With the continuous development of machine vision technology, its impact on industrial production and other fields is becoming increasingly profound. However, machine vision systems are often affected by factors such as lighting, angle, and occlusion in practical applications, resulting in errors in the recognition and positioning of target objects. Visual alignment and visual correction techniques are important means to improve the accuracy of machine vision. This article will explore how to improve the accuracy of machine vision through these two technologies.

1、 Visual alignment technology

Visual alignment technology is the process of matching feature points on images to calculate the geometric transformation relationships between images, thereby achieving accurate alignment of images. In machine vision, visual alignment technology is mainly used to improve the recognition and positioning accuracy of target objects. The following are the key steps of visual alignment technology:

1. Feature point extraction is the first step in visual alignment technology. By extracting feature points from an image, key information such as edges, corners, textures, etc. can be obtained. These feature points can be used for subsequent matching and transformation calculations. Common feature point extraction algorithms include SIFT, SURF, ORB, etc.

2. Feature point matching is the pairing of feature points in different images based on the extraction of feature points. During the pairing process, it is necessary to consider the similarity and uniqueness of feature points. Commonly used feature point matching algorithms include Brute Force matcher and RANSAC algorithm.

3. After completing feature point matching, geometric transformation calculation needs to calculate the geometric transformation relationship between images based on the matching results. Common geometric transformations include translation, rotation, scaling, etc. By calculating these transformation parameters, accurate image alignment can be achieved.

4. Align the image based on the calculated geometric transformation relationship, and perform the image alignment operation. Common alignment algorithms include nearest neighbor interpolation, bilinear interpolation, and cubic convolution. By aligning images, errors caused by factors such as lighting and angle can be reduced, and the recognition and positioning accuracy of target objects can be improved.

2、 Visual correction technology

Visual correction technology improves the quality and readability of images by preprocessing, enhancing, and repairing them. In machine vision, visual correction technology is mainly used to reduce errors caused by factors such as occlusion and noise, and improve the recognition and positioning accuracy of target objects. The following are the key steps of visual correction technology:

1. Image preprocessing is the first step in visual correction technology. By preprocessing the image, errors caused by factors such as lighting and angle can be reduced, and the quality and readability of the image can be improved. Common preprocessing methods include grayscale, binarization, filtering, etc.

2. Image enhancement is the core step of visual correction technology. By enhancing the image, key information such as edges and textures can be highlighted, improving the recognition accuracy of the target object. Common enhancement methods include contrast enhancement, sharpening, etc.

3. After completing image enhancement, image restoration is necessary for image damage or missing caused by factors such as occlusion and noise. Through image restoration, the true appearance of the image can be restored and the recognition accuracy of the target object can be improved. Common repair methods include interpolation based repair and deep learning based repair.

4. Color correction is also an important component of visual correction technology. In practical applications, images may experience color deviation or distortion due to factors such as lighting and angle. Through color correction, the true color of the image can be restored and the recognition accuracy of the target object can be improved. Common color correction methods include grayscale based color correction and color based color correction.

3、 Conclusion

By applying visual alignment and visual correction techniques, the accuracy and reliability of machine vision systems can be improved. These two technologies can be used separately or in combination to meet the needs of different scenarios. In practical applications, it is necessary to select appropriate algorithms and methods based on specific problems and scenarios to achieve optimal results. With the continuous development of machine learning and deep learning technologies in the future, visual alignment and visual correction technologies will have more application prospects and development directions.