Article
Computer Science, Software Engineering
Ankit Garg, Anuj Kumar Singh
Summary: This paper presents an improved technique for image retargeting, which uses the gradient energy map generation method to safeguard the prominent regions of the image. The efficiency of the technique is validated through objective and subjective image quality assessment.
Article
Computer Science, Information Systems
Kun Zeng, Jiangchuan Hu, Yongyi Gong, Kanoksak Wattanachote, Runpeng Yu, Xiaonan Luo
Summary: Two seam coupling strategies, real mapping and virtual mapping, were proposed for vertical retargeting to address the problems of multiple assignments and missing assignments. Experimental results demonstrated that the method overcomes the limitations of vertical retargeting and effectively preserves geometric consistency.
ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Seung-Hun Nam, Wonhyuk Ahn, In-Jae Yu, Myung-Joon Kwon, Minseok Son, Heung-Kyu Lee
Summary: This paper proposes a CNN-based approach for classifying seam-carving forgery, with specialized network blocks designed to capture local artifacts caused by seam carving. Experimental results demonstrate that the method has good classification performance and robustness.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2021)
Article
Computer Science, Software Engineering
Sean Flynn, David Hart, Bryan Morse, Seth Holladay, Parris Egbert
Summary: This paper introduces a novel method for intelligently resizing a wide range of volumetric data, including fluids, allowing for more versatile post-processing. Additionally, a faster seam computation method is presented to improve production workflow viability.
ACM TRANSACTIONS ON GRAPHICS
(2021)
Article
Computer Science, Information Systems
Hai Su, Zigui Ye, Yaping Liu, SongSen Yu
Summary: Seam carving algorithm is widely used for content-based image scaling. We propose a dynamic energy regulation method to improve the effect of seam carving by simulating the energy change in each carving. Our method adjusts the energy value of each pixel after each carving to simulate the extra energy introduced by carving.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Ankit Garg, Anand Nayyar, Anuj Kumar Singh
Summary: This paper proposes an improved seam carving technique to overcome the limitations of traditional techniques by restricting the intersection of optimal seam points on line structures. Through subjective and objective evaluations, the effectiveness and high similarity of the proposed technique have been demonstrated.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Ankit Garg, Anuj Kumar Singh
Summary: This paper introduces the seam diversion-based image retargeting (SDIR) algorithm and analyzes its performance through two experiments. Experiment 1 analyzes the impact of different edge detection operators on the algorithm, while Experiment 2 evaluates the performance based on the visual quality and quality of importance maps. The results show that the SDIR algorithm performs well in minimizing structural deformations on prominent objects.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Zehra Karapinar Senturk, Devrim Akgun
Summary: The paper introduces a new blind detection method based on seam carving for identifying retargeted images, which shows improved accuracies compared to traditional solutions through experiments.
ELEKTRONIKA IR ELEKTROTECHNIKA
(2021)
Article
Engineering, Electrical & Electronic
Yingchun Guo, Dan Wang, Ye Zhu, Gang Yan
Summary: This paper proposes a multi-operator image retargeting method based on salient object ranking and similarity evaluation metric. The method includes two stages: importance map generation and multi-operator image retargeting. Experimental results show that the proposed method achieves state-of-the-art performance and preserves important content effectively.
SIGNAL PROCESSING-IMAGE COMMUNICATION
(2023)
Article
Computer Science, Information Systems
Xin Chen, Mei Yu, Yang Song
Summary: This study proposes an optimized seam-driven image stitching method considering depth, color, and texture information. By introducing depth information, the method can find the seam that adapts to the depth of the scene and effectively avoid passing through salient objects, resulting in high-quality stitching results.
Article
Computer Science, Information Systems
Jila Ayubi, Mehdi Chehel Amirani, Morteza Valizadeh
Summary: This paper introduces a new method based on information entropy to extract the importance map and proposes a new method for selecting the most optimal seam based on the calculation of the Lyapunov exponents. Through simulation on the MSRA and RetargetMe datasets, it is demonstrated that the proposed algorithm performs better than the classical seam-carving and generalized seam-carving algorithms.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Cui Jia, Song Lei, Lu Hongju, Tang Mingxi, Qi Meng
Summary: Image retargeting technologies aim to preserve important information and reduce edge distortion. The JND algorithm is proposed to detect and pass distortion information to reduce retargeting distortions, offering promising results compared to other approaches at 'Retarget Me' database.
JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY
(2021)
Article
Computer Science, Information Systems
Zhenhua Tang, Jiemei Yao, Qian Zhang, Yuanting Luo
Summary: Most existing content-aware retargeting algorithms introduce undesired geometric distortions or information loss when resizing images. To address this issue, we propose a width-height synchronization resizing strategy and integrate it into a multi-operator retargeting method. Experimental results demonstrate that the proposed method outperforms existing approaches in the quality of retargeted images.
MULTIMEDIA SYSTEMS
(2023)
Article
Computer Science, Information Systems
Mahdi Ahmadi, Nader Karimi, Shadrokh Samavi
Summary: Image retargeting is a crucial task that requires preserving high-level visual information and low-level features, with different types of images needing different processing methods. The relationship between image retargeting and image saliency detection is significant, with newer approaches considering high-level semantic knowledge. The proposed methods in the study show excellent performance in practice and can be used as a benchmark for further research on retargeting quality assessment.
MULTIMEDIA TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Lin Wang, Chengfeng Liao, Runzhao Yao, Rui Zhang, Wanxu Zhang, Xiaoxuan Chen, Na Meng, Zenghui Yan, Bo Jiang, Cheng Liu
Summary: In this paper, a fixing algorithm of Kinect depth image based on non-local means (NLM) is proposed, which fills the holes in depth image using weights calculated on the corresponding gray image by distance factor and value consistent factor. The experiment results demonstrate good performance in both evaluation in metrics and subjectively visual effect. This research provides a solution idea for depth image fixing algorithm with low complexity.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Engineering, Electrical & Electronic
Yuwu Lu, Chun Yuan, Xuelong Li, Zhihui Lai, David Zhang, Linlin Shen
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2019)
Article
Automation & Control Systems
Chang Liu, Wenguan Wang, Jianbing Shen, Ling Shao
IEEE TRANSACTIONS ON CYBERNETICS
(2019)
Article
Engineering, Electrical & Electronic
Hui Wang, Jianbing Shen, Junbo Yin, Xingping Dong, Hanqiu Sun, Ling Shao
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2020)
Article
Biochemistry & Molecular Biology
Jian-Bing Shen, Kiran S. Toti, Saibal Chakraborty, T. Santhosh Kumar, Chunxia Cronin, Bruce T. Liang, Kenneth A. Jacobson
PURINERGIC SIGNALLING
(2020)
Editorial Material
Computer Science, Artificial Intelligence
Shuo Shang, Jianbing Shen, Ji-Rong Wen, Panos Kalnis
Article
Ophthalmology
Huazhu Fu, Fei Li, Yanwu Xu, Jingan Liao, Jian Xiong, Jianbing Shen, Jiang Liu, Xiulan Zhang
TRANSLATIONAL VISION SCIENCE & TECHNOLOGY
(2020)
Article
Computer Science, Artificial Intelligence
Xingping Dong, Jianbing Shen, Wenguan Wang, Ling Shao, Haibin Ling, Fatih Porikli
Summary: This paper introduces a novel dynamical hyperparameter optimization method utilizing an action-prediction network based on continuous deep Q-learning, to adaptively optimize hyperparameters for a given sequence and improve visual object tracking performance.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2021)
Article
Optics
Timothy O'Connor, Jian-Bing Shen, Bruce T. Liang, Bahram Javidi
Summary: This study presents a rapid screening method for COVID-19 infection in red blood cells using a compact, field-portable 3D-printed shearing digital holographic microscope. By analyzing the spatiotemporal behavior of individual red blood cells, a bi-directional long short-term memory network is used to classify between healthy and COVID positive red blood cells. The proposed system may benefit under-resourced healthcare systems.
Article
Chemistry, Analytical
Hao Li, Sanyuan Zhao, Wenjun Zhao, Libin Zhang, Jianbing Shen
Summary: The proposed anchor-free 3D vehicle detection algorithm encodes object positions as keypoints in the bird's-eye view of LiDAR point clouds, mapping them to a single-channel 2D heatmap using voxel/pillar feature extractor and convolutional blocks. The method achieves high average orientation similarity without direction classification tricks, eliminating the need for anchor boxes in target assignment and bounding box decoding processes.
Article
Computer Science, Artificial Intelligence
Xiankai Lu, Wenguan Wang, Jianbing Shen, David Crandall, Jiebo Luo
Summary: We introduce a novel network called COSNet that addresses the zero-shot video object segmentation task by incorporating a global co-attention mechanism. COSNet outperforms current alternatives by capturing global correlations and scene context through joint computation and appending co-attention responses into a joint feature space.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Xiankai Lu, Chao Ma, Jianbing Shen, Xiaokang Yang, Ian Reid, Ming-Hsuan Yang
Summary: This paper addresses the problem of data imbalance in learning deep models for visual object tracking. The proposed shrinkage loss function helps balance the training data and improves the performance of both deep regression and classification trackers. Experimental results on six benchmark datasets demonstrate the effectiveness of the shrinkage loss.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Artificial Intelligence
Keren Fu, Deng-Ping Fan, Ge-Peng Ji, Qijun Zhao, Jianbing Shen, Ce Zhu
Summary: This paper proposes a novel RGB-D salient object detection model, which achieves effective feature learning from RGB and depth inputs through joint learning and densely cooperative fusion. Experimental results demonstrate significant improvements over state-of-the-art models on multiple datasets, as well as comparable or better performance on other multi-modal detection tasks and RGB-D semantic segmentation tasks.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
Qiuxia Lai, Salman Khan, Yongwei Nie, Hanqiu Sun, Jianbing Shen, Ling Shao
Summary: Both human and machine attention play crucial roles in deep learning models, and understanding the relationship between the two is essential for interpreting and designing neural networks. Recent studies have shown that artificial attention does not always align with human intuition, highlighting the importance of better aligning machine and human attention for improved performance and explainability in neural network design.
IEEE TRANSACTIONS ON MULTIMEDIA
(2021)
Article
Computer Science, Artificial Intelligence
Fan Yang, Xin Li, Jianbing Shen
Summary: State-of-the-art object skeleton detection methods leveraging Convolutional Neural Networks have been enhanced by a new architecture called Multi-Scale Bidirectional Fully Convolutional Network (MSB-FCN), which aims to improve the accuracy by better gathering and enhancing multi-scale high-level contextual information. By solely utilizing deep features and a bidirectional structure, along with dense connections and an attention pyramid, MSB-FCN successfully learns semantic-level information and achieves significant improvements over existing algorithms in various benchmarks.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
ZheHui Wang, Sanyuan Zhao, Jianbing Shen, Zhengchao Lei
2020 IEEE INTERNATIONAL CONFERENCE ON VISUAL COMMUNICATIONS AND IMAGE PROCESSING (VCIP)
(2020)