English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

This paper presents a model and an algorithm for the detection of specularities from Lambertian reflections using multiple color images from different viewing directions. The algorithm, called spectral differencing, is based on the Larabertian consistency that color image irradiance from Lambertian reflection at an object surface does not change depending on viewing directions, but color image irradiance from specular reflection or from a mixture of Lambertian and specular reflections does change. The spectral differencing is a pixelwise parallel algorithm, and it detects specularities by color differences between a small number of images without using any feature correspondence or image segmentation. Applicable objects include uniformly or nonuniformly colored dielectrics and metals, under extended and multiply colored scene illumination. Experimental results agree with the model, and the algorithm performs well within the limitations discussed. 1 I n t r o d u c t i o n Recently there has been a growing interest in the visual measurement of surface reflectance properties in both basic and applied computer vision research. Most vision algorithms are based on the assumption that visually observable surfaces consist only of Lambertian reflection. Specularity is one of the major hindrances to vision tasks such as image segmentation, object recognition and shape or structure determination. Without any means of correctly identifying reflectance types, image segmentation algorithms can be easily misled into interpreting specular highlights as separate regions or as different objects with high albedo. Algorithms such as shape from shading and structure from stereo or motion can also produce false surface orientation or depth from the nonLambertian nature of specularity. Therefore it is desirable to have algorithms for estimating reflectance properties as a very early stage or an integral part of many visual processes. In many industrial applications, there is a great demand for visual inspection of surface reflectance which is directly related to the quality of surface finish and paint. Although the measurement of surface reflectance properties in applied physics has been the topic of many research efforts, only a few attempts in computer vision have been made until recently. There has been an approach to the detection of specularity with a single gray-level image using the Lambertian constraints by Brelstaff and Blake [BB88]. They attempted to extract maximal information from a single gray-scale image. * This work was partly supported by E. I. du Pont de Nemours and Company, Inc. and partly by the following grants: Navy Grant N0014-88-K-0630, AFOSR Grants 88-0244, AFOSR 88-0296; Army/DAAL 03-89-C-0031PRI; NSF Grants CISE/CDA 88-22719, IRI 89-06770, and ASC 91 0813. We thank Ales Leonardis at University of Ljubljana, Slovenija, for his collaboration on our color research during his stay at the GRASP Lab. Special thanks to Steve Sharer at Carnegie Mellon University for helpful discussions and comments.

Detection of specularity using color and multiple views