Parisian camera placement for vision metrology

Dunn, Enrique; Olague, Gustavo; Lutton, Évelyne

doi:10.1016/j.patrec.2005.07.019

Cited by 35 publications

(28 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[3]; this is true for all reasonable choices of U . Finding the global minimum is a difficult problem, and the prevailing approach in the literature seems to be more or less exhaustive search over a discretized parameter space, [4,7], or stochastic optimization methods, [13,14]. In the interest of speed, however, we adopt a gradient based optimization scheme, using the well-known Levenberg-Marquardt (LM) method.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…The emphasis is on obtaining the most accurate reconstruction given a limited number of cameras, and time can be spent finding an optimal configuration. For example, in [14] a genetic optimization algorithm is used to search the high-dimensional parameter space of camera placements. Similar stochastic algorithms are usually employed since the problem is intrinsically multi-modal i.e.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal View Path Planning for Visual SLAM

Haner

Heyden

2011

Image Analysis

View full text Add to dashboard Cite

Abstract. In experimental design and 3D reconstruction it is desirable to minimize the number of observations required to reach a prescribed estimation accuracy. Many approaches in the literature attempt to find the next best view from which to measure, and iterate this procedure. This paper discusses a continuous optimization method for finding a whole set of future imaging locations which minimize the reconstruction error of observed geometry along with the distance traveled by the camera between these locations. A computationally efficient iterative algorithm targeted toward application within real-time SLAM systems is presented and tested on simulated data.

show abstract

Section: Proposed Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal View Path Planning for Visual SLAM

Haner

Heyden

2011

Image Analysis

View full text Add to dashboard Cite

show abstract

“…Cooperative coevolution methods (e.g. Parisian evolution) have also produced good results for obstacle detection [26] and 3D reconstruction, the latter used either for computing the 3D coordinates from a pair of images [25], or for optimizing the placement of the different cameras [5]. A recent tutorial on evolutionary computer vision was given by Cagnoni [2].…”

Section: Related Workmentioning

confidence: 99%

Evolution of visual controllers for obstacle avoidance in mobile robotics

Barate

Manzanera

2009

Evol. Intel.

View full text Add to dashboard Cite

The purpose of this work is to automatically design vision algorithms for a mobile robot, adapted to its current visual context. In this paper we address the particular task of obstacle avoidance using monocular vision. Starting from a set of primitives composed of the different techniques found in the literature, we propose a generic structure to represent the algorithms, using standard resolution video sequences as an input, and velocity commands to control a wheel robot as an output. Grammar rules are then used to construct correct instances of algorithms, that are then evaluated using different protocols: evaluation of trajectories performed in a goal reaching task, or imitation of a hand-guided trajectory. A genetic program is applied to evolve populations of algorithms in order to optimize the performances of the controllers. The first results obtained in a simulated environment show that the evolution produces algorithms that can be easily interpreted and which are clearly adapted to the visual context. However, the resulting trajectories are often erratic, and the generalization capacities are poor. To improve the results, we propose to use a two-phase evolution combining imitation and goal reaching evaluations, and to add some constraints in the grammar rules to enforce a more generic behavior. The results obtained in simulation show that the evolved algorithms are more efficient and more generic. Finally, we apply the imitation based evolution on real sequences and test the evolved algorithms on a real robot. Though simplified by dropping the goal reaching constraint, the resulting algorithms behave well in a corridor centering task, and show certain generalization capacities.

show abstract

“…Our work is inspired from those that have followed the 0-1 canonical model of optimization [16] to solve the problem formulated in (1). But work of Olague proposed the use of genetic algorithms that are multi-cellular [13] or "Parisian" [14]. The genetic algorithms are known for their effectiveness to solve very complex and non-linear problems, whose form of the solution set is badly known, or when the problem is difficult to formalize by traditional methods.…”

Section: Resolving Methodsmentioning

confidence: 99%

“…Within this framework these techniques are used to set up networks of cameras allowing a very precise measurement of the objects either with very complex geometry or whose size forbid the more classical methods of measurement. Those cases are the measurement of industrial pieces [13], [14], or of buildings of complex architecture [15]. Finally, the third field that uses placement and camera control method is the localization of objects or humans.…”

Section: Problems Of Camera Placementmentioning

confidence: 99%

A Sensor Placement Approach for the Monitoring of Indoor Scenes

David¹,

Idasiak²,

Kratz³

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract.Within the framework of a French project, which aims at developing a new human presence sensor, we intend to design a sensor system simulator. During the establishment of the requirements of that new sensor we raised that the mission of a global scene survey could only be performed by a collection of several systems using very diverse technologies. This article presents the development of a method for the placement of multi-technology and multisensor systems. The considered environments are room or set of rooms in office buildings or individual homes. We will explain how we managed to represent the use of different sensors considering their various environments. Then, the way of exploiting these models using genetic algorithms is discussed. Those models are oriented for finding system placement and therefore for helping sensor networks deployment.

show abstract

Parisian camera placement for vision metrology

Cited by 35 publications

References 8 publications

Optimal View Path Planning for Visual SLAM

Optimal View Path Planning for Visual SLAM

Evolution of visual controllers for obstacle avoidance in mobile robotics

A Sensor Placement Approach for the Monitoring of Indoor Scenes

Contact Info

Product

Resources

About