Impact of Different Kernels on Image 🎯
Ever wondered how algorithms actually "looks" at images? This is it.
Same dog photo → 14 different convolution kernels → 14 completely different perspectives
What you're seeing:
Blur Filters - The dog becomes a soft, dreamy version of itself. Fur details vanish, but noise disappears too.
Sharpen - Every whisker pops. Every fur strand screams for attention. But so does every imperfection.
Sobel X & Y - The image splits into geometry. Sobel X sees only vertical boundaries (ears standing tall), Sobel Y sees only horizontal lines (collar, mouth). The dog becomes directional data.
Laplacian - The photo transforms into a pencil sketch. Pure edges, no fill. This is what "outline detection" literally looks like.
Emboss - Suddenly the dog looks carved in stone, with artificial shadows creating depth that never existed.
Gabor Filters - These are texture hunters. Rotate them (0°, 45°, 90°) and watch different fur patterns light up like a scanner sweeping across orientations.
The mind-blowing part?
CNNs learn these patterns automatically. These kernels are literally what the first layer of a CNN "sees" before it understands "dog."
#ComputerVision #MachineLearning #DeepLearning #AI #ImageProcessing #NeuralNetworks #Python #DataScience #CNN #EdgeDetection