A hybrid approach for categorizing images based on complex networks and neural networks

There are several methods for categorizing images, the most of which are statistical, geometric, model-based and structural methods. In this paper, a new method for describing images based on complex network models is presented. Each image contains a number of key points that can be identified through standard edge detection algorithms. To understand each image better, we can use these points to create a graph of the image. In order to facilitate the use of graphs, generated graphs are created in the form of a complex network of small-worlds. Complex grid features such as topological and dynamic features can be used to display image-related features. After generating this information, it normalizes them and uses them as suitable features for categorizing images. For this purpose, the generated information is given to the neural network. Based on these features and the use of neural networks, comparisons between new images are performed. The results of the article show that this method has a good performance in identifying similarities and finally categorizing them

A clique-based method for the edit distance between unordered trees and its application to analysis of glycan structures

[Background]Measuring similarities between tree structured data is important for analysis of RNA secondary structures, phylogenetic trees, glycan structures, and vascular trees. The edit distance is one of the most widely used measures for comparison of tree structured data. However, it is known that computation of the edit distance for rooted unordered trees is NP-hard. Furthermore, there is almost no available software tool that can compute the exact edit distance for unordered trees. [Results]In this paper, we present a practical method for computing the edit distance between rooted unordered trees. In this method, the edit distance problem for unordered trees is transformed into the maximum clique problem and then efficient solvers for the maximum clique problem are applied. We applied the proposed method to similar structure search for glycan structures. The result suggests that our proposed method can efficiently compute the edit distance for moderate size unordered trees. It also suggests that the proposed method has the accuracy comparative to those by the edit distance for ordered trees and by an existing method for glycan search. [Conclusions]The proposed method is simple but useful for computation of the edit distance between unordered trees. The object code is available upon request

Graph matching a skeletonized theoretical morphospace with a cladogram for gomphonemoid-cymbelloid diatoms (Bacillariophyta)

A three-dimensional (3D) theoretical morphospace of gomphonemoid and cymbelloid diatoms was skeletonized using concepts from extended Reeb graph analysis and Morse theory. The resultant skeleton tree was matched to a cladogram of the same group of related taxa using adjacency matrices of the trees and ordinated with multidimensional scaling (MDS) of leaf nodes. From this, an unweighted path matrix based on the number of branches between leaf nodes was ordinated to determine degree of matched tree structures. A constrained MDS of the path matrix, weighted by ranked MDS leaf node groups as facets, was used to interpret taxon environmental tolerances and habitat preferences with respect to adaptive value. The methods developed herein provided a way to combine results from morphological and phylogenetic analyses and interpret those results with respect to an aspect of evolutionary process, namely, adaptation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83675/1/JLP-JBS2011.pdf-

MSClique: Multiple structure discovery through the maximum weighted clique problem

We present a novel approach for feature correspondence and multiple structure discovery in computer vision. In contrast to existing methods, we exploit the fact that point-sets on the same structure usually lie close to each other, thus forming clusters in the image. Given a pair of input images, we initially extract points of interest and extract hierarchical representations by agglomerative clustering. We use the maximum weighted clique problem to find the set of corresponding clusters with maximum number of inliers representing the multiple structures at the correct scales. Our method is parameter-free and only needs two sets of points along with their tentative correspondences, thus being extremely easy to use. We demonstrate the effectiveness of our method in multiple-structure fitting experiments in both publicly available and in-house datasets. As shown in the experiments, our approach finds a higher number of structures containing fewer outliers compared to state-of-the-art methods.Peer ReviewedPostprint (published version

Efficient similarity computations on parallel machines using data shaping

Similarity computation is a fundamental operation in all forms of data. Big Data is, typically, characterized by attributes such as volume, velocity, variety, veracity, etc. In general, Big Data variety appears as structured, semi-structured or unstructured forms. The volume of Big Data in general, and semi-structured data in particular, is increasing at a phenomenal rate. Big Data phenomenon is posing new set of challenges to similarity computation problems occurring in semi-structured data. Technology and processor architecture trends suggest very strongly that future processors shall have ten\u27s of thousands of cores (hardware threads). Another crucial trend is that ratio between on-chip and off-chip memory to core counts is decreasing. State-of-the-art parallel computing platforms such as General Purpose Graphics Processors (GPUs) and MICs are promising for high performance as well high throughput computing. However, processing semi-structured component of Big Data efficiently using parallel computing systems (e.g. GPUs) is challenging. Reason being most of the emerging platforms (e.g. GPUs) are organized as Single Instruction Multiple Thread/Data machines which are highly structured, where several cores (streaming processors) operate in lock-step manner, or they require a high degree of task-level parallelism. We argue that effective and efficient solutions to key similarity computation problems need to operate in a synergistic manner with the underlying computing hardware. Moreover, semi-structured form input data needs to be shaped or reorganized with the goal to exploit the enormous computing power of \textit{state-of-the-art} highly threaded architectures such as GPUs. For example, shaping input data (via encoding) with minimal data-dependence can facilitate flexible and concurrent computations on high throughput accelerators/co-processors such as GPU, MIC, etc. We consider various instances of traditional and futuristic problems occurring in intersection of semi-structured data and data analytics. Preprocessing is an operation common at initial stages of data processing pipelines. Typically, the preprocessing involves operations such as data extraction, data selection, etc. In context of semi-structured data, twig filtering is used in identifying (and extracting) data of interest. Duplicate detection and record linkage operations are useful in preprocessing tasks such as data cleaning, data fusion, and also useful in data mining, etc., in order to find similar tree objects. Likewise, tree edit is a fundamental metric used in context of tree problems; and similarity computation between trees another key problem in context of Big Data. This dissertation makes a case for platform-centric data shaping as a potent mechanism to tackle the data- and architecture-borne issues in context of semi-structured data processing on GPU and GPU-like parallel architecture machines. In this dissertation, we propose several data shaping techniques for tree matching problems occurring in semi-structured data. We experiment with real world datasets. The experimental results obtained reveal that the proposed platform-centric data shaping approach is effective for computing similarities between tree objects using GPGPUs. The techniques proposed result in performance gains up to three orders of magnitude, subject to problem and platform

Multiple graph matching and applications

En aplicaciones de reconocimiento de patrones, los grafos con atributos son en gran medida apropiados. Normalmente, los vértices de los grafos representan partes locales de los objetos i las aristas relaciones entre estas partes locales. No obstante, estas ventajas vienen juntas con un severo inconveniente, la distancia entre dos grafos no puede ser calculada en un tiempo polinómico. Considerando estas características especiales el uso de los prototipos de grafos es necesariamente omnipresente. Las aplicaciones de los prototipos de grafos son extensas, siendo las más habituales clustering, clasificación, reconocimiento de objetos, caracterización de objetos i bases de datos de grafos entre otras. A pesar de la diversidad de aplicaciones de los prototipos de grafos, el objetivo del mismo es equivalente en todas ellas, la representación de un conjunto de grafos. Para construir un prototipo de un grafo todos los elementos del conjunto de enteramiento tienen que ser etiquetados comúnmente. Este etiquetado común consiste en identificar que nodos de que grafos representan el mismo tipo de información en el conjunto de entrenamiento. Una vez este etiquetaje común esta hecho, los atributos locales pueden ser combinados i el prototipo construido. Hasta ahora los algoritmos del estado del arte para calcular este etiquetaje común mancan de efectividad o bases teóricas. En esta tesis, describimos formalmente el problema del etiquetaje global i mostramos una taxonomía de los tipos de algoritmos existentes. Además, proponemos seis nuevos algoritmos para calcular soluciones aproximadas al problema del etiquetaje común. La eficiencia de los algoritmos propuestos es evaluada en diversas bases de datos reales i sintéticas. En la mayoría de experimentos realizados los algoritmos propuestos dan mejores resultados que los existentes en el estado del arte.In pattern recognition, the use of graphs is, to a great extend, appropriate and advantageous. Usually, vertices of the graph represent local parts of an object while edges represent relations between these local parts. However, its advantages come together with a sever drawback, the distance between two graph cannot be optimally computed in polynomial time. Taking into account this special characteristic the use of graph prototypes becomes ubiquitous. The applicability of graphs prototypes is extensive, being the most common applications clustering, classification, object characterization and graph databases to name some. However, the objective of a graph prototype is equivalent to all applications, the representation of a set of graph. To synthesize a prototype all elements of the set must be mutually labeled. This mutual labeling consists in identifying which nodes of which graphs represent the same information in the training set. Once this mutual labeling is done the set can be characterized and combined to create a graph prototype. We call this initial labeling a common labeling. Up to now, all state of the art algorithms to compute a common labeling lack on either performance or theoretical basis. In this thesis, we formally describe the common labeling problem and we give a clear taxonomy of the types of algorithms. Six new algorithms that rely on different techniques are described to compute a suboptimal solution to the common labeling problem. The performance of the proposed algorithms is evaluated using an artificial and several real datasets. In addition, the algorithms have been evaluated on several real applications. These applications include graph databases and group-wise image registration. In most of the tests and applications evaluated the presented algorithms have showed a great improvement in comparison to state of the art applications

Identification and three-dimensional positioning of urban energy lines from optical images to aid a teleoperated pruning robot

Orientador : Prof. Dr. Leandro dos Santos CoelhoDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia Elétrica. Defesa: Curitiba, 30/08/2016Inclui referências : f. 138-144Área de concentraçãoResumo: Diversos fatores podem impactar a qualidade da distribuição de energia elétrica, entre eles, um dos mais impactantes é o contato de vegetação com linhas aéreas energizadas. Assim sendo, é de suma importância a poda de vegetação próxima à linhas energizadas. Visando-se aprimorar esse processo, pode-se empregar um robô teleoperado de poda, de forma que a poda possa ser realizada de maneira remota e segura. As câmeras instaladas no braço robótico permitem que o operador tenha visão da área de corte mesmo quando a visada direta do solo estiver obstruída. Um dos problemas de se visualizar a região de corte por meio de um monitor é a perda de noção de profundidade, o que pode dificultar a operação. Dessa forma, seria relevante uma técnica de visão computacional capaz de detectar as linhas de energia e seu posicionamento tridimensional (3D) a fim de auxiliar o operador. Revisando-se a literatura, avaliou-se que, no geral, os trabalhos já propostos para detecção de linhas em imagens operam em situações com fundo limpo, não urbanizado e com vista superior das linhas de energia. Assim sendo, nesse trabalho é proposta uma técnica para detecção de linhas energizadas em imagens de regiões urbanas e a obtenção de seu posicionamento 3D, fator ainda não explorado na literatura recente. Para se alcançar esse objetivo é proposta a utilização de câmeras de espectro visível posicionadas em paralelo. Assim, regiões com potencial para serem linhas de energia são selecionadas utilizando-se detecção de bordas seguidas por filtragens geométricas aplicando-se técnicas inspiradas em algoritmos de grafos e ajuste de pontos selecionados a uma curva. Após a seleção de regiões candidatas a linha de energia, o posicionamento 3D é obtido utilizando-se de visão estéreo. Para tal, a correspondência entre pontos visíveis em ambas as câmeras é encontrada e com triangulação o posicionamento 3D da linha de energia é recuperado. Com a informação 3D disponível falsos candidatos são reduzidos por um fator de aproximadamente sete vezes e finalmente as linhas são detectadas. Para avaliação do método foi criada uma base de dados contendo imagens estéreo obtidas de um cenário montado com dois postes, três linhas de energia e uma árvore entre essas, na qual foi possível atingir níveis de precisão de 98% ao término do processo de detecção, contando-se com 91% de taxa de verdadeiro positivos. As causas dos falsos negativos são evidenciadas para que trabalhos futuros possam encontrar alternativas às dificuldades apresentadas. O algoritmo aqui proposto fornece como saída um mapa de cor sobre as linhas de energia para identificação da profundidade em 2D e uma nuvem de pontos para visualização em 3D. Palavras-chave: Visão Computacional. Reconhecimento de objetos. Visão estéreo. Linhas de energia. Robô de poda.Abstract: Different factors may affect energy distribution quality, among them, one of the main causes is when vegetation gets into contact with overhead energy lines. Therefore, it is of main importance to prune vegetation close to energy lines. To improve this process it is possible to use a teleoperated robot, what allows the pruning activity to be accomplished in a remote and safe way. Cameras installed in the robot arm provide images from the pruning region to the operator even when direct sight is not an option. One of the main problems viewing the prunning region using a display is the lost of depth perception, what could make the operator unintentialy colide the robot with energy lines. Therefore, it would be of great aid a computer vision method capable of detecting energy lines and their three-dimensional (3D) positioning to aid the operator. During the state of the art review of energy line detection in images, it was perceived that, in general, the already proposed works operate in regions where the images present a clear background, not urbanized, and with the energy lines seen from above. Therefore, in this work, it is proposed a technique to detect energy lines and their 3D positioning in images taken in urban settings, factor yet unexplored in the recent literature. To reach this objective it is proposed the use of two visible spectrum cameras installed in parallel. In this way, regions with potential to be energy line are selected using edge detection followed by the geometric filtering designed using techniques inspired in graphs algorithms and curve fitting. After the regions with potential to be energy lines are found, their 3D position is obtained with stereo vision. To do so, the matching among points visible by both cameras is found and with triangulation, it is possible to recover the energy line 3D position. With the 3D information available, false positives are reduced by a factor of about seven and finally the energy lines are detected. A dataset containing stereo images of a scenario built with two power poles, three energy lines, and a tree between them was created in order to evaluate the presented method. In the commented dataset it was possible to reach accuracy of 98% at the end of the detection process, with 91% true positive rate. The causes of the false negatives cases are put in evidence in order to allow them to be overcame by future works. The algorithm proposed here outputs a colormap projected over the energy lines to inform the depth of each one in 2D and a point cloud to visualize each line in 3D. Key words: Computer vision. Object Recognition. Stereo vision. Overhead Energy Lines. Pruning Robo