| This paper presents a method for smoothing of noisy carto-graphic boundaries. The resulting smoothed description preserves the basic shape's structures of the contour while smoothing out noise as well as un-wanted detail. The performance of any simpliication method relies on the quality of the levels of smoothing that represent the structures present. In our formulation the most signiicant le...

Smoothing (say by a Guassian kernel) has been a very popular technique for optimizing a nonconvex objective function. The rationale behind smoothing is that the smoothed function has less spurious local minima than the original one. This technique has seen tremendous success in many real world tasks such as those arising in machine learning and computer vision. Despite its empirical success, th...

Autonomous range acquisition for 3D modeling requires reliable range registration, for both the precise localization of the sensor and combining the data from multiple scans for view-planning computation. We introduce and present a novel approach to improve the reliability and robustness of the ICP (Iterative Closest Point) 3D shape registration algorithm by smoothing the shape’s surface into m...

After introducing several results relating to the modification of the homotopy of gradient functions based on extrema in the base image and building on earlier results in morphological scale-space, we introduce a scale-space monotonicity theorem for regions of an image defined by watersheds of a gradient function modified to retain only the local minima or maxima of its smoothed parent image. W...

Population risk—the expectation of the loss over the sampling mechanism—is always of primary interest in machine learning. However, learning algorithms only have access to empirical risk, which is the average loss over training examples. Although the two risks are typically guaranteed to be pointwise close, for applications with nonconvex nonsmooth losses (such as modern deep networks), the eff...

We describe a method to estimate the power spectral density of nonstationary noise when a noisy speech signal is given. The method can be combined with any speech enhancement algorithm which requires a noise power spectral density estimate. In contrast to other methods, our approach does not use a voice activity detector. Instead it tracks spectral minima in each frequency band without any dist...

Objective: Currently, the evaluation of baseflow components have been of a worldwide concern due to the influential role of streamflow (base flow and direct flow)in agriculture, water sources management as well as supplying the potable water. Direct and field measurement of baseflow is not practicable especially in large areas with statistics deficiencies. Also, this would not be economically e...

Objective: Currently, the evaluation of baseflow components have been of a worldwide concern due to the influential role of streamflow (base flow and direct flow)in agriculture, water sources management as well as supplying the potable water. Direct and field measurement of baseflow is not practicable especially in large areas with statistics deficiencies. Also, this would not be economically e...

Deformable model fitting has been actively pursued in the computer vision community for over a decade. As a result, numerous approaches have been proposed with varying degrees of success. A class of approaches that has shown substantial promise is one that makes independent predictions regarding locations of the model’s landmarks, which are combined by enforcing a prior over their joint motion....