Stefano Cagnoni

Genetic design of optimum linear and nonlinear QRS detectors

Describes an approach to the design of optimum QRS detectors. The authors report on detectors including a linear or nonlinear polynomial filter, which enhances and rectifies the QRS complex, and a simple, adaptive maxima detector. The parameters of the filter and the detector, and the samples to be processed are selected by a genetic algorithm which minimizes the detection errors made on a set of reference ECG signals. Three different architectures and the experimental results achieved on the MIT-BIH Arrhythmia Database are described.<<ETX>>

Genetic algorithm-based interactive segmentation of 3D medical images

This article describes a method for evolving adaptive procedures for the contour-based segmentation of anatomical structures in 3D medical data sets. With this method, the user first manually traces one or more 2D contours of an anatomical structure of interest on parallel planes arbitrarily cutting the data set. Such contours are then used as training examples for a genetic algorithm to evolve a contour detector. By applying the detector to the rest of the image sequence it is possible to obtain a full segmentation of the structure. The same detector can then be used to segment other image sequences of the same sort. Segmentation is driven by a contour-tracking strategy that relies on an elastic-contour model whose parameters are also optimized by the genetic algorithm. We report results obtained on a software-generated phantom and on real tomographic images of different sorts.

Evaluation of parallel particle swarm optimization algorithms within the CUDA TM architecture

Particle swarm optimization (PSO), like other population-based meta-heuristics, is intrinsically parallel and can be effectively implemented on Graphics Processing Units (GPUs), which are, in fact, massively parallel processing architectures. In this paper we discuss possible approaches to parallelizing PSO on graphics hardware within the Compute Unified Device Architecture (CUDA(TM)), a GPU programming environment by nVIDIA(TM) which supports the companys latest cards. In particular, two different ways of exploiting GPU parallelism are explored and evaluated. The execution speed of the two parallel algorithms is compared, on functions which are typically used as benchmarks for PSO, with a standard sequential implementation of PSO (SPSO), as well as with recently published results of other parallel implementations. An in-depth study of the computation efficiency of our parallel algorithms is carried out by assessing speed-up and scale-up with respect to SPSO. Also reported are some results about the optimization effectiveness of the parallel implementations with respect to SPSO, in cases when the parallel versions introduce some possibly significant difference with respect to the sequential version.

Particle Swarm Optimization and Differential Evolution for model-based object detection

Automatically detecting objects in images or video sequences is one of the most relevant and frequently tackled tasks in computer vision and pattern recognition. The starting point for this work is a very general model-based approach to object detection. The problem is turned into a global continuous optimization one: given a parametric model of the object to be detected within an image, a function is maximized, which represents the similarity between the model and a region of the image under investigation. In particular, in this work, the optimization problem is tackled using Particle Swarm Optimization (PSO) and Differential Evolution (DE). We compare the performances of these optimization techniques on two real-world paradigmatic problems, onto which many other real-world object detection problems can be mapped: hippocampus localization in histological images and human body pose estimation in video sequences. In the former, a 2D deformable model of a section of the hippocampus is fit to the corresponding region of a histological image, to accurately localize such a structure and analyze gene expression in specific sub-regions. In the latter, an articulated 3D model of a human body is matched against a set of images of a human performing some action, taken from different perspectives, to estimate the subjects posture in space. Given the significant computational burden imposed by this approach, we implemented PSO and DE as parallel algorithms within the nVIDIA(TM) CUDA computing architecture.

Vision-based localization for mobile robots

Abstract Robust self-localization and repositioning strategies are essential capabilities for robots operating in highly dynamic environments. Environments are considered highly dynamic, if objects of interest move continuously and quickly, and if chances of hitting or getting hit by other robots are quite significant. An outstanding example for such an environment is provided by RoboCup. Vision system designs for robots used in RoboCup are based on several approaches, aimed at fitting both the mechanical characteristics of the players and the strategies and operations that the different roles or playing situations may require. This paper reviews three approaches to vision-based self-localization used in the RoboCup middle-size league competition and describes the results they achieve in the robot soccer environment for which they have been designed.

Real-World Applications of Evolutionary Computing

This book constitutes the refereed proceedings of six workshops on evolutionary computation held concurrently as EvoWorkshops 2000 in Edinburgh, Scotland, UK, in April 2000. The 37 revised papers presented were carefully reviewed and selected by the respective program committees. All in all, the book demonstrates the broad application potential of evolutionary computing in a variety of fields. In accordance with the individual workshops, the book is divided into sections on image and signal processing; systems, controls, and drives in industry; telecommunications; scheduling and timetabling; robotics; and aeronautics

GPU-Based Road Sign Detection Using Particle Swarm Optimization

Road Sign Detection is a major goal of Advanced Driving Assistance Systems (ADAS). Since the dawn of this discipline, much work based on different techniques has been published which shows that traffic signs can be first detected and then classified in video sequences in real time. While detection is usually performed using classical computer vision techniques based on color and/or shape matching, most often classification is performed by neural networks. In this work we present a novel approach based on both sign shape and color which uses Particle Swarm Optimization (PSO) for detection. Remarkably, a single fitness function can be used both to detect a sign belonging to a certain category and, at the same time, to estimate its actual position with respect to the camera reference frame. To speed up execution times, the algorithm exploits the parallelism offered by modern graphics cards and, in particular, the CUDA™ architecture by nVIDIA. The effectiveness of the approach has been assessed on a synthetic video sequence, which has been successfully processed in real time at full frame rate.

Integrating content-based retrieval in a medical image reference database

Image reference databases (IRDBs) are a recurrent research topic in medical imaging. Most IRDBs are designed to help experienced physicians in diagnostic tasks and require that users have prior extensive knowledge of the field for their use to be fruitful. Therefore, the educational potential of such image collections cannot be exploited thoroughly. In this paper we propose an image-indexing method to extend the functionalities of an existing medical IRDB and allow for its use in educational applications, as well as in computer-assisted diagnosis. Our method, based on the Kahrunen-Leève transform, has been used to develop a content-based search engine for tomographic image databases on which we are presently experimenting and which we aim to integrate into a working radiological IRDB installed at the University of Florence. Results achieved in our preliminary tests are also reported.

Omnidirectional stereo systems for robot navigation

This paper discusses how stereo vision achieved through the use of omnidirectional sensors can help mobile robot navigation providing advantages, in terms of both versatility and performance, with respect to the classical stereo system based on two horizontally-displaced traditional cameras. The paper also describes an automatic calibration strategy for catadioptric omnidirectional sensors and results obtained using a stereo obstacle detection algorithm devised within a general framework in which, with some limitations, many existing algorithm designed for traditional cameras can be adapted for use with omnidirectional sensors.

Preface: Introduction to the special issue on evolutionary computer vision and image understanding

Genetic and Evolutionary Computation (GEC) is a recent research field in computer science which deals with adaptive systems and optimization techniques inspired by the rules of natural evolution. One of its goals is to endow computers with information-processing capabilities comparable to those found in nature (Holland, 1992; Poli and Cagnoni, 2003; Koza, 1992; Schwefel, 1981; Mitchell, 1996; Landon and Poli, 2002; Goldberg, 1989). The general applicability of its methods makes it possible to use GEC to solve problems in a large number of applications. In particular, GEC methods can be applied effectively to those fields whose tasks require robust and flexible techniques to optimize performance in the many possible scenarios that characterize real-world problems (GECCO; CEC; PPSN; EuroGP). Among those fields, computer vision and image understanding (CVIU) represents one of the most challenging for the complexity of the tasks that are being solved in order to provide computers with human-like perception capabilities, allowing them to sense the environment, understand the sensed data, identify patterns, take appropriate actions and learn from experience to enhance future performance (CVPR; ICCV; ECCV; ICPR). Real-world applications of CVIU presently include autonomous robot or vehicle navigation, inspection, quality control, surveillance, to mention but a few. To achieve these high-level tasks, lower-level problems need to be solved, such as feature extraction, 3D modeling, and object classification. These real-world tasks require to be robust and flexible to optimize performance in diverse scenarios encountered in a given application. CVIU is steadily gaining relevance within the large number of application fields of GEC techniques, thanks to the capability of the latter to explore huge search domains effectively, searching and often finding solutions that lie well far away from the rather limited region spanned by more traditional, hand-coded ones. A first benefit of studying GEC techniques within the computational CVIU framework is to mature the information-processing capabilities of artificial systems based on challenging real-world problems. A second benefit is the promise of advancing the CVIU techniques with a bet-