1
Department of Electrical Engineering, ShahidRajaee Teacher Training University, Tehran
2
Department of Computer Engineering, ShahidRajaee Teacher Training University, Tehran
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.
Karimi Rouzbahani, H., Ebrahimpour, R., & Bagheri, N. (2016). Quantitative evaluation of human ventral visual stream in invariant object recognition: Human behavioral experiments and brain-plausible computational model simulations. Journal of Machine Vision and Image Processing, 3(2), 59-72.
MLA
Hamid Karimi Rouzbahani; Reza Ebrahimpour; Nasour Bagheri. "Quantitative evaluation of human ventral visual stream in invariant object recognition: Human behavioral experiments and brain-plausible computational model simulations". Journal of Machine Vision and Image Processing, 3, 2, 2016, 59-72.
HARVARD
Karimi Rouzbahani, H., Ebrahimpour, R., Bagheri, N. (2016). 'Quantitative evaluation of human ventral visual stream in invariant object recognition: Human behavioral experiments and brain-plausible computational model simulations', Journal of Machine Vision and Image Processing, 3(2), pp. 59-72.
VANCOUVER
Karimi Rouzbahani, H., Ebrahimpour, R., Bagheri, N. Quantitative evaluation of human ventral visual stream in invariant object recognition: Human behavioral experiments and brain-plausible computational model simulations. Journal of Machine Vision and Image Processing, 2016; 3(2): 59-72.