Robot vision
Determining Reflectance Properties of an Object Using Range and Brightness Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inverting an illumination model from range and intensity maps
CVGIP: Image Understanding
Object shape and reflectance modeling from observation
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Texture and reflection in computer generated images
Communications of the ACM
IEEE Computer Graphics and Applications
Estimating Reflection Parameters from a Single Color Image
IEEE Computer Graphics and Applications
Separating Reflection Components Based on Chromaticity and Noise Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Registration of Range and Color Images Using Gradient Constraints and Range Intensity Images
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Correction of Color Information of a 3D Model Using a Range Intensity Image
3DIM '05 Proceedings of the Fifth International Conference on 3-D Digital Imaging and Modeling
Correction of intensity of a color image using a range intensity image
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Registration of 3D geometric model and color images using SIFT and range intensity images
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
An integrated framework for biometrics security
iUBICOM'10 Proceedings of the 5th international conference on Ubiquitous and Collaborative Computing
Evaluating a color-based active basis model for object recognition
Computer Vision and Image Understanding
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Most active optical range sensors record, simultaneously with the range image, the amount of light reflected at each measured surface location: this information forms what is called a range intensity image, also known as a reflectance image. This paper proposes a method that uses this type of image for the correction of the color information of a textured 3D model. This color information is usually obtained from color images acquired using a digital camera. The lighting condition for the color images are usually not controlled, thus this color information may not be accurate. On the other hand, the illumination condition for the range intensity image is known since it is obtained from a controlled lighting and observation configuration, as required for the purpose of active optical range measurement. The paper describes a method for combining the two sources of information, towards the goal of compensating for a reference range intensity image is first obtained by considering factors such as sensor properties, or distance and relative surface orientation of the measured surface. The color image of the corresponding surface portion is then corrected using this reference range intensity image. A B-spline interpolation technique is applied to reduce the noise of range intensity images. Finally, a method for the estimation of the illumination color is applied to compensate for the light source color. Experiments show the effectiveness of the correction method using range intensity images.