IGOR Pro 6.2使用方法详细介绍-睿驰整理 联系客服

发布时间 : 星期三 文章IGOR Pro 6.2使用方法详细介绍-睿驰整理更新完毕开始阅读1663f2ef856a561252d36f4f

北京环中睿驰科技有限公司 http://www.reahsoft.com.cn

Page layouts

A page layout, or layout for short, is a type of window that you can use to compose pages containing:

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graphs tables

annotations (textboxes and legends) pictures

drawing elements (lines, arrows, rectangles, polygons, etc.)

Each layout represents one page. You can have as many layouts as memory allows. Here is an example of a layout window.

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北京环中睿驰科技有限公司 http://www.reahsoft.com.cn A page layout has a number of layers. One layer, the layout layer, is for graphs, tables, annotations and pictures. The other layers are for drawing elements. Here are the notable features of page layouts.

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You can combine graphs, tables, pictures, annotations and drawing elements. Graphs, tables and legends in layouts are updated automatically. Complex graphs can be quickly and smoothly positioned. Layouts print at the full resolution of the printer.

You can export all or part of a layout to another program. There are two ways to add a graph or table to the layout layer:

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By creating a graph or table object. An object is a representation of a separate standalone graph or table window.

By creating an embedded graph or table subwindow. A subwindow is a self-contained graph or table embedded in a layout window.

2.4 图形处理方法

Image Processing

IGOR Pro contains a full set of operations and functions for scientific image analysis applications which make it an ideal cross-platform tool for image acquisition, display and processing.

Image acquisition can be as simple as loading multi-dimensional data from disk file or as complicated as using an XOP to grab live video frames to disk (see XOP Toolkit for information on creating your own XOP). In both cases the images can be displayed on the screen for visual inspection and analysis or they could be automatically analyzed without user intervention. The processing and analysis stage depends on the nature of the images and the information of interest. The main component of the image processing tools are the ImageXXX

operations which are supplemented by the image processing procedure files. The latter are combined as the Image Processing Package which you can load from Analysis menu. In addition to the dedicated ImageXXX operations you can also take advantage of general analysis functions such as FFT and curve fitting in image processing applications. Rounding up the list of built-in operations is MatrixOP which provides efficient means for formulating and performing mathematical operations on images.

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北京环中睿驰科技有限公司 http://www.reahsoft.com.cn Image display can be as simple as placing an RGB image in a graph window or as complicated as creating an overlay of multiple images combined with contour lines and legend. Being able to display images in false color or using a non-linear level mapping is sometimes helpful when trying to visually analyze images. The conventional approach to image processing involves the following steps: (1) image transformations and color conversions where the acquired image is converted into standard form in colorspace and in range.

(2) Image filtering (cleaning up the image to improve S/N ratio) can be accomplished using localized filters or mathematical transforms.

(3) Threshold operation to convert the image from a gray-scale to a binary form.

(4) Morphological filtering usually follows the threshold operations but some morphological operations can actually precede the threshold step. Typical morphological filters include: erosion/dilation, opening/closing, tophat and watershed.

(5) Particle analysis is the operation where the filtered binary image is analyzed by quantifying various spatial properties of different \regions) in the image. The spatial measurements include location, area, perimeter and moments for calculating a fitting ellipse.

Image Transforms

Image transforms can be simple arithmetic operations on images or complex mathematical operations which convert images from one representation to another.

Mathematical Operations include simple image arithmetic, Fourier, fast Hartley transform, Hough transform and Radon transform.

Histogram Modification include histogram equalization and adaptive histogram equalization.

Image Interpolation includes various methods for scaling, Kriging, image warping and radial aberration correction.

Image Registration is a tool for registering two 2D or 3D similar images and finding an affine transformation that can be used to convert one into the other. The operation is suitable for registering medical images of the same object.

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北京环中睿驰科技有限公司 http://www.reahsoft.com.cn Background Removal is a process to correct an image for non-uniform background or non-uniform illumination.

Image Rotation is a simple tool to rotate an image about its center by the specified number of degrees.

Mathematical Transformations

Mathematical transformations of images may be as simple as image arithmetic or as complex as an iterating Fourier transform. You can handle most image arithmetic by executing IGOR commands. For example, to subtract a background image:

Duplicate noisyImage,outputImage

outputImage=inputImage-backgroundImage

When working with images that are 8 or 16 bit per pixel you can perform level mapping using

outputImage=LUT[inputImage[p][q]]

here LUT is a lookup table for the mapping. For example, LUT can be used for Gamma correction using an expression like LUT=p^(1/Gamma)

Most arithmetic operations are performed more efficiently using MatrixOP.

Fourier Transforms

The Fast Fourier Transform (FFT) can be used to decompose a grayscale image into its spatial frequency components or to perform efficient 2D convolutions and correlations (RGB images are usually handled on a channel by channel basis).

In the following example we illustrate simple FFT filtering. We created an image that consists of a one dimensional slow quadratic ramp and added single frequency sinusoidal noise. The filter consists of a 2D constant wave with a single null pixel. The filtering consists of a single command line: MatrixOP/o filtered=IFFT(filter*FFT(inputImage,2),3)

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