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Learn Hog Computer Vision Tutorial, validate concepts with Hog Computer Vision MCQ Questions, and prepare interviews through Hog Computer Vision Interview Questions and Answers.
HOG (Histogram of Oriented Gradients) MCQ
Compute gradients, accumulate signed orientation votes per cell, concatenate block-normalized histograms for a fixed-length template.
Gradients
Gx, Gy
Cells
8×8 etc.
Blocks
Normalize
Pedestrian
Window
HOG descriptors
HOG summarizes local edge orientations in a dense grid. Block-wise contrast normalization makes the descriptor robust to illumination while preserving shape cues—classic for rigid pedestrian templates + linear SVM.
Why blocks?
L2 normalize over overlapping blocks of concatenated cell histograms to cancel local lighting gradients.
Pipeline
Gradients
Finite differences with optional Gaussian pre-smoothing; magnitude weights orientation votes.
Cells
Unsigned orientations binned (e.g., 9 bins over 0–180°) per cell.
Blocks & stride
2×2 cells per block with stride controls overlap and final dimensionality.
Modern use
CNNs largely replaced hand-tuned HOG+SVM, but HOG teaches gradient histogram ideas still used inside networks.
Sliding window
Fixed aspect window scans image; each window → one HOG vector → classifier score