The Müller-Lyer Illusion in Interpolated Figures

1998 ◽  
Vol 87 (2) ◽  
pp. 499-504 ◽  
Author(s):  
Vito Di Maio ◽  
Petr Lánsky
Keyword(s):  
Visual Perception ◽  
Function Theory ◽  
Input Image ◽  
Model Based ◽  
Lyer Illusion ◽  
Image Function

The Müller-Lyer patterns formed by separate dots have been used as stimuli in an experiment on visual perception to assess the influence of the number of dots composing the figures on the magnitude of the illusion. As predicted by our model, based on the Image Function theory, an increase was noted in the magnitude of illusion when the number of dots composing the arrowheads was increased. It follows from the model that filtering of the input image plays a central role in the formation of the illusion.

Visual Perception of Area and Hypnotic Susceptibility

1995 ◽  
Vol 81 (3_suppl) ◽  
pp. 1315-1327 ◽  
Author(s):  
Vito Di Maio ◽  
Enrica L. Santarcangelo ◽  
Knut Busse
Keyword(s):  
Visual Perception ◽  
Function Theory ◽  
General Rule ◽  
Hypnotic Susceptibility ◽  
Area Estimation ◽  
Model Based ◽  
Image Function

The visual perception of area of geometrical figures was compared for subjects of high and low hypnotizability in experiments with direct comparison of two different geometrical figures. The Stanford Hypnotic Susceptibility Scale (Form C) was used to assess subjects' hypnotizability. No differences between 17 highly hypnotizable and 10 low bypnorizable subjects were found. Present results were also compared with those previously obtained for subjects of unknown hypnotizability. The model based on the Image Function Theory proposed earlier to explain the errors in area estimation committed by subjects of unknown hypnotizability was confirmed as a general rule.

Threshold Effect in Visual Perception of Geometrical Figures

1998 ◽  
Vol 87 (1) ◽  
pp. 340-342 ◽  
Author(s):  
Vito Di Maio
Keyword(s):  
Visual Perception ◽  
Threshold Effect ◽  
Input Image ◽  
Visual Illusions ◽  
Experimental Results ◽  
Perceptual Error ◽  
Image Function ◽  
Random Dot Patterns

Filtering of the input image has been shown to play a central role in several aspects of visual perception. In our experiments in visual perception of the area of geometrical figures the orientation in random dot patterns, and some visual illusions, we have shown that a threshold effect inferred from the filtering of the input image produces a perceptual error. This error has been explained by using the concept of Image Function. The present paper is a brief review of our experimental results and of the models we have proposed.

Weighted Positional Averaging in the Illusions of the Müller-Lyer Type

2017 ◽  
Author(s):  
Aleksandr Bulatov
Keyword(s):  
Experimental Data ◽  
Visual Perception ◽  
Computational Model ◽  
Spatial Separation ◽  
Visual Illusions ◽  
Limited Ability ◽  
Model Based ◽  
Lyer Illusion ◽  
Linear Extent

The limited ability to estimate properly the linear extent or spatial separation of objects is one of the well-tested and documented features of visual perception. However, despite a large amount of experimental data collected in various studies of the Müller-Lyer illusion and related visual illusions of extent, the generally accepted view concerning the origin of this phenomenon is still absent. This chapter addresses a possible role of the perceptual positional shifts of the stimulus parts in occurrence of the illusions. It also discusses the most important features of the computational model based on the hypothesis of positional coding via centroids.

Towards stratified model-based environmental visual perception for humanoid robots

2011 ◽  
Vol 32 (16) ◽  
pp. 2254-2260 ◽  
Author(s):  
D. Gonzalez-Aguirre ◽  
T. Asfour ◽  
R. Dillmann
Keyword(s):  
Visual Perception ◽  
Humanoid Robots ◽  
Model Based ◽  
Stratified Model

Probabilistic Model Based Hybrid Classifier for Character Recognition

2017 ◽  
Vol 25 (04) ◽  
pp. 621-647
Author(s):  
A. K. Sampath ◽  
N. Gomathi
Keyword(s):  
Probabilistic Model ◽  
Character Recognition ◽  
Recognition Rate ◽  
Size Variation ◽  
Orientation Angle ◽  
Recognition System ◽  
Input Image ◽  
Hybrid Classifier ◽  
Form Processing ◽  
Model Based

Handwritten character recognition is most crucial one indulging in many of the applications like forensic search, searching historical manuscripts, mail sorting, bank check reading, tax form processing, book and handwritten notes transcription etc. The problem occurrence in the recognition is mainly because of the writing style variation, size variation (length and height), orientation angle etc. In this paper a probabilistic model based hybrid classifier is proposed for the character recognition combining the neural network and decision tree classifiers. In addition to the local gradient features i.e. histogram oriented feature and grid level feature, an additional feature called GLCM feature is extracted from the input image in the proposed recognition system and are concatenated for the image recognition procedure to encode color, shape, texture, local as well as the statistical information. These extracted features considered are given to the hybrid classifier which recognises the character. In the test set, recognition accuracy of 95% is achieved. The proposed probabilistic model based hybrid classifier tends to contribute more accurate character recognition rate compared to the existing character recognition system.

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