Ordem supervisionada baseada em valores fuzzy para morfologia matemática multivalorada

Orientador: Marcos Eduardo Ribeiro do Valle MesquitaDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação CientíficaResumo: Morfologia Matemática foi concebida como uma ferramenta para a análise e processamento de imagens binárias e foi subsequentemente generalizada para o uso em imagens em tons de cinza e imagens multivaloradas. Reticulados completos, que são conjuntos parcialmente ordenados em que todo subconjunto tem extremos bem definidos, servem como a base matemática para uma definição geral de morfologia matemática. Em contraste a imagens em tons de cinza, imagens multivaloradas não possuem uma ordem não-ambígua. Essa dissertação trata das chamadas ordens reduzidas para imagens multivaloradas. Ordens reduzidas são definidas por meio de uma relação binária que ordena os elementos de acordo com uma função h do conjunto de valores em um reticulado completo. Ordens reduzidas podem ser classificadas em ordens não-supervisionadas e ordens supervisionadas. Numa ordem supervisionada, o função de ordenação h depende de conjuntos de treinamento de valores de foreground e de background. Nesta dissertação, estudamos ordens supervisionadas da literatura. Também propomos uma ordem supervisionada baseada em valores fuzzy. Valores fuzzy generalizam cores fuzzy - conjuntos fuzzy que modelam o modo que humanos percebem as cores - para imagens multivaloradas. Em particular, revemos como construir o mapa de ordenação baseado em conjuntos fuzzy para o foreground e para o background. Também introduzimos uma função de pertinência baseada numa estrutura neuro-fuzzy e generalizamos a função de pertinência baseada no diagrama de Voronoi. Por fim, as ordens supervisionadas são avaliadas num experimento de segmentação de imagens hiperespectrais baseado num perfil morfológico modificadoAbstract: Mathematical morphology has been conceived initially as a tool for the analysis and processing of binary images and has been later generalized to grayscale and multivalued images. Complete lattices, which are partially ordered sets in whose every subset has well defined extrema, serve as the mathematical background for a general definition of mathematical morphology. In contrast to gray-scale images, however, there is no unambiguous ordering for multivalued images. This dissertation addresses the so-called reduced orderings for multi-valued images. Reduced orderings are defined by means of a binary relation which ranks elements according to a mapping h from the value set into a complete lattice. Reduced orderings can be classified as unsupervised and supervised ordering. In a supervised ordering, the mapping h depends on training sets of foreground and background values. In this dissertation, we study some relevant supervised orderings from the literature. We also propose a supervised ordering based on fuzzy values. Fuzzy values are a generalization of fuzzy colors - fuzzy sets that model how humans perceive colors - to multivalued images other than color images. In particular, we review how to construct the fuzzy ordering mapping based on fuzzy sets that model the foreground and the background. Also, we introduce a membership function based on a neuro-fuzzy framework and generalize the membership function based on Voronoi diagrams. The supervised orderings are evaluated in an experiment of hyperspectral image segmentation based on a modified morphological profileMestradoMatematica AplicadaMestre em Matemática Aplicada131635/2018-2CNP

Contribució a l'estudi de les uninormes en el marc de les equacions funcionals.

Les uninormes són uns operadors d'agregació que, per la seva definició, es poden considerar com a conjuncions o disjuncions, i que han estat aplicades a camps molt diversos. En aquest treball s'estudien algunes equacions funcionals que tenen com a incògnites les uninormes, o operadors definits a partir d'elles. Una d'elles és la distributivitat, que és resolta per les classes d'uninormes conegudes, solucionant, en particular, un problema obert en la teoria de l'anàlisi no-estàndard. També s'estudien les implicacions residuals i fortes definides a partir d'uninormes, trobant solució a la distributivitat d'aquestes implicacions sobre uninormes. Com a aplicació d'aquests estudis, es revisa i s'amplia la morfologia matemàtica borrosa basada en uninormes, que proporciona un marc inicial favorable per a un nou enfocament en l'anàlisi d'imatges, que haurà de ser estudiat en més profunditat.Las uninormas son unos operadores de agregación que, por su definición se pueden considerar como conjunciones o disjunciones y que han sido aplicados a campos muy diversos. En este trabajo se estudian algunas ecuaciones funcionales que tienen como incógnitas las uninormas, o operadores definidos a partir de ellas. Una de ellas es la distributividad, que se resuelve para las classes de uninormas conocidas, solucionando, en particular, un problema abierto en la teoría del análisis no estándar. También se estudian las implicaciones residuales y fuertes definidas a partir de uninormas, encontrando solución a la distributividad de estas implicaciones sobre uninormas. Como aplicación de estos estudios, se revisa y amplía la morfología matemática borrosa basada en uninormas, que proporciona un marco inicial favorable para un nuevo enfoque en el análisis de imágenes, que tendrá que ser estudiado en más profundidad.Uninorms are aggregation operators that, due to its definition, can be considered as conjunctions or disjunctions, and they have been applied to very different fields. In this work, some functional equations are studied, involving uninorms, or operators defined from them as unknowns. One of them is the distributivity equation, that is solved for all the known classes of uninorms, finding solution, in particular, to one open problem in the non-standard analysis theory. Residual implications, as well as strong ones defined from uninorms are studied, obtaining solution to the distributivity equation of this implications over uninorms. As an application of all these studies, the fuzzy mathematical morphology based on uninorms is revised and deeply studied, getting a new framework in image processing, that it will have to be studied in more detail

Optimization of scientific algorithms in heterogeneous systems and accelerators for high performance computing

Actualmente, la computación de propósito general en GPU es uno de los pilares básicos de la computación de alto rendimiento. Aunque existen cientos de aplicaciones aceleradas en GPU, aún hay algoritmos científicos poco estudiados. Por ello, la motivación de esta tesis ha sido investigar la posibilidad de acelerar significativamente en GPU un conjunto de algoritmos pertenecientes a este grupo. En primer lugar, se ha obtenido una implementación optimizada del algoritmo de compresión de vídeo e imagen CAVLC (Context-Adaptive Variable Length Encoding), que es el método entrópico más usado en el estándar de codificación de vídeo H.264. La aceleración respecto a la mejor implementación anterior está entre 2.5x y 5.4x. Esta solución puede aprovecharse como el componente entrópico de codificadores H.264 software, y utilizarse en sistemas de compresión de vídeo e imagen en formatos distintos a H.264, como imágenes médicas. En segundo lugar, se ha desarrollado GUD-Canny, un detector de bordes de Canny no supervisado y distribuido. El sistema resuelve las principales limitaciones de las implementaciones del algoritmo de Canny, que son el cuello de botella causado por el proceso de histéresis y el uso de umbrales de histéresis fijos. Dada una imagen, esta se divide en un conjunto de sub-imágenes, y, para cada una de ellas, se calcula de forma no supervisada un par de umbrales de histéresis utilizando el método de MedinaCarnicer. El detector satisface el requisito de tiempo real, al ser 0.35 ms el tiempo promedio en detectar los bordes de una imagen 512x512. En tercer lugar, se ha realizado una implementación optimizada del método de compresión de datos VLE (Variable-Length Encoding), que es 2.6x más rápida en promedio que la mejor implementación anterior. Además, esta solución incluye un nuevo método scan inter-bloque, que se puede usar para acelerar la propia operación scan y otros algoritmos, como el de compactación. En el caso de la operación scan, se logra una aceleración de 1.62x si se usa el método propuesto en lugar del utilizado en la mejor implementación anterior de VLE. Esta tesis doctoral concluye con un capítulo sobre futuros trabajos de investigación que se pueden plantear a partir de sus contribuciones

Collected Papers (on Neutrosophics, Plithogenics, Hypersoft Set, Hypergraphs, and other topics), Volume X

This tenth volume of Collected Papers includes 86 papers in English and Spanish languages comprising 972 pages, written between 2014-2022 by the author alone or in collaboration with the following 105 co-authors (alphabetically ordered) from 26 countries: Abu Sufian, Ali Hassan, Ali Safaa Sadiq, Anirudha Ghosh, Assia Bakali, Atiqe Ur Rahman, Laura Bogdan, Willem K.M. Brauers, Erick González Caballero, Fausto Cavallaro, Gavrilă Calefariu, T. Chalapathi, Victor Christianto, Mihaela Colhon, Sergiu Boris Cononovici, Mamoni Dhar, Irfan Deli, Rebeca Escobar-Jara, Alexandru Gal, N. Gandotra, Sudipta Gayen, Vassilis C. Gerogiannis, Noel Batista Hernández, Hongnian Yu, Hongbo Wang, Mihaiela Iliescu, F. Nirmala Irudayam, Sripati Jha, Darjan Karabašević, T. Katican, Bakhtawar Ali Khan, Hina Khan, Volodymyr Krasnoholovets, R. Kiran Kumar, Manoranjan Kumar Singh, Ranjan Kumar, M. Lathamaheswari, Yasar Mahmood, Nivetha Martin, Adrian Mărgean, Octavian Melinte, Mingcong Deng, Marcel Migdalovici, Monika Moga, Sana Moin, Mohamed Abdel-Basset, Mohamed Elhoseny, Rehab Mohamed, Mohamed Talea, Kalyan Mondal, Muhammad Aslam, Muhammad Aslam Malik, Muhammad Ihsan, Muhammad Naveed Jafar, Muhammad Rayees Ahmad, Muhammad Saeed, Muhammad Saqlain, Muhammad Shabir, Mujahid Abbas, Mumtaz Ali, Radu I. Munteanu, Ghulam Murtaza, Munazza Naz, Tahsin Oner, ‪Gabrijela Popović‬‬‬‬‬, Surapati Pramanik, R. Priya, S.P. Priyadharshini, Midha Qayyum, Quang-Thinh Bui, Shazia Rana, Akbara Rezaei, Jesús Estupiñán Ricardo, Rıdvan Sahin, Saeeda Mirvakili, Said Broumi, A. A. Salama, Flavius Aurelian Sârbu, Ganeshsree Selvachandran, Javid Shabbir, Shio Gai Quek, Son Hoang Le, Florentin Smarandache, Dragiša Stanujkić, S. Sudha, Taha Yasin Ozturk, Zaigham Tahir, The Houw Iong, Ayse Topal, Alptekin Ulutaș, Maikel Yelandi Leyva Vázquez, Rizha Vitania, Luige Vlădăreanu, Victor Vlădăreanu, Ștefan Vlăduțescu, J. Vimala, Dan Valeriu Voinea, Adem Yolcu, Yongfei Feng, Abd El-Nasser H. Zaied, Edmundas Kazimieras Zavadskas.‬

Automated Resolution Selection for Image Segmentation

It is well known in image processing in general, and hence in image segmentation in particular, that computational cost increases rapidly with the number and dimensions of the images to be processed. Several fields, such as astronomy, remote sensing, and medical imaging, use very large images, which might also be 3D and/or captured at several frequency bands, all adding to the computational expense. Multiresolution analysis is one method of increasing the efficiency of the segmentation process. One multiresolution approach is the coarse-to-fine segmentation strategy, whereby the segmentation starts at a coarse resolution and is then fine-tuned during subsequent steps. Until now, the starting resolution for segmentation has been selected arbitrarily with no clear selection criteria. The research conducted for this thesis showed that starting from different resolutions for image segmentation results in different accuracies and speeds, even for images from the same dataset. An automated method for resolution selection for an input image would thus be beneficial. This thesis introduces a framework for the selection of the best resolution for image segmentation. First proposed is a measure for defining the best resolution based on user/system criteria, which offers a trade-off between accuracy and time. A learning approach is then described for the selection of the resolution, whereby extracted image features are mapped to the previously determined best resolution. In the learning process, class (i.e., resolution) distribution is imbalanced, making effective learning from the data difficult. A variant of AdaBoost, called RAMOBoost, is therefore used in this research for the learning-based selection of the best resolution for image segmentation. RAMOBoost is designed specifically for learning from imbalanced data. Two sets of features are used: Local Binary Patterns (LBP) and statistical features. Experiments conducted with four datasets using three different segmentation algorithms show that the resolutions selected through learning enable much faster segmentation than the original ones, while retaining at least the original accuracy. For three of the four datasets used, the segmentation results obtained with the proposed framework were significantly better than with the original resolution with respect to both accuracy and time

Fuzzy Sets, Fuzzy Logic and Their Applications 2020

The present book contains the 24 total articles accepted and published in the Special Issue “Fuzzy Sets, Fuzzy Logic and Their Applications, 2020” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of fuzzy sets and systems of fuzzy logic and their extensions/generalizations. These topics include, among others, elements from fuzzy graphs; fuzzy numbers; fuzzy equations; fuzzy linear spaces; intuitionistic fuzzy sets; soft sets; type-2 fuzzy sets, bipolar fuzzy sets, plithogenic sets, fuzzy decision making, fuzzy governance, fuzzy models in mathematics of finance, a philosophical treatise on the connection of the scientific reasoning with fuzzy logic, etc. It is hoped that the book will be interesting and useful for those working in the area of fuzzy sets, fuzzy systems and fuzzy logic, as well as for those with the proper mathematical background and willing to become familiar with recent advances in fuzzy mathematics, which has become prevalent in almost all sectors of the human life and activity

Apprenticeship Bootstrapping for Autonomous Aerial Shepherding of Ground Swarm

Aerial shepherding of ground vehicles (ASGV) musters a group of uncrewed ground vehicles (UGVs) from the air using uncrewed aerial vehicles (UAVs). This inspiration enables robust uncrewed ground-air coordination where one or multiple UAVs effectively drive a group of UGVs towards a goal. Developing artificial intelligence (AI) agents for ASGV is a non-trivial task due to the sub-tasks, multiple skills, and their non-linear interaction required to synthesise a solution. One approach to developing AI agents is Imitation learning (IL), where humans demonstrate the task to the machine. However, gathering human data from complex tasks in human-swarm interaction (HSI) requires the human to perform the entire job, which could lead to unexpected errors caused by a lack of control skills and human workload due to the length and complexity of ASGV. We hypothesise that we can bootstrap the overall task by collecting human data from simpler sub-tasks to limit errors and workload for humans. Therefore, this thesis attempts to answer the primary research question of how to design IL algorithms for multiple agents. We propose a new learning scheme called Apprenticeship Bootstrapping (AB). In AB, the low-level behaviours of the shepherding agents are trained from human data using our proposed hierarchical IL algorithms. The high-level behaviours are then formed using a proposed gesture demonstration framework to collect human data from synthesising more complex controllers. The transferring mechanism is performed by aggregating the proposed IL algorithms. Experiments are designed using a mixed environment, where the UAV flies in a simulated robotic Gazebo environment, while the UGVs are physical vehicles in a natural environment. A system is designed to allow switching between humans controlling the UAVs using low-level actions and humans controlling the UAVs using high-level actions. The former enables data collection for developing autonomous agents for sub-tasks. At the same time, in the latter, humans control the UAV by issuing commands that call the autonomous agents for the sub-tasks. We baseline the learnt agents against Str\"{o}mbom scripted behaviours and show that the system can successfully generate autonomous behaviours for ASGV