Planar-Equirectangular Image Stitching

Abstract

The 360° cameras have served as a convenient tool for people to record their special moments or everyday lives. The supported panoramic view allowed for an immersive experience with a virtual reality (VR) headset, thus adding viewer enjoyment. Nevertheless, they cannot deliver the best angular resolution images that a perspective camera may support. We put forward a solution by placing the perspective camera planar image onto the pertinent 360° camera equirectangular image region of interest (ROI) through planar-equirectangular image stitching. The proposed method includes (1) tangent image-based stitching pipeline to solve the equirectangular image spherical distortion, (2) feature matching scheme to increase correct feature match count, (3) ROI detection to find the relevant ROI on the equirectangular image, and (4) human visual system (HVS)-based image alignment to tackle the parallax error. The qualitative and quantitative experiments showed improvement of the proposed planar-equirectangular image stitching over existing approaches on a collected dataset: (1) less distortion on the stitching result, (2) 29.0% increased on correct matches, (3) 5.72° ROI position error from the ground truth and (4) lower aggregated alignment-distortion error over existing alignment approaches. We discuss possible improvement points and future research directions.

Geological Borehole Video Image Stitching Method Based on Local Homography Matrix Offset Optimization

“…In recent years, image stitching technology has been widely used in the military, agriculture, geological exploration and other fields [ 4 , 5 ], and it has gradually become an important branch of image processing [ 6 , 7 , 8 ]. Image matching is at the core of image stitching [ 9 , 10 ], and its essence is to calculate the geometric transformation relationship between two images using overlapping areas to obtain the rotation, scale, displacement and other parameter values, and then realize the mosaic of the overlapping areas [ 11 , 12 ].…”

An Inspection Technique Using Fit Clearance Based on Microscopic Vision in Precision Assembly

“…There are several methods to extract ROI. These include image segmentation [10][11][12], histogram clustering [13,14], a fixed-area method [15,16], a template matching method [17,18], a golden template method [19,20], and a feature point method [21][22][23][24]. For the parts with micro multi-section arcs, due to machining errors, the background of feature images can differ due to structural deformation, and the shape of feature images can be irregular due to machining texture.…”