Surface Reconstruction from Multiple Images Filtering Non Lambert Regions

Halıcı, Uğur
In this study a new algorithm for 3D surface reconstruction from multiple images using a modified photometric stereo method is proposed and tested. The new algorithm, Filtered Lambert Photometric Stereo (FLPS), determines the non-Lambert pixels in the available images using a linearity test and constructs filtering masks for each image that corresponds to specular and self or cast shadow regions. Then, the photometric stereo is applied after eliminating the points in these masks. Tests carried out on synthetic images show that LPS on filtered images is a feasible solution when more than 4 images are available.


Performance results of the improved method of photometric stereo using local shape from shading on variable albedo samples
Sakarya, U; Erkmen, İsmet (2004-04-30)
This paper presents the performance results of the improved photometric stereo (PS) method [1], which integrates PS and local shape from shading (SFS) algorithms, on the variable albedo samples. PS problem can be solved with the linear equation system by using at least three input images illuminated on different directions. SFS recovers the shape of object from a single image and it does not work on the variable albedo samples. The main idea of the integration of PS and SFS can be explained briefly as follo...
Iterative Photometric Stereo with Shadow and Specular Region Detection for 3D Reconstruction
BUYUKATALAY, Soner; BİRGÜL, ÖZLEM; Halıcı, Uğur (2009-04-11)
Photometric stereo is a 3D reconstruction algorithm that uses the images of an object with different light conditions and its performance is affected by the shades and specular regions in the images. Especially, the use of Lambert reflectance model results in errors in the reconstructed surface normals. In this study an iterative approach was used to generate masks corresponding to these problematic regions and the surface normals were reconstructed using a Lambert based algorithm that excludes these region...
3D Object Modeling by Structured Light and Stereo Vision
Ozenc, Ugur; Tastan, Oguzhan; GÜLLÜ, MEHMET KEMAL (2015-05-19)
In this paper, we demonstrate a 3D object modeling system utilizing a setup which consists of two CMOS cameras and a DLP projector by making use of structured light and stereo vision. The calibration of the system is carried out using calibration pattern. The images are taken with stereo camera pair by projecting structured light onto the object and the correspondence problem is solved by both epipolar constraint of stereo vision and gray code constraint of structured light. The first experimental results s...
3d face representation and recognition using spherical harmonics
Tunçer, Fahri; Halıcı, Uğur; Department of Electrical and Electronics Engineering (2008)
In this study, a 3D face representation and recognition method based on spherical harmonics expansion is proposed. The input data to the method is range image of the face. This data is called 2.5 dimensional. Input faces are manually marked on the two eyes, nose and chin points. In two dimensions, using the marker points, the human face is modeled as two concentric half ellipses for the selection of region of interest. These marker points are also used in three dimensions to register the faces so that the n...
3D object recognition using scale space of curvatures
Akagündüz, Erdem; Ulusoy, İlkay; Department of Electrical and Electronics Engineering (2011)
In this thesis, a generic, scale and resolution invariant method to extract 3D features from 3D surfaces, is proposed. Features are extracted with their scale (metric size and resolution) from range images using scale-space of 3D surface curvatures. Different from previous scale-space approaches; connected components within the classified curvature scale-space are extracted as features. Furthermore, scales of features are extracted invariant of the metric size or the sampling of the range images. Geometric ...
Citation Formats
S. BÜYÜATALAY, Ö. BİRGÜL, and U. Halıcı, “Surface Reconstruction from Multiple Images Filtering Non Lambert Regions,” 2009, Accessed: 00, 2020. [Online]. Available: