Object Contour Extraction Algorithm Combined Snake with Level Set

In medical imaging, accurate anatomical structure extraction is important for diagnosis and therapeutic interventional planning. So, for easier, quicker and accurate diagnosis of medical images, image processing technologies may be employed in analysis and feature extraction of medical images. In this paper, some modifications to level set algorithm are made and modified algorithm is used for extracting contour of foetal objects in an image. The proposed approach is applied on foetal ultrasound images. In traditional approach, foetal parameters are extracted manually from ultrasound images. Due to lack of consistency and accuracy of manual measurements, an automatic technique is highly desirable to obtain foetal biometric measurements. This proposed approach is based on global & local region information for foetal contour extraction from ultrasonic images. The primary goal of this research is to provide a new methodology to aid the analysis and feature extraction from foetal images.

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A Novel Hybrid Level Set Model for Non-Rigid Object Contour Tracking

IEEE Transactions on Image Processing ◽

10.1109/tip.2021.3112051 ◽

2022 ◽

Vol 31 ◽

pp. 15-29

Author(s):

Qing Cai ◽

Huiying Liu ◽

Yiming Qian ◽

Sanping Zhou ◽

Jinjun Wang ◽

...

Keyword(s):

Level Set ◽

Contour Tracking ◽

Rigid Object ◽

Object Contour ◽

Object Contour Tracking ◽

Level Set Model

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Moving Object Detection and Tracking Based on the Contour Extraction and Centroid Representation

Advanced Methodologies and Technologies in Artificial Intelligence, Computer Simulation, and Human-Computer Interaction - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-5225-7368-5.ch012 ◽

2019 ◽

pp. 147-157

Author(s):

Naveenkumar M ◽

Sriharsha K. V. ◽

Vadivel A

Keyword(s):

Object Detection ◽

Motion Tracking ◽

Moving Object Detection ◽

Moving Object ◽

Absolute Difference ◽

Contour Extraction ◽

Object Detection And Tracking ◽

Detection And Tracking ◽

Novel Approach ◽

Extraction Algorithm

This chapter presents a novel approach for moving object detection and tracking based on contour extraction and centroid representation (CECR). Firstly, two consecutive frames are read from the video, and they are converted into grayscale. Next, the absolute difference is calculated between them and the result frame is converted into binary by applying gray threshold technique. The binary frame is segmented using contour extraction algorithm. The centroid representation is used for motion tracking. In the second stage of experiment, initially object is detected by using CECR and motion of each track is estimated by Kalman filter. Experimental results show that the proposed method can robustly detect and track the moving object.

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Research and Implementation of Moving Object Contour Extraction Algorithm Based on GPU

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.643.253 ◽

2014 ◽

Vol 643 ◽

pp. 253-257

Author(s):

Kai Min Zhao ◽

Yu Ting Chen ◽

Jun Li ◽

Hao Yang ◽

Li Ying Chen

Keyword(s):

Parallel Processing ◽

Mathematical Morphology ◽

Implementation Process ◽

Differential Method ◽

Massive Data ◽

Contour Extraction ◽

Moving Targets ◽

Video File ◽

Extraction Algorithm ◽

Morphology Algorithm

In this paper, frame differential method will be used to detect moving targets in a static background video file, and pre-contour can be obtained by binarizing the detected targets . However,, the result are not what was expected, so the expansion and corrosion of mathematical morphology are used to extract the final contour of moving targets. in the progress of dealing with massive data , mathematical morphology is not good enough to achieve the need of the real-time in video surveillance. Considering the dilation and erosion is a kind of parallel processing operations, in order to improve the speed of mathematical morphology operations, this paper offers detailed implementation process of the dilation algorithm for parallel computing on GPU. Experimental results showed that GPU parallel processing on mathematical morphology algorithm faster than the CPU serial processing.

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