Histogram computation in Computer Vision - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of grayscale histogram computation

What is the output of this code that computes a grayscale histogram of a 3x3 image?

Computer Vision

import numpy as np
image = np.array([[0, 128, 255], [128, 128, 0], [255, 0, 128]], dtype=np.uint8)
histogram, bins = np.histogram(image, bins=4, range=(0, 256))
print(histogram.tolist())

A [3, 3, 0, 3]

B [2, 3, 2, 2]

C [3, 0, 4, 2]

D [2, 2, 3, 3]

💡 Hint

Bins divide the pixel values into ranges: 0-63, 64-127, 128-191, 192-255.

The histogram counts pixels in these ranges: 0-63 has 3 pixels (0s), 64-127 has 0 pixels, 128-191 has 4 pixels (128s), 192-255 has 2 pixels (255s).

🧠 Conceptual

intermediate

Purpose of histogram equalization

What is the main purpose of histogram equalization in image processing?

A To increase the contrast of an image by spreading out the most frequent intensity values.

B To reduce the image size by compressing pixel values.

C To convert a color image into grayscale by averaging channels.

D To blur the image by averaging neighboring pixels.

💡 Hint

Think about how the image looks after equalization compared to before.

Histogram equalization redistributes pixel intensities to enhance contrast, making details more visible.

❓ Metrics

advanced

Interpreting histogram similarity metrics

Which metric is best to compare two image histograms to measure similarity?

A Histogram intersection

B Mean Squared Error (MSE)

C Euclidean distance

D Correlation coefficient

💡 Hint

Consider which metric measures overlap between histograms.

Histogram intersection measures the common area under two histograms, indicating similarity.

🔧 Debug

advanced

Bug in histogram calculation code

What error does this code raise? import cv2 import numpy as np image = np.array([[10, 20], [30, 40]], dtype=np.uint8) hist = cv2.calcHist([image], [1], None, [256], [0, 256])

A TypeError: argument must be a sequence

B IndexError: list index out of range

C ValueError: channels must be 0 or 1

D No error, returns histogram

💡 Hint

Check the channel index used for a single-channel image.

The channel index is 1, but the image has only one channel (index 0), causing IndexError.

❓ Model Choice

expert

Choosing histogram features for image classification

You want to classify images of fruits by color distribution. Which histogram feature approach is best?

A Use grayscale histogram with 256 bins

B Use histogram of oriented gradients (HOG)

C Use binary threshold histogram

D Use RGB histograms concatenated for each channel

💡 Hint

Color information is important for fruit classification.

RGB histograms capture color distribution across channels, useful for distinguishing fruits by color.