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Curve reconstruction from a set of dense scattered points using A* algorithm

Curve reconstruction from a set of dense scattered points us...

MIPPR 2009 - Automatic Target Recognition and image Analysis: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Sun, Yangguang Zhou, Chengping Cai, Chao Ding, Mingyue Institute for Pattern Recognition and Artificial Intelligence State Key Laboratory for Multi-spectral Information Processing Technologies HUST Wuhan 430074 China College of Life Science and Technology Image Processing and Intelligence Control Key Laboratory of Education Ministry of China HUST Wuhan 430074 China

ISBN: (纸本)9780819478061

In this paper, a novel curve reconstruction method based on A* algorithm from a set of dense scattered points was proposed. Our method can not only reconstruct dense scattered points with single connected complicated form, but also solve the problem building the curve from the scattered points with close form and multiply connected form. Moreover, the influence of noise points on reconstructed curves is farther decreased, and the shape of dense scattered points will be well hold. Experimental results have demonstrated that the proposed method has the feasibility and robustness. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Pattern recognition

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An optimal software maintenance policy based on reliability and risk

An optimal software maintenance policy based on reliability ...

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2014 International Joint Conference on Neural Networks, IJCNN 2014

作者： Xiaoping, Wang Fang, Zhou Yi, Shen School of Automation Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781479914845

Software maintenance is assuming ever more a crucial role in the lifecycle of software due to the increase of software requirements and the high variability of software environment. Common approaches of studying software maintenance are to consider them as a static by-product of software operation and only the maintenance cost is covered. In this paper, software maintenance policies are studied with the consideration of software reliability and risk. An optimization model is defined to drive the choice of a maintenance schedule. The solution of the model provides the best maintenance policy and the choice of actual actions that will minimize the average maintenance cost while the software reliability and risk are acceptable. Finally, a numerical example is given to show the analysis process of our proposed policy. © 2014 IEEE.

关键词： Software reliability

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DeNet: An explicit distance ensemble model for person re-identification 3

DeNet: An explicit distance ensemble model for person re-ide...

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3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015

作者： Wang, Jin Gao, Changxin Hu, Jing Sang, Nong Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781479961009

In this paper, we address the problem of person reidentification (re-id), which remains to be challenging due to view point changes, pose variations, different camera settings, etc. Different from common methods that concatenate descriptors extracted from different support regions and feature channels directly as a long vector, we encode the importance of different feature channels and support regions explicitly and propose a two-layer distance ensemble model called DeNet to measure the similarity between two images. The first layer of DeNet combines distances of different support regions while the second layer weights different feature channels. Weight parameters of DeNet are learnt under the large margin framework with the goal of maximizing the difference between distances of positive and negative matching pairs. Our method achieves very competitive results on the widely used VIPeR and PRID 450S datasets. © 2015 IEEE.

关键词： Computers

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Selective Bokeh Effect Transformation

Selective Bokeh Effect Transformation

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2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Peng, Juewen Pan, Zhiyu Liu, Chengxin Luo, Xianrui Sun, Huiqiang Shen, Liao Xian, Ke Cao, Zhiguo Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control China Nanyang Technological University S-Lab Singapore

ISBN: (纸本)9798350302493

Bokeh effect transformation is a novel task in computer vision and computational photography. It aims to convert bokeh effects from one camera lens to another. To this end, we introduce a new concept of blur ratio, which represents the ratio of the blur amount of a target image to that of a source image, and propose a novel framework SBTNet based on this concept. For cat-eye simulation and lens type transformation, a two-channel coordinate map and a two-channel one-hot map are added as extra inputs. The core of the framework is a sequence of parallel FeaNets, along with a feature selection and integration strategy, which aims to transform the blur amount with arbitrary blur ratio. The effectiveness of the proposed framework is demonstrated through extensive experiments, and our solution has achieved the top LPIPS metric in NTIRE 2023 Bokeh Effect Transformation Challenge. © 2023 IEEE.

关键词： Color photography

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Aero-thermal radiation correction via multi-scale bias field estimation 3

Aero-thermal radiation correction via multi-scale bias field...

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3rd IAPR Asian Conference on Pattern Recognition, ACPR 2015

作者： Li, Lerenhan Yan, Luxin Sang, Nong Gao, Changxin Hu, Jing Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781479961009

In this paper, a multi-scale bias field estimation is proposed to carry out the aero-thermal radiation correction. The bias field is estimated at scales from coarse to fine by an alternative minimization, after which, the degraded image is restored via subtraction. In the minimization, Tiknov regularization is employed to constrain the smoothness of bias field while Total Variation (TV) is adopted to preserve the details and structures of the latent image. Besides, Split Bregman iteration is introduced to conquer the difficulty in the non-smooth problem when resolving the minimization. Compared with conventional approaches, experiments verified that the proposed method contributes to the rapidness and precision of the algorithm. © 2015 IEEE.

关键词： Iterative methods

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Region of interest image encryption method based on panoramic segmentation and a novel coupled chaotic map

Region of interest image encryption method based on panorami...

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2022 International Conference on Artificial intelligence, Virtual Reality, and Visualization, AIVRV 2022

作者： Zhang, Hangming Hu, Hanping School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Information Processing and Intelligent Control Ministry of Education Wuhan430074 China

ISBN: (纸本)9781510662988

Social media is a platform for people to share their lives and interact with others. image sharing is an integral component. Privacy information will inevitably be compromised during the process of sharing whole photos. Sometimes, it is not even caused by the person who shares the image but the third party who forwards the image. In the majority of cases, we do not attempt to protect the entirety of the media;rather, we seek to protect the most crucial portion, whether it is the background or specific objects, which decreasing superfluous cryptography procedures. Unfortunately, current research on such field is extremely rare. In this research, we established a novel pixel-level image encryption technique relying on Panoptic FCN panoramic segmentation and chaos for client-intended images on social media sites. Our suggested technology is capable of automated picture encryption on either the whole images or user-selected areas, whether they are rectangular or irregular, which is suited for all region of interest (ROI) encryption. Relying on a novel coupled chaotic map, this universal new encryption method flattens the array of the image ROI into a series of pixels. The module of Panoptic FCN can be replaced by any other panoptic segmentation models which are stronger in performance. Statistical and cryptographic evaluations demonstrate that our technique preserves the high efficiency for practical applications. © 2023 SPIE. All rights reserved.

关键词： Cryptography

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RP-MSTC∗: Multi-Agent Coverage Path Planning Algorithm Emphasizing Local Area Updates

RP-MSTC∗: Multi-Agent Coverage Path Planning Algorithm Emph...

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2023 International Conference on Neuromorphic Computing, ICNC 2023

作者： Yang, Yong He, Dingxin Mao, Huantao Chen, Hong Wu, Hongtao Liu, Zhi-Wei School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent ControlMinistry of Education Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9798350316889

Compared to traditional area coverage algorithms, this paper proposes a re-plannable multi-agent coverage path planning algorithm to address issues such as low coverage rate of multiple agents in large-scale scenarios like warehouses and inability to cope with local environmental changes. Initially, the MSTC∗ algorithm is utilized to create the coverage path based on the available map information. To address scenarios like idle areas for material placement, the method introduces local area detection and path re-planning to enhance the coverage rate. This paper verifies the feasibility and effectiveness of the algorithm under different maps through simulation experiments. Compared with the coverage situation of other coverage path planning algorithms in environments with local area updates, the algorithm proposed in this paper has a higher coverage rate in this scenario. © 2023 IEEE.

关键词： Motion planning

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Closer to pre-trained network transfer better 9

Closer to pre-trained network transfer better

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9th IEEE Joint International Information Technology and Artificial intelligence Conference, ITAIC 2020

作者： Chen, Siyu Li, Wei Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China Image Processing and Intelligent Control Key Laboratory Education Ministry of China Wuhan430074 China

In recent years, Deep Neural Network (DNN) has been widely used in the domain of computer vision, but its further development is restricted because of the lack of train samples. Fine-tuning is one of deep transfer learning methods to solve this problem. Nowdays, some existing Fine-tuning approaches assume that if the fine-tuned network could be as close to the pre-trained network as possible, the source knowledge would be fully utilized to improve the study effect. Although these approaches work pretty well, the assumptions on which they are based have not been systematically proved. Due to the complexity of DNNs, offering a reasonable and accurate explanation of the performance of the network is significant for designing new algorithms. In this paper, we design a series of experiments with or without transfer learning respectively, to characterize the relationship between network differences and network performance. The results show that there is an obvious negative correlation between them under certain circumstances. This conclusion can not only provide a more reasonable explanation for the relevant transfer learning algorithm, but also provide support and in-spiration for the future work. © 2020 IEEE.

关键词： Deep neural networks

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Fast KNN search for big data with set compression tree and best bin first 2

Fast KNN search for big data with set compression tree and b...

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2nd International Conference on Cloud Computing and Internet of Things, CCIOT 2016

作者： Chen, Zhenjie Yan, Jingqi Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of Ministry of Education for System Control and Information Processing China Shanghai China

ISBN: (纸本)9781467398213

This paper proposes k nearest neighbors (kNN) search based on set compression tree (SCT) and best bin first (BBF) to deal with the problem for big data. The large compression rate by set compression tree is achieved by compressing the set of descriptors jointly instead of compressing on a per-descript or basis. So set compression tree has a good performance in kNN search at a low bit rate. At the same time, the best bin first (BBF) is a very efficient algorithm to find the approximately kNN from a large number of high dimensional feature descriptors. SCT-BBF is a novel exploration and it improves search performance in three aspects: First, SCT-BBF requires less memory footprint, which is important in big data age. Second, it increases accuracy compared traditional method like KD-Tree and original SCT. SCT-BBF can be used with other data processing methods like PCA and SIFT to perform better. Third, this paper adopts finer search to increase accuracy at a slight loss of speed. And it can extend to big data easily. © 2016 IEEE.

关键词： Big data

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Learning Probabilistic Coordinate Fields for Robust Correspondences

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IEEE Transactions on Pattern Analysis and Machine intelligence 2023年第10期45卷 12004-12021页

作者： Zhao, Weiyue Lu, Hao Ye, Xinyi Cao, Zhiguo Li, Xin Huazhong University of Science and Technology The Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Wuhan430074 China West Virginia University The Lane Department of Computer Science and Electrical Engineering MorgantownWV26506-6109 United States

We introduce Probabilistic Coordinate Fields (PCFs), a novel geometric-invariant coordinate representation for image correspondence problems. In contrast to standard Cartesian coordinates, PCFs encode coordinates in correspondence-specific barycentric coordinate systems (BCS) with affine invariance. To know when and where to trust the encoded coordinates, we implement PCFs in a probabilistic network termed PCF-Net, which parameterizes the distribution of coordinate fields as Gaussian mixture models. By jointly optimizing coordinate fields and their confidence conditioned on dense flows, PCF-Net can work with various feature descriptors when quantifying the reliability of PCFs by confidence maps. An interesting observation of this work is that the learned confidence map converges to geometrically coherent and semantically consistent regions, which facilitates robust coordinate representation. By delivering the confident coordinates to keypoint/feature descriptors, we show that PCF-Net can be used as a plug-in to existing correspondence-dependent approaches. Extensive experiments on both indoor and outdoor datasets suggest that accurate geometric invariant coordinates help to achieve the state of the art in several correspondence problems, such as sparse feature matching, dense image registration, camera pose estimation, and consistency filtering. Further, the interpretable confidence map predicted by PCF-Net can also be leveraged to other novel applications from texture transfer to multi-homography classification. © 1979-2012 IEEE.

关键词： image registration

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