Easy interview questions in Computer Vision
RGB versus HSV color spaces
RGB mixes three light channels; HSV separates hue, saturation, value so color identity decouples from brightness.
What is the difference between lossy and lossless image compression?
This tests irreversible discard versus perfect reconstruction. A strong answer defines lossy as dropping detail, lossless as fully reversible, names JPEG, PNG, and chooses lossless for masters, lossy for web. Red flag: claiming lossless is always smaller.
Describe a grayscale histogram and its use in exposure and equalization
Tests pixel distribution intuition. A strong answer covers intensity bin counts, left or right clustering for exposure errors, and CDF-based redistribution for equalization. Red flag: calling equalization min-max stretching without cumulative mapping.
How would you implement a simple box blur on a grayscale image?
Iterate interior pixels, sum the N by N neighborhood, divide by kernel area, write to a new buffer.
What is an image histogram and how does histogram equalization improve contrast?
Define a histogram as pixel counts per intensity; explain equalization normalizes the CDF to spread intensities across the full range.
Harris corner detector and corner stability
Harris finds points where intensity changes strongly in all directions using the structure tensor of gradients; corners are well localized in two directions, unlike edges.
Image gradients, Sobel, and Canny
The gradient measures local intensity change in x and y; Sobel approximates it via convolution kernels; Canny uses gradient magnitude and direction plus non-max suppression and hysteresis.
Epipolar constraint for correspondence search
The match for p1 must lie on its epipolar line in the second image, reducing a 2D search to 1D; the relation is encoded by the fundamental matrix.
Disparity and depth in stereo vision
Disparity is the horizontal shift of a point between left and right images; depth is inversely proportional to disparity given baseline and focal length.
How a convolutional layer works
Learnable kernels slide over the input computing dot products, with stride controlling step size and padding controlling output size.
Max pooling versus strided convolution
Pooling downsamples and adds small translation invariance with no parameters; strided conv learns its downsampling but adds parameters.
Why CNNs need nonlinear activations like ReLU
ReLU introduces nonlinearity letting stacked layers model complex functions; without it any stack collapses to a single linear map.
Image classification versus object detection
Classification assigns one label to the whole image; detection localizes and labels multiple objects with bounding boxes and class scores.
Intersection over Union for detection
IoU is the area of overlap divided by the area of union of predicted and ground-truth boxes; a threshold decides true positives.
Mean Average Precision in object detection
AP is the area under the precision-recall curve per class; mAP averages AP over classes, and COCO also averages over IoU thresholds.
Semantic versus instance segmentation
Semantic segmentation labels each pixel by class but merges objects of the same class; instance segmentation also separates individual objects.
Sparse vs dense optical flow and Lucas-Kanade
Sparse flow tracks selected feature points, dense flow computes a vector per pixel; Lucas-Kanade solves brightness constancy in a local window assuming constant motion.
Design a tracking-by-detection tracker
Detect per frame, then associate boxes across frames by IoU or appearance using Hungarian matching, maintaining track ids.
How ViT and CNN process an image differently
A CNN slides local filters over the raw pixel grid; a ViT splits the image into patches, flattens and linearly embeds each into a token, adds positional embeddings, and feeds the sequence to…
Self-attention over image patches explained
Each patch projects to query, key, value; a patch's query is scored against all keys, softmax-normalized into weights, used to combine all values.
We are hiring for this. Every open role lists the topics its interview covers, so you can prepare for the real thing rather than guessing.
See open roles