Disparity disambiguation by fusion of signal- and symbolic-level information

Date

2012-01-01

Author

Ralli, Jarno
Diaz, Javier
Kalkan, Sinan
Krueger, Norbert
Ros, Eduardo

Metadata

Show full item record

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

Item Usage Stats

216
views

0
downloads

We describe a method for resolving ambiguities in low-level disparity calculations in a stereo-vision scheme by using a recurrent mechanism that we call signal-symbol loop. Due to the local nature of low-level processing it is not always possible to estimate the correct disparity values produced at this level. Symbolic abstraction of the signal produces robust, high confidence, multimodal image features which can be used to interpret the scene more accurately and therefore disambiguate low-level interpretations by biasing the correct disparity. The fusion process is capable of producing more accurate dense disparity maps than the low- and symbolic-level algorithms can produce independently. Therefore we describe an efficient fusion scheme that allows symbolic- and low-level cues to complement each other, resulting in a more accurate and dense disparity representation of the scene.

Subject Keywords

Hardware and Architecture, Software, Computer Vision and Pattern Recognition, Computer Science Applications

URI

https://hdl.handle.net/11511/45941

Journal

MACHINE VISION AND APPLICATIONS

DOI

https://doi.org/10.1007/s00138-010-0266-z

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

Continuous dimensionality characterization of image structures Felsberg, Michael; Kalkan, Sinan; Kruger, Norbert (Elsevier BV, 2009-05-04) Intrinsic dimensionality is a concept introduced by statistics and later used in image processing to measure the dimensionality of a data set. In this paper, we introduce a continuous representation of the intrinsic dimension of an image patch in terms of its local spectrum or, equivalently, its gradient field. By making use of a cone structure and barycentric co-ordinates, we can associate three confidences to the three different ideal cases of intrinsic dimensions corresponding to homogeneous image patche...
Optimal lot-sizing/vehicle-dispatching policies under stochastic lead times and stepwise fixed costs Alp, O; Erkip, NK; Gullu, R (Institute for Operations Research and the Management Sciences (INFORMS), 2003-01-01) We characterize optimal policies of a dynamic lot-sizing/vehicle-dispatching problem under dynamic deterministic demands and stochastic lead times. An essential feature of the problem is the structure of the ordering cost, where a fixed cost is incurred every time a batch is initiated (or a vehicle is hired) regardless of the portion of the batch (or vehicle) utilized. Moreover, for every unit of demand not satisfied on time, holding and backorder costs are incurred. Under mild assumptions we show that the ...
Monte Carlo analysis of ridged waveguides with transformation media Ozgun, Ozlem; Kuzuoğlu, Mustafa (Wiley, 2013-07-01) A computational model is presented for Monte Carlo simulation of waveguides with ridges, by combining the principles of transformation electromagnetics and the finite methods (such as finite element or finite difference methods). The principle idea is to place a transformation medium around the ridge structure, so that a single and easy-to-generate mesh can be used for each realization of the Monte Carlo simulation. Hence, this approach leads to less computational resources. The technique is validated by me...
Sphere-packing bound for block-codes with feedback and finite memory Giacomo, Como; Nakiboğlu, Barış (2010-07-23) A lower bound bound is established on the error probability of fixed-length block-coding systems with finite memory feedback, which can be described in terms of a time dependent finite state machine. It is shown that the reliability function of such coding systems over discrete memoryless channels is upper-bounded by the sphere-packing exponent.
Learning customized and optimized lists of rules with mathematical programming Rudin, Cynthia; Ertekin Bolelli, Şeyda (Springer Science and Business Media LLC, 2018-12-01) We introduce a mathematical programming approach to building rule lists, which are a type of interpretable, nonlinear, and logical machine learning classifier involving IF-THEN rules. Unlike traditional decision tree algorithms like CART and C5.0, this method does not use greedy splitting and pruning. Instead, it aims to fully optimize a combination of accuracy and sparsity, obeying user-defined constraints. This method is useful for producing non-black-box predictive models, and has the benefit of a clear ...

Citation Formats

J. Ralli, J. Diaz, S. Kalkan, N. Krueger, and E. Ros, “Disparity disambiguation by fusion of signal- and symbolic-level information,” MACHINE VISION AND APPLICATIONS, pp. 65–77, 2012, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/45941.