Dichromatic Reflection Separation from a Single Image

Computer Analysis of Images and Patterns

Kersten

Schrater

2009

Abstract. We propose a method for rapidly classifying surface reflectance directly from the output of spatio-temporal filters applied to an image sequence of rotating objects. Using image data from only a single frame, we compute histograms of image velocities and classify these as being generated by a specular or a diffusely reflecting object. Exploiting characteristics of material-specific image velocities we show that our classification approach can predict the reflectance of novel 3D objects, as well as human perception.

Section: Introductionmentioning

confidence: 99%

Rapid Classification of Surface Reflectance from Image Velocities

Computer Analysis of Images and Patterns

Kersten

Schrater

2009

“…Light reflected by specular regions is highly polarized while that reflected by diffuse body color is not, thus, an alternative approach to using color information has been to identify specular highlights by looking at the amount of polarization in the reflected light, e.g., [14] and more recently [46]. Nayar et al [15] combines color and polarization profiles, and Chung et al [47] proposed an integrative feature-based technique that does not rely on the color signature of diffuse and specular reflectance components.…”

Section: Specular Highlight Detectionmentioning

confidence: 99%

Detection and localization of specular surfaces using image motion cues

Yılmaz

Machine Vision and Applications

2014

Successful identification of specularities in an image can be crucial for an artificial vision system when extracting the semantic content of an image or while interacting with the environment. We developed an algorithm that relies on scale and rotation invariant feature extraction techniques and uses motion cues to detect and localize specular surfaces. Appearance change in feature vectors is used to quantify the appearance distortion on specular surfaces, which has previously been shown to be a powerful indicator for specularity (Doerschner et al. in Curr Biol, 2011). The algorithm combines epipolar deviations (Swaminathan et al. in Lect Notes Comput Sci 2350:508-523, 2002) and appearance distortion, and succeeds in localizing specular objects in computer-rendered and real scenes, across a wide range of camera motions and speeds, object sizes and shapes, and performs well under image noise and blur conditions. © 2014 Springer-Verlag Berlin Heidelberg

“…However, their iterative approach takes a substantial amount of time. A more efficient algorithm has been developed by Chung et al [10]. They proposed a integrative feature based technique that classifies boundary pixels as either belonging to a highlight or not, without relying on the color signature of the diffuse and specular reflectance components.…”

Section: Introductionmentioning

confidence: 99%

Rapid classification of specular and diffuse reflection from image velocities

Kersten

Schrater

2011

Pattern Recognition

a b s t r a c tWe propose a method for rapidly classifying surface reflectance directly from the output of spatiotemporal filters applied to an image sequence of rotating objects. Using image data from only a single frame, we compute histograms of image velocities and classify these as being generated by a specular or a diffusely reflecting object. Exploiting characteristics of material-specific image velocities we show that our classification approach can predict the reflectance of novel 3D objects, as well as human perception.