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IS and T International Symposium on Electronic Imaging: Media Watermarking, Security, and Forensics, MWSF 2023

作者： Yadav, Amit Kumar Singh Bartusiak, Emily R. Bhagtani, Kratika Delp, Edward J. Video & Image Processing Laboratory School of Electrical and Computer Engineering Purdue University West LafayetteIN United States

The ability to synthesize convincing human speech has become easier due to the availability of speech generation tools. This necessitates the development of forensics methods that can authenticate and attribute speech signals. In this paper, we examine a speech attribution task, which identifies the origin of a speech signal. Our proposed method known as Synthetic Speech Attribution Transformer (SSAT) converts speech signals into mel spectrograms and uses a self-supervised pretrained transformer for attribution. This transformer is pretrained on two large publicly available audio datasets: Audio Set and LibriSpeech. We finetune the pretrained transformer on three speech attribution datasets: the DARPA SemaFor Audio Attribution dataset, the ASVspoof2019 dataset, and the 2022 IEEE SP Cup dataset. SSAT achieves high closed-set accuracy on all datasets (99.8% on ASVspoof2019 dataset, 96.3% on SP Cup dataset, and 93.4% on DARPA SemaFor Audio Attribution dataset). We also investigate the method's ability to generalize to unknown speech generation methods (open-set scenario). SSAT has high performance, achieving an open-set accuracy of 90.2% on the ASVspoof2019 dataset and 88.45% on DARPA SemaFor Audio Attribution dataset. Finally, we show that our approach is robust to typical compression rates used by YouTube for speech signals. © 2023, Society for Imaging Science and Technology.

关键词： Large dataset

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Distributed and networked analysis of volumetric image data for remote collaboration of microscopy image analysis

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Journal of Medical Imaging 2025年第2期12卷 024001页

作者： Chen, Alain Han, Shuo Lee, Soonam Fu, Chichen Yang, Changye Wu, Liming Winfree, Seth Dunn, Kenneth W. Salama, Paul Delp, Edward J. Purdue University School of Electrical and Computer Engineering Video and Image Processing Laboratory West LafayetteIN United States National Institute of Allergy and Infectious Diseases Rocky Mountain Laboratories HamiltonMT United States Indiana University School of Medicine IndianapolisIN United States Purdue University at Indianapolis School of Electrical and Computer Engineering IndianapolisIN United States

Purpose: The advancement of high-content optical microscopy has enabled the acquisition of very large three-dimensional (3D) image datasets. The analysis of these image volumes requires more computational resources than a biologist may have access to in typical desktop or laptop computers. This is especially true if machine learning tools are being used for image analysis. With the increased amount of data analysis and computational complexity, there is a need for a more accessible, easy-to-use, and efficient network-based 3D image processing system. The distributed and networked analysis of volumetric image data (DINAVID) system was developed to enable remote analysis of 3D microscopy images for biologists. Approach: We present an overview of the DINAVID system and compare it to other tools currently available for microscopy image analysis. DINAVID is designed using open-source tools and has two main sub-systems, a computational system for 3D microscopy image processing and analysis and a 3D visualization system. Results: DINAVID is a network-based system with a simple web interface that allows biologists to upload 3D volumes for analysis and visualization. DINAVID enables the image access model of a center hosting image volumes and remote users analyzing those volumes, without the need for remote users to manage any computational resources. Conclusions: The DINAVID system, designed and developed using open-source tools, enables biologists to analyze and visualize 3D microscopy volumes remotely without the need to manage computational resources. DINAVID also provides several image analysis tools, including pre-processing and several segmentation models. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Photointerpretation

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Uli-Ri: A Benchmark for Person Re-Identification With Quantitative Annotations

Uli-Ri: A Benchmark for Person Re-Identification With Quanti...

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IEEE Southwest Symposium on image Analysis and Interpretation

作者： Jiaqi Guo Amy R. Reibman Edward J. Delp Video and Image Processing Laboratory School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA

Person re-identification (re-ID) has wide applications in surveillance and security. It is also challenging due to viewpoint, occlusion and illumination variations across different cameras. One solution to unsupervised person re-ID problems is synthetic data augmentation. Generative neural networks have been used to translate images from the source domain into the target domain. In this paper, we introduce a new virtual-human image dataset that can be used as the source domain for person re-ID. This new dataset has images labeled by person identity, background, viewpoint and illumination intensity. We also explore GAN-based and Diffusion-based generative methods for unpaired image-to-image translation and provide qualitative and quantitative evaluation for the synthetic results.

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Illumination Correction for Unsupervised Person Re-Identification

Illumination Correction for Unsupervised Person Re-Identific...

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IEEE Southwest Symposium on image Analysis and Interpretation

作者： Jiaqi Guo Amy R. Reibman Edward J. Delp Video and Image Processing Laboratory School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA

Unsupervised person re-identification (re-ID) aims to learn identity information from a source domain (e.g. one surveillance system) and apply it to a target domain (e.g. a different surveillance system). This is challenging due to occlusion, viewpoint, and illumination variations between the different domains (i.e. systems). In this paper, we propose a neural network architecture, known as Synthetic Model Bank (SMB), to address illumination variation in unsupervised person re-ID. The basic idea of SMB is to use synthetic data for training different re-ID models for different illumination conditions. From our experiments, the proposed SMB outperforms other synthetic augmentation methods on several re-ID benchmarks.

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Rotation Adaptive Plot Extraction from UAV RGB images

Rotation Adaptive Plot Extraction from UAV RGB Images

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jiaqi Guo Changye Yang Enyu Cai Edward J. Delp Video and Image Processing Laboratory School of Electrical and Computer Engineering Purdue University West Lafayette Indiana

Unmanned Aerial Vehicles (UAVs) have the ability to acquire high resolution RGB images from plant fields. A field used for plant research is usually divided into smaller groups of plants known as "plots" to evaluate varieties or management practices. In this paper, we propose an optimization-based, rotation-adaptive approach for extracting plots in a UAV RGB orthomosaic image. From the experiments, our proposed method is robust against range/plot rotation and achieves higher segmentation accuracy compared with existing plot extraction approaches.

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Semi-Supervised Object Detection for Sorghum Panicles in UAV imagery

Semi-Supervised Object Detection for Sorghum Panicles in UAV...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Enyu Cai Jiaqi Guo Changye Yang Edward J. Delp Video and Image Processing Laboratory (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette Indiana USA

The sorghum panicle is an important trait related to grain yield and plant development. Detecting and counting sorghum panicles can provide significant information for plant phenotyping. Current deep-learning-based object detection methods for panicles require a large amount of training data. The data labeling is time-consuming and not feasible for real application. In this paper, we present an approach to reduce the amount of training data for sorghum panicle detection via semi-supervised learning. Results show we can achieve similar performance as supervised methods for sorghum panicle detection by only using 10% of original training data.

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Transformer Ensemble for Synthesized Speech Detection

Transformer Ensemble for Synthesized Speech Detection

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Asilomar Conference on Signals, Systems & computers

作者： Emily R. Bartusiak Kratika Bhagtani Amit Kumar Singh Yadav Edward J. Delp Video and Image Processing Lab School of Electrical and Computer Engineering Purdue University West Lafayette IN

As voice synthesis systems and deep learning tools continue to improve, so does the possibility that synthesized speech can be used for nefarious purposes. Methods that determine if audio signals contain synthesized or authentic speech are needed. In this paper, we investigate three transformers to detect synthesized speech: Compact Convolutional Transformer (CCT), Patchout faSt Spectrogram Transformer (PaSST), and Self-Supervised Audio Spectrogram Transformer (SSAST). We show that each transformer independently detects synthesized speech well. Then, we propose an ensemble of transformers that can provide even better performance. Finally, we explore how much of an audio signal is needed for high synthesized speech detection. Evaluated on the ASVspoof2019 dataset, we demonstrate that our transformer ensemble detects synthesized speech from shorter segments of audio signals, even on a highly imbalanced dataset.

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Supervised machine learning-based salp swarm algorithm for fault diagnosis of photovoltaic systems

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Journal of engineering and Applied Science 2024年第1期71卷 12页

作者： Hichri, Amal Hajji, Mansour Mansouri, Majdi Nounou, Hazem Bouzrara, Kais Research Unit Advanced Materials and Nanotechnologies Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kairouan Tunisia Electrical and Computer Engineering Program Texas A&M University at Qatar Doha Qatar Laboratory of Automatic Signal and Image Processing National School of Engineers of Monastir University of Monastir Monastir5019 Tunisia

The diagnosis of faults in grid-connected photovoltaic (GCPV) systems is a challenging task due to their complex nature and the high similarity between faults. To address this issue, we propose a wrapper approach called the salp swarm algorithm (SSA) for feature selection. The main objective of SSA is to extract only the most important features from the raw data and eliminate unnecessary ones to improve the classification accuracy of supervised machine learning (SML) classifiers. Subsequently, the selected features are used to train supervised machine learning (SML) techniques in distinguishing between various operating modes. To evaluate the efficiency of the technique, we used healthy and faulty data from GCPV systems that have been injected with frequent faults, 20 different types of faults were introduced, including line-to-line, line-to-ground, connectivity faults, and those affecting the operation of bay-pass diodes. These faults present diverse conditions, such as simple and multiple faults in the PV arrays and mixed faults in both arrays. The performances of the developed SSA-SML are compared with those using principal component analysis (PCA) and kernel PCA (KPCA) based SML techniques through different criteria (i.e., accuracy, recall, precision, F1 score, and computation time). The experimental findings demonstrated that the proposed diagnosis paradigm outperformed the other techniques and achieved a high diagnostic accuracy (an average accuracy greater than 99%) while significantly reducing computation time. © 2023, The Author(s).

关键词： Failure analysis

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Multi-objective Optimal Operation of Centralized Battery Swap Charging System with Photovoltaic

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Journal of Modern Power Systems and Clean Energy 2022年第1期10卷 149-162页

作者： Yuanzheng Li Yihan Cai Tianyang Zhao Yun Liu Jian Wang Lei Wu Yong Zhao School of Artificial Intelligence and Automation Ministry of Education Key Laboratory of Image Processing and Intelligence ControlHuazhong University of Science and TechnologyWuhanChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore College of Mechatronics and Control Engineering Shenzhen UniversityShenzhenChina Department of Electrical&Computer Engineering Stevens Institute of TechnologyHobokenUSA

Electric vehicles(EVs)are widely deployed throughout the world,and photovoltaic(PV)charging stations have emerged for satisfying the charging demands of EV *** paper proposes a multi-objective optimal operation method for the centralized battery swap charging system(CBSCS),in order to enhance the economic efficiency while reducing its adverse effects on power *** proposed method involves a multi-objective optimization scheduling model,which minimizes the total operation cost and smoothes load fluctuations,***,we modify a recently proposed multi-objective optimization algorithm of non-sorting genetic algorithm III(NSGA-III)for solving this scheduling ***,simulation studies verify the effectiveness of the proposed multi-objective operation method.

关键词： Multi-objective optimization electric vehicle battery swap charging system scheduling photovoltaic

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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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