Holoscopic 3D imaging is a true 3D imaging system mimics fly’s eye technique to acquire a true 3D
optical model of a real scene. To reconstruct the 3D image computationally, an efficient implementation
of an Auto-Feature-Edge (AFE) descriptor algorithm is required that provides an individual
feature detector for integration of 3D information to locate objects in the scene. The AFE
descriptor plays a key role in simplifying the detection of both edge-based and region-based objects.
The detector is based on a Multi-Quantize Adaptive Local Histogram Analysis (MQALHA) algorithm.
This is distinctive for each Feature-Edge (FE) block i.e. the large contrast changes (gradients)
in FE are easier to localise. The novelty of this work lies in generating a free-noise 3D-Map
(3DM) according to a correlation analysis of region contours. This automatically combines the exploitation
of the available depth estimation technique with edge-based feature shape recognition
technique. The application area consists of two varied domains, which prove the efficiency and
robustness of the approach: a) extracting a set of setting feature-edges, for both tracking and
mapping process for 3D depthmap estimation, and b) separation and recognition of focus objects
in the scene. Experimental results show that the proposed 3DM technique is performed efficiently
compared to the state-of-the-art algorithms