[New] 1300+ Computer Vision Interview Practice Questions

Description

Unlock the world of computer vision with our comprehensive course titled “Master Computer Vision: 1300+ Interview Questions & Practice.” This meticulously crafted program offers over 1300 practice questions that span all levels of difficulty—beginner, intermediate, and advanced—across critical categories such as image processing fundamentals, deep learning techniques, object detection methods, and more.

Throughout this course, you will engage with topics including convolutional neural networks (CNNs), image segmentation strategies, real-time vision systems, and generative models like GANs. Each section is designed not only to test your knowledge but also to deepen your understanding through practical applications and real-world scenarios.

By completing this course, you will gain confidence in your ability to tackle complex computer vision problems and prepare effectively for technical interviews. Whether you are aiming for a career in artificial intelligence or simply wish to enhance your skill set, our course provides the resources you need to succeed.

These practice tests cover:

1. Fundamentals of Image Processing

• Image representation (pixels, RGB, grayscale)

• Filters (blur, sharpening, edge detection)

• Histogram and contrast adjustments

• Thresholding (binary, Otsu’s method)

• Morphological operations (erosion, dilation, opening, closing)

2. Computer Vision Basics

• Convolutional filters and kernels

• Image transformations (rotation, translation, scaling)

• Interpolation techniques (bilinear, bicubic)

• Color spaces (RGB, HSV, Lab, etc.)

• Contours and shape detection

• Hough Transform (line and circle detection)

• Feature extraction (SIFT, SURF, ORB)

3. Deep Learning for Computer Vision

• Convolutional Neural Networks (CNNs)

  • Architecture (Conv layers, Pooling, Activation functions)

  • Famous CNN architectures (AlexNet, VGG, ResNet, etc.)

• Backpropagation and optimization techniques (Gradient Descent, Adam)

• Transfer Learning

• Fine-tuning pre-trained models

• Activation functions (ReLU, Leaky ReLU, Softmax)

• Loss functions (Cross-Entropy, MSE)

• Batch Normalization and Dropout

4. Object Detection and Localization

• Sliding Window Technique

• Region-based CNNs (R-CNN, Fast R-CNN, Faster R-CNN)

• YOLO (You Only Look Once)

• SSD (Single Shot MultiBox Detector)

• Anchor Boxes, Intersection over Union (IoU)

• Non-Max Suppression (NMS)

5. Image Segmentation

• Threshold-based segmentation

• Watershed Algorithm

• Edge detection-based segmentation

• Region Growing

• Deep learning-based segmentation (Fully Convolutional Networks, U-Net, Mask R-CNN)

• Semantic Segmentation vs Instance Segmentation

6. Optical Flow and Motion Analysis

• Optical flow algorithms (Lucas-Kanade, Farneback)

• Background subtraction

• Tracking algorithms (Kalman Filter, Mean-Shift, CAMShift)

• Object tracking with Deep Learning (Siamese Networks, DeepSORT)

7. 3D Computer Vision

• Depth Estimation (Stereo Vision, Structured Light)

• Epipolar Geometry (Fundamental Matrix, Essential Matrix)

• Camera Calibration

• 3D Reconstruction (Structure from Motion, Multiview Stereo)

• Point Clouds, 3D meshes

• LiDAR data processing

8. Face Detection, Recognition, and Pose Estimation

• Viola-Jones algorithm for face detection

• Haar cascades and HOG (Histogram of Oriented Gradients)

• Deep Learning-based face detection (MTCNN, SSD for faces)

• Facial landmark detection

• Face Recognition techniques (Eigenfaces, Fisherfaces, LBPH)

• Deep learning-based face recognition (FaceNet, VGGFace)

• Pose Estimation (OpenPose, PnP problem)

9. Generative Models and Image Synthesis

• Autoencoders and Variational Autoencoders (VAE)

• Generative Adversarial Networks (GANs)

• Super-resolution techniques

• Image-to-image translation

10. Time-Series in Computer Vision (Video Analysis)

• Action recognition

• Video frame segmentation

• Video classification (CNN + LSTM architecture)

• Temporal Convolutional Networks (TCN)

• Spatio-temporal feature extraction

11. Optimization Techniques

• Hyperparameter tuning (learning rate, momentum)

• Techniques to avoid overfitting (Dropout, Data Augmentation)

• Early stopping, learning rate schedules

• Model quantization and pruning for efficiency

12. Edge AI and Embedded Vision

• Running vision models on embedded systems (NVIDIA Jetson, Raspberry Pi)

• Model compression (Quantization, Pruning)

• ONNX and TensorRT optimizations

• Efficient architectures (MobileNet, SqueezeNet, ShuffleNet)

13. Image Annotation Tools and Data Preparation

• Manual annotation vs automatic annotation

• Tools like LabelImg, CVAT

• Data preprocessing (augmentation, normalization)

• Synthetic data generation

14. Popular Computer Vision Libraries

• OpenCV (image processing, object detection)

• Dlib (face detection, object tracking)

• TensorFlow/Keras (deep learning)

• PyTorch (deep learning)

• Scikit-image (image processing)

15. Real-Time Vision Systems

• Real-time object detection

• Frame rate optimization

• Video stream processing (OpenCV, GStreamer)

• GPU vs CPU processing for real-time applications

16. Model Evaluation Metrics

• Precision, Recall, F1-score

• Accuracy, Confusion Matrix

• Intersection over Union (IoU) for object detection

• Mean Average Precision (mAP)

• Pixel Accuracy and Mean IoU for segmentation

• Receiver Operating Characteristic (ROC) Curve, AUC

17. Explainability and Interpretability

• Visualizing CNN layers and filters

• Grad-CAM, Layer-wise Relevance Propagation (LRP)

• SHAP, LIME for interpretability in vision models

• Bias and fairness in computer vision models

Join us on this exciting journey into the realm of computer vision! With lifetime access to updated materials and a supportive community of learners, you will be well-equipped to take on challenges in this dynamic field. Enroll now and start transforming your understanding of computer vision today!

Embrace the challenge—your journey into the fascinating world of computer vision begins here!