Improving junction detection by semantic interpretation

Date

2007-03-08

Author

Kalkan, Sinan
Shi, Yan
Pilz, Florian
Kruger, Norbert

Metadata

Show full item record

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

Item Usage Stats

61
views

0
downloads

Every junction detector has a set of thresholds to make decisions about the junctionness of image points. Low-contrast junctions may pass such thresholds and may not be detected. Lowering the thresholds to find such junctions will lead to spurious junction detections at other image points. In this paper, we implement a junction-regularity measure to improve localization of junctions, and we develop a method to create semantic interpretations of arbitrary junction configurations at improved junction positions. We propose to utilize such a semantic interpretation as a feedback mechanism to filter false-positive junctions. We show results of our proposals on natural images using Harris and SUSAN operators as well as a continuous concept of intrinsic dimensionality.

URI

https://hdl.handle.net/11511/96633

Conference Name

Proceedings of the Second International Conference on Computer Vision Theory and Applications

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

A tracker-aware detector threshold optimization formulation for tracking maneuvering targets in clutter Aslan, Murat Samil; Saranlı, Afşar (Elsevier BV, 2011-09-01) In this paper, we consider a tracker-aware radar detector threshold optimization formulation for tracking maneuvering targets in clutter. The formulation results in an online method with improved transient performance. In our earlier works, the problem was considered in the context of the probabilistic data association filter (PDAF) for non-maneuvering targets. In the present study, we extend the ideas in the PDAF formulation to the multiple model (MM) filtering structures which use PDAFs as modules. Althou...
An Optimal Radar Detector Threshold Adaptation for Maneuvering Targets in Clutter Aslan, Murat Samil; Saranlı, Afşar (2009-04-11) In this paper, we consider the problem of radar detector threshold optimization for maneuvering targets in clutter In the earlier works, the problem was studied in the context of the probabilistic data association filter (PDAF) for non-maneuvering targets. In this study, we have extended the ideas, which were applied to the PDAF to the interacting multiple model PDAF (IMM-PDAF) for maneuvering targets. The proposed optimization problem and its solution show better results over the traditional approaches in ...
Improved Target Tracking with Road Network Information Orguner, Umut; Gustafsson, Fredrik (2009-03-14) In this paper we consider the problem of tracking targets, which can move both on-road and off-road, with particle filters utilizing the road-network information. It is argued that the constraints like speed-limits and/or one-way roads generally incorporated into on-road motion models make it necessary to consider additional high-bandwidth off-road motion models. This is true even if the targets under consideration are only allowed to move on-road due to the possibility of imperfect road-map information and...
Controlling a network of signalized intersections from temporal logical specifications Coogan, Samuel; Aydın Göl, Ebru; Arcak, Murat; Belta, Calin (2015-07-30) We propose a framework for generating a control policy for a traffic network of signalized intersections to accomplish control objectives expressed in linear temporal logic. Traffic management indeed calls for a rich class of objectives and offers a novel domain for these formal methods tools. We show that traffic networks possess structural properties that allow significant reduction in the time required to compute a finite state abstraction. We further extend our approach to a probabilistic framework by m...
IMPROVING PROPOSAL-BASED OBJECT DETECTION USING CONVOLUTIONAL CONTEXT FEATURES Kaya, Emre Can; Alatan, Abdullah Aydın (2018-10-10) A novel extension to proposal-based detection is proposed in order to learn convolutional context features for determining boundaries of objects better. Objects and their context are aimed to be learned through parallel convolutional stages. The resulting object and context feature maps are combined in such a way that they preserve their spatial relationship. The proposed algorithm is trained and evaluated on PASCAL VOC 2007 detection benchmark dataset and yielded improvements in performance over baseline, ...

Citation Formats

S. Kalkan, Y. Shi, F. Pilz, and N. Kruger, “Improving junction detection by semantic interpretation,” presented at the Proceedings of the Second International Conference on Computer Vision Theory and Applications, Barcelona, İspanya, 2007, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/96633.