Jesús Manuel de la Cruz

A wavelet-based image fusion tutorial

Abstract The objective of image fusion is to combine information from multiple images of the same scene. The result of image fusion is a new image which is more suitable for human and machine perception or further image-processing tasks such as segmentation, feature extraction and object recognition. Different fusion methods have been proposed in literature, including multiresolution analysis. This paper is an image fusion tutorial based on wavelet decomposition, i.e. a multiresolution image fusion approach. We can fuse images with the same or different resolution level, i.e. range sensing, visual CCD, infrared, thermal or medical. The tutorial performs a synthesis between the multiscale-decomposition-based image approach (Proc. IEEE 87 (8) (1999) 1315), the ARSIS concept (Photogramm. Eng. Remote Sensing 66 (1) (2000) 49) and a multisensor scheme (Graphical Models Image Process. 57 (3) (1995) 235). Some image fusion examples illustrate the proposed fusion approach. A comparative analysis is carried out against classical existing strategies, including those of multiresolution.

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.

eUDEVS: Executable UML with DEVS Theory of Modeling and Simulation

Modeling and simulation (M&S) for system design and prototyping is practiced today both in industry and academia. M&S are two different areas altogether and have specific objectives. However, most of the time these two separate areas are taken together. The developed code is woven tightly around both the model and the underlying simulator that executes it. This constrains both the model development and the simulation engine that has an impact on the scalability of the developed code. Furthermore, a lot of time is spent in developing a model because it needs both domain knowledge and simulation techniques, which also requires communication among users and developers. The Unified Modeling Language (UML) is widely accepted in industry, whereas discrete event specification (DEVS)-based modeling that separates the model and the simulator, provides a cleaner methodology to develop models and is much used in academia. DEVS today is used by engineers who understand discrete event modeling at a highly detailed level and are able to translate requirements to DEVS modeling code. There have been earlier efforts to integrate UML and DEVS but they have not succeeded in providing a transformation mechanism owing to inherent differences in these two modeling paradigms. In this paper we present an integrated approach to cross-transformations between UML and DEVS using the proposed eUDEVS, which stands for executable UML based on DEVS. Further, we also show that the obtained DEVS models belong to a specific class of DEVS models called finite deterministic DEVS (FD-DEVS) that is available as a W3C XML schema in XFD-DEVS. We also put the proposed eUDEVS in a much larger unifying framework called the DEVS unified process that allows bifurcated model-continuity-based lifecycle methodology for systems M&S. Finally, we demonstrate the concepts with a complete example.

Relaxation by Hopfield network in stereo image matching

Abstract This paper outlines a relaxation approach using the Hopfield neural network for solving the global stereovision matching problem. The primitives used are edge segments. The similarity, smoothness and uniqueness constraints are transformed into the form of an energy function whose minimum value corresponds to the best solution of the problem. We combine two methods: (a) optimization/relaxation [1] and (b) relaxation merit [2] with the above three constraints mapped in an energy function. The main contribution is made (1) by applying a learning strategy in the similarity constraint and (2) by introducing specific conditions to overcome the violation of the smoothness constraint and to avoid the serious problem arising from the required fixation of a disparity limit. So, we improve the stereovision matching process. A better performance of the proposed method is illustrated with a comparative analysis against a classical relaxation method.

Remote Control Laboratory Using EJS Applets and TwinCAT Programmable Logic Controllers

This paper presents a new methodology to develop remote laboratories for systems engineering and automation control courses, based on the combined use of TwinCAT, a laboratory Java server application, and Easy Java Simulations (EJS). The TwinCAT system is used to close the control loop for the selected plants by means of programmable logic controllers (PLCs) deployed in PCs with the TwinCAT run-time tool. EJS is used to develop the laboratory front-end applets that let teachers and students parametrize and observe the behavior of the PLCs from any computer. The laboratory Java server application establishes the connection between the EJS applets and the PLCs, fulfilling the TwinCAT connection requirements while ensuring an individualized access to each PLC. This paper also shows how the practical work in some undergraduate control courses at the Complutense University of Madrid, Spain, already uses the TwinCAT PLC + Java server + EJS applet strategy to provide real-time support to the controllers, remote individualized access to the experiments, and a user-friendly graphic controller interface for the students.

Evolutionary path planner for UAVs in realistic environments

This paper presents a path planner for Unmanned Air Vehicles (UAVs) based on Evolutionary Algorithms (EA) that can be used in realistic risky scenarios. The path returned by the algorithm fulfills and optimizes multiple criteria which (1) are calculated based on properties of real UAVs, terrains, radars and missiles, and (2) are used to rank the solutions according to the priority levels and goals selected for each mission. Developed originally to work with only one UAV, the planner currently allows us to obtain the optimal path of several UAVs that are flying simultaneously. It works globally offline and locally online to recalculate a part of the path when an unexpected threat appears. Finally, the effectiveness of the solutions given by this planner has been successfully tested against a simulator that implements a complex model of the UAV and its environment.

Stereo matching technique based on the perceptron criterion function

Abstract Classical stereo matching techniques use features representing objects in both images and compute the minimum difference attribute values. No knowledge of the environment is taken into account. This paper proposes an image understanding stereo matching method using a supervised networks: the perceptron.

Fuzzy Cognitive Maps for stereovision matching

This paper outlines a method for solving the stereovision matching problem using edge segments as the primitives. In stereovision matching the following constraints are commonly used: epipolar, similarity, smoothness, ordering and uniqueness. We propose a new matching strategy under a fuzzy context in which such constraints are mapped. The fuzzy context integrates both Fuzzy Clustering and Fuzzy Cognitive Maps. With such purpose a network of concepts (nodes) is designed, each concept represents a pair of primitives to be matched. Each concept has associated a fuzzy value which determines the degree of the correspondence. The goal is to achieve high performance in terms of correct matches. The main findings of this paper are reflected in the use of the fuzzy context that allows building the network of concepts where the matching constraints are mapped. Initially, each concept value is loaded via the Fuzzy Clustering and then updated by the Fuzzy Cognitive Maps framework. This updating is achieved through the influence of the remainder neighboring concepts until a good global matching solution is achieved. Under this fuzzy approach we gain quantitative and qualitative matching correspondences. This method works as a relaxation matching approach and its performance is illustrated by comparative analysis against some existing global matching methods.

A neural network model in stereovision matching

Abstract The paper outlines a method for solving the stereovision matching problem through a Neural Network approach based on self-organizing technique. The goal is to classify pairs of features (edge segments) as true or false matches; giving rise to two classes. Thus, the corresponding parameter vector from two component density functions, representing both classes and drawn as Normal densities, are to be estimated by using an unsupervised learning method. A three layer neural network topology implements the mixture density function and Bayess rule, all required computations are realized with the simple “sum of product” units commonly used in connectionist models. The unsupervised learning method leads to a learning rule, while all applicable constraints from stereovision field yield an activation rule. A training process receives the samples to learn, and a matching process classifies the pairs. The method is illustrated with two images from an indoor scene.

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.