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To simulate the depth of field, each object, p i, is assigned a z position, z i, using a median filtered Perlin noise image, scaled to encompass the desired depth in the image.
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Additional post-processing stage using a median filter was investigated and proved to be useful in significantly reducing the errors of the produced disparity images.
Preprocessing of the image using a median filter.
Preprocessing of the image using a median filter for noise removal.
This finite state machine smooths the input probabilities given by the MLP using a median filter over a small window.
Previous to SVM training, it is crucial to preprocess each image where this procedure involves histogram equalization, filtering using a median filter, followed by the sharpen filter.
As with the previous approach (Section 3.4) input images to the weight calculation step are pre-filtered using a median filter.
Following the methodology of Bakker et al., (Bakker et al., 2011), we filtered the data using a median filter with a window size of 110.
In a first step, called image pre-processing, the quality of the image is improved using a median filter and a contrast enhancement technique.
The following are the steps in experiment 2 for compressed images: 1. Preprocessing of the image using a median filter 2.
The following are the steps in experiment 1 for uncompressed images: 1. Preprocessing of the image using a median filter for noise removal 2.
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