Image Descriptors Based on Curvature Histograms

Fischer, Philipp; Brox, Thomas

doi:10.1007/978-3-319-11752-2_19

Cited by 14 publications

(8 citation statements)

References 12 publications

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“…The curvature computed at points along a contour is often used to represent shape information [6,14,20]. For differential curvature, this is defined as the change of the angle of the normal vector along the length of the contour [6]. This representation is sensitive to noise, however, and instead we use integral curvature.…”

Section: Related Workmentioning

confidence: 99%

Integral Curvature Representation and Matching Algorithms for Identification of Dolphins and Whales

Weideman

Jablons

Holmberg³

et al. 2017

2017 IEEE International Conference on Computer Vision Workshops (ICCVW)

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We address the problem of identifying individual cetaceans from images showing the trailing edge of their fins. Given the trailing edge from an unknown individual, we produce a ranking of known individuals from a database. The nicks and notches along the trailing edge define an individual's unique signature. We define a representation based on integral curvature that is robust to changes in viewpoint and pose, and captures the pattern of nicks and notches in a local neighborhood at multiple scales. We explore two ranking methods that use this representation. The first uses a dynamic programming time-warping algorithm to align two representations, and interprets the alignment cost as a measure of similarity. This algorithm also exploits learned spatial weights to downweight matches from regions of unstable curvature. The second interprets the representation as a feature descriptor. Feature keypoints are defined at the local extrema of the representation. Descriptors for the set of known individuals are stored in a tree structure, which allows us to perform queries given the descriptors from an unknown trailing edge. We evaluate the top-k accuracy on two real-world datasets to demonstrate the effectiveness of the curvature representation, achieving top-1 accuracy scores of approximately 95% and 80% for bottlenose dolphins and humpback whales, respectively.

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Section: Related Workmentioning

confidence: 99%

Integral Curvature Representation and Matching Algorithms for Identification of Dolphins and Whales

Weideman

Jablons

Holmberg³

et al. 2017

2017 IEEE International Conference on Computer Vision Workshops (ICCVW)

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“…Curvature Descriptor Histogram is a shape based feature [26]. For each of the pixel of an image I(x,y) it is defined as:…”

Section: ) Curvature Descriptor Histogram (Cdh)mentioning

confidence: 99%

Exploiting Cascaded Ensemble of Features for the Detection of Tuberculosis Using Chest Radiographs

et al. 2021

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Tuberculosis (TB) is a communicable disease that is one of the top 10 causes of death worldwide according to the World Health Organization [1]. Hence, Early detection of Tuberculosis is an important task to save millions of lives from this life threatening disease. For diagnosing TB from chest X-Ray, different handcrafted features were utilized previously and they provided high accuracy even in a small dataset. However, at present, deep learning (DL) gains popularity in many computer vision tasks because of their better performance in comparison to the traditional manual feature extraction based machine learning approaches and Tuberculosis detection task is not an exception. Considering all these facts, a cascaded ensembling method is proposed that combines both the hand-engineered and the deep learningbased features for the Tuberculosis detection task. To make the proposed model more generalized, rotationinvariant augmentation techniques are introduced which is found very effective in this task. By using the proposed method, outstanding performance is achieved through extensive simulation on two benchmark datasets that verifies the effectiveness of the method.

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“…Curvature detection is one of the most important techniques of second-order derivative-based approaches used for extracting the structure properties. Recently, [17] has proposed a detector based on curvature κ expressed as the change of the image gradient along the tangent to obtain a scalar q approximating κ. In addition, [24] presented PCBR, Principal Curvature-Based Regions, detector that uses the maximum or minimum eigenvalue of the Hessian matrix to find the principal curvature in a multi-scale space.…”

Section: A Classic 2d Feature Detectionmentioning

confidence: 99%

Using Curvilinear Features in Focus for Registering a Single Image to a 3D Object

Rashwan

Chambon

Gurdjos

et al. 2019

IEEE Trans. on Image Process.

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In the context of 2D/3D registration, this paper introduces an approach that allows to match features detected in two different modalities: photographs and 3D models, by using a common 2D reprensentation. More precisely, 2D images are matched with a set of depth images, representing the 3D model. After introducing the concept of curvilinear saliency, related to curvature estimation, we propose a new ridge and valley detector for depth images rendered from 3D model. A variant of this detector is adapted to photographs, in particular by applying it in multi-scale and by combining this feature detector with the principle of focus curves. Finally, a registration algorithm for determining the correct viewpoint of the 3D model and thus the pose is proposed. It is based on using histogram of gradients features adapted to the features manipulated in 2D and in 3D, and the introduction of repeatability scores. The results presented highlight the quality of the features detected, in term of repeatability, and also the interest of the approach for registration and pose estimation.

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Image Descriptors Based on Curvature Histograms

Cited by 14 publications

References 12 publications

Integral Curvature Representation and Matching Algorithms for Identification of Dolphins and Whales

Integral Curvature Representation and Matching Algorithms for Identification of Dolphins and Whales

Exploiting Cascaded Ensemble of Features for the Detection of Tuberculosis Using Chest Radiographs

Using Curvilinear Features in Focus for Registering a Single Image to a 3D Object

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