Abstract
In this research a proposed technique is used to enhance the frame difference technique performance for extracting moving objects in video file. One of the most effective factors in performance dropping is noise existence, which may cause incorrect moving objects identification. Therefore it was necessary to find a way to diminish this noise effect. Traditional Average and Median spatial filters can be used to handle such situations. But here in this work the focus is on utilizing spectral domain through using Fourier and Wavelet transformations in order to decrease this noise effect. Experiments and statistical features (Entropy, Standard deviation) proved that these transformations can stand to overcome such problems in an elegant way.
Highlights
Image processing techniques can be categorized into spatial and frequency domain techniques
Image filtering can be accomplished in such way
There are two choices to do image filtering, One of them is in spatial domain by convolving the image under consideration with an adequate window, while filtering in frequency domain occurs through multiplying the transformed image with an appropriate low pass filter (1)
Summary
Image processing techniques can be categorized into spatial and frequency domain techniques. There are two choices to do image filtering, One of them is in spatial domain by convolving the image under consideration with an adequate window (basis image), while filtering in frequency domain occurs through multiplying the transformed image with an appropriate low pass filter (in this research a circle of “ones” with appropriate radius) (1). Examples of spatial domain techniques are mean (average), median, Gaussian....etc. Spatial domain techniques produce different resulted output signals (images in this research context). Filtering images in spatial domain are more computation consumer (2). It is preferable to try the other techniques (frequency). Examples of such techniques are Wavelet, Fourier, Walsh...etc. Spatial domain techniques depend directly on pixel intensity levels, whereas frequency domain techniques depend on frequency coefficients (2). Transformation in spatial domain is done pixel in one domain to a pixel in other
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