# Biologically Inspired Visual System Architecture for Object Recognition in Autonomous Systems
## 4 The Proposed Architecture
### Inferotemporal Cortex Module  
Although it is widely accepted that the brain fuse pre-attentive bottom-up processing and attentive top-down task-specific cues, current computer vision models are still based on a bottom-up process only. The proposed model suggests a framework where the recognition process includes integrating the traditional bottom-up process in deep CNN with rapid top-down contextual information, in order to improve the performance in cases of ambiguity. In the human visual system the bottom-up High Spatial Frequencies (HSF) ventral stream meets the top-down LSF scene and object hypothesis in the ITC. Similarly, in the VAP model the bottom-up stream and the top-down prediction meet in a module named the ITC mechanism. It is important to note that there is evidence to suggest that the integration of top-down and bottom-up processes in the human brain is not exclusive to ITC and may also take place in other areas of the brain. Nevertheless, we limited our scope of work to the major role of the ITC in this process. The VAP model decision is based on adding a correction factor to each element in the probabilities vector given by the bottom-up classification process. The ITC mechanism in the VAP model uses two functions to regulate the integration of the two top-down pathways into the bottom-up pathway. Therefore, the output of the ITC module can be written as:  
\[P_{ITC}=P_{BottomUp}+C_{Context}+C_{Gist}, \tag{1}\]  
where \(P_{Bottom-up}\) is the probability vector from the CNN, marked in blue in Figure 5. \(C_{Context}\) and \(C_{Gist}\) are the two top-down correction pathways, marked in green and red, respectively. \(C_{Context}\) is the correction vector that takes into account the top-down LSF scene hypothesis, while \(C_{Gist}\) is the correction vector that takes into account the top-down LSF object hypothesis. As the formulas governing both pathways are similar, we will focus our explanation on one of them, the \(C_{Context}\) correction vector. First, a Gaussian function is used to regulate the correction factors magnitude and give a higher correction factor in cases of ambiguity. The Gaussian function, which has high values in the center and low values at its edges, expresses the notion that if the bottom-up classifier is \"pretty sure\" that the analyzed object is very similar (probability above a specific threshold) or very different (probability below a specific threshold) from the object model stored in the long term memory module, the correction should be minimal. This correction factor magnitude regulator, \(R_{Magitude}\), is given by:  
\[R_{Magitude}=ae^{-\frac{\left(P_{BottomUp}-\mu\right)^{2}}{2\mu^{2}}}, \tag{2}\]  
where \(\mu\) and \(\sigma\) are the parameters governing the shape of the \(R_{Magitude}\) regulation function and where set to \(\frac{1}{5}\) and \(\frac{1}{3}\), respectively. Second, a hyperbolic tangent function (tanh) is used to regulate the sign of the correction factors. The hyperbolic tangent function, which has positive values on one side and negative values on the other side, expresses the notion that for objects with high probability to appear in the current context the correction will be positive and that for objects with low probability to appear in the current context the correction will be negative. This correction factor sign regulator, \(R_{Sign}\), is given by:  
\[R_{Sign}=tanh\left(b\left(P_{Object|Context}-c\right)\right), \tag{3}\]where \(P_{Object|Context}\) is the probability of each object to be in the scene given the current context. \(b\) and \(c\) are the parameters governing the shape of the \(R_{Sign}\) regulation function and where set to 10 and \(\frac{1}{8}\), respectively. Following this logic, the correction coefficients for each top-down pathway is calculated as follows:  
\[C_{Context}=P_{Context}*R_{Magnitude}*R_{Sign}, \tag{4}\]  
where \(P_{Context}\) is the confidence level in the context classification. Figure 5 illustrates this structure of the ITC mechanism.