scholarly journals Does the luminance condition for test figures change the illusion?

2010 ◽  
Vol 8 (6) ◽  
pp. 727-727
Author(s):  
S. Naito ◽  
M. Kaite
Keyword(s):  
Luminance Condition

The Role of Transparency in Perceptual Grouping and Pattern Recognition

Perception ◽  
1992 ◽  
Vol 21 (1) ◽  
pp. 133-139 ◽  
Author(s):  
Takeo Watanabe ◽  
Patrick Cavanagh
Keyword(s):  
Pattern Recognition ◽  
Perceptual Grouping ◽  
Exposure Duration ◽  
The Other ◽  
Short Exposure ◽  
Stimulus Exposure ◽  
Digit Recognition ◽  
Invalid Condition ◽  
Luminance Condition

The shortest stimulus exposure time for which transparency can be seen was examined. In the first experiment, overlapping digits were presented for 120 ms and the luminance in the overlapping regions was varied. Subjects reported, in separate blocks of trials, either the apparent transparency of the digits or the identity of the digits. When the luminance was set so that one set of digits appeared to be seen through the other, recognition of the digits was high. When the luminance in the overlapping regions did not produce impressions of transparency, digit recognition was low. In the second experiment, digit identification at several stimulus durations was compared between stimuli that had luminance that was valid for transparency and stimuli that had invalid luminance. Performance was found to be higher in the valid luminance condition than in the invalid condition after as little as 60 ms exposure duration. This result suggests that the impression of transparency requires only relatively short exposure durations.

ROTATION INVARIANT MULTIVIEW FACE DETECTION USING SKIN COLOR REGRESSIVE MODEL AND SUPPORT VECTOR REGRESSION

2010 ◽  
Vol 24 (08) ◽  
pp. 1261-1280 ◽  
Author(s):  
PADMA POLASH PAUL ◽  
MD. MARUF MONWAR ◽  
MARINA L. GAVRILOVA ◽  
PATRICK S. P. WANG
Keyword(s):  
Support Vector Regression ◽  
Face Detection ◽  
Skin Color ◽  
Detection Method ◽  
Gaussian Mixture ◽  
Support Vector ◽  
Color Model ◽  
Rotation Invariant ◽  
Gray Scale Image ◽  
Luminance Condition

In this paper, an automatic rotation invariant multiview face detection method, which utilizes modified Skin Color Model (SCM), is presented. First, Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) based hybrid models are used to classify human skin regions from color images. The novelty of the adaptive hybrid model is its ability to predict the chromatic skin color band for individual images based on calibration differences of camera and luminance condition of environment. Classified skin regions are then converted to gray scale image with a threshold based on the predicted chromatic skin color bands, which further enhances detection performance. Next, Principle Component Analysis (PCA) is applied to gray segmented regions. Face detection is carried out based on the PCA-based extracted features, along with selected features, using support vector regression. The output of this procedure is used to report the final result of face detection. The proposed method is also beneficial for the rotation invariant face recognition problem.

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