C++ image processing technology analysis: the key to realizing image recognition and processing

Analysis of C image processing technology: the key to realizing image recognition and processing

Abstract: This article will introduce C image processing technology and discuss in depth the key to realizing image recognition and processing. Key technologies and methods. Including image preprocessing, feature extraction, pattern recognition and other processes.

Keywords: C, image processing, image recognition, image preprocessing, feature extraction, pattern recognition

Overview

In today's digital era, image processing technology has been widely used in all walks of life. From medical imaging to intelligent transportation, from image search to virtual reality, image processing is everywhere. C, as a high-level programming language, is widely accepted and used for its efficiency and portability. This article will be based on C and deeply explore the key technologies and methods to achieve image recognition and processing.

1. Image preprocessing

Image preprocessing is an important step in image processing, which includes processes such as denoising, enhancement, and normalization. First, denoising technology can improve the quality and clarity of images. Commonly used denoising methods include mean filtering, median filtering and Gaussian filtering. Secondly, enhancement techniques can improve features such as contrast and brightness of images. For example, histogram equalization can adjust the distribution of image pixels to make the image clearer and brighter. Finally, normalization techniques convert images into a standardized format for subsequent processing. For example, convert images into grayscale images or binary images to facilitate feature extraction and pattern recognition.

2. Feature extraction

Feature extraction is the core link of image processing. It represents the content and structure of the image by extracting different features from the image. Common features include color, texture, and shape. Color features can describe the color distribution of an image through statistical methods such as histograms. Texture features can describe the texture characteristics of the image through methods such as gray level co-occurrence matrix. Shape features can describe the shape information of images through methods such as edge detection and contour extraction. In addition, techniques such as filters and transformations can be used to extract specific features. For example, use Sobel operator for edge detection, use Haar wavelet transform for face detection, etc.

3. Pattern Recognition

Pattern recognition is the ultimate goal of image processing, which is achieved by classifying and identifying the extracted features. Commonly used pattern recognition methods include nearest neighbor classifier, support vector machine and neural network. The nearest neighbor classifier is a simple and effective pattern recognition method that determines its category by calculating the distance between the characteristics of the sample to be identified and the characteristics of the known sample. Support vector machine can be used for binary classification and multi-classification problems. It maps samples to high-dimensional feature space so that the samples are linearly separable in this space. Neural network is a pattern recognition method that simulates the human brain neuron network. It achieves sample classification and identification by training the weights and biases of the network.

Conclusion

C Image processing technology is the key to realizing image recognition and processing. This article introduces the key technologies and methods of C image processing from the aspects of image preprocessing, feature extraction and pattern recognition. By rationally selecting and combining these technologies and methods, efficient and accurate image recognition and processing can be achieved. At the same time, the efficiency and portability of the C language also provide powerful support and convenience for image processing. It is believed that C image processing technology will play a huge role in more fields in the near future.

Reference:

1. Milenkovic, Aleksandar, et al. "The major steps of the image processing for satellite imagery." Information systems and technologies (CISTI), 2014 9th Iberian conference on. IEEE, 2014.
2. Gonzalez, Rafael C., and Richard E. Woods. Digital image processing. Pearson/Prentice Hall, 2008.
3. Duda, Richard O., Peter E. Hart, and David G. Stork. Pattern classification. Wiley, 2012.

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