Better than SIFT?

Khan, Nabeel; McCane, Brendan; Mills, Steven

doi:10.1007/s00138-015-0689-7

Cited by 37 publications

(17 citation statements)

References 52 publications

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“…By doing this, we can compare the performance of matching method under the same conditions. In addition, SIFT showed better performance compared with the other feature descriptors such as SURF and BRISK in our experiment which is consistent with other findings [35,36] for images with various deformations. Although SIFT has slower speed for extracting features, it was determined to be an appropriate choice for the feature descriptor.…”

Section: Image Set Annotationssupporting

confidence: 91%

Deformable Object Matching Algorithm Using Fast Agglomerative Binary Search Tree Clustering

et al. 2017

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Deformable objects have changeable shapes and they require a different method of matching algorithm compared to rigid objects. This paper proposes a fast and robust deformable object matching algorithm. First, robust feature points are selected using a statistical characteristic to obtain the feature points with the extraction method. Next, matching pairs are composed by the feature point matching of two images using the matching method. Rapid clustering is performed using the BST (Binary Search Tree) method by obtaining the geometric similarity between the matching pairs. Finally, the matching of the two images is determined after verifying the suitability of the composed cluster. An experiment with five different image sets with deformable objects confirmed the superior robustness and independence of the proposed algorithm while demonstrating up to 60 times faster matching speed compared to the conventional deformable object matching algorithms.

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Section: Image Set Annotationssupporting

confidence: 91%

Deformable Object Matching Algorithm Using Fast Agglomerative Binary Search Tree Clustering

et al. 2017

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“…Note that without our orientation estimation, although the best among the competitors, the gap is small. Also, as pointed out in descriptor performance surveys [1,19,28,29], SIFT or EF-SIFT generally Table 1. Average rank of each method which summarizes the results in Fig.…”

Section: Descriptor Matching Performancesmentioning

confidence: 92%

Learning to Assign Orientations to Feature Points

Verdie

Fua

et al. 2016

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

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We show how to train a Convolutional Neural Network to assign a canonical orientation to feature points given an image patch centered on the feature point. Our method improves feature point matching upon the state-of-the art and can be used in conjunction with any existing rotation sensitive descriptors. To avoid the tedious and almost impossible task of finding a target orientation to learn, we propose to use Siamese networks which implicitly find the optimal orientations during training. We also propose a new type of activation function for Neural Networks that generalizes the popular ReLU, maxout, and PReLU activation functions. This novel activation performs better for our task. We validate the effectiveness of our method extensively with four existing datasets, including two non-planar datasets, as well as our own dataset. We show that we outperform the state-of-the-art without the need of retraining for each dataset.

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“…For keypoint description, SIFT uses spatial histogram of the image gradients, while SURF introduced many approximations to this approach, using Haar wavelet responses determined for a scale-dependent window, or integral images to speed up computations. Despite high-quality description provided by SIFT [16], this technique, and also SURF, suffers from long computation and matching time. Therefore, binary descriptors have been developed.…”

Section: Proposed Methodsmentioning

confidence: 99%

An approach to vision-based localisation with binary features for partially sighted people

Oszust

Padjasek

Kasprzyk

2017

SIViP

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In this paper, an approach to the development of a localisation system for supporting visually impaired people is proposed. Instead of using unique visual markers or radio tags, this approach relies on image recognition with local feature descriptors. In order to provide fast and robust keypoint description, a new binary descriptor is introduced. The descriptor computation pipeline selects four image patches with scale-dependent sizes around the keypoint and then places five square pixel blocks within each patch. The binary string is obtained in pairwise tests between directional gradients obtained for blocks. In contrary to other binary descriptors, tests take into account gradient values obtained for blocks from all patches. The proposed approach is extensively tested using six demanding image datasets. Some of them contain labelled indoor and outdoor images under different real-world transformations, as well as challenging illumination conditions. Two datasets were prepared for the needs of this research. Experimental evaluation reveals that the introduced binary descriptor is more robust and achieves shorter computation time than state-of-the-art floating-point and binary descriptors. Furthermore, the approach outperforms other techniques in image recognition tasks, making it more suitable for the vision-based localisation.

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Better than SIFT?

Cited by 37 publications

References 52 publications

Deformable Object Matching Algorithm Using Fast Agglomerative Binary Search Tree Clustering

Deformable Object Matching Algorithm Using Fast Agglomerative Binary Search Tree Clustering

Learning to Assign Orientations to Feature Points

An approach to vision-based localisation with binary features for partially sighted people

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