José J. Ruz

Automatic detection of crop rows in maize fields with high weeds pressure

This paper proposes a new method, oriented to crop row detection in images from maize fields with high weed pressure. The vision system is designed to be installed onboard a mobile agricultural vehicle, i.e. submitted to gyros, vibrations and undesired movements. The images are captured under image perspective, being affected by the above undesired effects. The image processing consists of three main processes: image segmentation, double thresholding, based on the Otsus method, and crop row detection. Image segmentation is based on the application of a vegetation index, the double thresholding achieves the separation between weeds and crops and the crop row detection applies least squares linear regression for line adjustment. Crop and weed separation becomes effective and the crop row detection can be favorably compared against the classical approach based on the Hough transform. Both gain effectiveness and accuracy thanks to the double thresholding that makes the main finding of the paper.

Automatic expert system for 3D terrain reconstruction based on stereo vision and histogram matching

This paper proposes an automatic expert system for 3D terrain reconstruction and automatic intensity correction in stereo pairs of images based on histogram matching. Different applications in robotics, particularly those based on autonomous navigation in rough and natural environments, require a high-quality reconstruction of the surface. The stereo vision system is designed with a defined geometry and installed onboard a mobile robot, together with other sensors such as an Inertial Measurement Unit (IMU), necessary for sensor fusion. It is generally assumed the intensities of corresponding points in two images of a stereo pair are equal. However, this assumption is often false, even though they are acquired from a vision system composed of two identical cameras. We have also found this issue in our dataset. Because of the above undesired effects the stereo matching process is significantly affected, as many correspondence algorithms are very sensitive to these deviations in the brightness pattern, resulting in an inaccurate terrain reconstruction. The proposed expert system exploits the human knowledge which is mapped into three modules based on image processing techniques. The first one is intended for correcting intensities of the stereo pair coordinately, adjusting one as a function of the other. The second one is based in computing disparity, obtaining a set of correspondences. The last one computes a reconstruction of the terrain by reprojecting the computed points to 2D and applying a series of geometrical transformations. The performance of this method is verified favorably.

A parallel prolog system for distributed memory

Abstract This paper presents a parallel execution system (PDP: Prolog Distributed Processor) for efficiently supporting both Independent_AND OR parallelism on distributed-memory multiprocessors. The system is composed of a set of workers with a hierarchical structure scheduler. Each worker operates on its own private memory and interprocessor communication is performed only by the passing of messages. The execution model follows a multisequential approach in order to maintain the sequential optimizations. Independent AND_parallelism is exploited following a fork-join approach and OR_parallelism is exploited following a recomputation approach. PDP deals with OR_under_AND parallelism by producing the solutions of a set of parallel goals in a distributed way, that is, by creating a new task for each element of the cross product. This approach has the advantage of avoiding both storing partial solutions and synchronizing workers, resulting in a largely increased performance. Different scheduling policies have been studied, and granularity controls have been introduced for each kind of parallelism. PDP has been implemented on a network of transputers and performance results show that PDP introduces very little overhead into sequential programs, and provides a high speedup for coarse-grain parallel programs.

Using MILP for UAVs Trajectory Optimization under Radar Detection Risk

This paper presents an approach to trajectories optimization for unmanned aerial vehicle (UAV) in presence of obstacles, waypoints, and threat zones such as radar detection regions, using mixed integer linear programming (MILP). The main result is the linear approximation of a nonlinear radar detection risk function with integer constraints and indicator 0-1 variables. Several results are presented to show that the approach can yields trajectories depending on the acceptable risk of detection.

A featured-based strategy for stereovision matching in sensors with fish-eye lenses for forest environments.

This paper describes a novel feature-based stereovision matching process based on a pair of omnidirectional images in forest stands acquired with a stereovision sensor equipped with fish-eye lenses. The stereo analysis problem consists of the following steps: image acquisition, camera modelling, feature extraction, image matching and depth determination. Once the depths of significant points on the trees are obtained, the growing stock volume can be estimated by considering the geometrical camera modelling, which is the final goal. The key steps are feature extraction and image matching. This paper is devoted solely to these two steps. At a first stage a segmentation process extracts the trunks, which are the regions used as features, where each feature is identified through a set of attributes of properties useful for matching. In the second step the features are matched based on the application of the following four well known matching constraints, epipolar, similarity, ordering and uniqueness. The combination of the segmentation and matching processes for this specific kind of sensors make the main contribution of the paper. The method is tested with satisfactory results and compared against the human expert criterion.

A hybrid evolutionary approach for solving constrained optimization problems over finite domains

A novel approach for the integration of evolution programs and constraint-solving techniques over finite domains is presented. This integration provides a problem-independent optimization strategy for large-scale constrained optimization problems over finite domains. In this approach, genetic operators are based on an arc-consistency algorithm, and chromosomes are arc-consistent portions of the search space of the problem. The paper describes the main issues arising in this integration: chromosome representation and evaluation, selection and replacement strategies, and the design of genetic operators. We also present a parallel execution model for a distributed memory architecture of the previous integration. We have adopted a global parallelization approach that preserves the properties, behavior, and fundamentals of the sequential algorithm. Linear speedup is achieved since genetic operators are coarse grained as they perform a search in a discrete space carrying out arc consistency. The implementation has been tested on a GRAY T3E multiprocessor using a complex constrained optimization problem.

A stereovision matching strategy for images captured with fish-eye lenses in forest environments.

We present a novel strategy for computing disparity maps from hemispherical stereo images obtained with fish-eye lenses in forest environments. At a first segmentation stage, the method identifies textures of interest to be either matched or discarded. This is achieved by applying a pattern recognition strategy based on the combination of two classifiers: Fuzzy Clustering and Bayesian. At a second stage, a stereovision matching process is performed based on the application of four stereovision matching constraints: epipolar, similarity, uniqueness and smoothness. The epipolar constraint guides the process. The similarity and uniqueness are mapped through a decision making strategy based on a weighted fuzzy similarity approach, obtaining a disparity map. This map is later filtered through the Hopfield Neural Network framework by considering the smoothness constraint. The combination of the segmentation and stereovision matching approaches makes the main contribution. The method is compared against the usage of simple features and combined similarity matching strategies.

UAV Trajectory Planning for Static and Dynamic Environments

An unmanned aerial vehicle (UAV) is a robotic aircraft that can fly with either a remote input from a ground-based operator, or autonomously without human intervention based on pre-programmed flight plans (How et al., 2004). UAVs offer advantages over conventional manned vehicles in many applications because they can be used in situations otherwise too dangerous for manned vehicles and without being weighed down by the systems required by a pilot. UAVs are currently receiving much attention in research because they can be used in a wide variety of fields, both civil and military, such as reconnaissance, geophysical survey, environmental and meteorological monitoring, aerial photography, and search-and-rescue tasks. Most of these missions are usually carried out in threatened environments, and then it is very important to fly along a route which keeps the UAV away from known threats. Detection radars are one of the main threats for an UAV, but there are others that should also be avoided, such as fires, electric storms, radio shadowing zones, no flight zones, and so on. One of the main goals in many UAV’s projects has been to establish the route that maximizes the likelihood of successful mission completion taking into account all known information about technological constraints, obstacles and threat zones on a static environment (Richards & How, 2002). Some papers that investigate path planning for UAVs presume that the location of the threats and their presence are deterministically known at planning-time, and interpret a path which avoids possible threat regions as an optimal path (Borto, 2000). However more recent projects are examining the possibilities of UAVs as realistic autonomous agents working on dynamic environments where threat zones called pop-up are present (Zengin & Dogan, 2004). The true presence of these types of zones is only known at flying-time, but the location and knowledge about the probability of appearance can be known at planning-time. In this chapter we will present an approach to trajectory optimization for UAV in presence of obstacles, waypoints, and risk zones. The approach has been implemented on SPASAS (System for Planning And Simulation of Aerial Strategy), an integrated system for definition of flight scenarios, flight planning, simulation and graphic representation of the results developed at Complutense University of Madrid. The system uses two alternative methods for trajectory generation: mixed integer linear programming (MILP) and a modification of the A* algorithm, depending on the characteristics of the scenario between two waypoints.

Performance analysis of homomorphic systems for image change detection

Under illumination variations image change detection becomes a difficult task. Some existing image change detection methods try to compensate this effect. It is assumed that an image can be expressed in terms of its illumination and reflectance components. Detection of changes in the reflectance component is directly related to scene changes. In general, scene illumination varies slowly over space, whereas the reflectance component contains mainly spatially high frequency details. The intention is to apply the image change detection algorithm to the reflectance component only. The aim of this work is to analyze the performance of different homomorphic pre-filtering schemes for extracting the reflectance component so that the image change detection algorithm is applied only to this component. This scheme is not suitable for scenes without spatial high frequency details.

Decision making among alternative routes for UAVs in dynamic environments

This paper presents an approach to trajectory generation for unmanned aerial vehicles (UAV) by using mixed integer linear programming (MILP) and a modification of the A* algorithm to optimize paths in dynamic environments, particularly having pop-ups with a known future probability of appearance. Each pop-up leads to one or several possible evasion maneuvers, characterized with a set of values used as decision making parameters in an integer linear programming (ILP) model that optimizes the final route by choosing the most suitable alternative trajectories, according to the imposed constrains such as maximum fuel consumption and spent time. The model of the system in MILP and A* algorithms is presented, as well as the ILP formulation for decision making. Results and discussions are given to promote future real time implementations.