scholarly journals An Effective Framework Using Spatial Correlation and Extreme Learning Machine for Moving Cast Shadow Detection

2019 ◽  
Vol 9 (23) ◽  
pp. 5042 ◽  
Author(s):  
Yugen Yi ◽  
Jiangyan Dai ◽  
Chengduan Wang ◽  
Jinkui Hou ◽  
Huihui Zhang ◽  
...  
Keyword(s):  
Spatial Correlation ◽  
Extreme Learning Machine ◽  
Moving Objects ◽  
Shadow Detection ◽  
Classification Model ◽  
Cast Shadow ◽  
Different Color ◽  
Learning Machine ◽  
Tracking Object ◽  
High Level

Moving cast shadows of moving objects significantly degrade the performance of many high-level computer vision applications such as object tracking, object classification, behavior recognition and scene interpretation. Because they possess similar motion characteristics with their objects, moving cast shadow detection is still challenging. In this paper, we present a novel moving cast-shadow detection framework based on the extreme learning machine (ELM) to efficiently distinguish shadow points from the foreground object. First, according to the physical model of shadows, pixel-level features of different channels in different color spaces and region-level features derived from the spatial correlation of neighboring pixels are extracted from the foreground. Second, an ELM-based classification model is developed by labelled shadow and un-shadow points, which is able to rapidly distinguish the points in the new input whether they belong to shadows or not. Finally, to guarantee the integrity of shadows and objects for further image processing, a simple post-processing procedure is designed to refine the results, which also drastically improves the accuracy of moving shadow detection. Extensive experiments on two publicly common datasets including 13 different scenes demonstrate that the performance of the proposed framework is superior to representative state-of-the-art methods.

An Effective Framework Using Region Merging and Learning Machine for Shadow Detection and Removal

2021 ◽  
Vol 12 (2) ◽  
pp. 2506-2514
Author(s):  
Mohan kumar Shilpa , Et. al.
Keyword(s):  
Moving Objects ◽  
Mean Shift ◽  
Texture Feature ◽  
Texture Features ◽  
Shadow Detection ◽  
Moving Targets ◽  
Region Merging ◽  
Motion Characteristics ◽  
Tracking Object ◽  
High Level

Moving cast shadows of moving objects significantly degrade the performance of many high-level computer vision applications such as object tracking, object classification, behavior recognition and scene interpretation. Because they possess similar motion characteristics with their objects, moving cast shadow detection is still challenging. In this paper, the foreground is detected by background subtraction and the shadow is detected by combination of Mean-Shift and Region Merging Segmentation. Using Gabor method, we obtain the moving targets with texture features. According to the characteristics of shadow in HSV space and texture feature, the shadow is detected and removed to eliminate the shadow interference for the subsequent processing of moving targets. Finally, to guarantee the integrity of shadows and objects for further image processing, a simple post-processing procedure is designed to refine the results, which also drastically improves the accuracy of moving shadow detection. Extensive experiments on publicly common datasets that the performance of the proposed framework is superior to representative state-of-the-art methods.

A New Point-of-Interest Classification Model with an Extreme Learning Machine

2018 ◽  
Vol 10 (6) ◽  
pp. 951-964 ◽  
Author(s):  
Zhen Zhang ◽  
Xiangguo Zhao ◽  
Guoren Wang ◽  
Xin Bi
Keyword(s):  
Extreme Learning Machine ◽  
Classification Model ◽  
Point Of Interest ◽  
Learning Machine

scholarly journals Deep Convolutional Extreme Learning Machine and Its Application in Handwritten Digit Classification

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Shan Pang ◽  
Xinyi Yang
Keyword(s):  
Deep Learning ◽  
Extreme Learning Machine ◽  
Neuron Network ◽  
Learning Methods ◽  
Training Time ◽  
Fast Training ◽  
Handwritten Digit ◽  
Learning Machine ◽  
Hidden Layer ◽  
High Level

In recent years, some deep learning methods have been developed and applied to image classification applications, such as convolutional neuron network (CNN) and deep belief network (DBN). However they are suffering from some problems like local minima, slow convergence rate, and intensive human intervention. In this paper, we propose a rapid learning method, namely, deep convolutional extreme learning machine (DC-ELM), which combines the power of CNN and fast training of ELM. It uses multiple alternate convolution layers and pooling layers to effectively abstract high level features from input images. Then the abstracted features are fed to an ELM classifier, which leads to better generalization performance with faster learning speed. DC-ELM also introduces stochastic pooling in the last hidden layer to reduce dimensionality of features greatly, thus saving much training time and computation resources. We systematically evaluated the performance of DC-ELM on two handwritten digit data sets: MNIST and USPS. Experimental results show that our method achieved better testing accuracy with significantly shorter training time in comparison with deep learning methods and other ELM methods.

scholarly journals Spectral-Spatial Hyperspectral Image Classification with Superpixel Pattern and Extreme Learning Machine

2019 ◽  
Vol 11 (17) ◽  
pp. 1983 ◽  
Author(s):  
Yongshan Zhang ◽  
Xinwei Jiang ◽  
Xinxin Wang ◽  
Zhihua Cai
Keyword(s):  
Remote Sensing ◽  
Extreme Learning Machine ◽  
Spatial Patterns ◽  
Hyperspectral Image ◽  
Classification Model ◽  
Image Texture ◽  
Spatial Characteristic ◽  
Spectral Features ◽  
Sensing Applications ◽  
Learning Machine

Spectral-spatial classification of hyperspectral images (HSIs) has recently attracted great attention in the research domain of remote sensing. It is well-known that, in remote sensing applications, spectral features are the fundamental information and spatial patterns provide the complementary information. With both spectral features and spatial patterns, hyperspectral image (HSI) applications can be fully explored and the classification performance can be greatly improved. In reality, spatial patterns can be extracted to represent a line, a clustering of points or image texture, which denote the local or global spatial characteristic of HSIs. In this paper, we propose a spectral-spatial HSI classification model based on superpixel pattern (SP) and kernel based extreme learning machine (KELM), called SP-KELM, to identify the land covers of pixels in HSIs. In the proposed SP-KELM model, superpixel pattern features are extracted by an advanced principal component analysis (PCA), which is based on superpixel segmentation in HSIs and used to denote spatial information. The KELM method is then employed to be a classifier in the proposed spectral-spatial model with both the original spectral features and the extracted spatial pattern features. Experimental results on three publicly available HSI datasets verify the effectiveness of the proposed SP-KELM model, with the performance improvement of 10% over the spectral approaches.

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