Test paper correction method based on image feature matching and alignment
A technology of image features and matching pairs, applied in the field of image processing, can solve the problems of not being able to find similar questions, not caring about the location of answers, and inaccurate cutting of questions, so as to achieve accurate corrections and avoid inaccurate positioning.
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Embodiment 1
[0028] A test paper correction method based on image feature matching and alignment, such as figure 1 As shown, the method includes the following steps S1 to S6:
[0029] S1, acquiring the original test paper image to be identified and the target test paper image;
[0030] Wherein, the original test paper image is collected by a camera, and the target test paper image is a test paper template PDF page.
[0031] S2, using an image extraction algorithm to extract the image features of the original test paper image and the target test paper image;
[0032] Preferably, the image extraction algorithm adopts the SIFT algorithm. The SIFT algorithm is invariant to image rotation and scale changes, has strong adaptability to changes in three-dimensional viewing angles and illumination changes, and is capable of performing extractions on massive amounts of information. Extract features quickly and accurately.
[0033] Among them, the specific feature extraction process is as follows:...
Embodiment 2
[0041] Taking one page of a test paper as an example, the specific process of the test paper correction method based on image feature matching and alignment is illustrated.
[0042] Step 1: Capture the original test paper image through the camera, and use the test paper template PDF page for the target test paper image; see figure 2 and image 3 .
[0043] Step 2: using the SIFT algorithm to extract the image features of the original test paper image and the target test paper image; the SIFT algorithm has invariance to image rotation and scale changes, and has strong adaptability to three-dimensional viewing angle changes and illumination changes, and It can quickly and accurately extract features from massive amounts of information.
[0044] Step 3: Set a certain threshold for the number of matching points, use the brute force matching algorithm to match the extracted image features, if it is lower than the threshold, output two images that do not correspond, otherwise out...
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