Quiz Computer Vision Basics 1 / 15 What is the process of dividing an image into meaningful regions called? Image enhancement Image segmentation Image restoration 2 / 15 Which of the following is NOT a core component of computer vision? Image acquisition Image processing Natural language processing 3 / 15 What is the term for identifying and classifying objects in an image? Object tracking Object recognition Image segmentation 4 / 15 Which technique is used to improve image quality? Image enhancement Image segmentation Feature extraction 5 / 15 What is the process of detecting changes between consecutive frames in a videocalled? Object tracking Optical flow Image registration 6 / 15 Which of the following is NOT a common feature extraction method? Histogram of Oriented Gradients (HOG) Scale-Invariant Feature Transform (SIFT) Principal Component Analysis (PCA) 7 / 15 What is the term for the smallest unit of a digital image? Pixel Voxel Bit 8 / 15 Which technique is used to create a 3D model from 2D images? Structure from Motion (SfM) Image stitching Image registration 9 / 15 What is the process of aligning multiple images to a common coordinate systemcalled? Image stitching Image registration Image rectification 10 / 15 Which of the following is NOT a common challenge in computer vision? Overfitting Lighting conditions Occlusions 11 / 15 What is the term for the process of teaching a computer to learn from data withoutexplicit programming? Machine learning Deep learning Artificial intelligence 12 / 15 Which type of neural network is commonly used for image recognition? Recurrent Neural Network (RNN) Convolutional Neural Network (CNN) Long Short-Term Memory (LSTM) 13 / 15 What is the term for the process of reducing the dimensionality of data? Feature extraction Dimensionality reduction Feature selection 14 / 15 Which of the following is NOT a common application of computer vision? Financial analysis Autonomous vehicles Medical image analysis 15 / 15 What is the term for the evaluation metric that measures the average precision ofa model? Mean Average Precision (mAP) F1-score Accuracy Your score isThe average score is 25% 0% Restart quiz AI Application Matchup Match the AI computer vision application with the appropriate real-worlduse case. 1 / 15 A self-driving car needs to identify pedestrians, traffic signs, and other vehicles. Image segmentation Object detection Image classification 2 / 15 A doctor wants to analyze an MRI scan to detect tumors. Image enhancement Image segmentation Object tracking 3 / 15 A retailer wants to count the number of people in a store. Object detection Image segmentation Image classification 4 / 15 A security system needs to recognize faces for access control. Image segmentation Facial recognition Object tracking 5 / 15 A manufacturing plant wants to inspect products for defects. Object detection Image segmentation Image enhancement 6 / 15 A scientist wants to track the movement of cells in a microscope video. Object detection Image segmentation Object tracking 7 / 15 A photographer wants to stitch multiple images together to create a panorama. Image segmentation Image stitching Object tracking 8 / 15 A meteorologist wants to analyze satellite images to predict weather patterns. Image segmentation Image classification Object tracking 9 / 15 A social media platform wants to automatically add captions to images. Image segmentation Object detection Image captioning 10 / 15 An autonomous drone needs to navigate through a forest. Object detection Image segmentation Image classification 11 / 15 A surgeon wants to overlay 3D models of organs on a patient during surgery. Image segmentation Augmented reality Object tracking 12 / 15 A marketing team wants to analyze customer behavior in a store. Object tracking Image segmentation Image classification 13 / 15 A self-driving car needs to estimate the distance to other vehicles. Depth estimation Object detection Image segmentation 14 / 15 A historian wants to restore old, damaged photographs. Image segmentation Image restoration Image restoration 15 / 15 A biologist wants to identify different types of plants in an image. Image classification Image segmentation Object detection Your score isThe average score is 50% 0% Restart quiz Computer Vision Terminology Match: Challenge Mode Match the term with its correct definition. 1 / 15 Homography: A robust estimation algorithm for fitting models to data. A mathematical representation of the relationship between two images. A technique for improving image contrast. 2 / 15 Superpixel: A group of pixels with similar characteristics. A type of image filter. A feature descriptor for object detection. 3 / 15 Epipolar Geometry: A technique for detecting corners in an image. The geometric relationship between corresponding points in two images. A method for measuring the distance between objects in an image. 4 / 15 Level Set: An implicit representation of curves and surfaces. A type of image filter. A feature descriptor for object recognition. 5 / 15 SIFT: A scale-invariant feature descriptor. A technique for image enhancement. A type of neural network layer. 6 / 15 RANSAC: A robust estimation algorithm for fitting models to data. A feature descriptor for image matching. A technique for image segmentation. 7 / 15 Graph Cuts: An optimization technique for image segmentation. An optimizatioA feature extraction method. A type of neural network layer. 8 / 15 Mean Shift: A clustering algorithm for image segmentation. A feature extraction method. A technique for image enhancement. 9 / 15 Lucas-Kanade: An optical flow algorithm. A feature detection method. A technique for image segmentation. 10 / 15 HOG: A histogram of oriented gradients for object detection. A color space representation. A feature extraction method for texture analysis. 11 / 15 Photometric Stereo: A method for recovering the 3D shape of an object from multiple images. A technique for image compression. A type of camera calibration method. 12 / 15 Semantic Segmentation: Assigning semantic labels to each pixel in an image. Detecting objects within an image. Enhancing image edges. 13 / 15 Instance Segmentation: Identifying and segmenting individual objects in an image. Classifying images into different categories Improving image resolution. 14 / 15 Harris Corner Detector: A technique for detecting corners in an image. A method for image registration. A type of feature descriptor. 15 / 15 Chamfer Distance: A distance metric between shapes. A type of image filter. A feature descriptor for object recognition. Your score isThe average score is 50% 0% Restart quiz
