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This paper presents a novel approach to background subtraction which aims to extract moving objects in video stream. To this end, a novel background model is proposed by using both working backgrounds and candidate backgrounds, which can be transferred to each other according to an adaptive mechanism. The input image (video frame) is compared and evaluated with these dual-class backgrounds (DCB) to...
In this paper, we propose an efficient and robust gross outlier removal method, called the Conceptual Space based Gross Outlier Removal (CSGOR) method, to remove gross outliers for geometric model fitting. In the proposed method, each data point is mapped to a conceptual space by computing the preference of "good" model hypotheses. In the conceptual space, the distributions of inliers and...
Since a decade multi-agents became a widespread solution to tackle different kinds of issues in various application fields. Among the two main trends in multi-agent approaches (cognitive vs. reactive), the reactive one is particularly interesting for applications that require both fast response time, adaptability and robustness. Reactive Multi-agent Systems rely on simple interaction schemes between...
Obstacle detection and tracking is essential module for autonomous driving. Vision based obstacle detection and tracking faces huge challenges due to factors like cluttered background, partial occlusion, inconsistent illumination, etc. In this paper, we propose a robust and low complexity stereo-vision based obstacle detection and tracking framework. Low complexity techniques are employed to detect...
Existing approaches to object detection address the generation of object hypotheses by extracting several cues in natural and automotive images, relying on objects with sufficiently high resolution. Very little to almost no approaches, however, address the generation of hypothesis of very small or distant objects in images such as on motorways. Here, we propose a simple yet effective approach to generating...
This paper presents an online self-supervised approach to improve the quality and relevance of input point cloud to a 3D registration algorithm. The suggested method considers the visibility of the model points and learns discriminative appearance of the object under gradual changes. It selectively reduces the amount of information to process by excluding non-visible points of the model and removing...
Student's-t distribution has attracted widely attention on model-based clustering analysis. In this paper, we propose a new level set energy function framework where the Markov random field-based Student's-t mixture model is incorporated for clustering both static images and time-series data. This algorithm provides a general strategy by taking the best of Bayesian technique and level set formulation...
Philipona & O’Regan (2006) [1] recently proposed a linear model of surface reflectance as it is sensed by the human eyes. In their model, the tristimulus response to reflected light is accurately approximated by a linear transformation of the tristimulus response to illumination, allowing the prediction of several perceptual characteristics of human vision. Later, Vazquez-Corral et al (2012) [2]...
We introduce the distributed camera model, a novel model for Structure-from-Motion (SfM). This model describes image observations in terms of light rays with ray origins and directions rather than pixels. As such, the proposed model is capable of describing a single camera or multiple cameras simultaneously as the collection of all light rays observed. We show how the distributed camera model is a...
In this paper, we address the problem of temporal alignment of surfaces for subjects dressed in wide clothing, as acquired by calibrated multi-camera systems. Most existing methods solve the alignment by fitting a single surface template to each instant's 3D observations, relying on a dense point-to-point correspondence scheme, e.g. by matching individual surface points based on local geometric features...
Background subtraction is a basic task of video analysis. Many methods have been proposed earlier for background subtraction, it is still challenging. In the present work, a novel method using canonical correlation analysis for background subtraction is proposed. The correlation between current image and background image obtained through canonical correlation analysis is used to detect the foreground...
Using Bhuman walking engine, this paper presents an LTI model for Nao robot walking control. The robot is assumed as a point mass in a 2-D maneuver. Model calculations and validation have been done using experimental data, performing various tests on the robot, and using system identification tools. Afterwards, a robust controller is designed to control the robot position in the horizontal (x-y) plane...
Object localization is a powerful technique to analyze certain features on particular objects for either general or specific purpose(s). To localize object within image frames, the background subtraction scheme is generally implemented as an unequivocal technique that pre-processes die corresponding input frame. Nevertheless, despite the prior developments and applications of various techniques for...
This study extends the application of Unbiased Criterion (UC) method to investigate the robustness of an identified fuzzy model for nonlinear dynamical systems in their different operating conditions. Heat Recovery Steam Generator (HRSG) as specific boilers in combined power plants exhibit challenging behaviors in startup, shut down and parallel conditions such as Swell and Shrink. In order to analyze...
Cauchy estimator has been successfully applied to the statistical learning owing to its unique distribution characteristics and robustness to outliers. In this paper, Cauchy loss function is utilized to update the weight of filter, generating a novel robust least Cauchy error (LCE) algorithm and its kernel extension (KLCE). The proposed filter algorithms are effective for the impulsive non-Gaussian...
Optimal experiment design (OED) is a well-developed concept for linear regression and linear dynamical modeling problems. In case of nonlinear models, the dilemma is that in order to evaluate the Fisher Information Matrix (FIM) for experiment design, the parameters to be estimated are required to evaluate the FIM. In case of locally affine Takagi-Sugeno (TS) models and D-optimal designs, even a ‘robust’...
A new nonlinear optimal control approach is proposed for autonomous navigation of unmanned surface vessels. The dynamic model of the surface vessels undergoes approximate linearization round local operating points which are redefined at each iteration of the control algorithm. These temporary equilibria consist of the last value of the vessel's state vector and of the last value of the control signal...
We analyze the correctness of an O(n log n) time divide-and-conquer algorithm for the convex hull problem when each input point is a location determined by a normal distribution. We show that the algorithm finds the convex hull of such probabilistic points to precision within some expected correctness determined by a user-given confidence value phi. In order to precisely explain how correct the resulting...
Lung nodule segmentation is the first and the most difficult step in every Computer Aided Diagnosis (CAD). Difficulty arises due to the boring and time-consuming nature of the manual lung segmentation process. In this paper, we propose a novel automatic lung segmentation method for accurate localization of the lung nodules in computer tomography (CT) images. We present a combination of the Graph Cut...
Software fault tolerance is an important issue when using software systems in safety-critical applications. In such systems, software robustness is an essential requirement for improving software fault tolerance. Since an operating system (OS) is a major part of a safety-critical system, its robustness has considerable influence on the system's overall robustness. In recent years, researchers have...
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