Scale invariant representation of 2 5D data

Date

2007-06-13

Author

AKAGUNDUZ, Erdem
ULUSOY PARNAS, İLKAY
BOZKURT, Nesli
Halıcı, Uğur

Metadata

Show full item record

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

Item Usage Stats

165
views

0
downloads

In this paper, a scale and orientation invariant feature representation for 2.5D objects is introduced, which may be used to classify, detect and recognize objects even under the cases of cluttering and/or occlusion. With this representation a 2.5D object is defined by an attributed graph structure, in which the nodes are the pit and peak regions on the surface. The attributes of the graph are the scales, positions and the normals of these pits and peaks. In order to detect these regions a "peakness" (or pitness) measure is defined based on Gaussian curvature calculation, which is performed at various scales on the surface. Finally a "position vs. scale" feature volume is obtained and the graph nodes are extracted from this feature space by volume segmentation techniques.

Subject Keywords

Object detection, Performance evaluation, Feature extraction, Solid modeling, Gaussian processes

URI

https://hdl.handle.net/11511/52695

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Rescoring detections based on contextual scores in object detection Zorlu, Ersan Vural; Akbaş, Emre; Department of Computer Engineering (2019) To detect objects in an image, current state-of-the-art object detectors firstly definecandidate object locations, and then classify each of them into one of the predefinedcategories or as background. They do so by using the visual features extracted locallyfrom the candidate locations; omitting the rich contextual information embedded inthe whole image. Contextual information can be utilized to complement the informa-tion extracted locally and thereby to improve object detection accuracy. Researchershave p...
Representation Learning for Contextual Object and Region Detection in Remote Sensing Firat, Orhan; Can, Gulcan; Yarman Vural, Fatoş Tunay (2014-08-28) The performance of object recognition and classification on remote sensing imagery is highly dependent on the quality of extracted features, amount of labelled data and the priors defined for contextual models. In this study, we examine the representation learning opportunities for remote sensing. First we attacked localization of contextual cues for complex object detection using disentangling factors learnt from a small amount of labelled data. The complex object, which consists of several sub-parts is fu...
Visual detection and tracking of moving objects Ergezer, Hamza; Leblebicioğlu, Mehmet Kemal (2007-06-13) In this paper, primary steps of a visual surveillance system are presented: moving object detection and tracking of these moving objects. Running average method has been used to detect the moving objects in the video, which is taken from a static camera. Tracking of foreground objects has been realized by using a Kalman filter. After background subtraction, morphological operators are used to remove noises detected as foreground. Active contour models (snakes) are the segmentation tools for the extracted fo...
3D Extended Object Tracking Using Recursive Gaussian Processes Kumru, Murat; Özkan, Emre (2018-07-10) In this study, we consider the challenging task of tracking dynamic 3D objects with unknown shapes by using sparse point cloud measurements gathered from the surface of the objects. We propose a Gaussian process based algorithm that is capable of tracking the dynamic behavior of the object and learn its shape in 3D simultaneously. Our solution does not require any parametric model assumption for the unknown shape. The shape of the objects is learned online via a Gaussian process. The proposed method can joi...
Continuous-time nonlinear estimation filters using UKF-aided gaussian sum representations Gökçe, Murat; Kuzuoğlu, Mustafa; Department of Electrical and Electronics Engineering (2014) A nonlinear filtering method is developed for continuous-time nonlinear systems with observations/measurements carried out in discrete-time by means of UKFaided Gaussian sum representations. The time evolution of the probability density function (pdf) of the state variables (or the a priori pdf) is approximated by solving the Fokker-Planck equation numerically using Euler’s method. At every Euler step, the values of the a priori pdf are evaluated at deterministic sample points. These values are used with Ga...

Citation Formats

E. AKAGUNDUZ, İ. ULUSOY PARNAS, N. BOZKURT, and U. Halıcı, “Scale invariant representation of 2 5D data,” 2007, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/52695.