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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-levels 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.

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