Seungchul Ryu

Seungchul Ryu, Hyunjin Yoo, Tara Akhavan

Stereoscopic 3D displays adopt a binocular depth cue to provide depth perception. However, users should be equipped with expensive special devices to appreciate depth perception based on the binocular depth cues. Also, visual fatigue induced by the stereoscopic display is still a challenging open problem. In order to overcome this limitation, this paper proposes a novel framework, JSM, to enhance monocular depth perception, significantly improving both depth volume perception and depth range perception. The proposed framework can not only provide an enhanced depth perception on any conventional 2D display devices, but also it can be applicable to the 3D display devices since it is complementary to binocular depth cues. The qualitative evaluation, ablation study, and subjective user evaluation proved the advantages and practicability of the proposed framework.

Seungchul Ryu

Establishing correspondences is a fundamental task in variety of image processing and computer vision applications. In particular, finding the correspondences between a non-linearly deformed image pair induced by different modality conditions is a challenging problem. This paper describes a efficient but powerful image transform called local area transform (LAT) for modality-robust correspondence estimation. Specifically, LAT transforms an image from the intensity domain to the local area domain, which is invariant under nonlinear intensity deformations, especially radiometric, photometric, and spectral deformations. In addition, robust feature descriptors are reformulated with LAT for several practical applications. Furthermore, LAT-convolution layer and Aception block are proposed and, with these novel components, deep neural network called LAT-Net is proposed especially for scene recognition task. Experimental results show that LATransformed images provide a consistency for nonlinearly deformed images, even under random intensity deformations. LAT reduces the mean absolute difference as compared to conventional methods. Furthermore, the reformulation of descriptors with LAT shows superiority to conventional methods, which is a promising result for the tasks of cross-spectral and modality correspondence matching. the local area can be considered as an alternative domain to the intensity domain to achieve robust correspondence matching, image recognition, and a lot of applications: such as feature matching, stereo matching, dense correspondence matching, image recognition, and image retrieval.