C language data structure: the application of data structure in image processing

Data structures are crucial in image processing. C language provides data structures such as arrays, linked lists, stacks and queues. Arrays are used to store image data, linked lists are used to represent edges or outlines, stacks are used to store operation history, and queues are used to store intermediate results. Practical applications include using arrays to implement grayscale image histograms and using linked lists to implement image edge detection.

C language data structure: the application of data structure in image processing

In image processing, data structures are essential for efficient storage, manipulation, and processing image data. Various data structures are provided in C language, which can easily and effectively implement image processing algorithms.

Array

Arrays are simple and effective linear data structures that store similar data. It allows efficient access to elements based on indexes. In image processing, arrays are often used as storage containers for image data. For example, a two-dimensional array may represent an image, where each element corresponds to an intensity value of one pixel in the image.

Link List

A linked list is a nonlinear data structure that allows dynamic addition and removal of elements. Linked lists are suitable for storing data that is irregular in shape or needs to be updated frequently. In image processing, linked lists can be used to represent edges or outlines of an image.

Stack

The stack is a last-in-first-out (LIFO) data structure. It is used to store temporary data and allows quick retrieval and deletion of the latest added elements. In image processing, the stack is used to store a history of image processing operations.

queue

Queues are a first-in-first-out (FIFO) data structure. It is used to store data that needs to be processed sequentially. In image processing, a queue is used to store intermediate results of the image processing algorithm.

Practical cases

Implementing a grayscale image histogram using arrays

A grayscale image histogram is a graph that displays the number of pixels at each grayscale level in an image. A one-dimensional array may be used to store histogram data, where the index corresponds to the grayscale level and the value corresponds to the number of pixels at the corresponding grayscale level.

 // Create an array to store the histogram int histogram[256];

// traverse the image and calculate the histogram for (int i = 0; i < imageHeight; i ) {
    for (int j = 0; j < imageWidth; j ) {
        histogram[image[i][j]] ;
    }
}

Image edge detection using linked list

Canny edge detection is a widely used edge detection algorithm. A linked list may be used to represent edges in an image, where each node corresponds to one pixel of the edge.

 // Create a linked list to store edge struct EdgeNode {
    int x;
    int y;
    struct EdgeNode *next;
};

struct EdgeNode *head = NULL;

// Use the Canny algorithm to detect edges for (int i = 0; i < imageHeight; i ) {
    for (int j = 0; j < imageWidth; j ) {
        if (isEdge(i, j)) {
            // Create a new node struct EdgeNode *newNode = malloc(sizeof(struct EdgeNode));
            newNode->x = i;
            newNode->y = j;
            newNode->next = NULL;

            // Add new node to the linked list if (head == NULL) {
                head = newNode;
            } else {
                newNode->next = head;
                head = newNode;
            }
        }
    }
}

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