This work is focused on the modeling and development of a CBIR (Content-based image retrieval) system applied to the recovery of digital medical images of a human body, denominated M-CBIR. This model is composed on two methodologies: features extraction techniques and metric data structures. When this set of techniques is applied to the search of different human body regions, it can retrieve the most relevant similar images to a query image. A real database of medical images composed of 772 medical studies was used to compare the robustness of the extraction techniques and evaluate the performance of the system, through four different extractors. The objective of this work will result in a digital atlas of human body for medical radiological center. Finally, analysis and conclusions are also discussed.

Most methods used to compare text documents are based on the space vector model; however, this model does not capture the relations between words, which is considered necessary to make better comparisons. In this research, we propose a method based on the creation of graphs to get semantic relations between words and we adapt algorithms of the theory of non-rigid 3D model analysis.

Due to the increasing amount of data and the reduction of costs in 3D data acquisition devices, there has been a growing interest, in developing efficient and robust feature extraction algorithms for 3D shapes, invariants to isometric, topological and noise changes, among others. One of the key tasks for feature extraction in 3D shapes is the interest points detection; where interest points are salient structures, which can be used, instead of the whole object. In this research, we present a new approach to detect interest points in 3D shapes by analyzing the triangles that compose the mesh which represent the shape, in different way to other algorithms more complex such as Harris 3D or HKS. Our results and experiments of repeatability, confirm that our algorithm is stable and robust, in addition, the computational complexity is O(n log n), where n represents the number of faces of the m...

Algorithm k-means is useful for grouping operations; however, when is applied to large amounts of data, its computational cost is high. This research propose an optimization of k-means algorithm by using parallelization techniques and synchronization, which is applied to image segmentation. In the results obtained, the parallel k-means algorithm, improvement 50% to the algorithm sequential k-means.

En la presente investigación se propone un enfoque novedoso para extraer características distintivas de imagenes basado en el modelo de color HSV y filtros wavelets, con la finalidad de hacer un agrupamiento de imágenes que son similares entre si, por ejemplo mariposas de la misma especie. Además se investiga la mejor combinacion de características de color y forma. Los experimentos han demostrado un mejor rendimiento en la combinación color con el filtro de Gabor.

To facilitate processing of 3D objects is common to use high-level representations. The interest points are one of them. An interest point should possess a distinctive feature regarding its locality and should be stable in different instances of the object. This article proposes a descriptor based on symmetry (GISIFs) and heat diffusion (HKS). From this features, we select a set of representative points. The GISIFs referenced in this article has not been used to extract local features. We compare our results with the results of other techniques, which make up the state of the art in interest point detection. We use a benchmark that evaluates the accuracy of the selected points with respect to an ideal set of interest points.

Due to the advancement of computing and the power of the new hardware, more economical, it is now feasible to have thousands of images which can be analyzed to allow classification for its shape and/or color. Furthermore, techniques and efficiency of the classification depends on the characteristics to be obtained of images in order to compare and classify them according to their similarity. Some images, such as model cars, planes and boats, can be discriminated by their shape. However, other images such as butterfly species where the shape is similar, the color plays an important role in the discrimination task. In this research we propose a novel approach to extract distinctive features of images by combining the HSV color model and wavelets filters. Furthermore, we investigate the best combination of features color and form. Experiments have shown improved performance by combining the...

One mechanism for estimating software quality is through the use of metrics, which are functions that evaluates certain characteristics of the product quality development. A software product can be evaluated from different points of view, and in that sense, the results of the evaluations are numeric vectors, which together describe the quality of the software. This research uses data from NASA's open access which undergo a process of reducing the dimensionality by principal component analysis (PCA), then applied three clustering techniques and evaluates the best grouping using Rand Index. Finally, the top clusters are tested with regression to find the metrics that are related to the error of the Software. The results suggest that groups consisting of software modules whose code source have a higher average of blank lines, show a higher density of error. This could be interpreted as an i...

El objetivo de esta tesis es el desarrollo de un nuevo algoritmo para la detección de simetría en cuerpos no rígidos. Esto implica el uso de modelos 3D, los cuales son nubes de puntos interconectados, formando una malla de triángulos. Dicha malla se aproxima a la superficie que representa el modelo. Se emplean métodos para la detección de puntos salientes en la superficie de la estructura. Estos puntos, conocidos como key points, son posteriormente utilizados para encontrar componentes clave (key components) dentro de la estructura. Finalmente, estos componentes son utilizados para la detección de simetría en los cuerpos no rígidos mediante el uso de la distancia de difusión. Como parte de la tesis, dos aportes significativos fueron desarrollados, el primero es un nuevo algoritmo de complejidad lineal respecto al número de triángulos, para detectar key points o puntos de inte...

Over the years, the scientific interest towards 3D models analysis has become more popular. Problems such as classification, retrieval and matching are studied with the idea to offer robust solutions. This paper introduces a 3D object classification method for non-rigid shapes, based on the detection of key points, the use of spectral descriptors and deep learning techniques. We adopt an approach of converting the models into a “spectral image”. By extracting interest points and calculating three types of spectral descriptors (HKS, WKS and GISIF), we generate a three-channel input to a convolutional neural network. This CNN is trained to automatically learn features such as topology of 3D models. The results are evaluated and analyzed using the Non-Rigid Classification Benchmark SHREC 2011. Our proposal shows promising results in classification tasks compared to other methods, and al...

Approximate similarity search algorithms based on hashing were proposed to query high-dimensional datasets due to its fast retrieval speed and low storage cost. Recent studies, promote the use of Convolutional Neural Network (CNN) with hashing techniques to improve the search accuracy. However, there are challenges to solve in order to find a practical and efficient solution to index CNN features, such as the need for heavy training process to achieve accurate query results and the critical dependency on data-parameters. Aiming to overcome these issues, we propose a new method for scalable similarity search, i.e., Deep frActal based Hashing (DAsH), by computing the best data-parameters values for optimal sub-space projection exploring the correlations among CNN features attributes using fractal theory. Moreover, inspired by recent advances in CNNs, we use not only activations of lower la...

Approximate similarity search algorithms based on hashing were proposed to query high-dimensional datasets due to its fast retrieval speed and low storage cost. Recent studies, promote the use of Convolutional Neural Network (CNN) with hashing techniques to improve the search accuracy. However, there are challenges to solve in order to find a practical and efficient solution to index CNN features, such as the need for heavy training process to achieve accurate query results and the critical dependency on data-parameters.

Common problems in climatological time series data are high dimensionality, correlation between the sequential values and noise due to calibration of meteorological stations influencing dramatically in the quality of clustering, classification, climate pattern finding and data processing. One way to deal with this problem is through feature extraction technique. In order to extract features from large climatological time series data, we propose a feature extraction method based on autoencoder neural network (AUTOE). As a first step, time series is standardized. Then, different architectures of autoencoder is applied on it to reduce dimensionality. Finally, k-means clustering algorithm are used to evaluate them through quality measures. As a result, autoencoder performs well and is competitive with other feature extraction techniques over Synthetic Control Chart Time Series.

Even though a computer science degree is unavoidably broken into semesters and courses, we always hope that our students form a holistic picture of the discipline by the time they graduate. Yet as educators, we do not have too many opportunities to make this point front and center for an extended period of time. This report es a well-defined portion of this problem: revealing conceptual connections between algorithmic courses (such as Discrete Math, Data Structures, Algorithms) and systems oriented courses (such as Organization, Computer Networks, Operating Systems, and Hardware) through the use of research papers. In particular, we provide a pedagogical framework as well as a set of carefully selected papers to crosscut our disciplinary space in a way that is orthogonal to conventional course design. This framework includes a paper taxonomy, strategies for covering topics that students ...