# which side of the greyscale is the components of the histogram concentrated in a dark image?

### Mohammed

Guys, does anyone know the answer?

get which side of the greyscale is the components of the histogram concentrated in a dark image? from screen.

## DIP 2

MCQ unit image enhancement digital image processing questions and answers relationship between pixels and image enhancement basics this set of digital image

Dr. A.P.J. Abdul Kalam Technical University

B.tech DIP 2 - MCQ

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Unit 2 Image Enhancement

Digital Image Processing Questions and Answers – Relationship between Pixels and Image Enhancement Basics

This set of Digital Image Processing Multiple Choice Questions & Answers (MCQs) focuses on “Relationship between Pixels and Image Enhancement Basics”.

A pixel p at coordinates (x, y) has neighbors whose coordinates are given by: (x+1, y), (x-1, y), (x, y+1), (x, y-1) This set of pixels is called ____________ a) 4-neighbors of p b) Diagonal neighbors c) 8-neighbors d) None of the mentioned

Answer: a Explanation: The given set of neighbor pixel are 1 unit distance to right, left, up and below respectively from pixel p(x, y). So, are called 4-neighbors of p.

A pixel p at coordinates (x, y) has neighbors whose coordinates are given by: (x+1, y+1), (x+1, y-1), (x-1, y+1), (x-1, y-1) This set of pixels is called ____________ a) 4-neighbors of p b) Diagonal neighbors c) 8-neighbors d) None of the mentioned View Answer

Answer: b Explanation: The given set of neighbor pixel are 1 unit distance to right-up diagonal, right-down diagonal, left-up diagonal and left-down diagonal respectively from pixel p(x, y). So, are called Diagonal neighbors of p.

What is the set of pixels of 8-neighbors of pixel p at coordinates (x, y)? a) (x+1, y), (x-1, y), (x, y+1), (x, y-1), (x+2, y), (x-2, y), (x, y+2), (x, y-2) b) (x+1, y), (x-1, y), (x, y+1), (x, y-1), (x+1, y+1), (x+1, y-1), (x-1, y+1), (x-1, y-1) c) (x+1, y+1), (x+1, y-1), (x-1, y+1), (x-1, y-1), (x+2, y+2), (x+2, y-2), (x-2, y+2), (x-2, y-2) d) (x+2, y), (x-2, y), (x, y+2), (x, y-2), (x+2, y+2), (x+2, y-2), (x-2, y+2), (x-2, y-2) View Answer

Answer: b Explanation: The set of pixels of 4-neighbors of p and Diagonal neighbors of p together are called as 8-neighbors of pixel p(x, y).

Two pixels p and q having gray values from V, the set of gray-level values used to define adjacency, are m-adjacent if: a) q is in N4(p) b) q is in ND(p) and the set N4(p) ∩ N4(q) has no pixels whose values are from V c) Any of the mentioned d) None of the mentioned View Answer

Answer: c Explanation: Mixed adjacency is a modified form of 8-adjacency. The above conditioned Two pixels p and q are m-adjacent if: q is in N4(p), or q is in ND(p) and the set N4(p) ∩ N4(q) has no pixels whose values are from V.

Let S, a subset of pixels in an image, is said to be a connected set if: a) If for any pixel p in S, the set of pixels that are connected to it in Sis only one b) If it only has one connected component c) If S is a region d) All of the mentioned View Answer

Answer: d Explanation: For a subset of pixels in an image S For any pixel p in S, the set of pixels is called a connected component of S if connected to p in S. The set S is called a connected set if it only has one connected component. S, is a region of the image if S is a connected set.

Let R be a subset of pixels in an image. How can we define the contour of R? a) If R is a region, and the set of pixels in R have one or more neighbors that are not in R b) If R is an entire image, then the set of pixels in the first and last rows and columns of R c) All of the mentioned d) None of the mentioned View Answer

Answer: c Explanation: For a subset of pixels in an image R The boundary or contour of a region R is the set of pixels in the region that have one or more neighbors that are not in R. In case R is an entire image, then its boundary is defined as the set of pixels in the first and last rows and columns of the image.

For pixels p(x, y), q(s, t), and z(v, w), D is a distance function or metric if: a) D(p, q) ≥ 0 b) D(p, q) = D(q, p) c) D(p, z) ≤ D(p, q) + D(q, z)

a) Spatial domain in both b) Frequency domain in both c) Spatial domain and Frequency domain respectively d) Frequency domain and Spatial domain respectively View Answer

Answer: c Explanation: Spatial domain itself refers to the image plane, and approachesin this category are based on direct manipulation of pixels in an image. Techniques based on Frequency domain processing are based on modifying the Fourier transform of an image.

What is the technique for a gray-level transformation function called, if the transformation would be to produce an image of higher contrast than the original by darkening the levels below some gray-level m and brightening the levels above m in the original image. a) Contouring b) Contrast stretching c) Mask processing d) Point processing View Answer

Answer: b Explanation: For a gray-level transformation function “s=T(r)”, where r ands are the gray-level of f(x, y) (input image) and g(x, y) (output image) respectively at any point (x, y). Then the technique, contrast stretching compresses the value of r below m by transformation function into a narrow range of s, towards black and brightens the value of r above m.

## Digital Image Processing Interview Questions and Answers for Freshers

This set of Digital Image Processing Interview Questions and Answers for freshers focuses on “Histogram Processing – 2”. 1. The histogram of a digital image with gray levels in the range [0, L-1] is represented by a discrete function: a) h(r_k)=n_k b) h(r_k )=n/n_k c) p(r_k )=n_k d) h(r_k )=n_k/n 2. How is the expression ... Read more

## Digital Image Processing Questions And Answers – Histogram Processing – 2

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This set of Digital Image Processing Interview Questions and Answers for freshers focuses on “Histogram Processing – 2”.

1. The histogram of a digital image with gray levels in the range [0, L-1] is represented by a discrete function:

a) h(r_k)=n_k b) h(r_k )=n/n_k c) p(r_k )=n_k d) h(r_k )=n_k/n View Answer

2. How is the expression represented for the normalized histogram?

a) p(r_k )=n_k b) p(r_k )=n_k/n c) p(r_k)=nn_k d) p(r_k )=n/n_k View Answer

3. Which of the following conditions does the T(r) must satisfy?

a) T(r) is double-valued and monotonically decreasing in the interval 0≤r≤1; and

0≤T(r)≤1 for 0≤r≤1

b) T(r) is double-valued and monotonically increasing in the interval 0≤r≤1; and

0≤T(r)≤1 for 0≤r≤1

c) T(r) is single-valued and monotonically decreasing in the interval 0≤r≤1; and

0≤T(r)≤1 for 0≤r≤1

d) T(r) is single-valued and monotonically increasing in the interval 0≤r≤1; and

0≤T(r)≤1 for 0≤r≤1 View Answer

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4. The inverse transformation from s back to r is denoted as:

a) s=T-1(r) for 0≤s≤1

b) r=T-1(s) for 0≤r≤1

c) r=T-1(s) for 0≤s≤1

d) r=T-1(s) for 0≥s≥1

View Answer

5. The probability density function p_s (s) of the transformed variable s can be obtained by using which of the following formula?

a) p_s (s)=p_r (r)|dr/ds|

b) p_s (s)=p_r (r)|ds/dr|

c) p_r (r)=p_s (s)|dr/ds|

d) p_s (s)=p_r (r)|dr/dr|

View Answer

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6. A transformation function of particular importance in image processing is represented in which of the following form?

a) s=T(r)=∫0 (2r)pr (ω)dω

b) s=T(r)=∫0 (r-1)pr (ω)dω

c) s=T(r)=∫0 (r/2)pr (ω)dω

d) s=T(r)=∫0 pr (ω)dω

View Answer

7. Histogram equalization or Histogram linearization is represented by of the following equation:

a) sk =∑k j =1 nj/n k=0,1,2,……,L-1

b) sk =∑k j =0 nj/n k=0,1,2,……,L-1

c) sk =∑k j =0 n/nj k=0,1,2,……,L-1

d) sk =∑k j =n nj/n k=0,1,2,……,L-1

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8. What is the method that is used to generate a processed image that have a specified histogram?

a) Histogram linearization

b) Histogram equalization

c) Histogram matching

d) Histogram processing

View Answer

9. Histograms are the basis for numerous spatial domain processing techniques.

a) True b) False View Answer advertisement

10. In a dark image, the components of histogram are concentrated on which side of the grey scale?

a) High b) Medium c) Low

d) Evenly distributed

View Answer

**Sanfoundry Global Education & Learning Series – Digital Image Processing.**

To practice all areas of Digital Image Processing for Interviews, here is complete set of 1000+ Multiple Choice Questions and Answers.

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## Image Histogram

An image histogram is a type of histogram that acts as a graphical representation of the tonal distribution in a digital image. It plots the number of pixels for each tonal value. By looking at the histogram for a specific image a viewer will be able to judge the entire tonal distribution at a glance.

Home / image histogram / image processing / Image Histogram | Bright Image | Dark Image | Low Contrast Image | High Contrast Image

## Image Histogram | Bright Image | Dark Image | Low Contrast Image | High Contrast Image

May 13, 2020 image histogram, image processing

**Image Histogram:**In general, the histogram can be defined as the frequency of occurrence of data. In terms of image processing, the graphical representation of the digital image is called the histogram. It can also be defined as the graphical representation of pixel intensity values in a digital image.

The histogram helps in understanding the digital image. It analyses the digital image pixel by pixel (at the pixel level). Fig 1. displays the graphical representation (histogram) of the cameraman image.

The histogram has two axes i.e. X-axis and Y-axis respectively. The X-axis represents the total number of gray levels (L) ranging from 0 to L-1. The Y-axis represents the total number of pixels. In Fig 1., L=256 means it is a grayscale image i.e. X-axis ranges from 0 to 255.

The Matlab provides an in-built function i.e. "imhist" to draw the histogram of any digital image. Using below code one can draw the histogram of any digital image:

**Drawback:**The histogram provides no information regarding the spatial distribution of an image’s pixel values.

**Applications of Histograms**

The digital image can be predicted based on its histogram.

The digital image can be analyzed based on its histogram.

It can be used in image thresholding.

It can be used in image enhancement.

It can be used to equalize an image.

The brightness and contrast of the digital image can be adjusted by its histogram.

If we have input and output histograms of an image, we can determine which type of transformation is applied in the algorithm.

Fig 1. Cameraman image with the histogram

### **Histogram of Bright Image**

The components of the histogram are biased towards the high side of the grayscale as shown in Fig 2. This shows that the brightness is high in the current image (Fig 2).

Fig 2. Bright cameraman image with the histogram

### **Histogram of Dark Image**

The components of the histogram are concentrated on the low (dark) side of the grayscale as shown in Fig 3. This shows that the brightness is very low (dark image) in the current image (Fig 3).

Fig 3. Dark cameraman image with the histogram

### **Histogram of Low Contrast Image**

The histogram will be narrow and will be centered towards the middle of the grayscale as shown in Fig 3. This shows that the contrast is low in the current image (Fig 4).

Fig 4. Low contrast cameraman image with the histogram

### **Histogram of High Contrast Image**

The components of the histogram cover a broad range of the grayscale as shown in Fig 4. This shows that the contrast is high in the current image (Fig 5).

Fig 5. High contrast cameraman image with the histogram

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How Synthetic Aperture Radar (SAR) Images are Created?

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Roughness and Brightness of SAR Image

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Guys, does anyone know the answer?