چكيده به لاتين
In this dissertation, we attempted to present an object recognition model, which is inspired by the human visual system, to cope with robustness and invariance challenges in machine vision. There are many challenges in the object recognition tasks such as illumination, scale, and rotational changes that make object recognition hard for machines. Besides, the human visual system is very robust to these challenges. Therefore, we presented a robust and invariant model for object recognition, which mimics the human visual system both structurally and functionally. In addition to improving robustness against image variation challenges, the computational load has been reduced. Accordingly, a model called RIMAX was proposed, which has six main layers. In RIMAX, the primary cortex was modeled helping two layers, S1, C1; also, the secondary cortex was modeled by the FE layer; and, the V4 region was modeled by three layers including FR, S2, C2. Each layer has an especial role in robustness improvement. Similar to the visual cortex, RIMAX has a hierarchical structure. Researches show that the first and second layers play an important role in robustness against light and minor local changes. Also, the FE layer which represents the secondary visual cortex plays a critical role in increasing accuracy, repeatability, and reliability. This layer, whose task is feature extraction from images, enhances the performance of the RIMAX, especially when there are not enough training samples or features. The FE layer improves robustness against scale and rotational variation. and template matching is implemented in S2 and C2 layers. Using the new template matching method, the computational load is significantly reduced. The results show that the RIMAX has 20% higher performance in terms of accuracy than previous models when threre aren't enough training samples. In addition, in terms of robustness against mentioned challenges, it has shown much better performance. For example, the accuracy of RIMAX has remained above 80% against scale variation in the range of 0.6 to 2 times. Also, in the worst case, the accuracy of RIMAX has decreased by only 15% against angle changes. The RIMAX has shown better performance in terms of speed rather than other previous models. Practically, RIMAX is eleven times faster than HMAX, and four time faster than AlexNet.
