Template Matching
57 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.
Benchmarks
These leaderboards are used to track progress in Template Matching
Most implemented papers
Learning Deep Features for One-Class Classification
We propose a deep learning-based solution for the problem of feature learning in one-class classification.
Deep Watershed Transform for Instance Segmentation
min2209/dwt
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CVPR 2017
Most contemporary approaches to instance segmentation use complex pipelines involving conditional random fields, recurrent neural networks, object proposals, or template matching schemes.
TernausNetV2: Fully Convolutional Network for Instance Segmentation
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.
Latent Fingerprint Recognition: Role of Texture Template
We propose a texture template approach, consisting of a set of virtual minutiae, to improve the overall latent fingerprint recognition accuracy.
Stochastic Distance Transform
We, thus, define a stochastic distance transform (SDT), which has an adjustable robustness to noise.
QATM: Quality-Aware Template Matching For Deep Learning
cplusx/QATM
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CVPR 2019
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.
Tracking Holistic Object Representations
xl-sr/THOR
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The framework leverages the idea of obtaining additional object templates during the tracking process.
GradNet: Gradient-Guided Network for Visual Object Tracking
LPXTT/GradNet-Pytorch
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ICCV 2019
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.
Deep Phase Correlation for End-to-End Heterogeneous Sensor Measurements Matching
The crucial step for localization is to match the current observation to the map.
Contrastive Learning of Relative Position Regression for One-Shot Object Localization in 3D Medical Images
LWHYC/RPR-Loc
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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.
