Fundamentals of digital image processing
Fundamentals of digital image processing
Estimation of 3-D translational motion parameters via Hadamard transform
Pattern Recognition Letters
Illumination independent change detection for real world image sequences
Computer Vision, Graphics, and Image Processing
MPEG: a video compression standard for multimedia applications
Communications of the ACM - Special issue on digital multimedia systems
Statistical model-based change detection in moving video
Signal Processing
Automatic partitioning of full-motion video
Multimedia Systems
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion Segmentation and Tracking Using Normalized Cuts
Motion Segmentation and Tracking Using Normalized Cuts
Separating non-stationary from stationary scene components in a sequence of real world TV-images
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 2
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Statistical change detection with moments under time-varying illumination
IEEE Transactions on Image Processing
Fast computation of a contrast-invariant image representation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Video segmentation for content-based coding
IEEE Transactions on Circuits and Systems for Video Technology
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The goal of the presented change detection algorithm is to extract objects that appear in only one of two input images. A typical application is surveillance, where a scene is captured at different times of the day or even on different days. In this paper we assume that there may be a significant noise or illumination differences between the input images. For example, one image may be captured in daylight while the other was captured during night with an infrared device. By using a connectivity analysis along gray-level technique, we extract significant blobs from both images. All the extracted blobs are candidates to be classified as changes or part of a change. Then, the candidate blobs from both images are matched. A blob from one image that does not satisfy the matching criteria with its corresponding blob from the other image is considered as an object of change. The algorithm was found to be reliable, fast, accurate, and robust even under extreme changes in illumination and some distortion of the images. The performance of the algorithm is demonstrated using real images. The worst-case time complexity of the algorithm is almost linear in the image size. Therefore, it is suitable for real-time applications.