https://wiki.hoku.emelibrary.org/ 2026-06-26T21:58:09+00:00 https://wiki.hoku.emelibrary.org/ https://wiki.hoku.emelibrary.org/_media/wiki:logo.png text/html 2025-06-26T09:43:33+00:00 Anonymous (anonymous@undisclosed.example.com) https://wiki.hoku.emelibrary.org/intermediate_level:algorithms_available_in_cupboard?rev=1750931013&do=diff Intermediate Level – Algorithms Available in Cupboard The Intermediate Level introduces new modules that enhance the ability to extract, enhance, and display low contrast information, while retaining all of the modules from the Basic Level. This level also introduces a new set of geometrically-shaped modules, located at the beginning of the Cupboard. These modules are elements of a flowchart, that can be combined to execute a job on a set of images, or on a set of directories. text/html 2026-04-28T12:53:40+00:00 Anonymous (anonymous@undisclosed.example.com) https://wiki.hoku.emelibrary.org/intermediate_level:batch_processing_with_flowcharts?rev=1777380820&do=diff Flowcharts Flowcharts consist of a collection of the geometrically-shaped modules. Together, they can execute a Star module several times on different images. Every flowchart begins with the “start” module and ends with the “end” module. In between, there is at least one “exec” module. To set the arguments of these modules, you left-click inside them to bring up their argument dialog boxes, as you do with any module. Attached to the “exec” module is a Star module that will be executed by the fl… text/html 2026-02-14T20:41:09+00:00 Anonymous (anonymous@undisclosed.example.com) https://wiki.hoku.emelibrary.org/intermediate_level:ica_independent_components_analysis?rev=1771101669&do=diff ICA – Independent Components Analysis [ ] What it does. ICA reads a multiband image, computes an Independent Components Analysis transform from a user‑selected ROI, then applies that transform to the full input image. Use ICA when you want to separate statistically independent sources (e.g., underwriting vs. overtext/substrate) that may be mixed across bands. text/html 2025-06-26T17:45:44+00:00 Anonymous (anonymous@undisclosed.example.com) https://wiki.hoku.emelibrary.org/intermediate_level:imagemath?rev=1750959944&do=diff ImageMath The “Image”Math” module reads in two images, an “input” image and an “operation” image. The two are combined mathematically. For example, in the image below, the links show that the image on the left of the ImageMath module is the “input” image and the image on the top is the “op” image. text/html 2025-06-26T17:47:34+00:00 Anonymous (anonymous@undisclosed.example.com) https://wiki.hoku.emelibrary.org/intermediate_level:mnf_minimum_noise_fraction?rev=1750960054&do=diff MNF – Minimum Noise Fraction [ ] Minimum Noise Fraction – MNF – looks for differences in variance in the multispectral data, similar to applying a PCA transform. The difference is that MNF whitens the data before applying the Principal Components Analysis transform. text/html 2025-06-26T17:46:23+00:00 Anonymous (anonymous@undisclosed.example.com) https://wiki.hoku.emelibrary.org/intermediate_level:normalize?rev=1750959983&do=diff Normalize [ ] ”Normalize” will convert an input image to 8-bit unsigned integers, while adjusting its contrast. This algorithm will enhance low contrast information. At each pixel, Normalize looks at the “statistics” of the rectangular region around each pixel and linearly stretches its value to maximize the visibility. Specifying a ”window” size is required, either a width and a height, or a single number, the side of a square. text/html 2025-06-26T17:47:12+00:00 Anonymous (anonymous@undisclosed.example.com) https://wiki.hoku.emelibrary.org/intermediate_level:sam_spectral_angle_map?rev=1750960032&do=diff SAM – Spectral Angle Map [ ] Spectral Angle Map – SAM – compares a reference region of an image with every other pixel in the image. The output image represents how closely the spectrum of each pixel in the image matches the spectrum of the reference region. text/html 2025-06-26T17:46:59+00:00 Anonymous (anonymous@undisclosed.example.com) https://wiki.hoku.emelibrary.org/intermediate_level:sid_spectral_angle_map?rev=1750960019&do=diff SID – Spectral Angle Map [ ] Spectral Information Divergence – SID – measures the spectral divergence between each pixel of the input image and one or more image references of the same spectral dimension, i.e. the same number of spectral bands, by measuring the distance between the probability measures of the two spectra. text/html 2025-06-26T17:40:55+00:00 Anonymous (anonymous@undisclosed.example.com) https://wiki.hoku.emelibrary.org/intermediate_level:slope?rev=1750959655&do=diff Slope Slope will fit a curve to the spectral variation of an image over a few wavelengths of illumination. Slope reads in a single multiband image. The wavelengths of each band are specified in the “wavelengths” entry. There are three different curves that can be fit to the spectral data, linear, parabola, and cubic. A ”linear” fit can output two bands with the parameters of “slope” and “intercept”. A parabolic fit will output three parameters, and a cubic fit will output 4. The default is to …