Image Retargeting by Content-Aware Synthesis
원문 초록
Real-world images usually contain vivid contents and rich textural details, which will complicate the manipulation on them. In this paper, we design a new framework based on content-aware synthesis to enhance content-aware image retargeting. By detecting the textural regions in an image, the textural image content can be synthesized rather than simply distorted or cropped. This method enables the manipulation of textural & non-textural regions with different strategy since they have different natures. We propose to retarget the textural regions by content-aware synthesis and non-textural regions by fast multi-operators. To achieve practical retargeting applications for general images, we develop an automatic and fast texture detection method that can detect multiple disjoint textural regions. We adjust the saliency of the image according to the features of the textural regions. To validate the proposed method, comparisons with state-of-the-art image targeting techniques and a user study were conducted. Convincing visual results are shown to demonstrate the effectiveness of the proposed method.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Prune and Repaint: Content-Aware Image Retargeting for any Ratio
Supervised Deep Learning for Content-Aware Image Retargeting with Fourier Convolutions
OAIR: Object-Aware Image Retargeting Using PSO and Aesthetic Quality Assessment
Self-Play Reinforcement Learning for Fast Image Retargeting
Cycle-IR: Deep Cyclic Image Retargeting
HALO: Human-Aligned End-to-end Image Retargeting with Layered Transformations
토론
이 논문과 연결된 포럼 글이 아직 없습니다.