SEGMENTATION USING THE EDGE STRENGTH FUNCTION AS A SHAPE PRIOR WITHIN A LOCAL DEFORMATION MODEL

Download

index.pdf

Date

2009-01-01

Author

Erdem, Erkut
Tarı, Zehra Sibel
Vese, Luminita

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

212
views

99
downloads

This paper presents a new image segmentation framework which employs a shape prior in the form of an edge strength function to introduce a higher-level influence on the segmentation process. We formulate segmentation as the minimization of three coupled functionals, respectively, defining three processes: prior-guided segmentation, shape feature extraction and local deformation estimation. Particularly, the shape feature extraction process is in charge of estimating an edge strength function from the evolving object region. The local deformation estimation process uses this function to determine a meaningful correspondence between a given prior and the evolving object region, and the deformation map estimated in return supervises the segmentation by enforcing the evolving object boundary towards the prior shape.

Subject Keywords

Prior-based image segmentation, Registration, Variational methods

URI

https://hdl.handle.net/11511/57219

DOI

https://doi.org/10.1109/icip.2009.5414504

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Segmentation and deciısion fusion for building detection Karadag, Ozge Oztimur; Senaras, Caglar; Yarman Vural, Fatoş Tunay (2014-07-18) Segment based classification is one of the popular approaches for object detection, where the performance of the classification task is sensitive to the accuracy of the output of the initial segmentation. Most of these studies includes generic segmentation methods and it is assumed that the segmentation output is compatible with the subsequent classification method. However, depending on the problem domain the properties of the regions such as size, shape etc. which are suitable for classification may vary....
REGION-BASED IMAGE SEGMENTATION VIA GRAPH CUTS Cigla, Cevahir; Alatan, Abdullah Aydın (2008-01-01) A graph theoretic color image segmentation algorithm is proposed, in which the popular normalized cuts image segmentation method is improved with modifications on its graph structure. The image is represented by a weighted undirected graph, whose nodes correspond to over-segmented regions, instead of pixels, that decreases the complexity of the overall algorithm. In addition, the link weights between the nodes are calculated through the intensity similarities of the neighboring regions. The irregular distri...
Segmentation Driven Object Detection with Fisher Vectors Cinbiş, Ramazan Gökberk; Schmid, Cordelia (2013-01-01) We present an object detection system based on the Fisher vector (FV) image representation computed over SIFT and color descriptors. For computational and storage efficiency, we use a recent segmentation-based method to generate class-independent object detection hypotheses, in combination with data compression techniques. Our main contribution is a method to produce tentative object segmentation masks to suppress background clutter in the features. Re-weighting the local image features based on these masks...
Segmentation Fusion for Building Detection Using Domain-Specific Information Karadag, Ozge Oztimur; Senaras, Caglar; Yarman Vural, Fatoş Tunay (2015-07-01) Segment-based classification is one of the popular approaches for object detection, where the performance of the classification task is sensitive to the accuracy of the output of the initial segmentation. Majority of the object detection systems directly use one of the generic segmentation algorithms, such as mean shift or k-means. However, depending on the problem domain, the properties of the regions such as size, color, texture, and shape, which are suitable for classification, may vary. Besides, fine tu...
Object recognition and segmentation via shape models Altınoklu, Metin Burak; Ulusoy, İlkay; Tarı, Zehra Sibel; Department of Electrical and Electronics Engineering (2016) In this thesis, the problem of object detection, recognition and segmentation in computer vision is addressed with shape based methods. An efficient object detection method based on a sparse skeleton has been proposed. The proposed method is an improved chamfer template matching method for recognition of articulated objects. Using a probabilistic graphical model structure, shape variation is represented in a skeletal shape model, where nodes correspond to parts consisting of lines and edges correspond to pa...

Citation Formats

E. Erdem, Z. S. Tarı, and L. Vese, “SEGMENTATION USING THE EDGE STRENGTH FUNCTION AS A SHAPE PRIOR WITHIN A LOCAL DEFORMATION MODEL,” 2009, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/57219.