Object Proposals-Based Significance Map for Image Retargeting

2018 ◽  
pp. 89-101
Author(s):  
Diptiben Patel ◽  
Shanmuganathan Raman
Keyword(s):  
Image Retargeting ◽  
Object Proposals

Unsupervised Stereoscopic Image Retargeting via View Synthesis and Stereo Cycle Consistency Losses

2021 ◽  
Author(s):  
Xiaoting Fan ◽  
Jianjun Lei ◽  
Jie Liang ◽  
Yuming Fang ◽  
Xiaochun Cao ◽  
...  
Keyword(s):  
View Synthesis ◽  
Stereoscopic Image ◽  
Image Retargeting

Deep Supervised Image Retargeting

2021 ◽  
Author(s):  
Yijing Mei ◽  
Xiaojie Guo ◽  
Di Sun ◽  
Gang Pan ◽  
Jiawan Zhang
Keyword(s):  
Image Retargeting

scholarly journals Ranking-Preserving Cross-Source Learning for Image Retargeting Quality Assessment

2020 ◽  
Vol 42 (7) ◽  
pp. 1798-1805
Author(s):  
Yong-Jin Liu ◽  
Yiheng Han ◽  
Zipeng Ye ◽  
Yu-Kun Lai
Keyword(s):  
Quality Assessment ◽  
Image Retargeting

Constrained sampling for image retargeting

2008 ◽  
Author(s):  
Tongwei Ren ◽  
Yanwen Guo ◽  
Gangshan Wu ◽  
Fuyan Zhang
Keyword(s):  
Image Retargeting

scholarly journals Meta-Transfer Learning Driven Tensor-Shot Detector for the Autonomous Localization and Recognition of Concealed Baggage Threats

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6450
Author(s):  
Taimur Hassan ◽  
Muhammad Shafay ◽  
Samet Akçay ◽  
Salman Khan ◽  
Mohammed Bennamoun ◽  
...  
Keyword(s):  
Transfer Learning ◽  
State Of The Art ◽  
Autonomous Systems ◽  
Aviation Security ◽  
Dual Energy ◽  
Average Precision ◽  
X Ray ◽  
Shot Classification ◽  
Object Proposals ◽  
The World

Screening baggage against potential threats has become one of the prime aviation security concerns all over the world, where manual detection of prohibited items is a time-consuming and hectic process. Many researchers have developed autonomous systems to recognize baggage threats using security X-ray scans. However, all of these frameworks are vulnerable against screening cluttered and concealed contraband items. Furthermore, to the best of our knowledge, no framework possesses the capacity to recognize baggage threats across multiple scanner specifications without an explicit retraining process. To overcome this, we present a novel meta-transfer learning-driven tensor-shot detector that decomposes the candidate scan into dual-energy tensors and employs a meta-one-shot classification backbone to recognize and localize the cluttered baggage threats. In addition, the proposed detection framework can be well-generalized to multiple scanner specifications due to its capacity to generate object proposals from the unified tensor maps rather than diversified raw scans. We have rigorously evaluated the proposed tensor-shot detector on the publicly available SIXray and GDXray datasets (containing a cumulative of 1,067,381 grayscale and colored baggage X-ray scans). On the SIXray dataset, the proposed framework achieved a mean average precision (mAP) of 0.6457, and on the GDXray dataset, it achieved the precision and F1 score of 0.9441 and 0.9598, respectively. Furthermore, it outperforms state-of-the-art frameworks by 8.03% in terms of mAP, 1.49% in terms of precision, and 0.573% in terms of F1 on the SIXray and GDXray dataset, respectively.

scholarly journals Image Retargeting Method for Arbitrary-shaped Display

2012 ◽  
Vol 66 (12) ◽  
pp. J524-J532
Author(s):  
Keisuke Nonaka ◽  
Takamichi Miyata ◽  
Yoshinori Hatori
Keyword(s):  
Image Retargeting
Sign in / Sign up

Export Citation Format

Share Document