Road-sign identification using ensemble learning

Ensemble learning that combines the decisions of multiple weak classifiers to from an output, has recently emerged as an effective identification method. This paper presents a road-sign identification system based upon the ensemble learning approach. The system identifies the regions of interest that are extracted from the scene into the road-sign groups that they belong to. A large road-sign image dataset is formed and used to train and test the system. Fifteen groups of road signs are chosen for identification. Five experiments are performed and the results are presented and discussed.<br /

Fast and robust road sign detection in driver assistance systems

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Road sign detection plays a critical role in automatic driver assistance systems. Road signs possess a number of unique visual qualities in images due to their specific colors and symmetric shapes. In this paper, road signs are detected by a two-level hierarchical framework that considers both color and shape of the signs. To address the problem of low image contrast, we propose a new color visual saliency segmentation algorithm, which uses the ratios of enhanced and normalized color values to capture color information. To improve computation efficiency and reduce false alarm rate, we modify the fast radial symmetry transform (RST) algorithm, and propose to use an edge pairwise voting scheme to group feature points based on their underlying symmetry in the candidate regions. Experimental results on several benchmarking datasets demonstrate the superiority of our method over the state-of-the-arts on both efficiency and robustness

Getting the most from medical VOC data using Bayesian feature learning

The metabolic processes in the body naturally produce a diverse set of Volatile Organic Compounds (VOCs), which are excreted in breath, urine, stool and other biological samples. The VOCs produced are odorous and influenced by disease, meaning olfaction can provide information on a person’s disease state. A variety of instruments exist for performing “artificial olfaction”: measuring a sample, such as patient breath, and producing a high dimensional output representing the odour. Such instruments may be paired with machine learning techniques to identify properties of interest, such as the presence of a given disease. Research shows good disease-predictive ability of artificial olfaction instrumentation. However, the statistical methods employed are typically off-the-shelf, and do not take advantage of prior knowledge of the structure of the high dimensional data. Since sample sizes are also typically small, this can lead to suboptimal results due to a poorly-learned model. In this thesis we explore ways to get more out of artificial olfaction data. We perform statistical analyses in a medical setting, investigating disease diagnosis from breath, urine and vaginal swab measurements, and illustrating both successful identification and failure cases. We then introduce two new latent variable models constructed for dimension reduction of artificial olfaction data, but which are widely applicable. These models place a Gaussian Process (GP) prior on the mapping from latent variables to observations. Specifying a covariance function for the GP prior is an intuitive way for a user to describe their prior knowledge of the data covariance structure. We also enable an approximate posterior and marginal likelihood to be computed, and introduce a sparse variant. Both models have been made available in the R package stpca hosted at https://github.com/JimSkinner/stpca. In experiments with artificial olfaction data, these models outperform standard feature learning methods in a predictive pipeline

DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applications

Convolutional Neural Networks (CNNs) are a cornerstone of the Deep Learning toolbox and have led to many breakthroughs in Artificial Intelligence. These networks have mostly been developed for regular Euclidean domains such as those supporting images, audio, or video. Because of their success, CNN-based methods are becoming increasingly popular in Cosmology. Cosmological data often comes as spherical maps, which make the use of the traditional CNNs more complicated. The commonly used pixelization scheme for spherical maps is the Hierarchical Equal Area isoLatitude Pixelisation (HEALPix). We present a spherical CNN for analysis of full and partial HEALPix maps, which we call DeepSphere. The spherical CNN is constructed by representing the sphere as a graph. Graphs are versatile data structures that can act as a discrete representation of a continuous manifold. Using the graph-based representation, we define many of the standard CNN operations, such as convolution and pooling. With filters restricted to being radial, our convolutions are equivariant to rotation on the sphere, and DeepSphere can be made invariant or equivariant to rotation. This way, DeepSphere is a special case of a graph CNN, tailored to the HEALPix sampling of the sphere. This approach is computationally more efficient than using spherical harmonics to perform convolutions. We demonstrate the method on a classification problem of weak lensing mass maps from two cosmological models and compare the performance of the CNN with that of two baseline classifiers. The results show that the performance of DeepSphere is always superior or equal to both of these baselines. For high noise levels and for data covering only a smaller fraction of the sphere, DeepSphere achieves typically 10% better classification accuracy than those baselines. Finally, we show how learned filters can be visualized to introspect the neural network.Comment: arXiv admin note: text overlap with arXiv:astro-ph/0409513 by other author

On some classification methods for high dimensional and functional data

In this study, we propose classification method based on multivariate rank. We show that this classifier is Bayes rule under suitable conditions. Multivariate ranks are not invariant under affine transformation of the data and so, the effect of deviation from property of spherical symmetry is investigated. Based on this, we construct affine invariant version of this classifier. When the distributions of competing populations have different covariance matrices, minimum rank classifier performs poorly irrespective of affine invariance. To overcome this limitation, we propose a classifier based on multivariate rank region. The asymptotic properties of this method and its associated probability of misclassification are studied. Also, we propose classifiers based on the distribution of the spatial rank and establish some theoretical results for this classification method. For affine invariant version of this method, two invariants are proposed. Many multivariate techniques fail to perform well when data are curves or functions. We propose classification method based on L$_2$ distance to spatial median and later generalise it to Lp distance to Lp median. The optimal choice of p is determined by cross validation of misclassification errors. The performances of our propose methods are examined by using simulation and real data set and the results are compared with the results from existing methods