drastically shrink the hardware resources and increase the compactness of our network. That is why we extended our original hybrid two-layer neural network to classify the MNIST problem, a much more demanding benchmark with about 160,000 trainable parameters. The 92% of the weights of the morphological layer were discarded, allowing a drastic shrinkage of the hardware resources and power dissipation without degrading test accuracy. An extensive comparison with other recent neural networks designs shows that the proposed design achieves significant improvements in terms of energy efficiency, throughput and hardware resources.

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.

Deep Morphological Neural Networks

Mathematical morphology intends to extract object features such as geometric and topological structures in digital images. Given a set of target images and original images, it is cumbersome and time-consuming to determine the suitable morphological operations and structuring elements. In this paper, we propose deep morphological neural networks, which include a nonlinear feature extraction layer to learn the structuring element correctly and an adaptive layer to select appropriate morphological operations automatically. We demonstrate the applications of object recognition, including hand-written digits, geometric shapes, traffic signs, and brain tumor. Experimental results show the higher computational efficiency and higher accuracy of our developed model as compared against existing convolutional neural network models.

Burnt-Net: Wildfire burned area mapping with single post-fire Sentinel-2 data and deep learning morphological neural network

Accurate and timely mapping of wildfire burned areas is crucial for post-fire management, planning, and next subsequent actions. The monitoring and mapping of the burned area by traditional and common methods are time-consuming and challenging while is vital to propose an advanced burned area detection framework for achieving reliable results. To this end, this study proposed a novel End-to-End framework based on deep learning and post-fire Sentinel-2 imagery. The proposed framework known as Burnt-Net combines quadratic morphological operators and standard convolution layers. The multi-patch multi-level residual morphological (MP-MRM) blocks are the main part of the decoder part of the Burnt-Net while the encoder part uses the multi-level residual morphological and transpose convolution layers. To evaluate the efficiency of Burnt-Net the post-fire Sentinel-2 for the latest wildfires over different countries was collected and then, the model was trained and evaluated based on them. Furthermore, the most common deep learning-based model implemented for comparing the result of burned areas by the proposed Burnt-Net. The results of burned areas mapping show the Burnt-Net is robust in the detection of burned areas and provides a mean accuracy of more than 97% by overall accuracy (OA). Furthermore, the Burnt-Net is fast and can provide the burned area map in the near real-time.

Recognition of Hydrophobicity Class of Polymeric Insulators Employing Residual Morphological Neural Network and Granulometry-Based Image Analysis

Hydrophobicity is an important property of polymeric insulators that allow formation of discrete water droplets on the insulator surface. However, with aging or under extreme humidification, the insulator surface loses its hydrophobic property, resulting in continuous water channel formation, which may lead to dry band arcing and subsequently flashover, thereby affecting the long-term performance of the insulator. Therefore, accurate recognition of hydrophobic class (HC) is important as it provides information regarding the surface condition of the insulator. Considering the above-said facts, this article investigates the feasibility of employing residual morphological neural network (Res-Morph-NN) to accurately recognize different HCs of polymeric insulators. In addition, for better understanding of the distribution of water droplets on the insulator surface, a novel approach based on multiscale mathematical morphology, i.e., granulometry, is used in this article. Our investigations revealed significant alterations in the granulometric patterns with the change in HC of polymeric insulators. The efficacy of the proposed framework employing Res-Morph-NN has been validated on HC images obtained from the experiment as well as on large set of images obtained from an online database, which returned satisfactory results. The proposed method can be efficiently used to discriminate different hydrophobicity classes, which can be implemented for monitoring insulator surface condition in real time.

DSMNN-Net: A Deep Siamese Morphological Neural Network Model for Burned Area Mapping Using Multispectral Sentinel-2 and Hyperspectral PRISMA Images

Wildfires are one of the most destructive natural disasters that can affect our environment, with significant effects also on wildlife. Recently, climate change and human activities have resulted in higher frequencies of wildfires throughout the world. Timely and accurate detection of the burned areas can help to make decisions for their management. Remote sensing satellite imagery can have a key role in mapping burned areas due to its wide coverage, high-resolution data collection, and low capture times. However, although many studies have reported on burned area mapping based on remote sensing imagery in recent decades, accurate burned area mapping remains a major challenge due to the complexity of the background and the diversity of the burned areas. This paper presents a novel framework for burned area mapping based on Deep Siamese Morphological Neural Network (DSMNN-Net) and heterogeneous datasets. The DSMNN-Net framework is based on change detection through proposing a pre/post-fire method that is compatible with heterogeneous remote sensing datasets. The proposed network combines multiscale convolution layers and morphological layers (erosion and dilation) to generate deep features. To evaluate the performance of the method proposed here, two case study areas in Australian forests were selected. The framework used can better detect burned areas compared to other state-of-the-art burned area mapping procedures, with a performance of >98% for overall accuracy index, and a kappa coefficient of >0.9, using multispectral Sentinel-2 and hyperspectral PRISMA image datasets. The analyses of the two datasets illustrate that the DSMNN-Net is sufficiently valid and robust for burned area mapping, and especially for complex areas.

Generalized morphological components based on interval descriptors and n-ary aggregation functions

Morphological perceptrons (MPs) can be characterized as feedforward morphological neural networks (MNNs) with applications in classification and regression. The neuronal aggregation functions of current MP versions are drawn from gray-scale mathematical morphology (MM) that can be described in terms of matrix products in a lattice algebra called minimax algebra. Specifically, MPs have components each of which computes a pair-wise infimum of an erosion and an anti-dilation that can be expressed in terms of products of matrices with entries in a complete l-group extension.In this paper, we use the novel concept of an interval descriptor and an n-ary aggregation function on a bounded poset in order to generalize existing gray-scale and fuzzy morphological components (MCs) of morphological and hybrid morphological/linear perceptrons (HMLPs). In addition, we present several other examples of generalized morphological components (GMCs) that can and will be incorporated as computational units into shallow and deep artificial neural networks.

Properties Regulation and Biological Applications of Decellularized Peripheral Nerve Matrix Hydrogel.

Decellularized peripheral nerve matrix hydrogel (DNM-G) has drawn increasing attention in the field of neural tissue engineering, owing to its high tissue-specific bioactivity, drug/cell delivery capability, and multifunctional processability. However, the mechanisms and influencing factors of DNM-G formation have been rarely reported. To enable potential biological applications, the relationship between gelation conditions (including digestion time and gel concentration) and mechanical properties/stability (sol-gel transition temperature, gelation time, nanotopology, and storage modulus) of the DNM-G were systematically investigated in this study. The adequate-digested decellularized nerve matrix solution exhibited higher mechanical property, shorter gelation time, and a lower gelation temperature. A noteworthy increase of β-sheet proportion was identified through Fourier-transform infrared spectroscopy (FTIR) and circular dichroism (CD) characterizations, which suggested the possible major secondary structure formation during the phase transition. Besides, the DNM-G degraded fast that over 70% mass loss was noted after 4 weeks when immersing in PBS. A natural cross-linking agent, genipin, was gently introduced into DNM-G to enhance its mechanical properties and stability without changing its microstructure and biological performance. As a prefabricated scaffold, DNM-G remarkably increased the length and penetration depth of dorsal root ganglion (DRG) neurites compared to collagen gel. Furthermore, the DNM-G promoted the myelination and facilitated the formation of the morphological neural network. Finally, we demonstrated the feasibility of applying DNM-G in support-free extrusion-based 3D printing. Overall, the mechanical and biological performance of DNM-G can be manipulated by tuning the processing parameters, which is key to the versatile applications of DNM-G in regenerative medicine.

Classification of Chest X-Ray Images Using Novel Adaptive Morphological Neural Networks

The chest X-ray images are difficult to classify for the radiologists due to the noisy nature. The existing models based on convolutional neural networks contain a giant number of parameters, and thus require multi-advanced GPUs to deploy. In this paper, we are the first to develop the adaptive morphological neural networks to classify chest X-ray images, such as pneumonia and COVID-19. A novel structure, which can self-learn morphological dilation and erosion, is proposed to determine the most suitable depth of the adaptive layer. Experimental results on the chest X-ray and the COVID-19 datasets show that the proposed model can achieve the highest classification rate as compared against the existing models. Moreover, it can significantly reduce the computational parameters of the existing models by 97%. The advantage makes the developed model more attractive than others to deploy in the internet and other device platforms.

Algorithmization of the combined approach to the synthesis of engineering solutions

This article focuses on solving the problem of designing new, more adapted and universal technical objects associated with the conceptual design of complex structural elements, and require the analysis and assessment of a large number of possible alternatives. To solve this problem, an urgent problem arises of creating new and improving existing methods for finding engineering solutions that will effectively solve the problem of synthesizing technical solutions using modern technologies. The solution to this problem is presented in this article, which proposes a combined approach to the synthesis of engineering solutions, based on the classical method of morphological research (to form a set of alternatives) and neural network technology (to form a rational technical solution). The article presents the developed algorithm for the operation of the block of intelligent decision making, which provides the process of synthesizing engineering solutions of the intellectual component and expands the technical level of the developed functional units, while reducing their design time. The paper presents a developed structural-functional model for the synthesis of engineering solutions, based on the proposed combined approach.

Forecast of export demand based on artificial neural network and fuzzy system theory

This paper analyses the significance and methods of foreign trade export forecasting. The index system of foreign trade export forecasting is determined based on the analysis of foreign trade export forecasting research results. The concepts and principles of artificial neural network and fuzzy system theory are expounded, and their respective advantages and disadvantages as well as their complementarities are analyzed. This paper introduces the types and training algorithms of evolutionary morphological neural network, combines the neural network with the fuzzy system theory, and establishes the prediction model. Finally, the evolutionary morphological neural network model is applied to the prediction of foreign trade export in view of the characteristics of export and considering the influence of various factors, the whole process of establishing evolutionary morphological neural network forecasting model is introduced in detail, and the change range of export is predicted, and the ideal forecasting results are obtained.

Reduced Dilation-Erosion Perceptron for Binary Classification

Dilation and erosion are two elementary operations from mathematical morphology, a non-linear lattice computing methodology widely used for image processing and analysis. The dilation-erosion perceptron (DEP) is a morphological neural network obtained by a convex combination of a dilation and an erosion followed by the application of a hard-limiter function for binary classification tasks. A DEP classifier can be trained using a convex-concave procedure along with the minimization of the hinge loss function. As a lattice computing model, the DEP classifier assumes the feature and class spaces are partially ordered sets. In many practical situations, however, there is no natural ordering for the feature patterns. Using concepts from multi-valued mathematical morphology, this paper introduces the reduced dilation-erosion (r-DEP) classifier. An r-DEP classifier is obtained by endowing the feature space with an appropriate reduced ordering. Such reduced ordering can be determined using two approaches: one based on an ensemble of support vector classifiers (SVCs) with different kernels and the other based on a bagging of similar SVCs trained using different samples of the training set. Using several binary classification datasets from the OpenML repository, the ensemble and bagging r-DEP classifiers yielded mean higher balanced accuracy scores than the linear, polynomial, and radial basis function (RBF) SVCs as well as their ensemble and a bagging of RBF SVCs.

Extreme learning machine for a new hybrid morphological/linear perceptron

Morphological neural networks (MNNs) can be characterized as a class of artificial neural networks that perform an operation of mathematical morphology at every node, possibly followed by the application of an activation function. Morphological perceptrons (MPs) and (gray-scale) morphological associative memories are among the most widely known MNN models. Since their neuronal aggregation functions are not differentiable, classical methods of non-linear optimization can in principle not be directly applied in order to train these networks. The same observation holds true for hybrid morphological/linear perceptrons and other related models. Circumventing these problems of non-differentiability, this paper introduces an extreme learning machine approach for training a hybrid morphological/linear perceptron, whose morphological components were drawn from previous MP models. We apply the resulting model to a number of well-known classification problems from the literature and compare the performance of our model with the ones of several related models, including some recent MNNs and hybrid morphological/linear neural networks.

Learning cellular morphology with neural networks

Reconstruction and annotation of volume electron microscopy data sets of brain tissue is challenging but can reveal invaluable information about neuronal circuits. Significant progress has recently been made in automated neuron reconstruction as well as automated detection of synapses. However, methods for automating the morphological analysis of nanometer-resolution reconstructions are less established, despite the diversity of possible applications. Here, we introduce cellular morphology neural networks (CMNs), based on multi-view projections sampled from automatically reconstructed cellular fragments of arbitrary size and shape. Using unsupervised training, we infer morphology embeddings (Neuron2vec) of neuron reconstructions and train CMNs to identify glia cells in a supervised classification paradigm, which are then used to resolve neuron reconstruction errors. Finally, we demonstrate that CMNs can be used to identify subcellular compartments and the cell types of neuron reconstructions.

A novel vessel detection and classification algorithm using a deep learning neural network model with morphological processing (M-DLNN)

In recent times, optical satellite images are gaining widespread significance due to its feasibility and compatibility with laser images to improve the contrast of the obtained image. Optical satellite images find numerous applications in object detection and tracking out of which ship detection and tracking is a quite significant field to be researched. A novel ship detection and tracking algorithm has been proposed and implemented in this research article and tested over a wide range of climatic conditions to quantify the efficiency of the proposed work. The proposed algorithm defines morphological traits of the object under study which is the ship in this case as its attributes to segment and detect the object. An input video sequence obtained from the optical imaging system is used as input in the proposed work and efficiency justified in terms of accuracy, precision and classification rates. A morphological deep learning neural network has been proposed in this research article which automates the function of both detection based on morphological attributes of the vessel under study and classifying the detected vessels into their constituent classes. The obtained results have been compared with recent and existing vessel detection algorithms like singular-value decomposition, salient mapping techniques and local binary pattern-based techniques.

Dendrite ellipsoidal neurons based on k-means optimization

Dendrite morphological neurons are a type of artificial neural network that can be used to solve classification problems. The major difference with respect to classical perceptrons is that morphological neurons create hyperboxes to separate patterns from different classes, while perceptrons use hyperplanes. In this paper, we introduce an improved version of dendrite morphological neural networks, which we have called dendrite ellipsoidal neuron that employs hyperellipsoids instead of hyperboxes. This ellipsoidal neuron is presented with a new training algorithm, to set the covariance matrix and the centroid of each hyperellipsoid based on k-means++, by applying hill climbing to search for an optimum number of hyperellipsoids. The main advantage of this approach is that dendrite ellipsoidal neuron creates smoother decision boundaries. The proposed neural model was tested on synthetic and real datasets from the UCI machine learning repository (in a paired t-test) achieving an average accuracy of 80.7%, while multi-layer perceptrons gave 78.4%, support vector machines obtained 74.2%, and radial basis networks 72.7%. Lastly, to test the proposed method performance in solving real practical problems, our model was used to detect lane lines on an urban highway, for classifying figures with a Nao robot and for traffic detection.

On simulating one-trial learning using morphological neural networks

“Learning once, remembering forever”, this wonderful cognitive phenomenon sometimes occurs in the learning process of human beings. Psychologists call this psychological phenomenon “one-trial learning”. The traditional artificial neural networks can simulate the psychological phenomenon of “implicit learning”, but can’t simulate the cognitive phenomenon of “one-trial learning”. Therefore, cognitive psychology gives a challenge to the traditional artificial neural networks. From two aspects of theory and practice in this paper, the possibility of simulating this kind of psychological phenomenon was explored by using morphological neural networks. This paper takes advantage of morphological associative memory networks to realize the simulation of “one-trial learning” for the first time, and gives 5 simulating practical examples. Theoretical analysis and simulation experiments show that the morphological associative memory networks are a higher effective machine learning method, and can better simulate the cognitive phenomenon of “one-trial learning”, therefore provide a theoretical basis and technological support for the study of intelligent science and cognitive science.