The image stitching function of the FVM-B manual image measuring instrument achieves seamless measurement of large workpieces through multi-view image acquisition, high-precision feature matching, and intelligent fusion algorithms. Its core mechanism involves dividing the workpiece surface into multiple local regions, acquiring high-resolution images of each region, and then using software algorithms to eliminate distortion and misalignment in overlapping areas, ultimately generating complete and continuous measurement data. This process integrates optical imaging, motion control, and image processing technologies to ensure the accuracy and completeness of the measurement results.
During the image acquisition phase, the FVM-B uses a precision guide rail system to control the movement of the measurement platform, sequentially bringing the workpiece surface into the field of view. After each movement, the system automatically triggers a high-resolution CCD camera to capture an image and simultaneously records the current position coordinates. To ensure stitching accuracy, adjacent images must maintain a certain percentage of overlap, typically 20%-30%. This design provides redundant information for subsequent feature matching, effectively offsetting motion errors and optical distortion.
Feature matching is the key step in image stitching. The FVM-B employs an algorithm based on SIFT (Scale Invariant Feature Transform) to extract rotation- and scaling-invariant feature points from the overlapping regions. By calculating the Euclidean distance and similarity between feature points, the system can accurately identify the correspondence between adjacent images. To improve matching efficiency, the algorithm prioritizes feature points with high saliency and uses the RANSAC (Random Sample Consensus) algorithm to eliminate mismatched point pairs, ensuring the reliability of feature correspondence.
The geometric correction stage unifies all local images to the same coordinate system through affine or projective transformation models. The system calculates transformation matrix parameters based on the feature matching results and adjusts the image by rotation, translation, and scaling to ensure complete overlap of overlapping areas. For radial and tangential distortions that may occur from optical lenses, the fvm-b manual image measuring instrument has a built-in distortion correction model that can preprocess the image based on calibration parameters to further eliminate imaging errors.
The intelligent fusion algorithm is responsible for eliminating stitching artifacts and optimizing image quality. The system employs a multi-band fusion strategy, decomposing the image into different frequency components and processing them separately: low-frequency components are smoothly transitioned through weighted averaging, while high-frequency components retain their original detail information. Furthermore, the algorithm can automatically detect and correct potential brightness differences and color deviations in the stitched area, ensuring visual consistency in the generated panoramic image.
For large-sized workpiece measurement, FVM-B's stitching function supports custom measurement path planning. Users can set the acquisition area and movement step size according to the workpiece shape, and the system automatically generates the optimal shooting sequence. During the measurement process, the software displays the stitching progress and overlap rate in real time, allowing operators to adjust the platform position or camera parameters to ensure data integrity. After stitching, the system supports geometric measurement, form and position tolerance analysis, and other operations on the panoramic image. The measurement results can be directly exported as CAD drawings or inspection reports.
FVM-B's image stitching function also boasts high environmental adaptability. Its light source system uses a programmable LED cold light source, supporting multi-mode switching of surface light, contour light, and coaxial light, which can optimize imaging contrast for workpieces of different materials. For example, using low-angle contour light on reflective metal surfaces can reduce glare interference; using coaxial light on transparent plastic parts can enhance edge clarity. This flexible light source configuration provides the stitching algorithm with high-quality original images, further improving measurement reliability.
