Matching lines and points in an active stereo vision system using genetic algorithms

Woo, Sanghyuk; Dipandà, Albert

doi:10.1109/icip.2000.899382

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…We are currently working on a heuristic based on genetic algorithms to eliminate some ambiguities. 18 All the points for which no straight line satisfying the preceding criteria can be found by either the current method or the genetic algorithm are considered as noise. These points will thus be removed from the list of points to be considered during the reconstruction step.…”

Section: Three-dimensional Reconstructionmentioning

confidence: 99%

Calibration of a three-dimensional reconstruction system using a structured light source

et al. 2002

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We present a method for calibrating a range finder system composed of a camera and a structured light source. The system is used to reconstruct the three-dimensional (3-D) surface of an object. This is achieved by projecting a pattern, represented by a set of regularly spaced spots, on the surface of the object using the structured light source. An image of the illuminated object is next taken and by analyzing the distortion of the projected pattern, the 3-D surface of the object can be reconstructed. This reconstruction operation can be envisaged only if the system is calibrated. Instead of using a classical calibration method, which is based on the determination of the matrices that characterize the intrinsic and extrinsic parameters of the system, we propose a fast and easy to set up methodology, consisting of taking a sequence of images of a plane in translation on which a set of regularly spaced spots is projected using the structured light projection system. Next, a relationship between the position of the plane and the coordinates of the spots in the image is established. Using this relationship, we are able to determine the 3-D coordinates of a set of points on the object's surface knowing the 2-D coordinates of the spots in the image of the object taken by the range finder system. Finally, from the 3-D coordinates of the set of points, the 3-D surface of the object is reconstructed.

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Section: Three-dimensional Reconstructionmentioning

confidence: 99%

Calibration of a three-dimensional reconstruction system using a structured light source

et al. 2002

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“…With the help of genetic algorithm and spline representation, our algorithm gives a dense disparity map for each scan line, which is very desirable for 3D reconstruction and also exhibits a strong feasibility for parallel processing. Most of the genetic algorithm based stereo vision techniques give a sparse disparity map, such as [2] and [3]. However, with our algorithm, no edge detection is required.…”

Section: Introductionmentioning

confidence: 97%

“…A significant amount of work has been done to solve correspondence problem and most of the work can be classified into two categories: feature-based matching [2] [3] and area-based matching [4] [5] . Feature-based stereo techniques use symbolic features derived from intensity images rather than image intensities themselves, hence feature extractions are required in the preprocessing stage.…”

Section: Introductionmentioning

confidence: 99%

<title>Solving correspondence problems with 1D signal matching</title>

2004

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Signal matching can be applied to many applications, such as shape matching, stereo vision, image registration, and so on. With the development of hardware, 1D signal matching can be implemented with hardware to make fast processing more feasible. This is especially important for many real-time 3D vision applications such as unmanned air vehicles and mobile robots. When lighting variance is not significant in a controlled lighting environment or when the baseline is short, images taken from two viewpoints are quite similar. It is also true for each scan line pair if the attention is drawn to 1D signal. By processing 1D signal line by line, a dense disparity map can be achieved and 3D scene can be reconstructed. In this paper, we present a robust 1D signal matching method, which combines spline representation and genetic algorithm to obtain a dense disparity map. By imposing smoothness constraint implicitly, matching parameters can be solved in terms of their spline representations by minimizing a certain cost function. Genetic algorithm can then be used to perform the optimization task. Reconstruction results of three different scene settings are shown to prove the validity of our algorithm. Due to the similarity of the problem in nature, this algorithm can be easily extended to solve image registration and motion detection problems.

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“…A genetic algorithm that is able to eliminate local extremities is then used to perform the optimized matching. Unlike most genetic algorithm-based stereo vision techniques (e.g., [8,9]) that result in sparse disparity maps, our new algorithm generates a dense disparity map from matching camera scan line pairs. The increased information is very desirable for applications requiring 3D information extraction.…”

Section: Introductionmentioning

confidence: 99%

Hardware Implementation of a Spline-Based Genetic Algorithm for Embedded Stereo Vision Sensor Providing Real-Time Visual Guidance to the Visually Impaired

Lee

Anderson

Archibald

2008

EURASIP J. Adv. Signal Process.

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ChungMany image and signal processing techniques have been applied to medical and health care applications in recent years. In this paper, we present a robust signal processing approach that can be used to solve the correspondence problem for an embedded stereo vision sensor to provide real-time visual guidance to the visually impaired. This approach is based on our new one-dimensional (1D) spline-based genetic algorithm to match signals. The algorithm processes image data lines as 1D signals to generate a dense disparity map, from which 3D information can be extracted. With recent advances in electronics technology, this 1D signal matching technique can be implemented and executed in parallel in hardware such as field-programmable gate arrays (FPGAs) to provide real-time feedback about the environment to the user. In order to complement (not replace) traditional aids for the visually impaired such as canes and Seeing Eyes dogs, vision systems that provide guidance to the visually impaired must be affordable, easy to use, compact, and free from attributes that are awkward or embarrassing to the user. "Seeing Eye Glasses," an embedded stereo vision system utilizing our new algorithm, meets all these requirements.

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Matching lines and points in an active stereo vision system using genetic algorithms

Cited by 6 publications

References 7 publications

Calibration of a three-dimensional reconstruction system using a structured light source

Calibration of a three-dimensional reconstruction system using a structured light source

<title>Solving correspondence problems with 1D signal matching</title>

Hardware Implementation of a Spline-Based Genetic Algorithm for Embedded Stereo Vision Sensor Providing Real-Time Visual Guidance to the Visually Impaired

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