  Classification and identification of wild animals for tracking and protection
purposes has become increasingly important with the deterioration of the
environment, and technology is the agent of change which augments this process
with novel solutions. Computer vision is one such technology which uses the
abilities of artificial intelligence and machine learning models on visual
inputs. Convolution neural networks (CNNs) have multiple layers which have
different weights for the purpose of prediction of a particular input. The
precedent for classification, however, is set by the image processing
techniques which provide nearly ideal input images that produce optimal
results. Image segmentation is one such widely used image processing method
which provides a clear demarcation of the areas of interest in the image, be it
regions or objects. The Efficiency of CNN can be related to the preprocessing
done before training. Further, it is a well-established fact that heterogeneity
in image sources is detrimental to the performance of CNNs. Thus, the added
functionality of heterogeneity elimination is performed by the image processing
techniques, introducing a level of consistency that sets the tone for the
excellent feature extraction and eventually in classification.
