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.
|Number of pages||14|
|Journal||Machine Vision and Image Processing|
|Publication status||Published - 1 Oct 2016|
- Object Recognition
- human visual cortex
- convolutional neural network
- invariant representation