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A new method based on the Discrete Cosine Transform (DCT) and the Otsu method for blur detection in image sequences is proposed in this paper. In the first step, the standard deviation (STD) and the DCT coefficients are utilized to detect blurred and homogeneous areas in each image. Then, the Otsu method is used to calculate an adaptive threshold in each segment of the image sequence. Our experiments...
We present a method for foreground-background video segmentation in real-time that may be used in applications as, for instance, Background Substitution, Analysis of Surveillance Cameras, Highway Cars Detection and so on. Our approach implements a probabilistic segmentation based on the binary Quadratic Markov Measure Fields models (QMMFs). That framework regularizes the likelihood of each pixel to...
We propose a region-based foreground object segmentation method capable of dealing with image sequences containing noise, illumination variations and dynamic backgrounds (as often present in outdoor environments). The method utilises contextual spatial information through analysing each frame on an overlapping block by-block basis and obtaining a low-dimensional texture descriptor for each block....
A robust foreground object segmentation technique is proposed, capable of dealing with image sequences containing noise, illumination variations and dynamic backgrounds. The method employs contextual spatial information by analysing each image on an overlapping patch-by-patch basis and obtaining a low-dimensional texture descriptor for each patch. Each descriptor is passed through an adaptive multi-stage...
Identifying handled objects, i.e. objects being manipulated by a user, is essential for recognizing the person's activities. An egocentric camera as worn on the body enjoys many advantages such as having a natural first-person view and not needing to instrument the environment. It is also a challenging setting, where background clutter is known to be a major source of problems and is difficult to...
In this paper we present a segmentation system for monocular video sequences with static camera that aims at foreground/background separation and tracking. We propose to combine a simple pixel-wise model for the background with a general purpose region based model for the foreground. The background is modeled using one Gaussian per pixel, thus achieving a precise and easy to update model. The foreground...
Background modelling is a key task in tracking applications. Our interest in this paper is the accurate estimation of static backgrounds in scientific imaging, such as those in automated stem cell tracking. In this paper, an effective background estimation method is proposed. First, the segmentation results are used to remove the foreground objects, then the background is robustly estimated over the...
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