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The rule of similarity states that images that are similar to each other can be grouped together as being the same type of object or part of the same object. Therefore, the more similar two images or objects are, the more likely it will be that they can be grouped together. For example, two squares among many circles will be grouped together.
A standard test of creativity is to list as many novel uses as one can for an everyday object (e.g., a paper clip) in a limited time. Wiseman et al. found that participants who easily could see the image as either a rabbit or duck came up with an average of about 5 novel uses for their everyday item, while those who could not flip between ...
Ambiguous image: These are images that can form two separate pictures. For example, the image shown forms a rabbit and a duck. Ambigram: A calligraphic design that has multiple or symmetric interpretations. Ames room illusion An Ames room is a distorted room that is used to create a visual illusion. Ames trapezoid window illusion
Testing various clustering algorithms and analyzing their results to find a suitable match for our task (determining which modules are similar and possible candidates to be merged). Also contains a brief literature review of code similarity detection. List of possible candidates for improvement of clustering using better algorithms.
In order to evaluate the image quality, this formula is usually applied only on luma, although it may also be applied on color (e.g., RGB) values or chromatic (e.g. YCbCr) values. The resultant SSIM index is a decimal value between -1 and 1, where 1 indicates perfect similarity, 0 indicates no similarity, and -1 indicates perfect anti-correlation.
An accidental viewpoint (i.e. eccentric or fixed viewpoint) is a singular position from which an image can be perceived, creating either an ambiguous image or an illusion. The image perceived at this angle is viewpoint-specific, meaning it cannot be perceived at any other position, known as generic or non-accidental viewpoints.
Split PDF files in a number of ways: After every page, even pages or odd pages; After a given set of page numbers; Every n pages; By bookmark level; By size, where the generated files will roughly have the specified size; Rotate PDF files where multiple files can be rotated, either every page or a selected set of pages (i.e. Mb).
These differences are summed to create a simple metric of block similarity, the L 1 norm of the difference image or Manhattan distance between two image blocks. The sum of absolute differences may be used for a variety of purposes, such as object recognition, the generation of disparity maps for stereo images, and motion estimation for video ...
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