Abstract
Template matching is a simple image detection algorithm that can easily detect different types of objects just by changing the template without tedious training procedures. Despite these advantages, template matching is not currently widely used. This is because traditional template matching is not very reliable for images that differ from the template. The reliability of template matching can be improved by using additional information (depths for the template) available from the vision sensor system. Methods of obtaining the depth of a template using stereo vision or a few (two or more) template images or a short template video via mono vision are well known in the vision literature and have been commercialized. In this strategy, this paper proposes a template matching vision sensor system that can easily detect various types of objects without prior training. To this end, by using the additional information provided by the vision sensor system, we study a method to increase the reliability of template matching, even when there is a difference in the 3D direction and size between the template and the image. Template images obtained through the vision sensor provide a depth template. Using this depth template, it is possible to predict the change of the image according to the difference in the 3D direction and the size of the object. Using the predicted changes in these images, the template is calibrated close to the given image, and then template matching is performed. For ease of use, the algorithm is proposed as a closed form solution that avoids tedious recursion or training processes. For wider application and more accurate results, the proposed method considers the 3D direction and size difference in the perspective projection model and the general 3D rotation model.
Highlights
Template matching is a well-known technique often used in many image-processing and computer vision tasks, e.g., object detection and recognition [1,2]
We studied the template matching method
To reduce the sensitivity of the conventional template matching methods to pose mismatches between the template image and the matching image, the proposed method adjusts the pose of the template image to that of the matching image
Summary
Template matching is a well-known technique often used in many image-processing and computer vision tasks, e.g., object detection and recognition [1,2]. This is due to the simplicity and efficiency of the method. Compared with learning-based methods e.g., deep-learning, NCC, and Viola-Jones. Object detection using template matching is often preferred in applications where the detection is required to perform using a single template supplied by the user and off-line learning every possible object class is impossible. The template matching method can be applied to various objects by changing only one template
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