53 papers with code • 0 benchmarks • 0 datasets
Template matching is a technique that is used to find a subimage or a patch (called the template) within a larger image. The basic idea behind template matching is to slide the template image over the larger image and compare the template to each portion of the larger image to determine the similarity between the template and the corresponding portion of the larger image.
These leaderboards are used to track progress in Template Matching
Most contemporary approaches to instance segmentation use complex pipelines involving conditional random fields, recurrent neural networks, object proposals, or template matching schemes.
The most common approaches to instance segmentation are complex and use two-stage networks with object proposals, conditional random-fields, template matching or recurrent neural networks.
We propose a texture template approach, consisting of a set of virtual minutiae, to improve the overall latent fingerprint recognition accuracy.
Finding a template in a search image is one of the core problems many computer vision, such as semantic image semantic, image-to-GPS verification \etc.
In this work, we propose a novel gradient-guided network to exploit the discriminative information in gradients and update the template in the siamese network through feed-forward and backward operations.
Contrastive Learning of Relative Position Regression for One-Shot Object Localization in 3D Medical Images
To address this problem, we present a one-shot framework for organ and landmark localization in volumetric medical images, which does not need any annotation during the training stage and could be employed to locate any landmarks or organs in test images given a support (reference) image during the inference stage.