The Potential of IMAGE Processing in Swift for Developers
summary
On-device preprocessing in Swift uses Core Image plus a small custom Metal kernel to turn messy camera strokes into MNIST-style 20×20 binary inputs, raising demo recognition from about 25% to near 100% confidence. The pipeline chains a custom metal kernel for grayscale, a CIColorThreshold (Otsu’s method) step, and a CIColorInvert before feeding the Apple drawing classification model; it uses CIImage, CIFilter and CIContext and demonstrates concrete accuracy gains on the sample dataset.
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chapters (17)
- 0:00Intro
- 1:18Main ideas and concepts
- 3:18Digital images
- 9:32Available tools
- 11:09Filters
- 13:05CIImage
- 14:03CIFilter
- 14:57CIContext
- 16:00Kernel
- 17:30How to use Core Image Filters
- 18:58Pay attention
- 20:15Machine Learning & Image Processing
- 22:25MNIST Specifics
- 23:50How to Binarise an image
- 25:12Otsu’s Method
- 26:35Custom Kernel
- 31:50The result
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