Computer Vision Techniques

What is the difference between image classification and object detection in computer vision?

• Image classification identifies the color of the objects in the image.

• Object detection only works on black and white images.

• Image classification and object detection are the same thing.

• Object detection identifies and localizes multiple objects within an image.

Explain the concept of feature extraction in computer vision.

• Identifying and extracting irrelevant information from an image

• Enhancing the resolution of an image

• Identifying and extracting important information or features from an image

• Converting images to text

What are some common computer vision techniques used for image segmentation?

• Thresholding, Edge detection, Region-based segmentation, Clustering

• Blurring, Noise reduction, Histogram equalization, Template matching

• Feature extraction, Object recognition, Depth estimation, Motion detection

• Color space conversion, Image resizing, Rotation, Cropping

Discuss the concept of edge detection in computer vision and its applications.

• Edge detection is used in applications such as image segmentation, object recognition, and feature extraction.

• Edge detection is not applicable in computer vision

• Edge detection is only used for blurring images

• Edge detection is primarily used for color correction in images

Explain the working principle of Convolutional Neural Networks (CNN) in computer vision.

• CNNs use multiplication to extract features from input images.

• CNNs use division to extract features from input images.

• CNNs use convolution to extract features from input images.

• CNNs use addition to extract features from input images.

Discuss the concept of optical character recognition (OCR) in computer vision.

• OCR is used to convert physical documents into virtual reality simulations

• OCR is a technology used to translate languages in real-time

• OCR is the process of converting different types of documents into editable and searchable data.

• OCR is the process of converting images into audio files

Explain the role of deep learning in advancing computer vision techniques.

• Deep learning makes computer vision techniques less accurate

• Deep learning enables the automatic learning of features from data, leading to more accurate and robust computer vision techniques.

• Deep learning only works for simple image recognition tasks

• Deep learning has no impact on computer vision techniques

What are some challenges faced in implementing computer vision techniques in real-world applications?

• Limited processing power

• High cost of hardware

• Difficulty in detecting objects in complex environments

• Large amounts of labeled data

Discuss the ethical considerations related to the use of computer vision techniques in surveillance and privacy.

• Enhancement of personal freedom

• Improvement of public safety

• Promotion of individual privacy rights

• Ethical considerations related to the use of computer vision techniques in surveillance and privacy include invasion of privacy, potential misuse of data, and impact on individual rights.