Quantitative evaluation of human ventral visual stream in invariant object recognition: Human behavioral experiments and brain-plausible computational model simulations

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

KARIMI ROUZBAHANI HAMID; EBRAHIMPOUR REZA; BAGHERI NASOUR

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: MACHINE VISION AND IMAGE PROCESSING Year:2017 | Volume:3 | Issue:2 Page(s): 59-72

Download Full-Text

Persian Verion

View:

693

Download:

Cites:

Information Journal Paper

Title

Quantitative evaluation of human ventral visual stream in invariant object recognition: Human behavioral experiments and brain-plausible computational model simulations

Author(s)

KARIMI ROUZBAHANI HAMID | EBRAHIMPOUR REZA | BAGHERI NASOUR | Issue Writer Certificate

Keywords

Object Recognition

human visual cortex

convolutional neural network

invariant representation

Abstract

A computational approach is proposed which is aimed at quantitatively assessing human recognition abilities when objects undergo different variations in the image. During the recent decades, as a result of its high accuracy and speed, human visual system has been considered an idolfora variety ofcomputational Object Recognition algorithms in machine vision. Therefore, quantification of its behavior in different situations can lead tobetter modeling algorithms. In this study, human ability is evaluated in an Object Recognition task in which object images underwent different levels of variation in lighting conditions, pose, size and position. To do this, a variation-controlled object image dataset is generated and presented to humans as well as to a computational model of visual cortex. The computational model is used to measure the effect of each variation on Object Recognition. Human behavioral results show a decline in recognition performance when objects underwent pose variation. The performance suppression is shown to be the result of disability of untangling object representations in highlevels of pose variation. Quantitatively speaking, images which underwent variations in lighting, pose, size and position, experienced respectively 0. 57, 0. 33, 0. 55 and 0. 73 of representational enhancement travelling from pixel to visual cortex space.

Cites

No record.

References

No record.

Cite

APA: Copy

KARIMI ROUZBAHANI, HAMID, EBRAHIMPOUR, REZA, & BAGHERI, NASOUR. (2017). Quantitative evaluation of human ventral visual stream in invariant object recognition: Human behavioral experiments and brain-plausible computational model simulations. MACHINE VISION AND IMAGE PROCESSING, 3(2 ), 59-72. SID. https://sid.ir/paper/265731/en

Vancouver: Copy

KARIMI ROUZBAHANI HAMID, EBRAHIMPOUR REZA, BAGHERI NASOUR. Quantitative evaluation of human ventral visual stream in invariant object recognition: Human behavioral experiments and brain-plausible computational model simulations. MACHINE VISION AND IMAGE PROCESSING[Internet]. 2017;3(2 ):59-72. Available from: https://sid.ir/paper/265731/en

IEEE: Copy

HAMID KARIMI ROUZBAHANI, REZA EBRAHIMPOUR, and NASOUR BAGHERI, “Quantitative evaluation of human ventral visual stream in invariant object recognition: Human behavioral experiments and brain-plausible computational model simulations,” MACHINE VISION AND IMAGE PROCESSING, vol. 3, no. 2 , pp. 59–72, 2017, [Online]. Available: https://sid.ir/paper/265731/en

Related Journal Papers