Moises Diaz-Cabrera

Bio: Moises Diaz-Cabrera is an academic researcher from University of Las Palmas de Gran Canaria. The author has contributed to research in topics: Signature (logic) & Signature recognition. The author has an hindex of 12, co-authored 19 publications receiving 546 citations.

Static Signature Synthesis: A Neuromotor Inspired Approach for Biometrics

Abstract: In this paper we propose a new method for generating synthetic handwritten signature images for biometric applications. The procedures we introduce imitate the mechanism of motor equivalence which divides human handwriting into two steps: the working out of an effector independent action plan and its execution via the corresponding neuromuscular path. The action plan is represented as a trajectory on a spatial grid. This contains both the signature text and its flourish, if there is one. The neuromuscular path is simulated by applying a kinematic Kaiser filter to the trajectory plan. The length of the filter depends on the pen speed which is generated using a scalar version of the sigma lognormal model. An ink deposition model, applied pixel by pixel to the pen trajectory, provides realistic static signature images. The lexical and morphological properties of the synthesized signatures as well as the range of the synthesis parameters have been estimated from real databases of real signatures such as the MCYT Off-line and the GPDS960GraySignature corpuses. The performance experiments show that by tuning only four parameters it is possible to generate synthetic identities with different stability and forgers with different skills. Therefore it is possible to create datasets of synthetic signatures with a performance similar to databases of real signatures. Moreover, we can customize the created dataset to produce skilled forgeries or simple forgeries which are easier to detect, depending on what the researcher needs. Perceptual evaluation gives an average confusion of 44.06 percent between real and synthetic signatures which shows the realism of the synthetic ones. The utility of the synthesized signatures is demonstrated by studying the influence of the pen type and number of users on an automatic signature verifier.

Suspended traffic lights detection and distance estimation using color features

Abstract: Traffic Light Detection is a problem differently approached by many research groups around the world. Here we present a novel technique to detect suspended traffic lights, based on colors and features such as black area of traffic lights or area of lighting lamps. Additionally, the traffic light distance is estimated aiming at slowing down and stopping in the correct position, in case of red light. Some preliminary test results are presented to assess both the detection rate and the distance estimation.

Robust real-time traffic light detection and distance estimation using a single camera

Abstract: A method to detect traffic lights both during day and night is designed.A mixed method based on fuzzy logic and sequential rules is developed.The distance between vehicle and traffic light is calculated using Bayesian filters.Different results and tests are presented to validate the method. This paper presents a robust technique to detect traffic lights during both day and night conditions and estimate their distance. The traffic light detection is based initially on color properties. To enhance the color on the video sequences, the acquisition is adapted according to the luminosity of the pixels on the top of the image. A fuzzy clustering provides a better division of the traffic light colors. The traffic light color properties have been estimated from registered sequences including both colors from LED spot lights and from traditional light bulbs. The filters rules based on the traffic light aspect ratios as well as the tracking stage are used to decide whether the spots on the frames are likely to be traffic lights. Then, the distance between traffic lights and the autonomous vehicle is estimated by applying Bayesian filters to the traffic lights represented on the frames. The tests are validated with more than an hour in real urban scenarios during day and night. The paper shows that the developed advanced driver assistance system is able to detect the traffic lights with 99.4% of accuracy in the range of 10-115m. The utility of this system has been demonstrated during the Public ROad Urban Driverless car test in Italy in 2013.

Synthetic off-line signature image generation

Abstract: This paper proposes a novel methodology to generate static/off-line signatures of new identities. The signature of the new synthetic identity is obtained particularizing the random variables of a statistical distribution of global signature properties. The results mimic real signature shapes and writing style properties, which are estimated from static signature databases. New instances, as well as forgeries, from the synthetic identities are obtained introducing a natural variability from the synthetic individual properties. As additional novelty, an ink deposition model based on a ballpoint is developed for realistic static signature image generation. The range of the static signature generator has been established matching the performance obtained with the synthetic databases and those obtained with two public databases.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Self-driving cars: A survey

Abstract: We survey research on self-driving cars published in the literature focusing on autonomous cars developed since the DARPA challenges, which are equipped with an autonomy system that can be categorized as SAE level 3 or higher. The architecture of the autonomy system of self-driving cars is typically organized into the perception system and the decision-making system. The perception system is generally divided into many subsystems responsible for tasks such as self-driving-car localization, static obstacles mapping, moving obstacles detection and tracking, road mapping, traffic signalization detection and recognition, among others. The decision-making system is commonly partitioned as well into many subsystems responsible for tasks such as route planning, path planning, behavior selection, motion planning, and control. In this survey, we present the typical architecture of the autonomy system of self-driving cars. We also review research on relevant methods for perception and decision making. Furthermore, we present a detailed description of the architecture of the autonomy system of the self-driving car developed at the Universidade Federal do Espirito Santo (UFES), named Intelligent Autonomous Robotics Automobile (IARA). Finally, we list prominent self-driving car research platforms developed by academia and technology companies, and reported in the media.

Handbook Of Biometrics

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A deep learning approach to traffic lights: Detection, tracking, and classification

Abstract: Reliable traffic light detection and classification is crucial for automated driving in urban environments. Currently, there are no systems that can reliably perceive traffic lights in real-time, without map-based information, and in sufficient distances needed for smooth urban driving. We propose a complete system consisting of a traffic light detector, tracker, and classifier based on deep learning, stereo vision, and vehicle odometry which perceives traffic lights in real-time. Within the scope of this work, we present three major contributions. The first is an accurately labeled traffic light dataset of 5000 images for training and a video sequence of 8334 frames for evaluation. The dataset is published as the Bosch Small Traffic Lights Dataset and uses our results as baseline. It is currently the largest publicly available labeled traffic light dataset and includes labels down to the size of only 1 pixel in width. The second contribution is a traffic light detector which runs at 10 frames per second on 1280×720 images. When selecting the confidence threshold that yields equal error rate, we are able to detect traffic lights as small as 4 pixels in width. The third contribution is a traffic light tracker which uses stereo vision and vehicle odometry to compute the motion estimate of traffic lights and a neural network to correct the aforementioned motion estimate.

Trajectory planning and tracking control for autonomous lane change maneuver based on the cooperative vehicle infrastructure system

Abstract: Autonomous lane change maneuver was developed using cooperative strategy.Proposed system can be potential to prevent lane change crashes and thus reducing injuries and fatalities.A trajectory planning method based on polynomial was developed.A trajectory tracking controller with global convergence ability was designed.Simulations and experimental results were presented to validate the method. Lane change maneuver is one of the most conventional behaviors in driving. Unsafe lane change maneuvers are key factor for traffic accidents and traffic congestion. For drivers' safety, comfort and convenience, advanced driver assistance systems (ADAS) are presented. The main problem discussed in this paper is the development of an autonomous lane change system. The system can be extended applied in intelligent vehicles in future. It solves two crucial issues - trajectory planning and trajectory tracking. Polynomial method was adopted for describing the trajectory planning issue. Movement of a host vehicle was abstracted into time functions. Moreover, collision detection was mapped into a parameter space by adopting infinite dynamic circles. The second issue was described by backstepping principle. According to the Lyapunov function, a tracking controller with global convergence property was verified. Both the simulations and the experimental results demonstrate the feasibility and effectiveness of the designed method for autonomous lane change.