1 / 6

Raster Filters

Explore the purpose, process, and design of raster filters to enhance image features. Learn neighborhood operations and feature detection using convolution and morphological approaches. Practice designing filters for specific image elements with hands-on examples.

nickyt
Download Presentation

Raster Filters

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Lecture 03-04: Neighborhood Operations Topics: Raster Filters References: Chapter 7 in Chrisman 2002, (pp. 169-194)

  2. Lecture 03-04: Neighborhood Operations Outlines 1. What is an image: An array of values – raster data layer (The Raster Values Figure) Pixel and spatial resolution Basic raster format (The Raster Format Figure) Raster layer, image, and picture Turn raster layer into image (picture) (User 3dMapper (www.terrainAnalytics.com) to demo it)

  3. 2. The purpose of raster filtering: To emphasize certain kind of info 3. The process of raster filtering: Convolution of a filter with the original image (The Convolution Figure) Basic issues: 1) neighborhood size (The Neighborhood Figure) 2) the filter (weight template, weight kernel) 3) methods used to compute the output value (The Convolution Figure) 4) the edge problem (The Edge Problem Figure)

  4. 4. Filter Design: 1) Types of filters: High pass filters (edge detectors) Low pass filters (average filters) 2) Design of filters: (specifying the weight kernel) The morphological approach: (1) the size (horizontal, plain-view) (2) shape: a) plain-view shape b) cross section shape (3) unbiased: Examples: (Valley and Ridge Filter Figures)

  5. 5. Detecting features: 1) The basic steps: a) determine the components b) design the filters for each components c) perform the computation for each component d) merge the components e) make a decision as to what is a valley 2) Examples: a) Detecting valleys: b) Detecting ridges:

  6. 6. Discussion: Filters are feature specific 7. For your practice: a) design a set of filters for valley of 3 pixel wide b) design a filter for detecting a flat mountain top with a size of 3 pixel on the each side c) do your homework using the following image and filters (The Homework Figure)

More Related