Javier I. Portillo

Breadth-first search and its application to image processing problems

This paper explores the use of breadth-first graph traversal for the processing of digital images. It presents efficient algorithms for eroding, dilating, skeletonizing, and distance-transforming regions. These algorithms work by traversing regions in a breadth-first manner using a queue for storage of unprocessed pixels. They use memory efficiently--pixels are removed from the queue as soon as their processing has been completed--and they process only pixels in the region (and their neighbors), rather than requiring a complete scan of the image. The image is still represented as a pixel matrix in memory; the graph is just a convenient framework for thinking about the algorithms.

A multitarget tracking video system based on fuzzy and neuro-fuzzy techniques

Automatic surveillance of airport surface is one of the core components of advanced surface movement, guidance, and control systems (A-SMGCS). This function is in charge of the automatic detection, identification, and tracking of all interesting targets (aircraft and relevant ground vehicles) in the airport movement area. This paper presents a novel approach for object tracking based on sequences of video images. A fuzzy system has been developed to ponder update decisions both for the trajectories and shapes estimated for targets from the image regions extracted in the images. The advantages of this approach are robustness, flexibility in the design to adapt to different situations, and efficiency for operation in real time, avoiding combinatorial enumeration. Results obtained in representative ground operations show the system capabilities to solve complex scenarios and improve tracking accuracy. Finally, an automatic procedure, based on neuro-fuzzy techniques, has been applied in order to obtain a set of rules from representative examples. Validation of learned system shows the capability to learn the suitable tracker decisions.

Aircraft identification integrated into an airport surface surveillance video system

Abstract.A video aircraft identification algorithm, based on tail number recognition, is proposed as part of a global airport surveillance video system. The recognition procedure searches and detects the presence of the tail number in the image and then recognizes the tail number using pattern matching techniques. The identification system has been designed to deal with airport real images, taking into account letter size differences and potential deformations. Finally, the aircraft identification system calculates the joint probability of each tail number in the airport database. The tail number maximizing the joint probability is selected. Results show that the identification procedure achieves a robust identification using the database.

Fuzzy data association for image-based tracking in dense scenarios

A new approach for data association problems in video image sequences is presented, which uses JPDA formulation adapted to cope with video data peculiarities. A correlation level is computed to weight each blob contribution to each track, by means of a fuzzy system integrating different heuristics inferred from system performance under real situations. Results obtained in representative ground operations show the system capabilities to solve complex scenarios and improve tracking accuracy.

A Review on Mobile Applications for Citizen Emergency Management

Mobile devices may be a powerful tool to help in case of emergency, not only for the person or people in danger but also for those ones giving assistance to them, professionally or not. In order to determine how mobile applications are currently being used in this area and the possibilities for innovation, this paper gathers the result of a review of about more than 250 applications commercially available. These applications have been featured to analyze their value proposition (e.g. main service goal, target user or pricing approach) and their operational features, with respect to their level of context-awareness and the discovery and notification of emergencies. Additionally, the paper proposes the functional design of a mobile application for Citizen Emergency Management, which takes advantage of the gap of the available offer.

A contactless identification system based on hand shape features

This paper aims at studying the viability of setting up a contactless identification system based on hand features, with the objective of integrating this functionality as part of different services for smart spaces. The final identification solution will rely on a commercial 3D sensor (i.e. Leap Motion) for palm feature capture. To evaluate the significance of different hand features and the performance of different classification algorithms, 21 users have contributed to build a testing dataset. For each user, the morphology of each of his/her hands is gathered from 52 features, which include bones length and width, palm characteristics and relative distance relationships among fingers, palm center and wrist. In order to get consistent samples and guarantee the best performance for the device, the data collection system includes sweet spot control; this functionality guides the users to place the hand in the best position and orientation with respect to the device. The selected classification strategies - nearest neighbor, supported vector machine, multilayer perceptron, logistic regression and tree algorithms - have been evaluated through available Weka implementations. We have found that relative distances sketching the hand pose are more significant than pure morphological features. On this feature set, the highest correct classified instances (CCI) rate (>96%) is reached through the multilayer perceptron algorithm, although all the evaluated classifiers provide a CCI rate above 90%. Results also show how these algorithms perform when the number of users in the database change and their sensitivity to the number of training samples. Among the considered algorithms, there are different alternatives that are accurate enough for non-critical, immediate response applications.

Robust object tracking with fuzzy shape estimation

This work presents a novel, efficient and robust approach for object tracking based on sequences of video images. A fuzzy system has been developed to ponder update decisions both for the trajectories and shapes estimated for targets with the set of image regions (blobs) extracted from each frame. Several numeric heuristics, describing the quality of gated groups of blobs and predicted tracks, are considered to generate confidence levels used in the update process. Rules are aimed to generate the most appropriate decisions under different conditions, emulating the reasoned decisions taken by an expert, and have been derived with a systematic analysis of performance. The application area is the Surveillance of airport surface, including situations with very closely spaced objects (aircraft and surface vehicles moving on apron). System performance with real image sequences of representative ground operations is shown at the end.

Design of an A-SMGCS prototype at Barajas airport: airport surveillance sensors bias estimation

This paper describes the design and implementation of a bias estimation system for airport surveillance. If not correctly calibrated, systematic errors may lead to track instability, and even to track splitting. Airport safety demands for very stable and accurate tracking, and so addressing this problem is mandatory if a data fusion system is to be used in operational procedures. The paper describes the design of an innovative sensor bias estimation system, and the practical issues related with its integration in the data processing chain. The simulation based results show estimators rapid convergence, and how the inclusion of these methods improves overall tracking performance.

Automatic video system for aircraft identification

Advanced surface movement guidance and control systems need the identification of aircraft and vehicles in airport movement areas. In this work, a video identification algorithm based on tail number recognition is proposed as a part of a global surveillance video system. The aircraft identification problem has to deal with three fundamental aspects: capturing and preprocessing the images, international regulations defining tail number grammar, and pattern recognition methodology. Considering these aspects, the developed system is based on three ideas: using the local grey level contrast to detect characters of the tail number; finding the zone where the fail number is written to isolate it from the rest of the image; and processing this zone to identify the aircraft, using an aircraft database.

Optimizing Statistical Character Recognition Using Evolutionary Strategies to Recognize Aircraft Tail Numbers

The design of statistical classification systems for optical character recognition (OCR) is a cumbersome task. This paper proposes a method using evolutionary strategies (ES) to evolve and upgrade the set of parameters in an OCR system. This OCR is applied to identify the tail number of aircrafts moving on the airport. The proposed approach is discussed and some results are obtained using a benchmark data set. This research demonstrates the successful application of ES to a difficult, noisy, and real-world problem.