scholarly journals Shadow Elimination Algorithm Using Color and Texture Features

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Minghu Wu ◽  
Rui Chen ◽  
Ying Tong
Keyword(s):  
Target Detection ◽  
Color Space ◽  
Texture Feature ◽  
Texture Features ◽  
Shadow Detection ◽  
Moving Targets ◽  
Elimination Algorithm ◽  
Local Variance ◽  
Actual Operation ◽  
Operation Results

Shadow detection and removal in real scene images are a significant problem for target detection. This work proposes an improved shadow detection and removal algorithm for urban video surveillance. First, the foreground is detected by background subtraction and the shadow is detected by HSV color space. Using local variance and OTSU 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, we embed our algorithm into C/S framework based on the HTML5 web socket protocol. Both the experimental and actual operation results show that the proposed algorithm is efficient and robust in target detection and shadow detection and removal under different scenes.

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.

scholarly journals Farmland Extraction from High Spatial Resolution Remote Sensing Images Based on Stratified Scale Pre-Estimation

2019 ◽  
Vol 11 (2) ◽  
pp. 108 ◽  
Author(s):  
Lu Xu ◽  
Dongping Ming ◽  
Wen Zhou ◽  
Hanqing Bao ◽  
Yangyang Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Color Space ◽  
Texture Features ◽  
Rate Of Change ◽  
Parameters Estimation ◽  
Remote Sensing Images ◽  
Image Region ◽  
Local Variance

Extracting farmland from high spatial resolution remote sensing images is a basic task for agricultural information management. According to Tobler’s first law of geography, closer objects have a stronger relation. Meanwhile, due to the scale effect, there are differences on both spatial and attribute scales among different kinds of objects. Thus, it is not appropriate to segment images with unique or fixed parameters for different kinds of objects. In view of this, this paper presents a stratified object-based farmland extraction method, which includes two key processes: one is image region division on a rough scale and the other is scale parameter pre-estimation within local regions. Firstly, the image in RGB color space is converted into HSV color space, and then the texture features of the hue layer are calculated using the grey level co-occurrence matrix method. Thus, the whole image can be divided into different regions based on the texture features, such as the mean and homogeneity. Secondly, within local regions, the optimal spatial scale segmentation parameter was pre-estimated by average local variance and its first-order and second-order rate of change. The optimal attribute scale segmentation parameter can be estimated based on the histogram of local variance. Through stratified regionalization and local segmentation parameters estimation, fine farmland segmentation can be achieved. GF-2 and Quickbird images were used in this paper, and mean-shift and multi-resolution segmentation algorithms were applied as examples to verify the validity of the proposed method. The experimental results have shown that the stratified processing method can release under-segmentation and over-segmentation phenomena to a certain extent, which ultimately benefits the accurate farmland information extraction.

scholarly journals Color space influence on ANN skin lesion classification using statistics texture feature

2021 ◽  
Vol 44 (1) ◽  
pp. 53-62
Author(s):  
Felicia Anisoara Damian ◽  
Simona Moldovanu ◽  
Luminita Moraru
Keyword(s):  
Color Space ◽  
Back Propagation ◽  
Texture Feature ◽  
Texture Features ◽  
Skin Lesions ◽  
Color Spaces ◽  
Color Channel ◽  
Feed Forward Back Propagation ◽  
Rgb Color Space ◽  
Hidden Layer

This study aims to investigate the ability of an artificial neural network to differentiate between malign and benign skin lesions based on two statistics terms and for RGB (R red, G green, B blue) and YIQ (Y luminance, and I and Q chromatic differences) color spaces. The targeted statistics texture features are skewness (S) and kurtosis (K) which are extracted from the histograms of each color channel corresponding to the color spaces and for the two classes of lesions: nevi and melanomas. The extracted data is used to train the Feed-Forward Back Propagation Networks (FFBPNs). The number of neurons in the hidden layer varies: it can be 8, 16, 24, or 32. The results indicate skewness features computed for the red channel in the RGB color space as the best choice to reach the goal of our study. The reported result shows the advantages of monochrome channels representation for skin lesions diagnosis.

Bridge Target Detection in SAR Images Based on Texture Feature

2013 ◽  
Vol 347-350 ◽  
pp. 3634-3638 ◽  
Author(s):  
Nan Zheng ◽  
Wei Zheng ◽  
Zhong Lin Xu ◽  
Da Cheng Wang
Keyword(s):  
False Alarm ◽  
Target Detection ◽  
Texture Feature ◽  
Texture Features ◽  
Histogram Equalization ◽  
Experimental Results ◽  
Gray Level ◽  
Contrastive Analysis ◽  
Sar Images ◽  
Occurrence Matrix

This paper carries out an algorithm research on bridge target detection in SAR images and presents a method that combines both texture features and correlation features. The method firstly extracts initial targets by using the algorithm of histogram equalization segmentation, and then conducts a contrastive analysis for targets and their surrounding background textures by using the gray level co-occurrence matrix to get rid of the false alarm target. The experimental results show that the method is simple, effective and has certain algorithm robustness.

An Efficient Image Retrieval Based on Combined Features

2013 ◽  
Vol 787 ◽  
pp. 1025-1029
Author(s):  
Ching Hung Su ◽  
Huang Sen Chiu ◽  
Mohd Helmy Abd Wahab ◽  
Tsai Ming Hsieh
Keyword(s):  
Image Retrieval ◽  
Color Space ◽  
Texture Feature ◽  
Texture Features ◽  
Texture Descriptor ◽  
Color Code ◽  
Hsv Color Space ◽  
Retrieval Scheme ◽  
Combined Features ◽  
Color Pixel

An efficient image retrieval scheme to retrieve images is proposed based on the issue of texture and color space features extractions. The algorithm for an effective image retrieval scheme to retrieve images is presented. We propose a scheme using color and texture features and address the unique algorithm to extract the color pixel features by the HSV color space and the texture features of Homogeneous Texture Descriptor (HTD). The proposed scheme transfers each image to a quantized color code using the regulations of the properties in compliance with HSV color space model and then employing the quantized color code along with the texture feature of Homogeneous Texture Descriptor (HTD) to compare the images of database. Experimental of the proposed scheme performed on SIMPLIcity image database to demonstrate more efficient and effective than the conventional schemes.

scholarly journals Research on college gymnastics teaching model based on multimedia image and image texture feature analysis

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Ying Wu ◽  
Jikun Liu
Keyword(s):  
Human Body ◽  
Rapid Development ◽  
Texture Feature ◽  
Texture Features ◽  
Dynamics Simulation ◽  
Image Texture ◽  
Whole Body ◽  
Database Modeling ◽  
Rigid Model ◽  
The Subject

AbstractWith the rapid development of gymnastics technology, novel movements are also emerging. Due to the emergence of various complicated new movements, higher requirements are put forward for college gymnastics teaching. Therefore, it is necessary to combine the multimedia simulation technology to construct the human body rigid model and combine the image texture features to display the simulation image in texture form. In the study, GeBOD morphological database modeling was used to provide the data needed for the modeling of the whole-body human body of the joint and used for dynamics simulation. Simultaneously, in order to analyze and summarize the technical essentials of the innovative action, this experiment compared and analyzed the hem stage of the cross-headstand movement of the subject and the hem stage of the 180° movement. Research shows that the method proposed in this paper has certain practical effects.

scholarly journals Shadow Detection and Compensation from Remote Sensing Images under Complex Urban Conditions

2021 ◽  
Vol 13 (4) ◽  
pp. 699
Author(s):  
Tingting Zhou ◽  
Haoyang Fu ◽  
Chenglin Sun ◽  
Shenghan Wang
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Mean Shift ◽  
Color Space ◽  
Compensation Method ◽  
Shadow Detection ◽  
Processing Unit ◽  
Shadow Region ◽  
Remote Sensing Images ◽  
Shadow Compensation

Due to the block of high-rise objects and the influence of the sun’s altitude and azimuth, shadows are inevitably formed in remote sensing images particularly in urban areas, which causes missing information in the shadow region. In this paper, we propose a new method for shadow detection and compensation through objected-based strategy. For shadow detection, the shadow was highlighted by an improved shadow index (ISI) combined color space with an NIR band, then ISI was reconstructed by the objects acquired from the mean-shift algorithm to weaken noise interference and improve integrity. Finally, threshold segmentation was applied to obtain the shadow mask. For shadow compensation, the objects from segmentation were treated as a minimum processing unit. The adjacent objects are likely to have the same ambient light intensity, based on which we put forward a shadow compensation method which always compensates shadow objects with their adjacent non-shadow objects. Furthermore, we presented a dynamic penumbra compensation method (DPCM) to define the penumbra scope and accurately remove the penumbra. Finally, the proposed methods were compared with the stated-of-art shadow indexes, shadow compensation method and penumbra compensation methods. The experiments show that the proposed method can accurately detect shadow from urban high-resolution remote sensing images with a complex background and can effectively compensate the information in the shadow region.

scholarly journals MRI texture feature repeatability and image acquisition factor robustness, a phantom study and in silico study

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Joshua Shur ◽  
Matthew Blackledge ◽  
James D’Arcy ◽  
David J. Collins ◽  
Maria Bali ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Coefficient Of Variation ◽  
In Silico ◽  
Image Acquisition ◽  
Texture Feature ◽  
Texture Features ◽  
Concordance Correlation Coefficient ◽  
Concordance Correlation ◽  
Simulation Data ◽  
Mri Features

Abstract Purpose To evaluate robustness and repeatability of magnetic resonance imaging (MRI) texture features in water and tissue phantom test-retest study. Materials and methods Separate water and tissue phantoms were imaged twice with the same protocol in a test-retest experiment using a 1.5-T scanner. Protocols were acquired to favour signal-to-noise ratio and resolution. Forty-six features including first order statistics and second-order texture features were extracted, and repeatability was assessed by calculating the concordance correlation coefficient. Separately, base image noise and resolution were manipulated in an in silico experiment, and robustness of features was calculated by assessing percentage coefficient of variation and linear correlation of features with noise and resolution. These simulation data were compared with the acquired data. Features were classified by their degree (high, intermediate, or low) of robustness and repeatability. Results Eighty percent of the MRI features were repeatable (concordance correlation coefficient > 0.9) in the phantom test-retest experiment. The majority (approximately 90%) demonstrated a strong or intermediate correlation with image acquisition parameter, and 19/46 (41%) and 13/46 (28%) of features were highly robust to noise and resolution, respectively (coefficient of variation < 5%). Agreement between the acquired and simulation data varied, with the range of agreement within feature classes between 11 and 92%. Conclusion Most MRI features were repeatable in a phantom test-retest study. This phantom data may serve as a lower limit of feature MRI repeatability. Robustness of features varies with acquisition parameter, and appropriate features can be selected for clinical validation studies.

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