In this paper a new method is introduced for path planning of an autonomous vehicle. In this method, the environment is considered cluttered and with some uncertainty sources. Thus, the state of detected object should be estimated using an optimal filter. To do so, the state distribution is assumed Gaussian. Thus the state vector is estimated by a Kalman filter at each time step. The estimation...

We propose an efficiently structured nonlinear finitememory filter for denoising (filtering) a Gaussian signal contaminated by additive impulsive colored noise. The noise is modeled as a zero-mean Gaussian mixture (ZMGM) process. We first derive the optimal estimator for the static case, in which a Gaussian random variable (RV) is contaminated by an impulsive ZMGM RV. We provide an analytical d...

The detection of hydrophobicity is an important way to evaluate the performance of composite insulators, which is helpful to the safe operation of composite insulators. Image processing technology is used to judge the hydrophobicity of composite insulators, which makes detection results more accurate and overcomes the subjective drawbacks of traditional detection methods. As the traditional Can...

— This paper presents review of techniques and algorithms used for filtering and data association in visual tracking. Kalman filter is an optimal Bayesian filter for linear dynamic models with Gaussian noise. Most of the processes and systems in real world are nonlinear, and in these situations there is extension of Kalman filter named the Extended Kalman filter (EKF). In case when the noise is...

We present a particle filter-based multitarget tracking method incorporating Gaussian process dynamical model (GPDM) to improve robustness in multitarget tracking. With the particle filter Gaussian process dynamical model (PFGPDM), a highdimensional target trajectory dataset of the observation space is projected to a low-dimensional latent space in a nonlinear probabilistic manner, which will t...

In this paper, we propose a fast circular edge detector for the iris region segmentation by detecting the inner and outer boundaries of the iris. In previous work, the circular edge detector which John G.Daugman proposed, searches the radius and the center of the iris to detect its outer boundary over an eye image. To do so, he used Gaussian filter to smooth texture patterns of the iris which c...

We present a particle filter-based multitarget tracking method incorporating Gaussian process dynamical model (GPDM) to improve robustness in multitarget tracking. With the particle filter Gaussian process dynamical model (PFGPDM), a highdimensional target trajectory dataset of the observation space is projected to a low-dimensional latent space in a nonlinear probabilistic manner, which will t...

In this paper, we propose a new partition-based filter for removing mixed high probability impulse and Gaussian noises by extraction of the signals. The proposed filter sorts the elements of sample vector and extracts the signals around the median from sorted signals. The extraction vector is classified into one of M partitions, and a particular filtering operation is then activated. The output...

A robust front-end pre-filter incorporating non-quadratic signal processing techniques is proposed. We will demonstrate the effectiveness of this filter in suppressing impulsive noises in a coded direct sequence code-division multiple access (DS-CDMA) communication system. Based on M-Estimation approach, the pre-filter works by minimizing an appropriate penalty function in an iterative fashion ...

The first order absolute moment provides a ridge map similar to the one provided by the GoG (gradient of Gaussian) filter. However, unlike the GoG filter, the first order absolute moment provides ridges at both edges and lines and gives rise to local extrema of the ridges at line endings, corners and junctions. Furthermore, a class of filters is obtained by introducing three low-pass filters at...