scholarly journals BACKGROUND ESTIMATION IN KERNEL SPACE

2011 ◽  
Vol 25 (01) ◽  
pp. 1-35 ◽  
Author(s):  
HAMIDREZA BARADARAN KASHANI ◽  
HADI SADOGHI YAZDI ◽  
SEYED ALIREZA SEYEDIN
Keyword(s):  
Function Approximation ◽  
Moving Objects ◽  
Learning Algorithm ◽  
Estimation Method ◽  
Least Mean Square ◽  
Background Estimation ◽  
Illumination Changes ◽  
Weighted Kernel ◽  
Kernel Least Mean Square ◽  
Two Stages

One problem in background estimation is the inherent change in the background such as waving tree branches, water surfaces, camera shakes, and the existence of moving objects in every image. In this paper, a new method for background estimation is proposed based on function approximation in kernel domain. For this purpose, Weighted Kernel-based Learning Algorithm (WKLA) is designed. WKLA includes a weighted type of kernel least mean square algorithm with ability to function approximation in the presence of noise. So, the proposed background estimation method includes two stages: firstly, a novel algorithm for outlier detection namely Fuzzy Outlier Detector (FOD) is applied. Then obtained results are fed to the WKLA. The proposed approach can handle scenes containing moving backgrounds, gradual illumination changes, camera vibrations, and non-empty backgrounds. The qualitative results and quantitative evaluations on various indoor and outdoor sequences relative to existing approaches show the high accuracy and effectiveness of the proposed method in background estimation and foreground detection.

scholarly journals Fast and Accurate Background Reconstruction Using Background Bootstrapping

2021 ◽  
Author(s):  
Bruno Sauvalle ◽  
Arnaud de La Fortelle
Keyword(s):  
Gradient Descent ◽  
Moving Objects ◽  
Stochastic Gradient Descent ◽  
Current Estimate ◽  
Background Estimation ◽  
Illumination Changes ◽  
Background Reconstruction ◽  
Image Pixels ◽  
Definition Of ◽  
Background Image

The goal of background reconstruction is to recover the background image of a scene from a sequence of frames showing this scene cluttered by various moving objects. This task is fundamental in image analysis, and is generally the first step before more advanced processing, but difficult because there is no formal definition of what should be considered as background or foreground and the results may be severely impacted by various challenges such as illumination changes, intermittent object motions, highly cluttered scenes, etc. We propose in this paper a new iterative algorithm for background reconstruction, where the current estimate of the background is used to guess which image pixels are background pixels and a new background estimation is performed using those pixels only. We then show that the proposed algorithm, which uses stochastic gradient descent for improved regularization, is more accurate than the state of the art on the challenging SBMnet dataset, especially for short videos with low frame rates, and is also fast, reaching an average of 52 fps on this dataset when parameterized for maximal accuracy using GPU acceleration and a Python implementation.

scholarly journals NLM-HS: Navigation Learning Model Based on a Hippocampal–Striatal Circuit for Explaining Navigation Mechanisms in Animal Brains

2021 ◽  
Vol 11 (6) ◽  
pp. 803
Author(s):  
Jie Chai ◽  
Xiaogang Ruan ◽  
Jing Huang
Keyword(s):  
Prefrontal Cortex ◽  
Learning Algorithm ◽  
Learning Model ◽  
Cognitive Map ◽  
Model Based ◽  
Animal Navigation ◽  
Environmental Cognition ◽  
Neurophysiological Studies ◽  
Planning Algorithm ◽  
Two Stages

Neurophysiological studies have shown that the hippocampus, striatum, and prefrontal cortex play different roles in animal navigation, but it is still less clear how these structures work together. In this paper, we establish a navigation learning model based on the hippocampal–striatal circuit (NLM-HS), which provides a possible explanation for the navigation mechanism in the animal brain. The hippocampal model generates a cognitive map of the environment and performs goal-directed navigation by using a place cell sequence planning algorithm. The striatal model performs reward-related habitual navigation by using the classic temporal difference learning algorithm. Since the two models may produce inconsistent behavioral decisions, the prefrontal cortex model chooses the most appropriate strategies by using a strategy arbitration mechanism. The cognitive and learning mechanism of the NLM-HS works in two stages of exploration and navigation. First, the agent uses a hippocampal model to construct the cognitive map of the unknown environment. Then, the agent uses the strategy arbitration mechanism in the prefrontal cortex model to directly decide which strategy to choose. To test the validity of the NLM-HS, the classical Tolman detour experiment was reproduced. The results show that the NLM-HS not only makes agents show environmental cognition and navigation behavior similar to animals, but also makes behavioral decisions faster and achieves better adaptivity than hippocampal or striatal models alone.

scholarly journals A Novel Neuron in Kernel Domain

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Zahra Khandan ◽  
Hadi Sadoghi Yazdi
Keyword(s):  
Neural Network ◽  
Online Learning ◽  
Mean Square ◽  
Least Mean Square ◽  
Backpropagation Neural Network ◽  
Adaptive Parameters ◽  
Kernel Parameter ◽  
Online Learning Algorithms ◽  
Adaptive Kernel ◽  
Kernel Least Mean Square

Kernel-based neural network (KNN) is proposed as a neuron that is applicable in online learning with adaptive parameters. This neuron with adaptive kernel parameter can classify data accurately instead of using a multilayer error backpropagation neural network. The proposed method, whose heart is kernel least-mean-square, can reduce memory requirement with sparsification technique, and the kernel can adaptively spread. Our experiments will reveal that this method is much faster and more accurate than previous online learning algorithms.

scholarly journals Real-time gaze estimation via pupil center tracking

2018 ◽  
Vol 9 (1) ◽  
pp. 6-18 ◽  
Author(s):  
Dario Cazzato ◽  
Fabio Dominio ◽  
Roberto Manduchi ◽  
Silvia M. Castro
Keyword(s):  
Real Time ◽  
Learning Algorithm ◽  
Natural Environments ◽  
Gaze Estimation ◽  
Head Pose ◽  
Data Set ◽  
Gaze Tracking ◽  
Illumination Changes ◽  
Wide Range ◽  
Estimation System

Abstract Automatic gaze estimation not based on commercial and expensive eye tracking hardware solutions can enable several applications in the fields of human computer interaction (HCI) and human behavior analysis. It is therefore not surprising that several related techniques and methods have been investigated in recent years. However, very few camera-based systems proposed in the literature are both real-time and robust. In this work, we propose a real-time user-calibration-free gaze estimation system that does not need person-dependent calibration, can deal with illumination changes and head pose variations, and can work with a wide range of distances from the camera. Our solution is based on a 3-D appearance-based method that processes the images from a built-in laptop camera. Real-time performance is obtained by combining head pose information with geometrical eye features to train a machine learning algorithm. Our method has been validated on a data set of images of users in natural environments, and shows promising results. The possibility of a real-time implementation, combined with the good quality of gaze tracking, make this system suitable for various HCI applications.

A Time-Dependent Reliability Estimation Method Based on Gaussian Process Regression

2018 ◽  
Author(s):  
Wang Han ◽  
Xiaoling Zhang ◽  
Xiesi Huang ◽  
Haiqing Li
Keyword(s):  
Machine Learning ◽  
Mechanical System ◽  
Physical Modeling ◽  
Learning Algorithm ◽  
Estimation Method ◽  
Gaussian Process Regression ◽  
Reliability Estimation ◽  
Time Dependent ◽  
Physics Of Failure ◽  
Gear Transmission System

This paper presents a time-dependent reliability estimation method for engineering system based on machine learning and simulation method. Due to the stochastic nature of the environmental loads and internal incentive, the physics of failure for mechanical system is complex, and it is challenging to include uncertainties for the physical modeling of failure in the engineered system’s life cycle. In this paper, an efficient time-dependent reliability assessment framework for mechanical system is proposed using a machine learning algorithm considering stochastic dynamic loads in the mechanical system. Firstly, stochastic external loads of mechanical system are analyzed, and the finite element model is established. Secondly, the physics of failure mode of mechanical system at a time location is analyzed, and the distribution of time realization under each load condition is calculated. Then, the distribution of fatigue life can be obtained based on high-cycle fatigue theory. To reduce the calculation cost, a machine learning algorithm is utilized for physical modeling of failure by integrating uniform design and Gaussian process regression. The probabilistic fatigue life of gear transmission system under different load conditions can be calculated, and the time-varying reliability of mechanical system is further evaluated. Finally, numerical examples and the fatigue reliability estimation of gear transmission system is presented to demonstrate the effectiveness of the proposed method.

scholarly journals APPLICATION OF LEARNING REINFORCEMENT METHOD IN ROBOTIZED AND AUTOMATED FORESTRY SYSTEMS

2020 ◽  
Vol 10 (1) ◽  
pp. 256-265
Author(s):  
Andrey Tolstyh ◽  
D Stupnikov ◽  
Sergey Malyukov ◽  
Aleksandr Luk'yanov ◽  
Yuriy Lunev
Keyword(s):  
Physical Environment ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Industrial Robots ◽  
Environment Model ◽  
Random Position ◽  
Learning Reinforcement ◽  
The Stability ◽  
Two Stages ◽  
The Cost

Abstract Currently, most large enterprises are actively using industrial robots and other automated solutions. This allows a significant increase in productivity and quality of work performed. This article gave a brief overview of modern industrial robots, their operating principle, basic components and systems. A reinforcement learning algorithm was developed and tested. The task of constructing a learning algorithm with reinforcement was divided into two stages: modeling the environment and description and optimization of the cost function. Since industrial robotic systems operate in the real world, the environment model should reflect basic physical laws. Therefore, the pyBullet library of the physical environment was chosen as the physical environment for testing. After modeling the manipulator in the selected physical medium, it was given the trivial task of touching a given object with the capture of the manipulator. An artificial neural network was used as an agent interacting with the environment. The inputs were the coordinates of the object and the existing angles of rotation of the articulated joints of the robot. Outputs - angle of rotation of joints at this step. This network was trained using the back propagation method, Adam modification. The system was trained for about 12 hours. Success is achieved in 95% of cases when testing the stability of the system (random position of the cylinder). In future, it is planned to test the obtained models on bench samples

scholarly journals Moving Objects Detection & Recognition using Hybrid Canny Edge Detection Algorithm in Digital Image Processing

2019 ◽  
Vol 8 (9S3) ◽  
pp. 56-60
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Detection Method ◽  
Learning Algorithm ◽  
Detection Algorithm ◽  
Moving Objects Detection ◽  
Canny Edge ◽  
Objects Detection ◽  
Canny Edge Detection Algorithm ◽  
Day By Day

Recognition and detection of an object in the watched scenes is a characteristic organic capacity. Animals and human being play out this easily in day by day life to move without crashes, to discover sustenance, dodge dangers, etc. Be that as it may, comparable PC techniques and calculations for scene examination are not all that direct, in spite of their exceptional advancement. Object detection is the process in which finding or recognizing cases of articles (for instance faces, mutts or structures) in computerized pictures or recordings. This is the fundamental task in computer. For detecting the instance of an object and to pictures having a place with an article classification object detection method usually used learning algorithm and extracted features. This paper proposed a method for moving object detection and vehicle detection.

scholarly journals Detection of signals of moving objects based on the time selection method

2021 ◽  
Vol 66 (3) ◽  
pp. 335-342
Author(s):  
V. M. Artemiev ◽  
S. M. Kostromitsky ◽  
A. O. Naumov
Keyword(s):  
Moving Objects ◽  
Average Duration ◽  
Threshold Level ◽  
Selection Method ◽  
Probability Of Detection ◽  
False Alarms ◽  
Threshold Method ◽  
Detection Of Signals ◽  
Two Stages ◽  
Time Selection

To increase the efficiency of detecting moving objects in radiolocation, additional features are used, associated with the characteristics of trajectories. The authors assumed that trajectories are correlated, that allows extrapolation of the coordinate values taking into account their increments over the scanning period. The detection procedure consists of two stages. At the first, detection is carried out by the classical threshold method with a low threshold level, which provides a high probability of detection with high values of the probability of false alarms. At the same time uncertainty in the selection of object trajectory embedded in false trajectories arises. Due to the statistical independence of the coordinates of the false trajectories in comparison with the correlated coordinates of the object, the average duration of the first of them is less than the average duration of the second ones. This difference is used to solve the detection problem at the second stage based on the time-selection method. The obtained results allow estimation of the degree of gain in the probability of detection when using the proposed method.

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