Question

challenge a the problem: work to digitally represent this image using sampling. reference your activity guide.

Explanation:

To digitally represent the image using sampling (assuming a grid - based sampling where we check each grid cell):

Step 1: Analyze the Grid and Image

Let's assume the grid has a certain number of rows and columns (from the visual, let's say we can define the grid cells). For each cell in the grid, we determine if it is part of the black (the bird) or white (the background/inside the bird) region.

Step 2: Create a Sampling Matrix (Example)

Suppose the grid is, for simplicity, an \(n\times m\) grid (we can count the cells from the image). Let's say we have a grid with, for example, 6 rows and 6 columns (visually estimating). We go through each cell:

• If the cell is filled with the bird's color (black), we mark it as 1; if it's white, we mark it as 0.
• For the body of the bird, the head, the legs, and the feet, we identify the cells that belong to them and mark them as 1. The inner white part of the bird (the "face" area) and the background cells are marked as 0.

Step 3: Represent the Sampling

We can then create a table or a matrix to represent this sampling. For example, if we consider a smaller grid (simplified for illustration):

Row \ Column	1	2	3	4	5	6
2	0	1	0	0	1	0
3	1	1	0	0	1	1
4	1	1	1	1	1	1
5	0	0	1	1	0	0
6	0	0	1	1	0	0

(This is a simplified example, and the actual sampling would depend on the exact grid and image details. The key is to sample each cell to determine its color/region and then represent it digitally, like in a matrix or a pixel - like representation.)

The final digital representation would be a data structure (like a matrix) where each element represents the sampled value (e.g., 0 for white, 1 for black) of the corresponding grid cell in the original image.

Answer:

To digitally represent the image using sampling (assuming a grid - based sampling where we check each grid cell):

Step 1: Analyze the Grid and Image

Let's assume the grid has a certain number of rows and columns (from the visual, let's say we can define the grid cells). For each cell in the grid, we determine if it is part of the black (the bird) or white (the background/inside the bird) region.

Step 2: Create a Sampling Matrix (Example)

Suppose the grid is, for simplicity, an \(n\times m\) grid (we can count the cells from the image). Let's say we have a grid with, for example, 6 rows and 6 columns (visually estimating). We go through each cell:

• If the cell is filled with the bird's color (black), we mark it as 1; if it's white, we mark it as 0.
• For the body of the bird, the head, the legs, and the feet, we identify the cells that belong to them and mark them as 1. The inner white part of the bird (the "face" area) and the background cells are marked as 0.

Step 3: Represent the Sampling

We can then create a table or a matrix to represent this sampling. For example, if we consider a smaller grid (simplified for illustration):

Row \ Column	1	2	3	4	5	6
2	0	1	0	0	1	0
3	1	1	0	0	1	1
4	1	1	1	1	1	1
5	0	0	1	1	0	0
6	0	0	1	1	0	0

(This is a simplified example, and the actual sampling would depend on the exact grid and image details. The key is to sample each cell to determine its color/region and then represent it digitally, like in a matrix or a pixel - like representation.)

The final digital representation would be a data structure (like a matrix) where each element represents the sampled value (e.g., 0 for white, 1 for black) of the corresponding grid cell in the original image.