Uncalibrated multi-viewpoint image correction method for parallel camera array
A multi-viewpoint image and camera array technology, applied in image communication, image analysis, image data processing, etc., can solve difficult application requirements, high precision requirements for multi-camera calibration parameters, inability to complete multi-camera calibration, etc. , to achieve the effect of being easy to implement and increasing the scope of multi-view correction
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Embodiment 1
[0056] Embodiment 1: The specific steps of the uncalibrated multi-viewpoint image correction method of this kind of parallel camera array are as follows: figure 1 The flow chart is shown. The method of the present invention is realized by programming on a computer platform, and the multi-viewpoint natural scene image collected by a parallel camera array is corrected; see figure 1 , the uncalibrated multi-viewpoint image correction method of the parallel camera array, first extracts feature points for the multi-viewpoint images collected by the parallel camera array and matches the feature points in the adjacent stereo pairs, then refines the matching information, and selects an appropriate amount of matching features Point correct each stereo pair, then project the multi-viewpoint image to the common correction plane and adjust the distance between the viewpoints, so that the corrected stereoscopic image can be synthesized to improve the quality of stereoscopic display; [0020...
Embodiment 2
[0062] Embodiment 2: This embodiment is basically the same as Embodiment 1, and the special features are as follows: see figure 2 , the specific process of the above step (1) is as follows:
[0063] (a) Establish the scale space of each viewpoint image at different scales, detect the extreme value in the scale space, and determine the position and scale of the feature point;
[0064] (b) Specify the direction parameter for each feature point according to the gradient direction distribution characteristics of the neighborhood pixels of the feature point;
[0065] (c) Generate its corresponding 128-dimensional SIFT feature vector according to the position, scale and direction characteristic information of each feature point;
[0066] (d) Take the Euclidean distance between the feature vectors as the similarity judgment measure, take a certain feature point in the left image, and find out the two feature points with the closest Euclidean distance to the right image, in these t...
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