Exploring Computer Vision Concepts by Mohd Mirza

What is computer vision?

• Computer vision is a type of computer programming language.

• Computer vision is solely focused on image compression techniques.

• Computer vision is a method for enhancing audio signals.

• Computer vision is a field of AI that enables computers to interpret and understand visual information.

Name one application of computer vision.

• Image editing software

• Autonomous vehicles

• Web development frameworks

• Data analysis tools

What is the role of machine learning in computer vision?

• Machine learning plays a crucial role in enabling computers to interpret and understand visual information.

• Machine learning has no impact on visual data processing.

• Machine learning is only applicable in text analysis.

• Machine learning is used solely for data storage.

Explain the difference between image processing and computer vision.

• Image processing is about manipulating images, while computer vision is about understanding and interpreting them.

• Image processing focuses on video analysis, while computer vision deals with still images.

• Computer vision is a subset of image processing that only uses black and white images.

• Image processing is primarily concerned with 3D modeling, whereas computer vision is about 2D image enhancement.

What is a convolutional neural network (CNN)?

• A convolutional neural network (CNN) is a deep learning model designed for processing grid-like data, particularly images, using convolutional and pooling layers.

• A convolutional neural network (CNN) is primarily used for natural language processing tasks.

• A convolutional neural network (CNN) is a simple linear regression model for predicting numerical values.

• A convolutional neural network (CNN) is a type of recurrent neural network used for time series data.

How does object detection work in computer vision?

• Object detection is a process of enhancing image brightness.

• Object detection works by extracting features from images, localizing objects with bounding boxes, classifying them, and refining results.

• Object detection relies solely on image resolution.

• Object detection only identifies colors in images.

What are some common challenges in computer vision?

• All computer vision tasks can be solved with deep learning

• Common challenges in computer vision include lighting variations, occlusions, viewpoint changes, scene complexity, real-time processing requirements, and data labeling difficulties.

• Computer vision is only used in robotics

• High-resolution images are always required

What is the significance of image segmentation?

• Image segmentation is only useful for image compression.

• Image segmentation is primarily used for color enhancement.

• Image segmentation is crucial for improving object recognition and analysis in computer vision.

• Image segmentation has no impact on machine learning models.

How can computer vision be used in autonomous vehicles?

• Computer vision helps in improving fuel efficiency by analyzing engine performance.

• Computer vision is primarily used for vehicle interior design and aesthetics.

• Computer vision is used in autonomous vehicles for object detection, scene understanding, and navigation.

• Computer vision is used for enhancing audio systems in vehicles.

What is the purpose of feature extraction in computer vision?

• To identify and isolate important characteristics or patterns from images.

• To compress image files for storage.

• To convert images into text format.

• To enhance the brightness of images.