Análisis de texturas mediante el histograma de frecuencias de elementos conexo

El Análisis de Texturas, y más concretamente, la segmentación de texturas es uno de los campos de mayor interés dentro de la Visión Artificial. La segmentación de una escena del mundo real es casi imposible llevarla a cabo sin poder segmentar los objetos texturizados que en ella se encuentran. Este proceso es más crítico cuando se pretende aplicar en sistemas de inspección automática en entornos industriales. Un error en esta fase se propagará en subsiguientes etapas y provocará una degeneración completa del sistema. Por este motivo, la inspección de productos industriales juega un papel muy importante en los procesos de producción, en los que cada vez más aumenta la demanda de calidad en un entorno de fuerte competitividad. El principal problema con el que nos enfrentamos en el Análisis de Texturas es el de encontrar la mejor representación de textura. Uno de los inconvenientes más significativos dentro de la caracterización e identificación de texturas es que la mayoría de sus descripciones son subjetivas y difusas. La mayor parte de las características utilizadas para su descripción dependen muy estrechamente del problema que se intenta resolver y desgraciadamente no existe un esquema estandarizado. Sobre esta problemática se centra la principal aportación de este trabajo. Hemos desarrollado un novedoso concepto al que hemos llamado: Histograma de Frecuencias de Elementos Conexos (HFEC). Se puede definir el HFEC de una imagen como una aproximación a la función de densidad de un suceso o evento aleatorio denominado “elemento conexo”. Este suceso no sólo representa la distribución de los niveles de gris de la textura, sino también la dependencia espacial que existe entre ellos. El trabajo se compone principalmente de tres partes. La primera parte del trabajo se centra en la descripción de esta novedosa herramienta, así como en el estudio de sus propiedades. Se presentan los parámetros que definen el HFEC: el Nivel de Conectividad y el Parámetro Morfológico. Mediante un estudio de sensibilidad de estos parámetros se demuestra como el Histograma de niveles de gris de una imagen es un caso particular de la configuración de un HFEC. En una segunda parte se presenta una arquitectura para el diseño de Sistemas de Reconocimiento Automático de Formas basados en la representación de un HFEC. Dicha arquitectura se divide en tres fases: (1) Extracción de características, (2) Selección de características y (3) Clasificación. En la primera fase, como su nombre indica, se extraen las características del HFEC que entran en juego en nuestro problema de reconocimiento y cuyo fin es la conversión del HFEC en características que representen, idealmente, la información condensada y más importante de un HFEC dado. En esta fase, además de las herramientas clásicas como la transformada de Fourier, se ha utilizado una herramienta matemática cuyo uso está siendo bastante extenso y exitoso en sistemas de reconocimientos de formas como es la transformada Wavelet. El objetivo principal de un HFEC es el de caracterizar la región de una textura para su posterior clasificación. El HFEC de una textura natural es una función no estacionaria y, por lo tanto, es deseable para su estudio poder trabajar con una representación espacio / escalar (frecuencia) de la forma que nos lo permite el análisis Wavelet. En la fase de selección de características se identifican el menor número de características que mejor identifican un HFEC dado con la mínima redundancia posible. Para ello se ha implementado un procedimiento estadístico que tiene en cuenta la información de dispersión entre/intra clases a la hora de seleccionar una característica. En la tercera y última fase se asigna una categoría de textura a un HFEC específico de acuerdo con las características seleccionadas en la etapa anterior. Hemos elegido como clasificador un tipo de redes neuronales como el modelo de perceptrón multicapa con propagación hacia delante (feedforward neural network), asociado al algoritmo de ajuste denominado Retropropagación del Gradiente (Back-Propagation). Por último, se presenta la aplicación de esta arquitectura y el HFEC a un sistema real de inspección automática de madera. El problema que se pretende resolver es el de la detección de defectos en objetos de madera. La inspección de estos objetos se realiza bajo condiciones de un entorno industrial y existe una gran variabilidad en la apariencia de los mismos. Parte del desarrollo expuesto es fruto del trabajo de investigación realizado durante más de seis años en un proyecto de colaboración entre el Departamento de Inteligencia Artificial de la Facultad de Informática de la Universidad Politécnica de Madrid y una empresa privada (por motivos de confidencialidad evitamos el nombre). A la hora de escribir estas líneas existen seis plantas industriales (tres en España, dos en Francia y una en Portugal) utilizando el sistema de inspección automática desarrollado en este proyecto. Textures Analysis, or more precisely, texture segmentation, is one of the most critical issues in computer vision. There are virtually no scenes taken from the real world that can be analyzed automatically without segmenting the texture elements present in the corresponding digital images. Focusing on the automatic quality inspection of industrial products, the results attained in the segmentation phase are critical for the performance of the whole computer-based vision system. On the other hand, because of ever-increasing competitiveness in business and industry, quality inspection has become a key issue in any production process. The main problem that we face ourselves in Textures Analysis is finding the best texture representation. One of the most significant drawbacks within texture characterization and identification is that most of the descriptions are subjective and vague. Although the description, selection and classification of texture features are crucial for automatic object inspection, there is, unfortunately, no universally accepted standard for such a strategic endeavor. Most of the texture features used in practice depend very closely on the application domain and, furthermore, there is a disturbing lack of agreement even in the terminology used by the different authors. For any domain-specific application, the problem of selecting the best set of texture features, aside from being crucial, is more an art than a science. The main contribution of this thesis centers on these problems. We have developed a novel concept that we have called: Frequency Histogram of Connected Elements (FHCE). The FHCE can be defined as an approximation to the probability density function of a random event called “Connected Element”. The FHCE represents the frequency of occurrence of a random event, which not only describes the texture’s gray-level distribution, but also the existing spatial dependence within the texture. The thesis is composed of three parts. The first part centers on the description of this novel concept, as well as on the study of its main properties. The parameters that define the so-called “Connected Element” are introduced: the Connectivity Level and the Morphology Parameter. A sensibility study of these parameters shows that the gray-level histogram of a digital image is a particular case of the FHCE. The second part introduces an architecture for the design of Automatic Recognition Systems based on the representation of an FHCE. This architecture is divided into three phases: (1) characteristics extraction, (2) characteristics selection and (3) classification. In the first phase, the characteristics of the FHCE that come into play in our recognition problem are extracted. The purpose is to convert the FHCE into characteristics that represent the condensed information of a given FHCE. In this phase, in addition to classic tools, such as the Fourier transform, we used a widely used and highly successful mathematical tool for recognition systems: the Wavelet transform. The main objective of an FHCE is to characterize the region of a texture for its subsequent classification. The FHCE of a natural texture is a non-stationary function and, therefore, it is desirable for its study to be able to work with a space/scalar (frequency) representation, as the Wavelet analysis does. In the characteristics selection phase, the smallest number of characteristics that best identify an FHCE, with the minimum possible redundancy, are identified. For this reason, a statistical procedure was implemented that takes into account the dispersion information between/within classes in order to select a characteristic. In the third and last phase, the FHCE is assigned to a texture category according to the characteristics selected in the previous stage. A feed-forward multilayer perceptron, trained with the back-propagation algorithm, is the specific ANN classifier applied for the detection and recognition of textures in digital images. Finally, this architecture and the FHCE are applied to a real automatic wood inspection system. The problem that is to be solved is the detection of defects in wooden objects. These objects are inspected under industrial environment conditions. Part of the development presented is the result of investigative work carried out over more than six years in a collaborative project between the Departamento de Inteligencia Artificial of the Facultad de Informática of the Universidad Politécnica de Madrid and a private company (to remain unnamed for confidentiality reasons). At the time of this writing, there are six industrial plants (three in Spain, two in France and one in Portugal) using the automatic inspection system developed in this project

Automatically Updating a Dynamic Region Connection Calculus for Topological Reasoning

Proceedings of: Workshop on User-Centric Technologies and Applications (CONTEXTS 2011), Salamanca, Spain, April 6-8, 2011During the last years ontology-based applications have been thought without taking in account their limitations in terms of upgradeability. In parallel, new capabilities such as topological sorting of instances with spatial characteristics have been developed. Both facts may lead to a collapse in the operational capacity of this kind of applications. This paper presents an ontology-centric architecture to solve the topological relationships between spatial objects automatically. The capability for automatic assertion is given by an object model based on geometries. The object model seeks to prioritize the optimization using a dynamic data structure of spatial data. The ultimate goal of this architecture is the automatic storage of the spatial relationships without a noticeable loss of efficiency.This work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/ TIC-1485) and DPS2008-07029-C02-02.Publicad

A practical approach for active camera coordination based on a fusion-driven multi-agent system

In this paper, we propose a multi-agent system architecture to manage spatially distributed active (or pan-tilt-zoom) cameras. Traditional video surveillance algorithms are of no use for active cameras, and we have to look at different approaches. Such multi-sensor surveillance systems have to be designed to solve two related problems: data fusion and coordinated sensor-task management. Generally, architectures proposed for the coordinated operation of multiple cameras are based on the centralisation of management decisions at the fusion centre. However, the existence of intelligent sensors capable of decision making brings with it the possibility of conceiving alternative decentralised architectures. This problem is approached by means of a MAS, integrating data fusion as an integral part of the architecture for distributed coordination purposes. This paper presents the MAS architecture and system agents.This work was supported in part by Projects MINECO TEC2012-37832-C02-01, CICYT TEC2011-28626-C02-02 and CAM CONTEXTS (S2009/TIC-1485).Publicad

MIJ2K Optimization using evolutionary multiobjective optimization algorithms

This paper deals with the multiobjective definition of video compression and its optimization. The optimization will be done using NSGA-II, a well-tested and highly accurate algorithm with a high convergence speed developed for solving multiobjective problems. Video compression is defined as a problem including two competing objectives. We try to find a set of optimal, so-called Pareto-optimal solutions, instead of a single optimal solution. The two competing objectives are quality and compression ratio maximization. The optimization will be achieved using a new patent pending codec, called MIJ2K, also outlined in this paper. Video will be compressed with the MIJ2K codec applied to some classical videos used for performance measurement, selected from the Xiph.org Foundation repository. The result of the optimization will be a set of near-optimal encoder parameters. We also present the convergence of NSGA-II with different encoder parameters and discuss the suitability of MOEAs as opposed to classical search-based techniques in this field.This work was supported in part by Projects CICYT TIN2008- 06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB, CAM MADRINET S-0505/TIC/0255 and DPS2008-07029-C02-02.publicad

MIJ2K: Enhanced video transmission based on conditional replenishment of JPEG2000 tiles with motion compensation

A video compressed as a sequence of JPEG2000 images can achieve the scalability, flexibility, and accessibility that is lacking in current predictive motion-compensated video coding standards. However, streaming JPEG2000-based sequences would consume considerably more bandwidth. With the aim of solving this problem, this paper describes a new patent pending method, called MIJ2K. MIJ2K reduces the inter-frame redundancy present in common JPEG2000 sequences (also called MJP2). We apply a real-time motion detection system to perform conditional tile replenishment. This will significantly reduce the bit rate necessary to transmit JPEG2000 video sequences, also improving their quality. The MIJ2K technique can be used both to improve JPEG2000-based real-time video streaming services or as a new codec for video storage. MIJ2K relies on a fast motion compensation technique, especially designed for real-time video streaming purposes. In particular, we propose transmitting only the tiles that change in each JPEG2000 frame. This paper describes and evaluates the method proposed for real-time tile change detection, as well as the overall MIJ2K architecture. We compare MIJ2K against other intra-frame codecs, like standard Motion JPEG2000, Motion JPEG, and the latest H.264-Intra, comparing performance in terms of compression ratio and video quality, measured by standard peak signal-to-noise ratio, structural similarity and visual quality metric metrics.This work was supported in part by Projects CICYT TIN2008– 06742-C02–02/TSI, CICYT TEC2008–06732-C02–02/TEC, SINPROB, CAM MADRINET S-0505/TIC/0255 and DPS2008–07029-C02–02.Publicad

Interactive Video Annotation Tool

Proceedings of: Forth International Workshop on User-Centric Technologies and applications (CONTEXTS 2010). Valencia, 7-10 September , 2010.Abstract: Increasingly computer vision discipline needs annotated video databases to realize assessment tasks. Manually providing ground truth data to multimedia resources is a very expensive work in terms of effort, time and economic resources. Automatic and semi-automatic video annotation and labeling is the faster and more economic way to get ground truth for quite large video collections. In this paper, we describe a new automatic and supervised video annotation tool. Annotation tool is a modified version of ViPER-GT tool. ViPER-GT standard version allows manually editing and reviewing video metadata to generate assessment data. Automatic annotation capability is possible thanks to an incorporated tracking system which can deal the visual data association problem in real time. The research aim is offer a system which enables spends less time doing valid assessment models.Publicad

A General Purpose Context Reasoning Environment to Deal with Tracking Problems: An Ontology-based Prototype

Proceedings of: 6th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2011). Wroclaw, Poland, May 23-25, 2011The high complexity of semantics extraction with automatic video analysis has forced the researchers to the generalization of mixed approaches based on perceptual and context data. These mixed approaches do not usually take in account the advantages and benefits of the data fusion discipline. This paper presents a context reasoning environment to deal with general and specific tracking problems. The cornerstone of the environment is a symbolic architecture based on the Joint Directors of Laboratories fusion model. This architecture may build a symbolic data representation from any source, check the data consistency, create new knowledge and refine it through inference obtaining a higher understanding level of the scene and providing feedback to autocorrect the tracking errors. An ontology-based prototype has been developed to carry out experimental tests. The prototype has been proved against tracking analysis occlusion problems.This work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/ TIC-1485) and DPS2008-07029-C02-02.Publicad

A Practical Case Study: Face Recognition on Low Quality Images Using Gabor Wavelet and Support Vector Machines

Face recognition is a problem that arises on many real world applications, such as those related with Ambient Intelligence (AmI). The specific nature and goals of AmI applications, however, requires minimizing the invasiveness of data collection methods, often resulting in a drastic reduction of data quality and a plague of unforeseen effects which can put standard face recognition systems out of action. In order to deal with this, a face recognition system for AmI applications must not only be carefully designed but also subject to an exhaustive configuration plan to ensure it offers the required accuracy, robustness and real-time performance. This document covers the design and tuning of a holistic face recognition system targeting an Ambient Intelligence scenario. It has to work under partially uncontrolled capturing conditions: frontal images with pose variation up to 40 degrees, changing illumination, variable image size and degraded quality. The proposed system is based on Support Vector Machine (SVM) classifiers and applies Gabor Filters intensively. A complete sensitivity analysis shows how the recognition accuracy can be boosted through careful configuration and proper parameter setting, although the most adequate setting depends on the requirements for the final system.This work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB,CAMMADRINET S-0505 /TIC/0255 and DPS2008-07029-C02-02.Publicad

Distributed Active-Camera Control Architecture Based on Multi-Agent Systems

Proceedings of: 10th Conference on Practical Applications of Agents and Multi-Agent Systems, Salamanca (Spain), 28-30 March 2012 (PAAMS`12)In this contribution a Multi-Agent System architecture is proposed to deal with the management of spatially distributed heterogeneous nets of sensors, specially is described the problem of Pan-Tilt-Zoom or active cameras. The design of surveillance multi-sensor systems implies undertaking to solve two related problems: data fusion and coordinated sensor-task management. Generally, proposed architectures for the coordinated operation of multiple sensors are based on centralization of management decisions at the fusion center. However, the existence of intelligent sensors capable of taking decisions brings the possibility of conceiving alternative decentralized architectures. This problem could be approached by means of a Multi-Agent System (MAS). In specific, this paper proposes a MAS architecture for automatically control sensors in video surveillance environments.This work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, CAM CONTEXTS (S2009/ TIC-1485) and DPS2008- 07029-C02-02.Publicad

Multi-camera and Multi-modal Sensor Fusion, an Architecture Overview

Proceedings of: Forth International Workshop on User-Centric Technologies and applications (CONTEXTS 2010). Valencia, 07-10 September , 2010.This paper outlines an architecture formulti-camera andmulti-modal sensor fusion.We define a high-level architecture in which image sensors like standard color, thermal, and time of flight cameras can be fused with high accuracy location systems based on UWB, Wifi, Bluetooth or RFID technologies. This architecture is specially well-suited for indoor environments, where such heterogeneous sensors usually coexists. The main advantage of such a system is that a combined nonredundant output is provided for all the detected targets. The fused output includes in its simplest form the location of each target, including additional features depending of the sensors involved in the target detection, e.g., location plus thermal information. This way, a surveillance or context-aware system obtains more accurate and complete information than only using one kind of technologyThis work was supported in part by Projects CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB, CAM CONTEXTS S2009/TIC-1485 and DPS2008-07029-C02-02Publicad