We present a method for filtering noisy point clouds, specifically those constructed from merged depth maps as obtained from a range scanner or multiple view stereo (MVS), applying techniques that have previously been used in finding outliers in clustered data, but not in MVS or range scanning. We estimate the probability density function (PDF) over the space of observed points via a technique ...

In recent years the use of remote sensing imagery to classify and map vegetation over different spatial scales has gained wide acceptance in the research community. Many national and regional datasets have been derived using remote sensing data. However, much of this research was undertaken using multispectral remote sensing datasets. With advances in remote sensing technologies, the use of hyp...

The objectives of the present study were to determine heterotic groups of germplasm lines of tropical maize by test crosses and by simple sequence repeat (SSR) markers and to compare five grouping methods of heterogeneous maize. Sixteen lines of nine populations in the S5 generation were evaluated in test crosses with three testers. The results of four experimental trials over two years were us...

We address the problem of tracking efficiently feature points along image sequences. To estimate the undergoing movement we use an approach based on Kalman filtering which performs the prediction and correction of the features movement in every image frame. In this paper measured data is incorporated by optimizing the global correspondence set based on efficient approximations of the Mahalanobi...

(x − x)A(x − x) Mahalanobis distance: Previous work on learning metrics has focused on learning a single distance metric for all instances. One of our primary contributions is to learn a distance function for every training image. Most visual categorization approaches make use of machine learning after computing distances between images (e.g. SVM with pyramid kernel). We want to learn how to co...

The validation of matching hypotheses using Mahalanobis distance is extensively utilized in robotic applications, and in general data-association techniques. The Ma-halanobis distance, deened by t h e i n n o vation and its covariance, is compared with a threshold deened by the chi-square distribution to validate a matching hypothesiss the validation test is a time-consuming operation. This pap...

The goal of this paper is to propose an improved competitive Hebbian learning for mode detection using a new activation function, to overcome its sensitivity to local irregularities in pattern distribution. This method is involved with an unsupervised clustering approach divided into four processing stages. It begins by the estimation of the probability density function, followed by a competiti...

INTRODUCTION Data analysis in Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) is highly sophisticated and can be thought of as a “pipeline” of closely connected processing and modeling steps. Cluster analysis of the orientation of the fiber direction and fiber tracts is typically carried on the major eigenvector. This type of cluster analysis is also important in reducing sorting bias in t...

Deep neural networks (DNNs) have been shown to be vulnerable against adversarial examples (AEs), which are maliciously designed cause dramatic model output errors. In this work, we reveal that normal (NEs) insensitive the fluctuations occurring at highly-curved region of decision boundary, while AEs typically over one single domain (mostly spatial domain) exhibit exorbitant sensitivity on such ...