TY  - JOUR
A1  - Kyprianidis, Jan Eric
A1  - Collomosse, John
A1  - Wang, Tinghuai
A1  - Isenberg, Tobias
T1  - State of the "Art" a taxonomy of artistic stylization techniques for images and video
JF  - IEEE transactions on visualization and computer graphics
N2  - This paper surveys the field of nonphotorealistic rendering (NPR), focusing on techniques for transforming 2D input (images and video) into artistically stylized renderings. We first present a taxonomy of the 2D NPR algorithms developed over the past two decades, structured according to the design characteristics and behavior of each technique. We then describe a chronology of development from the semiautomatic paint systems of the early nineties, through to the automated painterly rendering systems of the late nineties driven by image gradient analysis. Two complementary trends in the NPR literature are then addressed, with reference to our taxonomy. First, the fusion of higher level computer vision and NPR, illustrating the trends toward scene analysis to drive artistic abstraction and diversity of style. Second, the evolution of local processing approaches toward edge-aware filtering for real-time stylization of images and video. The survey then concludes with a discussion of open challenges for 2D NPR identified in recent NPR symposia, including topics such as user and aesthetic evaluation.
KW  - Image and video stylization
KW  - nonphotorealistic rendering (NPR)
KW  - artistic rendering
Y1  - 2013
U6  - https://doi.org/10.1109/TVCG.2012.160
SN  - 1077-2626
VL  - 19
IS  - 5
SP  - 866
EP  - 885
PB  - Inst. of Electr. and Electronics Engineers
CY  - Los Alamitos
ER  - 
TY  - JOUR
A1  - Richter, Rico
A1  - Kyprianidis, Jan Eric
A1  - Döllner, Jürgen Roland Friedrich
T1  - Out-of-core GPU-based change detection in massive 3D point clouds
JF  - Transactions in GIS
N2  - If sites, cities, and landscapes are captured at different points in time using technology such as LiDAR, large collections of 3D point clouds result. Their efficient storage, processing, analysis, and presentation constitute a challenging task because of limited computation, memory, and time resources. In this work, we present an approach to detect changes in massive 3D point clouds based on an out-of-core spatial data structure that is designed to store data acquired at different points in time and to efficiently attribute 3D points with distance information. Based on this data structure, we present and evaluate different processing schemes optimized for performing the calculation on the CPU and GPU. In addition, we present a point-based rendering technique adapted for attributed 3D point clouds, to enable effective out-of-core real-time visualization of the computation results. Our approach enables conclusions to be drawn about temporal changes in large highly accurate 3D geodata sets of a captured area at reasonable preprocessing and rendering times. We evaluate our approach with two data sets from different points in time for the urban area of a city, describe its characteristics, and report on applications.
Y1  - 2013
U6  - https://doi.org/10.1111/j.1467-9671.2012.01362.x
SN  - 1361-1682
VL  - 17
IS  - 5
SP  - 724
EP  - 741
PB  - Wiley-Blackwell
CY  - Hoboken
ER  -