**5. Cognitive object recognition architecture**

For the endangered species 1 and 2, we represent an object as a vector of an input image histogram's SAP for Red and Blue components. In effect, we assume that each input image of a species bird captured during an aerial survey can have a spectral signature which consists of those SAP Red and SAP Blue values. It is important to note here that K-means clustering algorithm is an unsupervised learning method. In effect, there is no a-priori information regarding the clusters size and final positions of the centroids. As we have assumed that each object is represented by two vector components, one SAP Red and one SAP Blue component, then we have a 2-Dimensional (2D) object space.

#### **5.1 Biologically-inspired hybrid digital-optical system**

We need to develop a new method to improve the speciation of the endangered bird species 1 and 2 for automating the image analysis from the collected datasets of the aerial surveys. There is an increased level of difficulty in correctly classifying and performing speciation for endangered bird species 1 and 2 during the winter aerial surveys due to the higher similarity of the birds' plumage between species 1 and species 2. Therefore, we propose the design and development of a novel biologicallyinspired hybrid digital-optical system [14] for increasing the accuracy of the bird

**Figure 3.** *Pure SDF correlator classifier for endangered bird species speciation.*

species speciation and the overall time it takes to process an aerial survey. As we are going to describe in the next sections, our proposed system is capable of performing both knowledge representation and knowledge learning by incorporating in its data: (i) the shape of each endangered species 1 and 2, (ii) the size of each endangered species 1 and 2, and (iii) the colour information of each endangered species 1 and 2.
