Contour Detection by CORF Operator

Azzopardi, George; Petkov, Nicolai

doi:10.1007/978-3-642-33269-2_50

Cited by 4 publications

(2 citation statements)

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“…A contour operator for edge detection (CORF) (Azzopardi and Petkov 2012) is another popular tool particularly suited to low contrast images. CORF also gives a bonus estimate for the object’s surface orientation, ω i at each point p i .…”

Section: Hierarchical Model For Multiple Curvesmentioning

confidence: 99%

Bayesian Multiscale Modeling of Closed Curves in Point Clouds

Gu¹,

Pati²,

Dunson³

2014

Journal of the American Statistical Association

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Modeling object boundaries based on image or point cloud data is frequently necessary in medical and scientific applications ranging from detecting tumor contours for targeted radiation therapy, to the classification of organisms based on their structural information. In low-contrast images or sparse and noisy point clouds, there is often insufficient data to recover local segments of the boundary in isolation. Thus, it becomes critical to model the entire boundary in the form of a closed curve. To achieve this, we develop a Bayesian hierarchical model that expresses highly diverse 2D objects in the form of closed curves. The model is based on a novel multiscale deformation process. By relating multiple objects through a hierarchical formulation, we can successfully recover missing boundaries by borrowing structural information from similar objects at the appropriate scale. Furthermore, the model’s latent parameters help interpret the population, indicating dimensions of significant structural variability and also specifying a ‘central curve’ that summarizes the collection. Theoretical properties of our prior are studied in specific cases and efficient Markov chain Monte Carlo methods are developed, evaluated through simulation examples and applied to panorex teeth images for modeling teeth contours and also to a brain tumor contour detection problem.

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Section: Hierarchical Model For Multiple Curvesmentioning

confidence: 99%

Bayesian Multiscale Modeling of Closed Curves in Point Clouds

Gu¹,

Pati²,

Dunson³

2014

Journal of the American Statistical Association

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“…Here we use Gabor filters as they have been widely used for more than two decades. Other orientation-selective filters, such as CORF [4,5], may also be used. A COSFIRE filter response is then computed as the weighted geometric mean of the responses of certain Gabor filters at specific locations with respect to its receptive field center.…”

Section: Computational Model and Its Implementationmentioning

confidence: 99%

COSFIRE: A Brain-Inspired Approach to Visual Pattern Recognition

Azzopardi

Petkov

2014

Lecture Notes in Computer Science

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Abstract. The primate visual system has an impressive ability to generalize and to discriminate between numerous objects and it is robust to many geometrical transformations as well as lighting conditions. The study of the visual system has been an active reasearch field in neuropysiology for more than half a century. The construction of computational models of visual neurons can help us gain insight in the processing of information in visual cortex which we can use to provide more robust solutions to computer vision applications. Here, we demonstrate how inspiration from the functions of shape-selective V4 neurons can be used to design trainable filters for visual pattern recognition. We call this approach COSFIRE, which stands for Combination of Shifted Filter Responses. We illustrate how a COSFIRE filter can be configured to be selective for the spatial arrangement of lines and/or edges that form the shape of a given prototype pattern. Finally, we demonstrate the effectiveness of the COSFIRE approach in three applications: the detection of vascular bifurcations in retinal fundus images, the localization and recognition of traffic signs in complex scenes and the recognition of handwritten digits. This work is a further step in understanding how visual information is processed in the brain and how information on pixel intensities is converted into information about objects. We demonstrate how this understanding can be used for the design of effective computer vision algorithms.

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