scholarly journals Classification image analysis reveals different cognitive strategies for symmetry and face processing

2012 ◽  
Vol 12 (9) ◽  
pp. 632-632
Author(s):  
R. M. Jones ◽  
J. D. Victor ◽  
M. M. Conte
Keyword(s):  
Image Analysis ◽  
Face Processing ◽  
Cognitive Strategies ◽  
Classification Image

scholarly journals Visual search in noise: Revealing the influence of structural cues by gaze-contingent classification image analysis

10.1167/6.4.7 ◽  
2006 ◽  
Vol 6 (4) ◽  
pp. 7 ◽  
Author(s):  
Umesh Rajashekar ◽  
Alan C. Bovik ◽  
Lawrence K. Cormack
Keyword(s):  
Image Analysis ◽  
Visual Search ◽  
Classification Image

scholarly journals Frequency tuning of shape perception revealed by classification image analysis

2018 ◽  
Vol 18 (8) ◽  
pp. 9 ◽  
Author(s):  
John Wilder ◽  
Ingo Fruend ◽  
James H. Elder
Keyword(s):  
Image Analysis ◽  
Frequency Tuning ◽  
Shape Perception ◽  
Classification Image

scholarly journals Contrast dependence of spatial summation revealed by classification image analysis

2004 ◽  
Vol 4 (8) ◽  
pp. 539-539 ◽  
Author(s):  
Y. Morgenstern ◽  
J. H. Elder ◽  
Y. Hou
Keyword(s):  
Image Analysis ◽  
Spatial Summation ◽  
Classification Image

scholarly journals Classification image analysis of oriented texture detection

2010 ◽  
Vol 7 (9) ◽  
pp. 359-359
Author(s):  
C. C.-F. Or ◽  
J. H. Elder
Keyword(s):  
Image Analysis ◽  
Classification Image ◽  
Oriented Texture ◽  
Texture Detection

scholarly journals Computational analysis of tongue image for health diagnosis

2017 ◽  
Author(s):  
◽  
Meng Zhang
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Image Segmentation ◽  
The Body ◽  
Support Vector ◽  
Classification Image ◽  
Machine Learning Model ◽  
Machine Learning Applications ◽  
User Friendly ◽  
Traditional Chinese Medical

Tongue, the primary taste organ in the mouth, can reflect the whole body's health conditions based on the Traditional Chinese Medical (TCM) theories. Watching the tongue is one of the most common, essential and reliable methods for the TCM doctor to make diagnoses. In this thesis, a new health system is introduced based on tongue image analysis. The technologies adopted in this system ranged from tongue image processing algorithms to machine learning applications. The tongue image algorithms used in this work include image segmentation, tongue recognition and tongue image classification. Image segmentation was used to get rid of other unrelated parts, such as lip, face and neck, while keeping the tongue only. Then two recognition methods were applied to check whether the segmented result is a tongue or not. For different tongue patterns, the Support Vector Machine is applied to train a machine learning model and make predictions to classify the tongue into different labeled groups. An app named 'iTongue' is designed to monitor the body status by taking and processing tongue images in smart phones. The app provides a user-friendly, fast and powerful health tool based on TCM theories. The whole system is implemented in a webbased environment. An advanced portal was developed to connect the users and the TCM doctors. The users will not only obtain the analysis label of tongue images, but also get some life style recommendations based on the tongue image analysis. This portal helps the user understand more about his body status and guide him to adopt a more suitable diet and improve exercise.

scholarly journals Reversing the Luminance Polarity of Control Faces: Why Are Some Negative Faces Harder to Recognize, but Easier to See?

2021 ◽  
Vol 11 ◽  
Author(s):  
Abigail L. M. Webb
Keyword(s):  
Image Analysis ◽  
Face Processing ◽  
Present Report ◽  
Holistic Processing ◽  
Behavioral Data ◽  
Future Research ◽  
Trade Off ◽  
Affective Evaluation ◽  
Trade Offs ◽  
Performance Bias

Control stimuli are key for understanding the extent to which face processing relies on holistic processing, and affective evaluation versus the encoding of low-level image properties. Luminance polarity (LP) reversal combined with face inversion is a popular tool for severely disrupting the recognition of face controls. However, recent findings demonstrate visibility-recognition trade-offs for LP-reversed faces, where these face controls sometimes appear more salient despite being harder to recognize. The present report brings together findings from image analysis, simple stimuli, and behavioral data for facial recognition and visibility, in an attempt to disentangle instances where LP-reversed control faces are associated with a performance bias in terms of their perceived salience. These findings have important implications for studies of subjective face appearance, and highlight that future research must be aware of behavioral artifacts due to the possibility of trade-off effects.

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