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Annual Conference of Industrial Electronics Society

作者： C. Olivier P. Courtellemont D. De Brucq O. Colot LACIS-UFR des Sciences Université de Rouen Aignan France

To take into account slow changes of characteristics in signal which are only locally stationary, an adaptive process for parameter modeling is carried out. An adaptive parametric modeling technique using windows is described. A general process with two thresholds for change detection is discussed. A more general method based on the histograms of the prediction errors is suggested.< >

关键词： Histograms Testing Performance evaluation Maximum likelihood detection change detection algorithms Signal processing Parametric statistics Maximum likelihood estimation Convergence Rhythm

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Application of neural networks algorithms to QuickBird imagery for classification and change detection of urban areas

Application of neural networks algorithms to QuickBird image...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： F.D. Frate G. Schiavon C. Solimini Dipt. di Informatica Sistemi e Produzione Tor Vergata Univ. Rome Italy Dipartimento di Informatica Sistemi e Produzione (DISP) Università Tor Vergata Rome Italy Dipartimento di Informatica Sistemi e Produzione (DISP) Università “Tor Vergata” Roma

Several studies have reported the potentialities of high resolution multi-spectral imagery for classifying and monitoring urban areas [A. K. Shackelford et al. (2003)], [M. Pesaresi et al. (2000)], [G. Schiavon et al. (2003)]. In this paper we present the results obtained by processing high resolution multispectral QuickBird images of an urban area. The high resolution QuickBird data have been used for two different purposes: for an automatic image classification using neural network techniques and for a change detection analysis. In the first case, we have carried out a pixel-based classification procedure aimed at the discrimination among 4 main classes: buildings, roads, vegetated areas, bare soil; then we have examined the potentialities of Kohonen maps for discovering new subclasses within those already established: e.g. for the asphalt category, different subclasses such as highways pixels and the other different types of roads such as secondary street pixels have been identified. In the second case we have processed multitemporal QuickBird images for detecting major changes occurred over the selected test area, like news buildings not visible in the first image.

关键词： Neural networks change detection algorithms Urban areas Image resolution Roads Multispectral imaging Monitoring Image classification Image analysis Soil

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PanoSCU: A Simulation-Based Dataset for Panoramic Indoor Scene Understanding

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IEEE ACCESS 2025年 13卷 72456-72476页

作者： Khan, Mariia Qiu, Yue Cong, Yuren Abu-Khalaf, Jumana Suter, David Rosenhahn, Bodo Edith Cowan Univ Ctr AI & Machine Learning Sch Sci Joondalup WA 6027 Australia Natl Inst Adv Ind Sci & Technol Artificial Intelligence Res Ctr Tokyo 1008921 Japan Leibniz Univ Hannover Inst Informat Proc D-30060 Hannover Germany

Panoramic images offer a comprehensive spatial view that is crucial for indoor robotics tasks such as visual room rearrangement, where an agent must restore objects to their original positions or states. Unlike existing 2D scene change understanding datasets, which rely on single-view images, panoramic views capture richer spatial context, object relationships, and occlusions-making them better suited for embodied artificial intelligence (AI) applications. To address this, we introduce Panoramic Scene change Understanding (PanoSCU), a dataset specifically designed to enhance the visual object rearrangement task. Our dataset comprises 5,300 panoramas generated in an embodied simulator, encompassing 48 common indoor object classes. PanoSCU supports eight research tasks: single-view and panoramic detection, single-view and panoramic segmentation, single-view and panoramic change understanding, embodied object tracking, and change reversal. We also present PanoStitch, a training-free method for automatic panoramic data collection within embodied environments. We evaluate state-of-the-art methods on panoramic segmentation and change understanding tasks. There is a gap in existing methods, as they are not designed for panoramic inputs and struggle with varying ratios and sizes, resulting from the unique challenges of visual object rearrangement. Our findings reveal these limitations and underscore PanoSCU's potential to drive progress in developing models capable of robust panoramic reasoning and fine-grained scene change understanding.

关键词： Visualization Robots Image processing Object segmentation Artificial intelligence Object detection Image segmentation Object recognition Navigation Image restoration change detection algorithms embodied artificial intelligence image stitching

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An Endmember-Oriented Transformer Network for Bundle-Based Hyperspectral Unmixing

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Xiang, Shu Li, Xiaorun Chen, Shuhan Zhejiang Univ Coll Elect Engn Hangzhou 310027 Peoples R China

In recent years, researchers have focused on mitigating the impact of spectral variability (SV) on unmixing performance, leading to the development of various deep-learning-based unmixing networks. Currently, most unmixing networks that account for SV mainly rely on probabilistic generative models, which lacks specific constraints for SV and suffers from the instability of solutions generated by probabilistic models. This results in the generated endmembers or SV components lacking clear physical meaning. To avoid the problems above, we propose an endmember-oriented Transformer network (EOT-Net) that leverages the advantages of endmember bundles to introduce variability while providing stable endmember results with clear physical meaning. We design an endmember-oriented Transformer (EOT) to capture endmember-specific features through directional subspace projection and a low-redundancy attention (LRA) mechanism. Subsequently, the proposed network is divided into two branches: endmember generation and abundance estimation, to process endmember-specific features. In the endmember generation branch, endmember-specific features are transformed into intraclass weights that are used to combine signatures within the bundles, and a set of endmembers is generated for each pixel. In the abundance estimation branch, endmember-specific features are integrated using a heterogeneous information fusion (HIF) module that leverages the spatial distribution heterogeneity of the endmembers, ultimately producing the abundance results. We applied the proposed algorithm to both synthetic and real datasets, and the experimental results demonstrated the model's superiority.

关键词： Feature extraction Transformers Hyperspectral imaging Attention mechanisms Estimation Classification algorithms Generators Data mining change detection algorithms Accuracy Autoencoder (AE) network endmember bundle hyperspectral unmixing (HU) spectral variability (SV) Transformer

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Temporal change detection in SAR Images Using Log Cumulants and Stacked Autoencoder

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2018年第2期15卷 297-301页

作者： Planinsic, Peter Gleich, Dusan Univ Maribor Fac Elect Engn & Comp Sci SLO-2000 Maribor Slovenia

This letter proposes a change detection algorithm for damage assessment caused by fires in Ireland using Sentinel 1 data. The novelty, in this letter, is a feature extraction within tunable Q discrete wavelet transform (TQWT) using higher order log cumulants of fractional Fourier transform (FrFT), which were fed into a stacked autoencoder (SAE) to distinguish changed and unchanged areas. The extracted features were used to train the SAE layerwise using an unsupervised learning algorithm. After training the decoding layer was replaced by a logistic regression layer to perform supervised fine-tuning and classification. The proposed algorithm was compared with the algorithm that used log cumulants of FrFT within the oriented dual-tree wavelet transform using support vector machine (SVM) classifier. The experimental results showed that the proposed combination of algorithms decreased the overall error (OE) for real synthetic aperture radar images by 6%, when TQWT was used instead of oriented dual-tree wavelet transform and OE was decreased by another 5% when SAE was used instead of the SVM classifier.

关键词： change detection algorithms feature extraction image processing neural networks synthetic aperture radar (SAR)

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Person-In-Bed detection using Frequency Domain Features and GLR-based CuSum

Person-In-Bed Detection using Frequency Domain Features and ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dhinesh Chandran G Srikrishna Bhashyam Srinivas Reddy Kota Department of Electrical Engineering Indian Institute of Technology Madras Chennai India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

We consider the problem of person-in-bed detection using accelerometer measurements in the segmented as well as streaming setting. For the segmented problem, we identify frequency domain features (4 features for each acceleration coordinate) that can be used to model the in-bed and not-in-bed hypotheses. We estimate the model parameters from the training data and apply the Generalized Likelihood Ratio (GLR) test. Using the same form as the GLR test statistic, we also propose an improvement using quadratic logistic regression. For the streaming problem, we model it as a sequential change detection problem using the models that we obtained for the in-bed and not-in-bed hypotheses and propose a GLRT-based Cumulative Sum (CuSum) algorithm.

关键词： Logistic regression change detection algorithms Frequency-domain analysis Training data Signal processing algorithms Signal processing Feature extraction Data models Acoustics Speech processing

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A Bayesian Theory of change detection in Statistically Periodic Random Processes

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IEEE TRANSACTIONS ON INFORMATION THEORY 2021年第4期67卷 2562-2580页

作者： Banerjee, Taposh Gurram, Prudhvi Whipps, Gene T. Univ Texas San Antonio Dept Elect & Comp Engn San Antonio TX 78249 USA Booz Allen Hamilton Mclean VA 22102 USA CCDC Army Res Lab Adelphi MD 20783 USA JHU Appl Phys Lab Laurel MD 20723 USA

A new class of stochastic processes called independent and periodically identically distributed (i.p.i.d.) processes is defined to capture periodically varying statistical behavior. A novel Bayesian theory is developed for detecting a change in the distribution of an i.p.i.d. process. It is shown that the Bayesian change point problem can be expressed as an optimal control problem of a Markov decision process (MDP) with periodic transition and cost structures. An optimal control theory is developed for periodic MDPs for discounted and undiscounted total cost criteria. A fixed-point equation is obtained that is satisfied by the optimal cost function. It is shown that a nonstationary but periodic policy is optimal. A value iteration algorithm is obtained to compute the optimal cost function. The results from the MDP theory are then applied to detect a change in the distribution of an i.p.i.d. process. It is shown that while a stopping rule based on a periodic sequence of thresholds is exactly optimal, a single-threshold policy is asymptotically optimal, as the probability of false alarm goes to zero. Numerical results are provided to demonstrate that the asymptotically optimal policy is not strictly optimal.

关键词： Optimal control Bayes methods Process control Mathematical model Delays change detection algorithms Social networking (online) Asymptotic optimality exact optimality generalized Shiryaev problem quickest change detection optimal control in nonstationary environments

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AGVS: A New change detection Dataset for Airport Ground Video Surveillance

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第11期23卷 20588-20600页

作者： Zhang, Xiang Shu, Chang Li, Shuai Wu, Celimuge Liu, Zhi Univ Elect Sci & Technol China Yangtze Delta Reg Inst Quzhou Sch Informat & Commun Engn Chengdu 611731 Peoples R China Shandong Univ Sch Control Sci & Engn Jinan 250100 Peoples R China Univ Electrocommun Dept Comp & Network Engn Tokyo 1828585 Japan

change detection is the foundation of intelligent video surveillance of the airport ground. However, experiments have shown that change detection algorithms with good performance on traditional datasets (e.g., CDnet2014) perform poorly in airport ground surveillance. The reason is that traditional datasets focus on the diversity of scenarios, while the practical application requires robustness against various changes in a single scene. We posit that the solution to this problem is to establish a unique dataset for airport ground surveillance and develop specific algorithms for this scenario. In this paper, we present an Airport Ground Video Surveillance benchmark (AGVS) for change detection of the airport ground. AGVS includes 25 long videos, amounting to about 100000 frames and accurate ground truth for all frames. Each video contains multiple challenges specific to the airport ground (e.g., haze, camouflage, strip shape, shadow and illumination change, simultaneous multi-scale objects) and various appearance changes of the aircraft). change detection ground truth is generated by manual annotation. The AGVS benchmark can be downloaded from ***. Furthermore, we conduct a simple review of current change detection algorithms, both unsupervised or supervised, and then 21 state-of-the-art algorithms are tested and analyzed on the AGVS benchmark. Finally, we conclude with algorithm design principles of change detection for airport ground surveillance.

关键词： Airports change detection algorithms detection algorithms Benchmark testing Adaptation models Video surveillance Lighting Benchmark change detection background subtraction airport ground video surveillance

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Automatic change detection in Sparse Repeat CT Scanning

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2020年第1期39卷 48-61页

作者： Shamul, N. Joskowicz, L. Hebrew Univ Jerusalem Sch Comp Sci & Engn CASMIP Lab IL-9190401 Jerusalem Israel

We describe a new method for the automatic detection of changes in repeat CT scanning with a reduced X-ray radiation dose. We present a theoretical formulation of the automatic change detection problem based on the on-line sparse-view repeat CT scanning dose optimization framework. We prove that the change detection problem is NP-hard and therefore cannot be efficiently solved exactly. We describe a new greedy change detection algorithm that is simple and robust and relies on only two key parameters. We demonstrate that the greedy algorithm accurately detects small, low contrast changes with only 12 scan angles. Our experimental results show that the new algorithm yields a mean changed region recall rate > 89% and a mean precision rate > 76%. It outperforms both our previous heuristic approach and a thresholding method using a low-dose prior image-constrained compressed sensing (PICCS) reconstruction of the repeat scan. The resulting changed region map may obviate the need for a high-quality repeat scan image when no major changes are detected and may streamline the radiologist's workflow by highlighting the regions of interest.

关键词： Computed tomography Image reconstruction Optimization Greedy algorithms change detection algorithms Compressed sensing Task analysis change detection repeat scanning sparse CT scanning

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Statistical change detection with moments under time-varying illumination

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IEEE TRANSACTIONS ON IMAGE PROCESSING 1998年第9期7卷 1258-1268页

作者： Liu, SC Fu, CW Chang, SY Nankai Coll Technol & Commerce Nantou Taiwan Chunghwa Telcom Co Ltd Telecommun Labs Taoyuan Taiwan Natl Tsing Hua Univ Dept Elect Engn Hsinchu Taiwan

In this paper, an illumination-independent statistical change detection method is proposed. The proposed method consists of two parts. First, based on our defined circular shift moments, structural changes can be distinguished from those due to time-varying illumination in the noise-free case, Moreover, the amount of computation is less than that of the shading model method, Second, in the light of the characteristics of the defined moments, a statistical decision rule is also proposed to cope with the effects of noise. The change detection problem can be treated as one of hypothesis testing. Critical values can be chosen according to the desired level of significance. Experimental results indicate that the proposed method detects changes accurately in the time-varying illumination case.

关键词： Lighting Layout Samarium Robustness detection algorithms Algorithm design and analysis Testing Machine vision Image segmentation change detection algorithms

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