What is the course title for the computer vision course?
Computer Vision (CS456)
This course covers various aspects of computer vision and image processing.
What are the pre-requisites recommended for the computer vision course?
- Basic knowledge of image processing
- Linear algebra
- Probability
- Some programming skills (in Python, MATLAB, or C++)
No prior experience with computer vision is required.
What percentage of the total assessment is allocated to assignments in the computer vision course?
10%
Assignments involve regular coding tasks to implement image processing and computer vision techniques.
What are the Course Learning Outcomes (CLOs) for the computer vision course?
- Understand basic principles and techniques of image processing and computer vision
- Apply fundamental image processing techniques
- Implement computer vision algorithms
- Analyze and evaluate computer vision systems
These outcomes guide the learning objectives of the course.
What is the primary text for the computer vision course?
“Computer Vision: Algorithms and Applications” by Richard Szeliski
This textbook serves as the main reference for the course.
Define computer vision.
A subfield of Artificial Intelligence (AI) that enables computers to interpret and understand visual information
The goal is to perform tasks similar to the human visual system.
What is the difference between image processing and computer vision?
- Image Processing: Focuses on improving image quality
- Computer Vision: Involves extracting meaningful information from images
Image processing prepares images for analysis, while computer vision makes decisions based on visual inputs.
List the fields in computer vision.
- Object Recognition
- Scene Understanding
- Motion Analysis
These fields encompass various applications and techniques within computer vision.
In which decade did machine learning algorithms emerge in computer vision?
2000s
This led to advancements in object detection, tracking, and classification.
What significant development in computer vision occurred in the 2010s?
The rise of Convolutional Neural Networks (CNNs)
CNNs transformed the field by enabling machines to achieve near-human accuracy in image classification.
What is the first component of a computer vision system?
Image Acquisition
Devices like cameras and sensors capture the raw visual data.
What techniques are involved in image preprocessing?
- Smoothing
- Denoising
- Contrast enhancement
These techniques enhance features and reduce noise before analysis.
What is the purpose of feature extraction in computer vision?
Detecting useful features (edges, corners, blobs) for recognizing objects
Algorithms like SIFT and SURF help identify distinctive points in images.
What is the final step in a computer vision system?
Decision Making
This involves interpreting data to make decisions, such as identifying a face.
What is illumination variance in the context of computer vision challenges?
Changes in lighting that can alter the appearance of objects
This makes it difficult for algorithms to recognize objects.
True or false: Occlusion complicates recognition by partially hiding objects.
TRUE
For example, detecting a person behind a tree can be challenging.
List some applications of computer vision.
- Healthcare: Detecting tumors in medical images
- Autonomous Vehicles: Identifying pedestrians and obstacles
- Surveillance: Monitoring public spaces
- Augmented Reality (AR): Enhancing the real world
- Manufacturing: Quality control
These applications demonstrate the versatility of computer vision technology.
Also give examples.
What is a future trend in computer vision related to 3D vision?
This advancement enhances virtual reality experiences and object interactions.
Hint: Focuses on realistic depth perception in 3D environments.
Improving depth perception and 3D reconstruction
This advancement is essential for enhancing user experiences in virtual environments, allowing for more realistic interactions. Additionally, it plays a significant role in improving navigation and object manipulation in 3D spaces.
What is Edge AI in computer vision?
Running vision algorithms on low-power devices for real-time analysis.
This enables analysis on devices like smartphones.
What is the goal of Explainable AI in computer vision?
Making decision-making processes of AI models transparent
This is especially important in critical applications like healthcare.
