Computer Vision Engineer Interview Questions

1.1Image Representation: Color Spaces and Data Structures

1.2Fundamental Pixel Operations and Image Filtering

1.3Convolution and Kernel-Based Image Processing

1.4Image Enhancement Techniques: Histogram Equalization and Contrast Adjustment

2.1Edge Detection: Sobel, Canny, and Laplacian Operators

2.2Corner and Keypoint Detection: Harris, FAST, and SIFT

2.3Local Feature Descriptors: SIFT, SURF, and ORB

2.4Blob Detection and Connected Component Analysis

3.1Thresholding Methods: Binary, Otsu, and Adaptive Thresholding

3.2Morphological Operations: Erosion, Dilation, Opening, and Closing

3.3Clustering-Based Segmentation: K-Means and Mean Shift

3.4Contour Detection and Bounding Box Extraction

4.1Affine and Perspective Transformations

4.2Camera Intrinsics, Extrinsics, and the Pinhole Camera Model

4.3Image Warping, Interpolation, and Rectification

4.4Homography and Epipolar Geometry

5.1Convolutional Neural Networks: Architecture, Layers, and Operations

5.2Backpropagation, Gradient Descent, and Optimizer Selection

5.3Activation Functions, Regularization, and Avoiding Overfitting

5.4Transfer Learning and Fine-Tuning Pre-Trained Models

6.1Classification Networks: VGG, ResNet, Inception, and EfficientNet

6.2Region-Based Detection: R-CNN, Fast R-CNN, and Faster R-CNN

6.3Single-Stage Detectors: YOLO, SSD, and RetinaNet

6.4Anchor-Free Detection and Feature Pyramid Networks

7.1Fully Convolutional Networks and Encoder-Decoder Architectures

7.2U-Net and Medical Image Segmentation

7.3Instance Segmentation: Mask R-CNN and YOLACT

7.4Semantic Segmentation at Scale: DeepLab and Pyramid Scene Parsing

8.1Facial Recognition: Detection, Alignment, and Verification

8.2Human Pose Estimation and Keypoint Detection

8.3Video Action Recognition and Temporal Modeling

8.4Optical Flow, Motion Estimation, and Video Object Tracking

9.1Model Quantization, Pruning, and Knowledge Distillation

9.2Optimization for Mobile and Edge Devices: TensorFlow Lite and ONNX

9.3Inference Frameworks, Batch Processing, and Latency Optimization

9.4Monitoring, Versioning, and Continuous Model Improvement in Production

10.13D Reconstruction, Structure-from-Motion, and SLAM

10.2Point Cloud Processing and 3D Object Detection

10.3Multimodal Learning: Vision-Language Models and Sensor Fusion

10.4Vision for Autonomous Vehicles: Perception, Prediction, and Planning