Computer Vision (AL-701) - Important Questions

Explain what computer vision is and how it differs from traditional image processing, with examples.

Explain data type conversion in digital image processing, including mathematical operations on images, OpenCV example, and impact on image quality and storage.

Perform the following numerical operations on a $3\times3$ image matrix: $$I = \begin{bmatrix}100 & 150 & 200 \\ 50 & 100 & 150 \\ 0 & 50 & 100\end{bmatrix}$$

Define computer vision, explain its significance in modern applications, and how it differs from traditional image processing.

Discuss various problems in computer vision and explain how basic image operations help in solving them.

Explain the difference between gray scale and colour image.

Explain binary image thresholding and demonstrate adaptive thresholding on a $4\times4$ grayscale image

Describe morphological erosion and dilation and compute dilation of a given binary image with a given structuring element.

Explain the concept of binary image processing. How do thresholding, erosion, and dilation contribute to object recognition?

What is contour analysis and how can contours be used for object shape recognition?

Discuss contour analysis and connected component analysis and how they are used in object segmentation.

Describe erosion and dilation in binary image processing and explain their use for noise removal and object size modification.

Explain thresholding in binary image processing and how to choose an optimal threshold value with an example

Provide an algorithm or method for performing connected component analysis on a binary image. Explain the steps involved.

Write a short note on any two:

What are color spaces and how are they used in image processing, and what is the role of histogram equalization in image enhancement?

Explain convolution in image filtering and compare smoothing techniques such as Box Blur, Gaussian Blur and Median Blur.

Explain different types of color spaces and their transformation equations for RGB$\rightarrow$HSV and RGB$\rightarrow$LAB.

Numerically compute Histogram Equalization for an image having pixel intensities. [52, 55, 61, 66, 70, 61, 64, 73]

Compare Box blur, Gaussian blur, and Median blur image filtering methods with examples.

Explain the concept of image enhancement and its importance in image processing. Provide examples of scenarios where image enhancement is necessary.

Define colour transforms in image processing. Provide an example of a colour transform and explain how it alters the appearance of an image?

Discuss scenarios where precise colour adjustment is essential, and how curves can be employed to achieve the desired result.

Describe a real-world application requiring image smoothing and identify the most appropriate blur filter.

Explain the role of convolution in image filtering and how a filter kernel is applied to an image.

Define image smoothing and explain its role in noise reduction and enhancing image appearance.

Discuss the various techniques for image segmentation and explain how image classification differs from object detection.

Derive the Sobel operator for gradient calculation and show its effect on a sample $3\times3$ image.

Differentiate between first-order and second-order derivative filters with mathematical examples.

Discuss various object detection techniques in Computer Vision with suitable examples.

Define image segmentation, explain its relation to image recognition, and describe the steps to segment objects in an image.

Explain the Canny Edge detection algorithm, including its key steps and the role of various parameters.

Explain the significance of gesture recognition in computer vision and applications of motion estimation in real-world scenarios.

How does face detection work in computer vision? Explain the role of deep learning techniques in improving face detection accuracy.

Explain the process of gesture recognition using OpenCV and deep learning.

Explain Motion Estimation and Object Tracking algorithms with help of examples.

What is OpenCV, and how is it used in deep learning for computer vision tasks?

Define gesture recognition in computer vision and explain how it enables human-computer interaction.

Discuss the ethical considerations in computer vision applications. How can bias in AI models be minimized?

How can computer vision techniques be adapted for remote sensing and satellite image analysis? Discuss the key challenges.

Explain the role of AI-driven computer vision in medical imaging. How has it transformed the healthcare industry?