Description: 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 Platform: |
Size: 3252 |
Author:wugang |
Hits:
Description: gaborconvolve - function for convolving each row of an image with 1D log-Gabor filters
％
％ Usage:
％ [template, mask] = createiristemplate(eyeimage_filename)
％
％ Arguments:-gaborconvolve- function for convolving each row of an image with 1D log-Gabor filters Usage: [template, mask] = createiristemplate (eyeimage_filename) Arguments: Platform: |
Size: 1024 |
Author:wpf |
Hits:
Description: 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 Platform: |
Size: 3072 |
Author:wugang |
Hits:
Description: 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 methods of edge detection involve convolving the image with an operator (a 2-D filter), which is constructed to be sensitive to large gradients in the image while returning values of zero in uniform regions. There is an extremely large number of edge detection operators available, each designed to be sensitive to certain types of edges. Variables involved in the selection of an edge detection operator include: Platform: |
Size: 438272 |
Author:Image |
Hits:
Description: 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 template, with an unknown signal to detect the presence of the template in the unknown signal. This is equivalent to convolving the unknown signal with a conjugated time-reversed version of the template. The matched filter is the optimal linear filter for maximizing the signal to noise ratio (SNR) in the presence of additive stochastic noise. Matched filters are commonly used in radar, in which a known signal is sent out, and the reflected signal is examined for common elements of the out-going signal. Pulse compression is an example of matched filtering. It is so called because impulse response is matched to input pulse signals. Two-dimensional matched filters are commonly used in image processing, e.g., to improve SNR for X-ray. Platform: |
Size: 1024 |
Author:何兴宇 |
Hits:
Description: 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. (Levels: 4)
2. Create directional kernels to detect edges at 0, 45, 90 and 135 orientations.
3. Convolve each image in the Gaussian pyramid with each orientation filter.
4. Combine the results of step 3 to create the Feature Map.
5. Dilate the resultant image using a sufficiently large structuring element (7x7 [1])
to cluster candidate text regions together.
6. Create final output image with text in white pixels against a plain black
background. Platform: |
Size: 2048 |
Author:Lee Kurian |
Hits:
Description: 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 function. At each point in the image, the result of the Prewitt operator is either the corresponding gradient vector or the norm of this vector. The Prewitt operator is based on convolving the image with a small, separable, and integer valued filter in horizontal and vertical directions and is therefore relatively inexpensive in terms of computations. On the other hand, the gradient approximation which it produces is relatively crude, in particular for high frequency variations in the image. The Prewitt operator was developed by Judith M. S. Prewitt. Platform: |
Size: 1024 |
Author:ali |
Hits:
Description: 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
% estimated and the true images, and utiliz Platform: |
Size: 4096 |
Author:pierovdz|
|
Hits:
Description: 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
% estimated and the tr Platform: |
Size: 4096 |
Author:pierovdz|
|
Hits:
Description: 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 Platform: |
Size: 4096 |
Author:pierovdz|
|
Hits:
Description: 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
% estimated and the true images Platform: |
Size: 4096 |
Author:pierovdz|
|
Hits:
Description: 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
% estimated and the tr Platform: |
Size: 4096 |
Author:pierovdz|
|
Hits:
Search - image convolving