Alberto Sanfeliu

Network robot systems

This article introduces the definition of Network Robot Systems (NRS) as is understood in Europe, USA and Japan. Moreover, it describes some of the NRS projects in Europe and Japan and presents a summary of the papers of this Special Issue.

Progress in Pattern Recognition, Speech and Image Analysis

Neurons, Dendrites, and Pattern Classification.- Robot Vision for Autonomous Object Learning and Tracking.- Graduated Scale Inspection Using Computer Vision.- Vision System for Subpixel Laser Stripe Profile Extraction with Real Time Operation.- Multi-channel Reconstruction of Video Sequences from Low-Resolution and Compressed Observations.- 3D Rigid Facial Motion Estimation from Disparity Maps.- Robust Techniques in Least Squares-Based Motion Estimation Problems.- Inexact Graph Matching for Facial Feature Segmentation and Recognition in Video Sequences: Results on Face Tracking.- Crater Marking and Classification Using Computer Vision.- Using Optical Flow for Tracking.- Another Paradigm for the Solution of the Correspondence Problem in Motion Analysis.- Improvement of the Fail-Safe Characteristics in Motion Analysis Using Adaptive Technique.- Spatially Adaptive Algorithm for Impulse Noise Removal from Color Images.- Improving Phase-Congruency Based Feature Detection through Automatic Scale-Selection.- Robust Estimation of Roughness Parameter in SAR Amplitude Images.- Two New Scale-Adapted Texture Descriptors for Image Segmentation.- Topological Query in Image Databases.- Reconstructing 3D Objects from Silhouettes with Unknown Viewpoints: The Case of Planar Orthographic Views.- Enforcing a Shape Correspondence between Two Views of a 3D Non-rigid Object.- A Colour Constancy Algorithm Based on the Histogram of Feasible Colour Mappings.- Reconstruction of Surfaces from Cross Sections Using Skeleton Information.- Extension of a New Method for Surface Reconstruction from Cross Sections.- Imposing Integrability in Geometric Shape-from-Shading.- Correcting Radial Lens Distortion Using Image and Point Correspondences.- Characterization of Surfaces with Sonars Using Time of Flight and Triangulation.- Non-speech Sound Feature Extraction Based on Model Identification for Robot Navigation.- Enhancement of Noisy Speech Using Sliding Discrete Cosine Transform.- Integrating High and Low Smoothed LMs in a CSR System.- Selection of Lexical Units for Continuous Speech Recognition of Basque.- Creating a Mexican Spanish Version of the CMU Sphinx-III Speech Recognition System.- Decision Tree-Based Context Dependent Sublexical Units for Continuous Speech Recognition of Basque.- Uniclass and Multiclass Connectionist Classification of Dialogue Acts.- A Technique for Extraction of Diagnostic Data from Cytological Specimens.- Segmentation and Morphometry of Histological Sections Using Deformable Models: A New Tool for Evaluating Testicular Histopathology.- Robust Markers for Blood Vessels Segmentation: A New Algorithm.- Discriminative Power of Lymphoid Cell Features: Factor Analysis Approach.- Retinal Angiography Based Authentication.- Suboptimal Classifier for Dysarthria Assessment.- Approximate Nearest Neighbour Search with the Fukunaga and Narendra Algorithm and Its Application to Chromosome Classification.- Characterization of Viability of Seeds by Using Dynamic Speckles and Difference Histograms.- An Adaptive Enhancer with Modified Signal Averaging Scheme to Detect Ventricular Late Potentials.- A Study on the Recognition of Patterns of Infant Cry for the Identification of Deafness in Just Born Babies with Neural Networks.- Patients Classification by Risk Using Cluster Analysis and Genetic Algorithms.- Mathematical Morphology on MRI for the Determination of Iberian Ham Fat Content.- Automatic Dark Fibres Detection in Wool Tops.- Musical Style Identification Using Grammatical Inference: The Encoding Problem.- New Distance Measures Applied to Marble Classification.- Online Handwritten Signature Verification Using Hidden Markov Models.- Fast Handwritten Recognition Using Continuous Distance Transformation.- Stroke Boundary Analysis for Identification of Drawing Tools.- Solving the Global Localization Problem for Indoor Mobile Robots.- Restricted Decontamination for the Imbalanced Training Sample Problem.- An Entropy Maximization Approach to Optimal Model Selection in Gaussian Mixtures.- Gaussian Mixture Models for Supervised Classification of Remote Sensing Multispectral Images.- Fast Multistage Algorithm for K-NN Classifiers.- Some Improvements in Tree Based Nearest Neighbour Search Algorithms.- Impact of Mixed Metrics on Clustering.- A Comparison between Two Fuzzy Clustering Algorithms for Mixed Features.- Extended Star Clustering Algorithm.- Two New Metrics for Feature Selection in Pattern Recognition.- Conditions of Generating Descriptive Image Algebras by a Set of Image Processing Operations.- Completeness Conditions of a Class of Pattern Recognition Algorithms Based on Image Equivalence.- Typical Segment Descriptors: A New Method for Shape Description and Identification.- A New Approach That Selects a Single Hyperplane from the Optimal Pairwise Linear Classifier.- A Characterization of Discretized Polygonal Convex Regions by Discrete Moments.- Learning probabilistic context-free grammars from treebanks.- Simulated Annealing for Automated Definition of Fuzzy Sets in Human Central Nervous System Modeling.- Automatic Tuning of Fuzzy Partitions in Inductive Reasoning.- Kernel Computation in Morphological Bidirectional Associative Memories.- Improving Still Image Coding by an SOM-Controlled Associative Memory.- A Morphological Methodology for Features Identification in Satellite Images for Semi-automatic Cartographic Updating.- Morphological Neural Networks with Dendrite Computation: A Geometrical Approach.- A Method for the Automatic Summarization of Topic-Based Clusters of Documents.- Improving Prepositional Phrase Attachment Disambiguation Using the Web as Corpus.- Determination of Similarity Threshold in Clustering Problems for Large Data Sets.- Content-Based Retrieval Using Color, Texture, and Shape Information.- Off the Shelf Methods for Robust Portuguese Cadastral Map Analysis.- Simultaneous Segmentation-Recognition-Vectorization of Meaningful Geographical Objects in Geo-Images.- Geomorphometric Analysis of Raster Image Data to detect Terrain Ruggedness and Drainage Density.- Morphological Applications for Maps Construction in Path Planning Tasks.- Compact Mapping in Plane-Parallel Environments Using Stereo Vision.- An Oscillatory Neural Network for Image Segmentation.- Generating Three-Dimensional Neural Cells Based on Bayes Rules and Interpolation with Thin Plate Splines.- A Maximum Entropy Approach to Sampling in EDA - The Single Connected Case.

Function-described graphs for modelling objects represented by sets of attributed graphs

We present in this article the model function-described graph (FDG), which is a type of compact representation of a set of attributed graphs (AGs) that borrow from random graphs the capability of probabilistic modelling of structural and attribute information. We define the FDGs, their features and two distance measures between AGs (unclassified patterns) and FDGs (models or classes) and we also explain an efficient matching algorithm. Two applications of FDGs are presented: in the former, FDGs are used for modelling and matching 3D-objects described by multiple views, whereas in the latter, they are used for representing and recognising human faces, described also by several views.

Graph-based representations and techniques for image processing and image analysis☆

In this paper we will discuss the use of some graph-based representations and techniques for image processing and analysis. Instead of making an extensive review of the graph techniques in this field, we will explain how we are using these techniques in an active vision system for an autonomous mobile robot developed in the Institut de Robotica i Informatica Industrial within the project “Active Vision System with Automatic Learning Capacity for Industrial Applications (CICYT TAP98-0473)”. Specifically we will discuss the use of graph-based representations and techniques for image segmentation, image perceptual grouping and object recognition. We first present a generalisation of a graph partitioning greedy algorithm for colour image segmentation. Next we describe a novel fusion of colour-based segmentation and depth from stereo that yields a graph representing every object in the scene. Finally we describe a new representation of a set of attributed graphs (AGs), denominated Function Described Graphs (FDGs), a distance measure for matching AGs with FDGs and some applications for robot vision.

Robot companion: A social-force based approach with human awareness-navigation in crowded environments

Robots accompanying humans is one of the core capacities every service robot deployed in urban settings should have. We present a novel robot companion approach based on the so-called Social Force Model (SFM). A new model of robot-person interaction is obtained using the SFM which is suited for our robots Tibi and Dabo. Additionally, we propose an interactive scheme for robots human-awareness navigation using the SFM and prediction information. Moreover, we present a new metric to evaluate the robot companion performance based on vital spaces and comfortableness criteria. Also, a multimodal human feedback is proposed to enhance the behavior of the system. The validation of the model is accomplished throughout an extensive set of simulations and real-life experiments.

Efficient rotation invariant object detection using boosted Random Ferns

We present a new approach for building an efficient and robust classifier for the two class problem, that localizes objects that may appear in the image under different orientations. In contrast to other works that address this problem using multiple classifiers, each one specialized for a specific orientation, we propose a simple two-step approach with an estimation stage and a classification stage. The estimator yields an initial set of potential object poses that are then validated by the classifier. This methodology allows reducing the time complexity of the algorithm while classification results remain high. The classifier we use in both stages is based on a boosted combination of Random Ferns over local histograms of oriented gradients (HOGs), which we compute during a preprocessing step. Both the use of supervised learning and working on the gradient space makes our approach robust while being efficient at run-time. We show these properties by thorough testing on standard databases and on a new database made of motorbikes under planar rotations, and with challenging conditions such as cluttered backgrounds, changing illumination conditions and partial occlusions.

The effects of partial observability when building fully correlated maps

This paper presents an analysis of the fully correlated approach to the simultaneous localization and map building (SLAM) problem from a control systems theory point of view, both for linear and nonlinear vehicle models. We show how partial observability hinders full reconstructibility of the state space, making the final map estimate dependent on the initial observations. Nevertheless, marginal filter stability guarantees convergence of the state error covariance to a positive semidefinite covariance matrix. By characterizing the form of the total Fisher information, we are able to determine the unobservable state space directions. Moreover, we give a closed-form expression that links the amount of reconstruction error to the number of landmarks used. The analysis allows the formulation of measurement models that make SLAM observable.

ON THE GRAPH EDIT DISTANCE COST: PROPERTIES AND APPLICATIONS

We model the edit distance as a function in a labeling space. A labeling space is an Euclidean space where coordinates are the edit costs. Through this model, we define a class of cost. A class of cost is a region in the labeling space that all the edit costs have the same optimal labeling. Moreover, we characterize the distance value through the labeling space. This new point of view of the edit distance gives us the opportunity of defining some interesting properties that are useful for a better understanding of the edit distance. Finally, we show the usefulness of these properties through some applications.

SECOND-ORDER RANDOM GRAPHS FOR MODELING SETS OF ATTRIBUTED GRAPHS AND THEIR APPLICATION TO OBJECT LEARNING AND RECOGNITION

The aim of this article is to present a random graph representation, that is based on second-order relations between graph elements, for modeling sets of attributed graphs (AGs). We refer to these models as Second-Order Random Graphs (SORGs). The basic feature of SORGs is that they include both marginal probability functions of graph elements and second-order joint probability functions. This allows a more precise description of both the structural and semantic information contents in a set of AGs and, consequently, an expected improvement in graph matching and object recognition. The article presents a probabilistic formulation of SORGs that includes as particular cases the two previously proposed approaches based on random graphs, namely the First-Order Random Graphs (FORGs) and the Function-Described Graphs (FDGs). We then propose a distance measure derived from the probability of instantiating a SORG into an AG and an incremental procedure to synthesize SORGs from sequences of AGs. Finally, SORGs are shown to improve the performance of FORGs, FDGs and direct AG-to-AG matching in three experimental recognition tasks: one in which AGs are randomly generated and the other two in which AGs represent multiple views of 3D objects (either synthetic or real) that have been extracted from color images. In the last case, object learning is achieved through the synthesis of SORG models.

SYNTHESIS OF FUNCTION-DESCRIBED GRAPHS AND CLUSTERING OF ATTRIBUTED GRAPHS

Function-Described Graphs (FDGs) have been introduced by the authors as a representation of an ensemble of Attributed Graphs (AGs) for structural pattern recognition alternative to first-order random graphs. Both optimal and approximate algorithms for error-tolerant graph matching, which use a distance measure between AGs and FDGs, have been reported elsewhere. In this paper, both the supervised and the unsupervised synthesis of FDGs from a set of graphs is addressed. First, two procedures are described to synthesize an FDG from a set of commonly labeled AGs or FDGs, respectively. Then, the unsupervised synthesis of FDGs is studied in he context of clustering a set of AGs and obtaining an FDG model for each cluster. Two algorithms based on incremental and hierarchical clustering, respectively, are proposed, which are parameterized by a graph matching method. Some experimental results both on synthetic data and a real 3D-object recognition application show that the proposed algorithms are effective for clustering a set of AGs and synthesizing the FDGs that describe the classes. Moreover, the synthesized FDGs are shown to be useful for pattern recognition thanks to the distance measure and matching algorithm previously reported.