Computer Vision
30 bites tagged Computer Vision — interview questions with model answers, and 60-second explainers.
Walk me through Canny edge detection and why it beats Sobel thresholding
Tests multi-scale edge detection and noise robustness versus raw gradient thresholding. Strong answer lists Gaussian blur, Sobel gradients, non-maximum suppression, double thresholding, hysteresis. Red flag: calling it blurred Sobel without hysteresis or NMS.
How does filter separability optimize Gaussian blur and its complexity?
This tests if you know a 2D Gaussian separates into two 1D convolutions. A strong answer gives complexity as O(N^2 K^2) dropping to O(N^2 K) for an N-by-N image and K-by-K kernel. A red flag is claiming all kernels are separable or omitting dimensions.
U-Net: Segmentation with Less Data
U-Net retrofits fully convolutional networks to segment images precisely with fewer training examples. It runs a 512 by 512 frame in under a second on a 2015 GPU, fitting latency-sensitive pipelines.
FLANN Matcher for Feature Correspondence
OpenCV's FLANN matcher pairs query and train descriptors to find cross-image feature correspondences as an alternative to Brute-Force. Engineers often assume FLANN shares Brute-Force's normType and crossCheck parameters, causing silent configuration errors…
FPGA in Computer Vision
An FPGA is reconfigurable silicon wired into a custom digital circuit rather than programmed as instructions, letting a vision pipeline like demosaicing and feature extraction run in dedicated hardware with low latency and high throughput per watt.
HSL and HSV: Intuitive Ways to Represent RGB Color
HSL and HSV are not new colors, but new ways to organize RGB. Think of them as cylindrical coordinates for color, making it easier for humans to select shades. You see them in every color picker.
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