C Language Image Pixel Data Input and Analysis [Image Recognition and Analysis] OpenCV: Open Source Computer Vision Library, Comprising Various Image Processing Algorithms

# 1. Introduction

### 1.1 Reading and Analyzing Image Pixel Data with C Language

In the realm of image processing, C language is a commonly used programming language capable of reading and processing image pixel data. With C, we can achieve the input, analysis, and processing of image data, laying the groundwork for image recognition and analysis.

### 1.2 The Importance of Image Recognition and Analysis

Image recognition and analysis have widespread applications in modern society, encompassing fields such as face recognition, license plate recognition, and medical image analysis. By recognizing and analyzing images, we can extract useful information to assist in decision-making and problem-solving. Therefore, image recognition and analysis are of significant importance, exerting profound impacts across various industries.

# 2. Overview of OpenCV

OpenCV (Open Source Computer Vision Library) is an open-source computer vision library initially developed by Intel and released under the BSD license. It boasts a rich set of image processing and computer vision functionalities, suitable for developing real-time image processing applications. Below, we will introduce an overview and history of OpenCV, as well as its functions and applications in different fields.

### 2.1 Introduction and History of OpenCV

Initially developed by Intel Research, OpenCV was first released in 1999. It is a widely used computer vision library that supports various programming languages, such as C++ and Python. OpenCV can run on multiple platforms and offers a plethora of image processing and computer vision algorithms.

### 2.2 Functions and Applications of OpenCV

OpenCV provides a variety of image processing and computer vision functionalities, including image transformations, feature detection, object recognition, and visual tracking. It is widely applied in many fields, such as face recognition, license plate recognition, medical image processing, and industrial visual inspection. OpenCV's powerful and easy-to-use features make it an indispensable tool in numerous research and application projects.

# 3. Fundamentals of Image Processing

#### 3.1 Representation and Storage of Image Data

Images are composed of two-dimensional matrices of pixels, ***mon image representation methods include RGB (Red, Green, Blue) and grayscale images. Images can be stored in various formats on computers, such as BMP, JPEG, PNG, each with its own storage structure and compression method.

In image processing, understanding the storage structure of images is essential for performing operations and analysis.

#### 3.2 Overview of Image Processing Algorithms

Image processing algorithms refer to methods that transform, enhance, ***mon image processing algorithms include:

- Blurring and Sharpening: Such as mean filtering, Gaussian filtering, Sobel operator, etc.
- Edge Detection: Such as Canny edge detection algorithm, Sobel operator, etc.
- Image Segmentation: Such as threshold segmentation, region growing algorithm, etc.
- Feature Extraction: Such as HOG (Histog