![]() ![]() In the past, Up-resing: Increasing the size of an image. Denoise: Removing unwanted pixels from an image. So, let’s talk about Figure 4 – Creating Seamless Textures these cases. ![]() ![]() In each of these cases, developers are working to apply machine learning to use the data available to help categorize, classify, and analyze the pixels on images (or frames in a video’s case) to determine how to adjust them. Tasks include up-resing, denoising, tiling, perspective correction, and object-aware/content fill. With the ability to accept infinite levels of information, no requirement for sleep, never aging, becoming exhausted, or dying, machine learning has extraordinary advantages. The computer will attempt to categorize and classify data, making their decision based on that input. For example, in the scenario with the ten people I provided, we could limit the computer to only having the gender of the people or input everything from what types of films the people like to anomalies in their DNA. A key strength of machine learning is the computer’s ability to accept and review near-infinite levels of data. The computer will attempt to accomplish these tasks using complex algorithms. We present a problem for the computer: determine which shirt ten different people should be wearing. Five have red shirts, and five have blue shirts based on various characteristics of the people. I will present a simple (perhaps crude) explanation of the machine learning process.
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