Novel convolution kernels for computer vision and shape analysis based on electromagnetism

Computer vision is a growing field with a lot of new applications in automation and robotics, since it allows the analysis of images and shapes for the generation of numerical or analytical information. One of the most used method of information extraction is image filtering through convolution kernels, with each kernel specialized for specific applications. The objective of this paper is to present a novel convolution kernels, based on principles of electromagnetic potentials and fields, for a general use in computer vision and to demonstrate its usage for shape and stroke analysis. Such filtering possesses unique geometrical properties that can be interpreted using well understood physics theorems. Therefore, this paper focuses on the development of the electromagnetic kernels and on their application on images for shape and stroke analysis. It also presents several interesting features of electromagnetic kernels, such as resolution, size and orientation independence, robustness to noise and deformation, long distance stroke interaction and ability to work with 3D images

Green Function and Electromagnetic Potential for Computer Vision and Convolutional Neural Network Applications

RÉSUMÉ Pour les problèmes de vision machine (CV) avancées, tels que la classification, la segmentation de scènes et la détection d’objets salients, il est nécessaire d’extraire le plus de caractéristiques possibles des images. Un des outils les plus utilisés pour l’extraction de caractéristiques est l’utilisation d’un noyau de convolution, où chacun des noyaux est spécialisé pour l’extraction d’une caractéristique donnée. Ceci a mené au développement récent des réseaux de neurones convolutionnels (CNN) qui permet d’optimiser des milliers de noyaux à la fois, faisant du CNN la norme pour l’analyse d’images. Toutefois, une limitation importante du CNN est que les noyaux sont petits (généralement de taille 3x3 à 7x7), ce qui limite l’interaction longue-distance des caractéristiques. Une autre limitation est que la fusion des caractéristiques se fait par des additions pondérées et des opérations de mise en commun (moyennes et maximums locaux). En effet, ces opérations ne permettent pas de fusionner des caractéristiques du domaine spatial avec des caractéristiques puisque ces caractéristiques occupent des positions éloignées sur l’image. L’objectif de cette thèse est de développer des nouveaux noyaux de convolutions basés sur l’électromagnétisme (EM) et les fonctions de Green (GF) pour être utilisés dans des applications de vision machine (CV) et dans des réseaux de neurones convolutionnels (CNN). Ces nouveaux noyaux sont au moins aussi grands que l’image. Ils évitent donc plusieurs des limitations des CNN standards puisqu’ils permettent l’interaction longue-distance entre les pixels de limages. De plus, ils permettent de fusionner les caractéristiques du domaine spatial avec les caractéristiques du domaine du gradient. Aussi, étant donné tout champ vectoriel, les nouveaux noyaux permettent de trouver le champ vectoriel conservatif le plus rapproché du champ initial, ce qui signifie que le nouveau champ devient lisse, irrotationnel et conservatif (intégrable par intégrale curviligne). Pour répondre à cet objectif, nous avons d’abord développé des noyaux convolutionnels symétriques et asymétriques basés sur les propriétés des EM et des GF et résultant en des noyaux qui sont invariants en résolution et en rotation. Ensuite, nous avons développé la première méthode qui permet de déterminer la probabilité d’inclusion dans des contours partiels, permettant donc d’extrapoler des contours fins en des régions continues couvrant l’espace 2D. De plus, la présente thèse démontre que les noyaux basés sur les GF sont les solveurs optimaux du gradient et du Laplacien.----------ABSTRACT For advanced computer vision (CV) tasks such as classification, scene segmentation, and salient object detection, extracting features from images is mandatory. One of the most used tools for feature extraction is the convolutional kernel, with each kernel being specialized for specific feature detection. In recent years, the convolutional neural network (CNN) became the standard method of feature detection since it allowed to optimize thousands of kernels at the same time. However, a limitation of the CNN is that all the kernels are small (usually between 3x3 and 7x7), which limits the receptive field. Another limitation is that feature merging is done via weighted additions and pooling, which cannot be used to merge spatial-domain features with gradient-domain features since they are not located at the same pixel coordinate. The objective of this thesis is to develop electromagnetic (EM) convolutions and Green’s functions (GF) convolutions to be used in Computer Vision and convolutional neural networks (CNN). These new kernels do not have the limitations of the standard CNN kernels since they allow an unlimited receptive field and interaction between any pixel in the image by using kernels bigger than the image. They allow merging spatial domain features with gradient domain features by integrating any vector field. Additionally, they can transform any vector field of features into its least-error conservative field, meaning that the field of features becomes smooth, irrotational and conservative (line-integrable). At first, we developed different symmetrical and asymmetrical convolutional kernel based on EM and GF that are both resolution and rotation invariant. Then we developed the first method of determining the probability of being inside partial edges, which allow extrapolating thin edge features into the full 2D space. Furthermore, the current thesis proves that GF kernels are the least-error gradient and Laplacian solvers, and they are empirically demonstrated to be faster than the fastest competing method and easier to implement. Consequently, using the fast gradient solver, we developed the first method that directly combines edges with saliency maps in the gradient domain, then solves the gradient to go back to the saliency domain. The improvement of the saliency maps over the F-measure is on average 6.6 times better than the nearest competing algorithm on a selected dataset. Then, to improve the saliency maps further, we developed the DSS-GIS model which combines edges with salient regions deep inside the network

Content-Based Image Retrieval Based on Electromagnetism-Like Mechanism

Recently, many researchers in the field of automatic content-based image retrieval have devoted a remarkable amount of research looking for methods to retrieve the best relevant images to the query image. This paper presents a novel algorithm for increasing the precision in content-based image retrieval based on electromagnetism optimization technique. The electromagnetism optimization is a nature-inspired technique that follows the collective attraction-repulsion mechanism by considering each image as an electrical charge. The algorithm is composed of two phases: fitness function measurement and electromagnetism optimization technique. It is implemented on a database with 8,000 images spread across 80 classes with 100 images in each class. Eight thousand queries are fired on the database, and the overall average precision is computed. Experimental results of the proposed approach have shown significant improvement in the retrieval performance in regard to precision

Numerical optimization for vibration and noise of the wheel based on PSO-GA method

Currently, those reported researches conducted optimal design for the wheel only in order to reduce the tread wear and increase the service life, but they did not consider the wheel vibration and radiation noise which seriously influence people’s life and did not achieve obvious noise reduction effects. Aiming at this question, a multi-body dynamic model of the high-speed train was established, and the vertical and radial force was extracted to input into the finite element model of the wheel to compute its vibration characteristics. Then, the wheel was conducted on a multi-objective optimization based on particle swarm optimization improved by genetic algorithm (PSO-GA) method. Finally, the optimized vibration results were mapped to the acoustic element model to compute the radiation noise of the wheel. The computational model was also validated by experimental test. In order to observe the optimized effect, the optimized results were compared with those of the traditional GA and PSO method. Solutions of the traditional GA and PSO method were relatively dispersed during iterations and the algorithm could easily fall into the locally optimal solution. The optimized results of PSO-GA method were obviously better. Compared with the original wheel, the vibration acceleration was reduced by 22.9 %, and the mass was reduced by 1.1 %. Finally, the optimized vibration was mapped to the boundary element model to compute the radiation noise of the wheel, and the computational results were compared with the original wheel. Radiation noises of the original wheel were obviously more than that of the optimized wheel, and there were a lot of obvious peak noises in the original wheel. Radiation noises of the optimized wheel only had two obvious noise peaks in the analyzed frequency. Therefore, a wheel with low noises and lightweight was achieved in this paper

Keypoint detection by wave propagation

We propose to rely on the wave equation for the detection of repeatable keypoints invariant up to image scale and rotation and robust to viewpoint variations, blur, and lighting changes. The algorithm exploits the properties of local spatial–temporal extrema of the evolution of image intensities under the wave propagation to highlight salient symmetries at different scales. Although the image structures found by most state-of-the-art detectors, such as blobs and corners, occur typically on highly textured surfaces, salient symmetries are widespread in diverse kinds of images, including those related to poorly textured objects, which are hardly dealt with by current pipelines based on local invariant features. The impact on the overall algorithm of different numerical wave simulation schemes and their parameters is discussed, and a pyramidal approximation to speed-up the simulation is proposed and validated. Experiments on publicly available datasets show that the proposed algorithm offers state-of-the-art repeatability on a broad set of different images while detecting regions that can be distinctively described and robustly matched