Engineering the Perception of Recognition Through Interactive Raw Primal Sketch by HNFGS and CNN-MRF

Abstract

The impression of a scene on human brain, specifically the primary visual cortex, is still a far-reached goal by the computer vision research community. This work is a proposal of a novel system to engineer the human perception of recognizing a subject of interest. This end-to-end solution implements all the stages from entropy-based unbiased cognitive interview to the final reconstruction of human perception in terms of machine sketch in the framework of forensic sketch of suspects. The lower mid-level vision as designed behaviorally in primary visual cortex honoring the scale-space concept of object identification has been modeled by hierarchical 2D filters, namely hierarchical neuro-visually inspired figure-ground segregation (HNFGS) for interactive sketch rendering. The aforementioned human–machine interaction is twofold: in gross structural design layer and finer/granular modification of the pre-realized digital perception. Pre-realized sketches are formed learning the characteristics of human artists while sketching an object through integrated framework of deep convolutional neural network (D-CNN) and Markov Random field (MRF). After few iterations of interactive fine-tuning of the sketch, a psycho-visual experiment has been designed and performed to evaluate the feasibility and effectiveness of the proposed algorithm.