S. Maldonado Bascón

Bio: S. Maldonado Bascón is an academic researcher from University of Alcalá. The author has contributed to research in topics: Symbolic computation & Second-generation wavelet transform. The author has an hindex of 3, co-authored 4 publications receiving 159 citations.

Computer Algebra Algorithms Applied to Computer Vision in a Parking Management System

Abstract: From this paper, we propose a novel methodology to compute a 2D homography applying some algorithms of computer algebra. We consider the classical problem of solving (exactly) a linear system of algebraic equations, and we suggest a new algorithm for computer vision, based on homomorphism methods over Zopf, to solve a system of equations necessary to achieve a 3 times 3 matrix H which lets us to compute the projective transformation which translates coordinates between points in different planes. From this work, we want to show that it is possible to apply a symbolic approach to some crucial issues of computer vision, moreover of the numerical methodology, in order to reduce the complexity of some algorithms, and to eliminate the problems associated with loss of precision and normalization. We test our technique in a real situation: a parking management system, which creates a pseudo-top-view of a parking area to determine if there are free parking lots or not.

Fixed kernel regression for voltammogram feature extraction

Abstract: Cyclic voltammetry is an electroanalytical technique for obtaining information about substances under analysis without the need for complex flow systems. However, classifying the information in voltammograms obtained using this technique is difficult. In this paper, we propose the use of fixed kernel regression as a method for extracting features from these voltammograms, reducing the information to a few coefficients. The proposed approach has been applied to a wine classification problem with accuracy rates of over 98%. Although the method is described here for extracting voltammogram information, it can be used for other types of signals.

The German Traffic Sign Recognition Benchmark: A multi-class classification competition

Abstract: The “German Traffic Sign Recognition Benchmark” is a multi-category classification competition held at IJCNN 2011. Automatic recognition of traffic signs is required in advanced driver assistance systems and constitutes a challenging real-world computer vision and pattern recognition problem. A comprehensive, lifelike dataset of more than 50,000 traffic sign images has been collected. It reflects the strong variations in visual appearance of signs due to distance, illumination, weather conditions, partial occlusions, and rotations. The images are complemented by several precomputed feature sets to allow for applying machine learning algorithms without background knowledge in image processing. The dataset comprises 43 classes with unbalanced class frequencies. Participants have to classify two test sets of more than 12,500 images each. Here, the results on the first of these sets, which was used in the first evaluation stage of the two-fold challenge, are reported. The methods employed by the participants who achieved the best results are briefly described and compared to human traffic sign recognition performance and baseline results.

An Efficient Method for Traffic Sign Recognition Based on Extreme Learning Machine

Abstract: This paper proposes a computationally efficient method for traffic sign recognition (TSR). This proposed method consists of two modules: 1) extraction of histogram of oriented gradient variant (HOGv) feature and 2) a single classifier trained by extreme learning machine (ELM) algorithm. The presented HOGv feature keeps a good balance between redundancy and local details such that it can represent distinctive shapes better. The classifier is a single-hidden-layer feedforward network. Based on ELM algorithm, the connection between input and hidden layers realizes the random feature mapping while only the weights between hidden and output layers are trained. As a result, layer-by-layer tuning is not required. Meanwhile, the norm of output weights is included in the cost function. Therefore, the ELM-based classifier can achieve an optimal and generalized solution for multiclass TSR. Furthermore, it can balance the recognition accuracy and computational cost. Three datasets, including the German TSR benchmark dataset, the Belgium traffic sign classification dataset and the revised mapping and assessing the state of traffic infrastructure (revised MASTIF) dataset, are used to evaluate this proposed method. Experimental results have shown that this proposed method obtains not only high recognition accuracy but also extremely high computational efficiency in both training and recognition processes in these three datasets.

PKLot – A robust dataset for parking lot classification

Abstract: Outdoor parking lot vacancy detection systems have attracted a great deal of attention in the last decade due the large number of practical applications. However, a common problem that researchers in this field very often face is the lack of a representative dataset to perform their experiments. To mitigate this difficulty, in this paper we introduce a new parking lot dataset composed of 695,899 images captured from two parking lots with three different camera views. The acquisition protocol allows obtaining static images showing illumination variance related to sunny, overcast and rainy days. We believe that researchers will find this dataset a very useful tool since it allows future benchmarking and evaluation. The dataset is currently available for research purposes upon request. To gain a better insight into this dataset we have evaluated two textural descriptors, Local Binary Patterns and Local Phase Quantization, with a Support Vector Machine classifier to detect parking lot vacancy. In the experiments where the same view was used for both training and testing, we have reached outstanding recognition rates, greater than 99%. The main challenge, though, lies in building a general classifier that is able to detect parking spaces from the parking lots that were not used for training. In this sense, the best result achieved by the texture-based classifier was about 89%. The observed drop in terms of performance shows that additional investigation is necessary to create classification schemes less dependent on the training set. Other researchers can use these results as a baseline performance when testing their own algorithms on this dataset.

Traffic sign detection and recognition based on random forests

Abstract: Graphical abstractDisplay Omitted In this paper we present a new traffic sign detection and recognition (TSDR) method, which is achieved in three main steps. The first step segments the image based on thresholding of HSI color space components. The second step detects traffic signs by processing the blobs extracted by the first step. The last one performs the recognition of the detected traffic signs. The main contributions of the paper are as follows. First, we propose, in the second step, to use invariant geometric moments to classify shapes instead of machine learning algorithms. Second, inspired by the existing features, new ones have been proposed for the recognition. The histogram of oriented gradients (HOG) features has been extended to the HSI color space and combined with the local self-similarity (LSS) features to get the descriptor we use in our algorithm. As a classifier, random forest and support vector machine (SVM) classifiers have been tested together with the new descriptor. The proposed method has been tested on both the German Traffic Sign Detection and Recognition Benchmark and the Swedish Traffic Signs Data sets. The results obtained are satisfactory when compared to the state-of-the-art methods.