Image Segmentation Quiz

What is the main goal of image segmentation?

• To enhance image brightness

• To divide an image into parts that correlate with real-world objects

• To compress image data

• To convert images to grayscale

What is complete segmentation?

• Regions that overlap with each other

• A method of image compression

• A set of disjoint regions corresponding uniquely to objects

• An image with no segmentation

Which of the following is a characteristic used in edge detection?

• File size

• Image resolution

• Brightness

• Color depth

What is the simplest segmentation process mentioned?

• Edge-based segmentation

• Adaptive thresholding

• Region-based segmentation

• Gray level thresholding

What does adaptive thresholding do?

• Uses a single global threshold for the entire image

• Changes the threshold dynamically based on local characteristics

• Ignores local variations in the image

• Only works with binary images

What is the Chow and Kaneko approach used for?

• Detecting edges in images

• Enhancing image contrast

• Finding the optimal threshold for each sub-image

• Creating a binary image

What is the purpose of the Otsu method?

• To apply adaptive thresholding

• To select an optimal threshold for image segmentation

• To reduce image noise

• To enhance image colors

What is a drawback of the Otsu method?

• It requires manual threshold selection

• It is computationally expensive

• It works well with variable illumination

• It assumes the histogram is bimodal

What is the result of multithresholding?

• A color image

• A binary image

• A grayscale image

• An image with multiple gray levels

What is the main challenge in achieving complete segmentation of complex scenes?

• High computational cost

• Ambiguity in image data

• Lack of available algorithms

• Insufficient image resolution