翻译The histogram of 𝒛𝒕 − 𝒛𝒕+𝟏 𝒈𝒓𝒆𝒂𝒕 𝒄𝒊𝒓𝒄𝒍𝒆 − 𝒙𝒕,𝒊 − 𝒙𝒕+𝟏,𝒋 𝒓𝒐𝒖𝒕𝒆 follows an exponential probability distribution.
时间: 2024-05-24 20:14:29 浏览: 11
The histogram of 𝒛𝒕 − 𝒛𝒕 𝟏 𝒈𝒓𝒆𝒂𝒕 𝒄𝒊𝒓𝒄𝒍𝒆 − 𝒙𝒕,𝒊 − 𝒙𝒕 𝟏,𝒋 𝒓𝒐𝒖𝒕𝒆 follows an exponential probability distribution. 翻译为中文是:𝒛𝒕 − 𝒛𝒕 𝟏 𝒈𝒓𝒆𝒂𝒕 𝒄𝒊𝒓𝒄𝒍𝒆 − 𝒙𝒕,𝒊 − 𝒙𝒕 𝟏,𝒋 的直方图满足指数概率分布。
相关问题
The sample SS12 is of size 10. As , is the histogram in (1) highly likely to look similar to a normal distribution curve?
It is difficult to determine whether the histogram of a sample of size 10 will look similar to a normal distribution curve without knowing the underlying population distribution and the specific values in the sample. In general, as the sample size increases, the shape of the histogram tends to become more bell-shaped and resemble a normal distribution. However, with a small sample size, the histogram may not necessarily resemble a normal distribution, even if the underlying population distribution is normal. Additionally, the presence of outliers or skewness in the sample can also affect the shape of the histogram. Therefore, it is important to examine the histogram and other measures of central tendency and variability to assess the normality of the data.
Implement a function that equalizes grey-scale images based on their histogram. The input is a given grey level image; the output is the derived image with uniform intensity distribution.
Sure, I can help you with that! Here is a Python implementation of the function:
```python
import numpy as np
import cv2
def hist_equalization(img):
# Calculate histogram
hist, bins = np.histogram(img.flatten(), 256, [0, 256])
# Calculate cumulative distribution function (CDF)
cdf = hist.cumsum()
# Normalize CDF
cdf_normalized = cdf * hist.max() / cdf.max()
# Create lookup table
lut = np.interp(np.arange(256), bins[:-1], cdf_normalized)
# Apply lookup table to image
equalized_img = cv2.LUT(img, lut)
return equalized_img
```
To use this function, simply pass in a grayscale image as the argument and it will return the equalized image. Here's an example of how to use it:
```python
# Load grayscale image
img = cv2.imread('image.jpg', 0)
# Apply histogram equalization
equalized_img = hist_equalization(img)
# Display original and equalized images
cv2.imshow('Original Image', img)
cv2.imshow('Equalized Image', equalized_img)
cv2.waitKey(0)
cv2.destroyAllWindows()
```
I hope this helps! Let me know if you have any questions.