Bob Fisher

How can we exploit typical architectural structures to improve model recovery

In this work we address the question of how to exploit typical architectural structures to improve recovery for CAD modeling of built environments from 3D data. In doing so we have examined the applicability of the GENOCOP III algorithm to the model fitting. The algorithm uses explicit domain knowledge, specifically geometric constraints in the form of parameterized surface models and a Euclidean fitting of geometric primitives that describe the parameterized surface models. Beside some results fitting parameterized surface models to real 3D datasets, example times for convergence and comparison with known ground truth are given.

Editorial for the Special Issue on 3D Data Processing, Visualization and Transmission

This special issue discusses new results, technologies and applications related to the capture, representation, processing, storage, transmission and visualization of 3D geometric and photometric models. The guest editors selected 7 papers from 128 as candidates for this publication. The topics of the accepted papers cover several important problems in the area of 3D vision. The first four papers discuss image registration through keypoint detection and matching. “Physical Scale Keypoints: Matching and Registration for Combined Intensity/Range Images” by Smith et al. presents a method to detect and match keypoints between images for which the depth of each pixel is known. “Interesting Interest Points—A Comparative Study of Interest Point Performance on a Unique Data Set” by Aanaes et al. presents a framework for the evaluation of keypoint detection with ground truth, using the recall rate as main criterion. The conference version of this paper received the Best Paper Award at 3DPVT 2010. “Imposing Semi-Local Geometric Constraints for Accurate Correspondences Selection in Structure from Motion: A GameTheoretic Perspective” by Albarelli et al. discusses the prob-

Are multimedia identification tools biodiversity-friendly?

This paper discusses the results of the LifeCLEF 2014 multimedia identification challenges with regards to the requirements of real-world ecological surveillance systems. In particular, we study the identification performances of the evaluated systems as a function of the ordinariness or rarity of the species in the dataset. This allows us to assess the ability of the underlying methods to be robust to heavily tailed distributions such as the ones encountered in real-world collections of life observations. Results show that all methods are more or less affected by the long-tail curse but that the best methods making use of classifiers with good discrimi- nation capacities do resist the phenomenon pretty well.

Proceedings of the 3rd ACM International Workshop on Multimedia Analysis for Ecological Data (MAED 2014)

This paper discusses the results of the LifeCLEF 2014 multimedia identification challenges with regards to the requirements of real-world ecological surveillance systems. In particular, we study the identification performances of the evaluated systems as a function of the ordinariness or rarity of the species in the dataset. This allows us to assess the ability of the underlying methods to be robust to heavily tailed distributions such as the ones encountered in real-world collections of life observations. Results show that all methods are more or less affected by the long-tail curse but that the best methods making use of classifiers with good discrimi- nation capacities do resist the phenomenon pretty well.