Blobs are defined as patches within an image where the intensity satisfies some threshold. The fundamental statistics of these areas may be determined by this option. The data obtained includes the area, centroid location, spread, maximum extent and (x,y) correlation of each blob. Such data may be useful for determining length scales, anisotropy, particle positions etc.
The fundamental purpose of this option is to determine perturbations to a refractive index field. In most cases the refractive index field will be due to some stratifying agent (e.g. salt or heat) and a knowledge of the refractive index field can be used to infer the density field.
Calculate, as a function of position, the cross correlation between two images and display the result on a third buffer.
This option evaluates the Kolmogorov capacity (fractal dimension) using a box counting algorithm within a user defined window. The box sizes are selected automatically to give an optimal coverage of the length scales. The boxes are approximately square in real displayed coordinates (ie. they are higher than they are wide in pixel coordinates). An optional log-log plot of number of boxes verses length scale may be produced either on a buffer or a PostScript file.
This option is essentially similar to F except that instead of the boxes being nominally square, two calculations are made. In one calculation the boxes are the full height of the user defined window (the scales being obtained from varying width), while in the other they are the full width of the window. This option is useful for investigating the anisotropy of the length scales. The fractal dimension calculated by this approach should be one less than that for the box counting for an isotropic situation.
This option produces a scatter plot showing how the intensities on two images are related on a point by point basis. For each pixel, the intensities on one image are used as the horizontal coordinate, and the other axis the vertical coordinate.
This option determines a least squares curve which approximates the scatter plot of the intensities of two images. The intensities of the first image are considered an independent variable. For each such intensity in the first image, the mean intensity for the corresponding pixels in the second image is calculated and used as the dependent variable. Finally, a least squares fit relates the two.
This option provides another method for investigating the statistics of a specified threshold level. Statistics are produced for both horizontal and vertical scales. The statistics produced, and optionally plotted, are the total length above and below the threshold, the average length of each segment above and below the threshold and the standard deviation about this average.
This option determines the mean, rms or standard deviation of the intensity for each line and/or column within a window. This is equivalent to evaluating the marginal sums for the intensity. The data may optionally be written to file and/or plotted on a frame buffer as either a histogram or intensity bar.
This option is designed to give an estimate of the integral length scale of the mixing region between two bodies of fluid. Suppose the concentration of some dye at some point in the mixing region is C. In the unmixed regions the concentration is either 0 or 1. We then define the integral length scale as the integral of C_bar^m*(1-C_bar)^n along some path (either horizontal or vertical), where C_bar is the mean concentration in the direction normal to this path. Here m and n are user-specified positive values. The routine also calculates the integrals of C^m*(1-C)^n, C^m and (1-C)^n over the domain to give information about the molecular mixing fraction.
This option determines the moments of intensity for each line and/or column within a window. The data may optionally be written to file and/or plotted on a frame buffer as either a histogram or intensity bar.
This option provides a simple particle image velocimetry (PIV) system within DigImage. Functionally the computations are very similar to those performed by [S Shift to minimise differences], but offer both improved (subpixel) resolution and output in a form compatible with Trk2DVel.
This option provides a method of evaluating the local translation required to maximise the matching between two images. Two main techniques can be used: image differencing and cross correlation. For image differencing the ALU is used to subtract the two images, one subject to a simple translation, and evaluate the absolute difference (or the square of the difference) between the images. Each difference is smoothed using a fast low pass filter, before being compared pixel-by-pixel with the shifts already computed to determine which shift minimises the difference. Cross correlation is implemented in a similar manner, searching for the translation which maximises the correlation. Note that this option offers only pixel resolution for the translations. In contrast [P Particle image velocimetry] utilises the differencing technique to in a slightly different way to provide subpixel resolution.
T Track centroids in array of windows This option is designed to find the intensity centroid for an array of windows in a range of buffers and to write the data out to a file. This facility may be used for determining the motion of an array of entities provided they remain within the original window specified.