Fast Hue-Division-Based Selective Color Transfer

Abstract

This paper introduces a new concept, selective color transfer (SCT), to allow for color experimentation and visualization on a selected object within an image, without affecting any other image content or color. To implement this new concept, this paper proposes a new fast SCT-I algorithm for image input data and a new SCT-V algorithm for video input data. For the SCT-I algorithm, its fundamental novelties include: 1) utilizing a new hue-division-based color region segmentation (HCR) based on defining a new nonlinear and circular property of the hue spectrum; 2) developing two new color artifact suppression strategies, including color region integration and circular statistical calculation; and 3) applying a new truncated color transfer equation to solve the problem of mismatches in the data range. The SCT-V algorithm utilizes: 1) object tracking to locate the target of interest (TOI) within each video frame; 2) the SCT-I algorithm to update the original color within TOIs; and 3) an expected color interpolation process to support different special color animation effects. The experimental results demonstrate: 1) there are no residual color artifacts introduced;2) the approaches have tremendous flexibility for modifying an object's color; and 3) these algorithms have a low computational cost. Furthermore, these algorithms require minimal manual intervention for the easy use of ordinary users. The performance analysis demonstrates the potential of these two methods for various applications, including creating special effects in videos, making histology images much clearer for medical inspection, and enabling low-cost experimentation of manufacturing the design of a product.

Considering Multisensor Data Fusion and Data-Driven Special Effects Animation Simulation

“…For convenience, a hexahedron is used here to define the scope of the macroscopic scene. It is assumed that the scope of the considered scene is limited to the following hexahedron [9]:…”

Object-Based Attention Mechanism for Color Calibration of UAV Remote Sensing Images in Precision Agriculture

“…In general, computational color constancy and exemplarbased color transfer methods are usually used to calibrate the color variations of the UAV imagery [4]. The former automatically adjusts the color value according to the illumination changes [5,6], whilst the latter considers one image with the correct color as the reference image before transferring the color pattern of the reference image to other images captured in the same UAV flight [7,8]. In practice, the sensor is hard to detect illumination changes during the highspeed flight process, thus, the commercial sensors established with color constancy still suffer from the color cast.…”