scholarly journals Image Segmentation in Video Sequences Using Modified Background Subtraction

2012 ◽  
Vol 4 (1) ◽  
pp. 93-104 ◽  
Author(s):  
Chinchkhede D W
Keyword(s):  
Image Segmentation ◽  
Background Subtraction ◽  
Video Sequences

scholarly journals DETECTION OF CRACKS IN PAVED ROAD SURFACE USING LASER SCAN IMAGE DATA

2016 ◽  
Vol XLI-B1 ◽  
pp. 559-562 ◽  
Author(s):  
J. Choi ◽  
L. Zhu ◽  
H. Kurosu
Keyword(s):  
Image Segmentation ◽  
Background Subtraction ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Image Data ◽  
Road Surface ◽  
Surface Model ◽  
Rolling Ball ◽  
The Difference ◽  
Paved Road

In the current study, we developed a methodology for detecting cracks in the surface of paved road using 3D digital surface model of road created by measuring with three-dimensional laser scanner which works on the basis of the light-section method automatically. For the detection of cracks from the imagery data of the model, the background subtraction method (Rolling Ball Background Subtraction Algorithm) was applied to the data for filtering out the background noise originating from the undulation and gradual slope and also for filtering the ruts that were caused by wearing, aging and excessive use of road and other reasons. We confirmed the influence from the difference in height (depth) caused by forgoing reasons included in a data can be reduced significantly at this stage. Various parameters of ball radius were applied for checking how the result of data obtained with this process vary according to the change of parameter and it becomes clear that there are not important differences by the change of parameters if they are in a certain range radius. And then, image segmentation was performed by multi-resolution segmentation based on the object-based image analysis technique. The parameters for the image segmentation, scale, pixel value (height/depth) and the compactness of objects were used. For the classification of cracks in the database, the height, length and other geometric property are used and we confirmed the method is useful for the detection of cracks in a paved road surface.

scholarly journals DETECTION OF CRACKS IN PAVED ROAD SURFACE USING LASER SCAN IMAGE DATA

2016 ◽  
Vol XLI-B1 ◽  
pp. 559-562
Author(s):  
J. Choi ◽  
L. Zhu ◽  
H. Kurosu
Keyword(s):  
Image Segmentation ◽  
Background Subtraction ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Image Data ◽  
Road Surface ◽  
Surface Model ◽  
Rolling Ball ◽  
The Difference ◽  
Paved Road

In the current study, we developed a methodology for detecting cracks in the surface of paved road using 3D digital surface model of road created by measuring with three-dimensional laser scanner which works on the basis of the light-section method automatically. For the detection of cracks from the imagery data of the model, the background subtraction method (Rolling Ball Background Subtraction Algorithm) was applied to the data for filtering out the background noise originating from the undulation and gradual slope and also for filtering the ruts that were caused by wearing, aging and excessive use of road and other reasons. We confirmed the influence from the difference in height (depth) caused by forgoing reasons included in a data can be reduced significantly at this stage. Various parameters of ball radius were applied for checking how the result of data obtained with this process vary according to the change of parameter and it becomes clear that there are not important differences by the change of parameters if they are in a certain range radius. And then, image segmentation was performed by multi-resolution segmentation based on the object-based image analysis technique. The parameters for the image segmentation, scale, pixel value (height/depth) and the compactness of objects were used. For the classification of cracks in the database, the height, length and other geometric property are used and we confirmed the method is useful for the detection of cracks in a paved road surface.

scholarly journals Colored Image Segmentation using K-Means Algorithm

2016 ◽  
Vol 15 (7) ◽  
pp. 6950-6956
Author(s):  
Ishita Vishnoi ◽  
Nikunj Khetan ◽  
Sreedevi Indu
Keyword(s):  
Image Segmentation ◽  
Sign Language ◽  
Background Subtraction ◽  
Skin Color ◽  
Recognition Accuracy ◽  
Low Cost ◽  
Human Beings ◽  
Computational Power ◽  
Impaired People ◽  
Natural Means

Hand gestures are natural means of communication for human beings and even more so for hearing and speech impaired people who communicate through sign language. Unfortunately, most people are not familiar with sign language and an interpreter is required to translate dialogues. Hence, there is a need to develop a low cost, easily implementable and efficient means to recognize sign language gestures to eliminate the interpreter and facilitate easier communication. The proposed work achieves a satisfactory recognition accuracy using in-built laptop webcam using combination of 3 skin color models(HSV,RGB,YCbCr) and background subtraction to eliminate noise from webcam low quality images to recognize sign language for helping the hearing and speech impaired in real-time without requiring too much computational power or any other device as it can be implemented in any laptop with a webcam.

scholarly journals Vehicle Counting and Detection

2020 ◽  
Vol 9 (8) ◽  
pp. 763-766
Keyword(s):  
Image Segmentation ◽  
Background Subtraction ◽  
Video Stream ◽  
Vehicle Tracking ◽  
Traffic Surveillance ◽  
Primary Key ◽  
Key Features ◽  
Blob Tracking

This paper describes about the system to count the number of vehicles on roads and highways by using adaptive background subtraction and blob tracking technologies. Overall, system requires a video stream captured from static cameras installed on roads and highways .The proposed system consists of four stages: 1) Adaptive background subtraction 2) image segmentation 3) vehicle counting 4) vehicle tracking. The necessity of tracing and counting the vehicles is helpful for traffic surveillance. The primary key features of the system are 1) Ability to count the vehicles 2) efficiency, to show that system would give the results with high perfection.

Real-Time Human Skeleton Extraction Based on Video Sequences

2013 ◽  
Vol 401-403 ◽  
pp. 1410-1414
Author(s):  
Qing Ye ◽  
Jun Feng Dong ◽  
Yong Mei Zhang
Keyword(s):  
Real Time ◽  
Background Subtraction ◽  
Adaptive Threshold ◽  
Video Sequences ◽  
Topological Properties ◽  
Skeleton Extraction ◽  
Subtraction Method ◽  
Computation Cost ◽  
Thinning Algorithm ◽  
Moving Body

Thinning algorithm is widely used in image processing and pattern recognition.In this paper we proposed an optimized thinning algorithm based on Zhan-Suen thinning and applied it to video sequences of moving human body to extract real-time body skeleton. We firstly used background subtraction method to detect moving body, then made use of adaptive threshold segmentation to gain the binary moving body image, finally we used the optimized algorithm to the binary image and got its skeleton. The skeleton not only maintains the movement geometry and body image’s topological properties, also reduces image redundancy and computation cost, and helps us clearly recognize the moving body posture.

scholarly journals Compressive Background Modeling for Foreground Extraction

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yong Wang ◽  
Qian Lu ◽  
Dianhong Wang ◽  
Wei Liu
Keyword(s):  
Background Subtraction ◽  
Background Modeling ◽  
Video Sequences ◽  
Computationally Efficient ◽  
Subtraction Method ◽  
Foreground Extraction ◽  
Image Frame ◽  
Computer Vision Applications ◽  
Background Image ◽  
Classical Background

Robust and efficient foreground extraction is a crucial topic in many computer vision applications. In this paper, we propose an accurate and computationally efficient background subtraction method. The key idea is to reduce the data dimensionality of image frame based on compressive sensing and in the meanwhile apply sparse representation to build the current background by a set of preceding background images. According to greedy iterative optimization, the background image and background subtracted image can be recovered by using a few compressive measurements. The proposed method is validated through multiple challenging video sequences. Experimental results demonstrate the fact that the performance of our approach is comparable to those of existing classical background subtraction techniques.

Researcher of Region Growing Medical Image Segmentation Based on Adaboost Algorithm

2010 ◽  
Vol 142 ◽  
pp. 21-25
Author(s):  
Peng Wang ◽  
X.F. Ye ◽  
Shi Wei Yin ◽  
Shao Chen Kang ◽  
Jing Lei Xin
Keyword(s):  
Image Segmentation ◽  
Background Subtraction ◽  
Medical Image ◽  
Region Growing ◽  
Medical Image Segmentation ◽  
Segmentation Algorithm ◽  
Adaboost Algorithm ◽  
Actual Application ◽  
Region Extraction ◽  
Seed Pixel

To obtain better region extraction results of medical image, a new segmentation algorithm is proposed based on improved Adaboost algorithm. The seed pixel is selected with background subtraction. The neighborhood point is judged. The primary selected seed is calibrated with label, and then the range of seed is reduced through growing label and the maximal saliency. The optimized Adaboost algorithm is taken as growing criterion to optimally combine the scrappy region when the region growing is over. The experiment result shows that the accuracy and robustness of the algorithm both meet the actual application required.

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