36,921 research outputs found
Modified and Ensemble Intelligent Water Drop Algorithms and Their Applications
1.1 Introduction Optimization is a process that concerns with finding the best solution of a given problem from among the possible solutions within an affordable time and cost (Weise et al., 2009). The first step in the optimization process is formulating the optimization problem through an objective function and a set of constrains that encompass the problem search space (ie, regions of feasible solutions). Every alternative (ie, solution) is represented by a set of decision variables. Each decision variable has a domain, which is a representation of the set of all possible values that the decision variable can take. The second step in optimization starts by utilizing an optimization method (ie, search method) to find the best candidate solutions. Candidate solution has a configuration of decision variables that satisfies the set of problem constrains, and that maximizes or minimizes the objective function (Boussaid et al., 2013). It converges to the optimal solution (ie, local or global optimal solution) by reaching the optimal values of the decision variables. Figure 1.1 depicts a 3D-fitness landscape of an optimization problem. It shows the concept of the local and global optima, where the local optimal solution is not necessarily the same as the global one (Weise et al., 2009). Optimization can be applied to many real-world problems in various domains. As an example, mathematicians apply optimization methods to identify the best outcome pertaining to some mathematical functions within a range of variables (Vesterstrom and Thomsen, 2004). In the presence of conflicting criteria, engineers use optimization methods t
Rain Removal in Traffic Surveillance: Does it Matter?
Varying weather conditions, including rainfall and snowfall, are generally
regarded as a challenge for computer vision algorithms. One proposed solution
to the challenges induced by rain and snowfall is to artificially remove the
rain from images or video using rain removal algorithms. It is the promise of
these algorithms that the rain-removed image frames will improve the
performance of subsequent segmentation and tracking algorithms. However, rain
removal algorithms are typically evaluated on their ability to remove synthetic
rain on a small subset of images. Currently, their behavior is unknown on
real-world videos when integrated with a typical computer vision pipeline. In
this paper, we review the existing rain removal algorithms and propose a new
dataset that consists of 22 traffic surveillance sequences under a broad
variety of weather conditions that all include either rain or snowfall. We
propose a new evaluation protocol that evaluates the rain removal algorithms on
their ability to improve the performance of subsequent segmentation, instance
segmentation, and feature tracking algorithms under rain and snow. If
successful, the de-rained frames of a rain removal algorithm should improve
segmentation performance and increase the number of accurately tracked
features. The results show that a recent single-frame-based rain removal
algorithm increases the segmentation performance by 19.7% on our proposed
dataset, but it eventually decreases the feature tracking performance and
showed mixed results with recent instance segmentation methods. However, the
best video-based rain removal algorithm improves the feature tracking accuracy
by 7.72%.Comment: Published in IEEE Transactions on Intelligent Transportation System
An ensemble of intelligent water drop algorithm for feature selection optimization problem
Master River Multiple Creeks Intelligent Water Drops (MRMC-IWD) is an ensemble model of the intelligent water drop, whereby a divide-and-conquer strategy is utilized to improve the search process. In this paper, the potential of the MRMC-IWD using real-world optimization problems related to feature selection and classification tasks is assessed. An experimental study on a number of publicly available benchmark data sets and two real-world problems, namely human motion detection and motor fault detection, are conducted. Comparative studies pertaining to the features reduction and classification accuracies using different evaluation techniques (consistency-based, CFS, and FRFS) and classifiers (i.e., C4.5, VQNN, and SVM) are conducted. The results ascertain the effectiveness of the MRMC-IWD in improving the performance of the original IWD algorithm as well as undertaking real-world optimization problems
3D spherical-cap fitting procedure for (truncated) sessile nano- and micro-droplets & -bubbles
In the study of nanobubbles, nanodroplets or nanolenses immobilised on a
substrate, a cross-section of a spherical-cap is widely applied to extract
geometrical information from atomic force microscopy (AFM) topographic images.
In this paper, we have developed a comprehensive 3D spherical cap fitting
procedure (3D-SCFP) to extract morphologic characteristics of complete or
truncated spherical caps from AFM images. Our procedure integrates several
advanced digital image analysis techniques to construct a 3D spherical cap
model, from which the geometrical parameters of the nanostructures are
extracted automatically by a simple algorithm. The procedure takes into account
all valid data points in the construction of the 3D spherical cap model to
achieve high fidelity in morphology analysis. We compare our 3D fitting
procedure with the commonly used 2D cross-sectional profile fitting method to
determine the contact angle of a complete spherical cap and a truncated
spherical cap. The results from 3D-SCFP are consistent and accurate, while 2D
fitting is unavoidably arbitrary in selection of the cross-section and has a
much lower number of data points on which the fitting can be based, which in
addition is biased to the top of the spherical cap. We expect that the
developed 3D spherical-cap fitting procedure will find many applications in
imaging analysis.Comment: 23 pages, 7 figure
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