Improved Recognition of Far Objects by using DPM method in Curving-Effective Integral Imaging

Abstract

In this paper, we propose a novel approach to enhance the recognition performance of a far and partially occluded three-dimensional (3-D) target in computational curving-effective integral imaging (CEII) by using the direct pixel-mapping (DPM) method. With this scheme, the elemental image array (EIA) originally picked up from a far and partially occluded 3-D target can be converted into a new EIA just like the one virtually picked up from a target located close to the lenslet array. Due to this characteristic of DPM, resolution and quality of the reconstructed target image can be highly enhanced, which results in a significant improvement of recognition performance of a far 3-D object. Experimental results reveal that image quality of the reconstructed target image and object recognition performance of the proposed system have been improved by 1.75 dB and 4.56% on the average in PSNR (peak-to-peak signal-to-noise ratio) and NCC (normalized correlation coefficient), respectively, compared to the conventional system.

Face recognition for occluded face with mask region convolutional neural network and fully convolutional network: a literature review

“…Objects that cannot be detected are mostly because the information from the object cannot be perfectly received by the computer, or in other words the object is not all visible or only part of it [2], [11], [12]. There are several methods of detection and recognition of traditional image objects such as Viola-Jones [15], Hog+ support vector machine (SVM) [16], and deformable part model (DPM) [17].…”