QR code region detection method for improving background prior and foreground prior

Abstract

The invention provides a QR code region detection method for improving background prior and foreground prior. The QR code region detection method comprises the following steps: performing convex hulldetection on an input original image G; carrying out super-pixel segmentation; respectively carrying out multi-feature extraction on the image under each super-pixel scale; calculating a background saliency map of the original image G; calculating a foreground saliency map of the original image G, fusing the obtained final background saliency map and the final foreground saliency map to obtain a weak saliency map, training by adopting a multi-kernel learning enhancement method according to a training sample generated by the weak saliency map to obtain a strong saliency model, and applying themodel to all test samples to obtain a strong saliency map; and finally, weighting and fusing the strong saliency map and the weak saliency map to obtain a final saliency map, wherein the highlighted part in the final saliency map is the QR code area in the image. According to the QR code region detection method, the salient targets can be highlighted accurately and consistently, and the QR code area in the image can be detected accurately. The QR code region detection method provided by the invention has more advantages in the aspect of accuracy of salient target detection.

Description

Technical field [0001] The invention relates to a two-dimensional code area detection method that improves the background prior and the foreground prior. Background technique [0002] As an important preprocessing step to reduce computational complexity in computer vision problems, visual saliency is an effective mechanism for highlighting visual focus, which can accurately and quickly acquire the most important areas in an image, thereby reducing image processing time. Saliency detection has attracted much attention in recent years. Although significant progress has been made in related research, it is still a challenging task to develop efficient algorithms for salient object detection. [0003] Visual saliency detection algorithms can generally be divided into two categories: one is based on low-level features, using a data-driven bottom-up method to distinguish the salient target area from the surrounding background area; the other is based on high-level information and task-dr...

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