A shape descriptor based on circular Hidden Markov Model

Given the shape information of an object, can we find visually meaningful "n" objects in an image database, which is ranked from the most similar to the n(th) similar one? The answer to this question depends on the complexity of the images in the database and the complexity of the objects in the query.


A probabilistic sparse skeleton based object detection
Altinoklu, Burak; Ulusoy, İlkay; Tarı, Zehra Sibel (Elsevier BV, 2016-11)
We present a Markov Random Field (MRF) based skeleton model for object shape and employ it in a probabilistic chamfer-matching framework for shape based object detection. Given an object category, shape hypotheses are generated from a set of sparse (coarse) skeletons guided by suitably defined unary and binary potentials at and between shape parts. The Markov framework assures that the generated samples properly reflect the observed or desired shape variability. As the model employs a sparsely sampled skele...
Extended Target Tracking Using Gaussian Processes
Wahlström, Niklas; Özkan, Emre (2015-08-15)
In this paper, we propose using Gaussian processes to track an extended object or group of objects, that generates multiple measurements at each scan. The shape and the kinematics of the object are simultaneously estimated, and the shape is learned online via a Gaussian process. The proposed algorithm is capable of tracking different objects with different shapes within the same surveillance region. The shape of the object is expressed analytically, with well-defined confidence intervals, which can be used ...
Okman, O. Erman; Akar, Gözde (2013-05-31)
In this paper a novel fast circle detection algorithm is proposed which depends on the spatial properties of the connected components on the image. Two 1-D transforms of each connected component is obtained by taking the Radon Transform of the image for two different directions, which are in fact the integrations of the image through horizontal and vertical directions. Circles are detected using the similarities of detected peaks on the transformed functions and the characteristics of the values in between ...
A PHD Filter for Tracking Multiple Extended Targets Using Random Matrices
Granstrom, Karl; Orguner, Umut (2012-11-01)
This paper presents a random set based approach to tracking of an unknown number of extended targets, in the presence of clutter measurements and missed detections, where the targets' extensions are modeled as random matrices. For this purpose, the random matrix framework developed recently by Koch et al. is adapted into the extended target PHD framework, resulting in the Gaussian inverse Wishart PHD (GIW-PHD) filter. A suitable multiple target likelihood is derived, and the main filter recursion is present...
Generalized beam angle statistics for shape descrition
Tola, Ömer Önder; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2004)
In this thesis, we introduce a new shape descriptor and a graph based matching algorithm to detect a template shape in an image that contains a single object. The shape descriptor, Generalized Beam Angle Statistics, GBAS is obtained with the generalization of the boundary based shape descriptor, Beam Angle Statistics, BAS
Citation Formats
N. Arica and F. T. Yarman Vural, “A shape descriptor based on circular Hidden Markov Model,” 2000, p. 924, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/62709.