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gaborconvolve
- gaborconvolve - function for convolving each row of an image with 1D log-Gabor filters ％ ％ Usage: ％ [template, mask] = createiristemplate(eyeimage_filename) ％ ％ Arguments:
gaborconvolve
- gaborconvolve.m Code for convolving an image with a bank of log-Gabor filters. A pre-processing step for texture analysis, feature detection and classification
gaborconvolve
- gaborconvolve - function for convolving each row of an image with 1D log-Gabor filters ％ ％ Usage: ％ [template, mask] = createiristemplate(eyeimage_filename) ％ ％ Arguments:-gaborconvolve- function for convolvin
gaborconvolve
- gaborconvolve.m Code for convolving an image with a bank of log-Gabor filters. A pre-processing step for texture analysis, feature detection and classification
hilbert2
- HILBERT - Compute Hilbert transform y of x. The Hilbert transform is computed by convolving x with a windowed (approximate) version of the ideal Hilbert transformer.-HILBERT- Compute Hilbert transform y of x. The H
Various_EdgeDetection
- Edge detection refers to the process of identifying and locating sharp discontinuities in an image. The discontinuities are abrupt changes in pixel intensity which characterize boundaries of objects in a scene. Classical
hilbert
- The Hilbert transform is computed by convolving x with a windowed (approximate) version of the ideal Hilbert transformer.
matched-filter
- 指滤波器的性能与信号的特性取得某种一致，使滤波器输出端的信号瞬时功率与噪声平均功率的比值最大.即当信号与噪声同时进入滤波器时，它使信号成分在某一瞬间出现尖峰值，而噪声成分受到抑制。-In signal processing, a matched filter (originally known as a North filter[1]) is obtained by correlating a known signal, or temp
Edge-based-text-region-extraction-from-natural-im
- The basic steps of the edge-based text extraction algorithm are given below 1. Create a Gaussian pyramid by convolving the input image with a Gaussian kernel and successively down-sample each direction by half. (Lev
prewwit
- The Prewitt operator is used in image processing, particularly within edge detection algorithms. Technically, it is a discrete differentiation operator, computing an approximation of the gradient of the image intensity f
hola mundo2
- hat the image I was created by convolving a true image with a % point-spread function PSF and possibly by adding noise. The algorithm % is optimal in a sense of least mean square error between the % estima
hola mundo2
- hat the image I was created by convolving a true image with a % point-spread function PSF and possibly by adding noise. The algorithm % is optimal in a sense of least mean square error between the % estima
hola mundo2
- hat the image I was created by convolving a true image with a % point-spread functionrithm % is optimal in a sense of least mean square error between the % estimated and the true images, and utiliz
hola mundo2
- hat the image I was created by convolving a true image with a % point-spread function PSF and possibly by adding noise. The algorithm % is optimal in a sense of least mean square error between the % estima
hola m
- hat the image I was created by convolving a true image with a % point-spread function PSF and possibly by adding noise. The algorithm % is optimal in a sense of least mean square error between the % estima