Template Matching

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.

Most implemented papers

Learning Deep Features for One-Class Classification

PramuPerera/DeepOneClass 16 Jan 2018

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 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

ternaus/TernausNetV2 3 Jun 2018

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

prip-lab/MSU-LatentAFIS 27 Apr 2018

We propose a texture template approach, consisting of a set of virtual minutiae, to improve the overall latent fingerprint recognition accuracy.

Stochastic Distance Transform

MIDA-group/sdt 18 Oct 2018

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 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 21 Jul 2019

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 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

ZJU-Robotics-Lab/DPCN 21 Aug 2020

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 13 Dec 2020

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.