Topic
Median filter
About: Median filter is a research topic. Over the lifetime, 12479 publications have been published within this topic receiving 178253 citations.
Papers published on a yearly basis
Papers
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TL;DR: In this article, an extension of Lee's local statistics method modified to utilize local gradient information is presented, where the local mean and variance are computed from a reduced set of pixels depending on the orientation of the edge.
819 citations
18 Jan 2004
TL;DR: This paper compares various background subtraction algorithms for detecting moving vehicles and pedestrians in urban traffic video sequences, considering approaches varying from simple techniques such as frame differencing and adaptive median filtering, to more sophisticated probabilistic modeling techniques.
Abstract: Identifying moving objects from a video sequence is a fundamental and
critical task in many computer-vision applications. A common approach
is to perform background subtraction, which identifies moving objects
from the portion of a video frame that differs significantly from a
background model. There are many challenges in developing a good
background subtraction algorithm. First, it must be robust against
changes in illumination. Second, it should avoid detecting
non-stationary background objects such as swinging leaves, rain, snow,
and shadow cast by moving objects. Finally, its internal background
model should react quickly to changes in background such as starting
and stopping of vehicles. In this paper, we compare various background subtraction algorithms for detecting moving vehicles and pedestrians in urban traffic video sequences. We consider approaches varying from simple techniques such as frame differencing and adaptive median filtering, to more sophisticated probabilistic modeling techniques. While complicated techniques often produce superior performance, our experiments show that simple techniques such as adaptive median filtering can produce good results with much lower computational complexity.
794 citations
TL;DR: In this article, the authors derived necessary and sufficient conditions for a signal to be invariant under a specific form of median filtering and proved that the form of successive median filtering of a signal (i.e., the filtered output is itself again filtered) eventually reduces the original signal to an invariant signal called a root signal.
Abstract: Necessary and sufficient conditions for a signal to be invariant under a specific form of median filtering are derived. These conditions state that a signal must be locally monotone to pass through a median filter unchanged. It is proven that the form of successive median filtering of a signal (i.e., the filtered output is itself again filtered) eventually reduces the original signal to an invariant signal called a root signal. For a signal of length L samples, a maximum of \frac{1}{2}(L - 2) repeated filterings produces a root signal.
793 citations
TL;DR: The Weighted Median Filter is described, a more general filter that enables filters to be designed with a wide variety of properties and the question of finding the number of distinct ways a class of filters can act is considered and solved for some classes.
Abstract: The median filter is well-known [1, 2]. However, if a user wishes to predefine a set of feature types to remove or retain, the median filter does not necessarily satisfy the requirements. A more general filter, called the Weighted Median Filter, of which the median filter is a special case, is described. It enables filters to be designed with a wide variety of properties. Particular cases of filter requirements are discussed and the corresponding filters are derived. The notion of a minimal weighted median filter, of a subclass that act identically, is introduced and discussed. The question of finding the number of distinct ways a class of filters can act is considered and solved for some classes.
789 citations
TL;DR: A new watermarking algorithm is presented: the method, which operates in the frequency domain, embeds a pseudo-random sequence of real numbers in a selected set of DCT coefficients, which is adapted to the image by exploiting the masking characteristics of the human visual system, thus ensuring the watermark invisibility.
743 citations