Data Visualization - Raster Graphics Or BitMap Image
Table of Contents
1 - About
Raster graphics (also called bitmap) represents an image, photo through a dot matrix data structure.
A raster is technically characterized by the:
- width and height of the image in pixels
- and by the number of bits per pixel (a color depth, which determines the number of colors it can represent)
A bitmap is a single-bit raster.
The word raster comes from the Latin word rastrum, or rake. Old cathode ray tubes created images on screens by literally drawing one line at a time, raking each across the screen.
2 - Articles Related
3 - Tools
- http://gimp.org/ - Bitmap Editor
3.1 - Diff
3.2 - Similarity
- SSIM: Structural Similarity Index is one method to measure the similarity between two images. Result of comparison between two images using SSIM, gives a value between 0 and 1. Closer to 1 implies more similarity.
- Feature matching, SURF, SIFT, FAST and so on. Find the number of match between the two images.
- Cross Correlation - a simple metrics which you can use for comparison of image areas. It's more robust than the simple euclidean distance but doesn't work on transformed images and you will again need a threshold.
- Histogram comparison - if you use normalized histograms, this method works well and is not affected by affine transforms. The problem is determining the correct threshold. It is also very sensitive to color changes (brightness, contrast etc).
- Detectors of salient points/areas - such as MSER (Maximally Stable Extremal Regions), SURF or SIFT. These are very robust algorithms and they might be too complicated for your simple task. Good thing is that you do not have to have an exact area with only one icon, these detectors are powerful enough to find the right match.
3.3 - Properties / Feature
Local features provide a limited set of well localized and individually identifiable anchor points. Their location must be determined accurately and in a stable manner over time. They can be used as a robust image representation, that allows to recognize objects or scenes without the need for segmentation. Features need to be described, such that they can be identified and matched.
- Local features are covariant.
- points (interest point)
- edge segment
- small image patches
- edges detected in aerial images often correspond to roads;
- blob detection can be used to identify impurities in some inspection task;
- matching or tracking applications,
- camera calibration or 3D reconstruction.
- image alignment or mosaicing
- object recognition
- scene classification,
- texture analysis,
- To localize features in images, a local neighborhood of pixels needs to be analyzed
- spatial extent means the local neighborhood of pixels (any subset of the image)