Rafael Marcos Luque-Baena

FOREGROUND DETECTION IN VIDEO SEQUENCES WITH PROBABILISTIC SELF-ORGANIZING MAPS

Background modeling and foreground detection are key parts of any computer vision system. These problems have been addressed in literature with several probabilistic approaches based on mixture models. Here we propose a new kind of probabilistic background models which is based on probabilistic self-organising maps. This way, the background pixels are modeled with more flexibility. On the other hand, a statistical correlation measure is used to test the similarity among nearby pixels, so as to enhance the detection performance by providing a feedback to the process. Several well known benchmark videos have been used to assess the relative performance of our proposal with respect to traditional neural and non neural based methods, with favourable results, both qualitatively and quantitatively. A statistical analysis of the differences among methods demonstrates that our method is significantly better than its competitors. This way, a strong alternative to classical methods is presented.

Assessment of geometric features for individual identification and verification in biometric hand systems

This paper studies the reliability of geometric features for the identification of users based on hand biometrics. Our methodology is based on genetic algorithms and mutual information. The aim is to provide a system for user identification rather than a classification. Additionally, a robust hand segmentation method to extract the hand silhouette and a set of geometric features in hard and complex environments is described. This paper focuses on studying how important and discriminating the hand geometric features are, and if they are suitable in developing a robust and reliable biometric identification. Several public databases have been used to test our method. As a result, the number of required features have been drastically reduced from datasets with more than 400 features. In fact, good classification rates with about 50 features on average are achieved, with a 100% accuracy using the GA-LDA strategy for the GPDS database and 97% for the CASIA and IITD databases, approximately. For these last contact-less databases, reasonable EER rates are also obtained.

Stochastic approximation for background modelling

Many background modelling approaches are based on mixtures of multivariate Gaussians with diagonal covariance matrices. This often yields good results, but complex backgrounds are not adequately captured, and post-processing techniques are needed. Here we propose the use of mixtures of uniform distributions and multivariate Gaussians with full covariance matrices. These mixtures are able to cope with both dynamic backgrounds and complex patterns of foreground objects. A learning algorithm is derived from the stochastic approximation framework, which has a very reduced computational complexity. Hence, it is suited for real time applications. Experimental results show that our approach outperforms the classic procedure in several benchmark videos.

Application of genetic algorithms and constructive neural networks for the analysis of microarray cancer data

BackgroundExtracting relevant information from microarray data is a very complex task due to the characteristics of the data sets, as they comprise a large number of features while few samples are generally available. In this sense, feature selection is a very important aspect of the analysis helping in the tasks of identifying relevant genes and also for maximizing predictive information.MethodsDue to its simplicity and speed, Stepwise Forward Selection (SFS) is a widely used feature selection technique. In this work, we carry a comparative study of SFS and Genetic Algorithms (GA) as general frameworks for the analysis of microarray data with the aim of identifying group of genes with high predictive capability and biological relevance. Six standard and machine learning-based techniques (Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Naive Bayes (NB), C-MANTEC Constructive Neural Network, K-Nearest Neighbors (kNN) and Multilayer perceptron (MLP)) are used within both frameworks using six free-public datasets for the task of predicting cancer outcome.ResultsBetter cancer outcome prediction results were obtained using the GA framework noting that this approach, in comparison to the SFS one, leads to a larger selection set, uses a large number of comparison between genetic profiles and thus it is computationally more intensive. Also the GA framework permitted to obtain a set of genes that can be considered to be more biologically relevant. Regarding the different classifiers used standard feedforward neural networks (MLP), LDA and SVM lead to similar and best results, while C-MANTEC and k-NN followed closely but with a lower accuracy. Further, C-MANTEC, MLP and LDA permitted to obtain a more limited set of genes in comparison to SVM, NB and kNN, and in particular C-MANTEC resulted in the most robust classifier in terms of changes in the parameter settings.ConclusionsThis study shows that if prediction accuracy is the objective, the GA-based approach lead to better results respect to the SFS approach, independently of the classifier used. Regarding classifiers, even if C-MANTEC did not achieve the best overall results, the performance was competitive with a very robust behaviour in terms of the parameters of the algorithm, and thus it can be considered as a candidate technique for future studies.

Wound image evaluation with machine learning

A pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue caused by pressure, shear or friction. Diagnosis, care and treatment of pressure ulcers can result in extremely expensive costs for health systems. A reliable diagnosis supported by precise wound evaluation is crucial in order to succeed on the treatment decision and, in some cases, to save the patient?s life. However, current clinical evaluation procedures, focused mainly on visual inspection, do not seem to be accurate enough to accomplish this important task. This paper presents a computer-vision approach based on image processing algorithms and supervised learning techniques to help detect and classify wound tissue types that play an important role in wound diagnosis. The system proposed involves the use of the k-means clustering algorithm for image segmentation and compares three different machine learning approaches-neural networks, support vector machines and random forest decision trees-to classify effectively each segmented region as the appropriate tissue type. Feature selection based on a wrapper approach with recursive feature elimination is shown to be effective in keeping the efficacy of the classifiers up and significantly reducing the number of necessary predictors. Results obtained show high performance rates from classifiers based on fitted neural networks, random forest models and support vector machines (overall accuracy on a testing set 95% CI], respectively: 81.87% 80.03%, 83.61%]; 87.37% 85.76%, 88.86%]; 88.08% 86.51%, 89.53%]), with significant differences found between the three machine learning approaches. This study seeks to provide, using standard classification algorithms, a consistent and robust methodological framework as a basis for the development of reliable computational systems to support ulcer diagnosis.

Robust gene signatures from microarray data using genetic algorithms enriched with biological pathway keywords

Genetic algorithms are widely used in the estimation of expression profiles from microarrays data. However, these techniques are unable to produce stable and robust solutions suitable to use in clinical and biomedical studies. This paper presents a novel two-stage evolutionary strategy for gene feature selection combining the genetic algorithm with biological information extracted from the KEGG database. A comparative study is carried out over public data from three different types of cancer (leukemia, lung cancer and prostate cancer). Even though the analyses only use features having KEGG information, the results demonstrate that this two-stage evolutionary strategy increased the consistency, robustness and accuracy of a blind discrimination among relapsed and healthy individuals. Therefore, this approach could facilitate the definition of gene signatures for the clinical prognosis and diagnostic of cancer diseases in a near future. Additionally, it could also be used for biological knowledge discovery about the studied disease.

Efficient Implementation of the Backpropagation Algorithm in FPGAs and Microcontrollers

The well-known backpropagation learning algorithm is implemented in a field-programmable gate array (FPGA) board and a microcontroller, focusing in obtaining efficient implementations in terms of a resource usage and computational speed. The algorithm was implemented in both cases using a training/validation/testing scheme in order to avoid overfitting problems. For the case of the FPGA implementation, a new neuron representation that reduces drastically the resource usage was introduced by combining the input and first hidden layer units in a single module. Further, a time-division multiplexing scheme was implemented for carrying out product computations taking advantage of the built-in digital signal processor cores. In both implementations, the floating-point data type representation normally used in a personal computer (PC) has been changed to a more efficient one based on a fixed-point scheme, reducing system memory variable usage and leading to an increase in computation speed. The results show that the modifications proposed produced a clear increase in computation speed in comparison with the standard PC-based implementation, demonstrating the usefulness of the intrinsic parallelism of FPGAs in neurocomputational tasks and the suitability of both implementations of the algorithm for its application to the real world problems.

A self-organizing map to improve vehicle detection in flow monitoring systems

The obtaining of perfect foreground segmentation masks still remains as a challenging task in video surveillance systems, since errors in that initial stage could lead to misleadings in subsequent tasks as object tracking and behavior analysis. This work presents a novel methodology based on self-organizing neural networks and Gaussian distributions to detect unusual objects in the scene, and to improve the foreground mask handling occlusions between objects. After testing the proposed approach on several traffic sequences obtained from public repositories, the results demonstrate that this methodology is promising and suitable to correct segmentation errors on crowded scenes with rigid objects.

A Competitive Neural Network for Multiple Object Tracking in Video Sequence Analysis

Tracking of moving objects in real situation is a challenging research issue, due to dynamic changes in objects or background appearance, illumination, shape and occlusions. In this paper, we deal with these difficulties by incorporating an adaptive feature weighting mechanism to the proposed growing competitive neural network for multiple objects tracking. The neural network takes advantage of the most relevant object features (information provided by the proposed adaptive feature weighting mechanism) in order to estimate the trajectories of the moving objects. The feature selection mechanism is based on a genetic algorithm, and the tracking algorithm is based on a growing competitive neural network where each unit is associated to each object in the scene. The proposed methods (object tracking and feature selection mechanism) are applied to detect the trajectories of moving vehicles in roads. Experimental results show the performance of the proposed system compared to the standard Kalman filter.

Image Compression and Video Segmentation Using Hierarchical Self-Organization

Both image compression based on color quantization and image segmentation are two typical tasks in the field of image processing. Several techniques based on splitting algorithms or cluster analyses have been proposed in the literature. Self-organizing maps have been also applied to these problems, although with some limitations due to the fixed network architecture and the lack of representation in hierarchical relations among data. In this paper, both problems are addressed using growing hierarchical self-organizing models. An advantage of these models is due to the hierarchical architecture, which is more flexible in the adaptation process to input data, reflecting inherent hierarchical relations among data. Comparative results are provided for image compression and image segmentation. Experimental results show that the proposed approach is promising for image processing, and the powerful of the hierarchical information provided by the proposed model.