# Biologically Inspired Visual System Architecture for Object Recognition in Autonomous Systems
## 1 Introduction  
While there are many technological gaps that inhibit the development of the autonomous systems field, there is no doubt that a significant factor in this delay is that it has been much harder than expected to give robotic agents the capabilities to analyze their ever-changing environment, detect and classify the objects surrounding them, and interpret the interaction between them. Visual object recognition is an extremely difficult computational task. The core problem is that each object in the world can cast an infinite number of different 2D images onto the retina as the object's position (translation and rotation), pose, lighting, and background vary relative to the viewer.  
The state-of-the-art computer vision algorithms, convolutional neural networks (CNNs), although achieving remarkable results in object detection and classification challenges, still are not robust enough for many applications. They are sensitive to ambient light conditions [1] and to additive noise [2] as a result of pockets in their manifold. These algorithms are based on bottom-up object detection and recognition process. As such, they do not include the means to use top-down contextual information for a more holistic process.  
In the human brain, for comparison, there are quick projections of Low Spatial Frequency (LSF) information, in parallel to the bottom-up systematic progression of the image details along the visual pathways. These coarse but rapid blurred representations are used for an initial rough classification hypothesis with low discrimination. These hypotheses are followed by higher discriminability tests for an overall improved efficiency of object recognition in cases of ambiguity. This model was proven by a series of functional Magnetic Resonance Imaging (fMRI) and Magnetoencephalography (MEG) studies, which showed early Orbitofrontal Cortex (OFC) activation by LSF information, preceding the corresponding object recognition activation in Inferior Temporal Cortex (ITC) by about 50 ms [3].  
The idea that context-based predictions make object recognition more efficient is prominent in Figure 1. The hairdryer in the right image and the drill in the left image are identical objects. Nevertheless, contextual information uniquely resolves ambiguity in each case. In this case, recognition cannot be accomplished quickly based only on the physical attributes of the target; contextual information provides more relevant input for the recognition of that object than can its intrinsic properties.  
The working assumption underlying this research is that setting the human visual system as a source of inspiration and getting better in imitating its architecture and mechanisms, will enable us to develop a more robust and accurate object recognition algorithm with better results than exists today. Specifically, integrating the state-of-the-art bottom-up feed-forward algorithms (CNNs), together with the top-down mechanisms of the human visual system. The main contributions in this paper can be highlighted in the following aspects:  
1. Design of a biologically inspired architecture for integration of top-down and bottom-up processes into one holistic solution (Section 4).
2. Demonstration of the importance of contextual-based predictions and their potential to improve the performance of bottom-up computer vision algorithms, such as deep CNNs (Section 5.1).
3. Evaluation of the performance of a methodology for continuously updating models based on encounters with visual stimuli different than the ones they have been trained on (Section 5.2).  
The rest of the paper is organized as follows: Review and analysis of the different pathways and mechanism of the human brain, focusing on the \"associative generation of predictions\" mechanism, is given in Section 2. Review of CNNs and their vulnerability to perturbations from the dataset they were trained on is in Section 3. Finally, discussion and future work are summarized in Section 6, followed by concluding remarks in Section 7.  
Figure 1: The powerful effects of context.