Video anomaly recovery from compressed spectral imaging

Ramirez, Ana B.; Argüello, Henry; Arce, Gonzalo R.

doi:10.1109/icassp.2011.5946655

Cited by 5 publications

(9 citation statements)

References 12 publications

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“…6 plots the 3-D ROC curves and Table III tabulates the area under curve (AUC) values of their corresponding 2-D ROC curves [2-D ROC curves of (P D , P F ), 2-D ROC curves of (P D , τ ), and 2-D ROC curves of (P F , τ )], which are all between 0 and 1 where the best cases for various values of ( p, m, j ) are boldfaced. In order to take advantage of the three 2-D ROC curves of (P D , P F ), (P D , τ ), and (P F , τ ), generated by the 3-D ROC curves, we further design a new metric as AUC OD to measure the overall detection performance, which is defined by AUC OD = AUC(P D , P F ) + AUC(P D , τ ) − AUC(P F , τ ) (43) where the values of AUC(P D , P F ) and AUC(P D , τ ) are summed with the subtraction of AUC(P F , τ ). This is because a higher value of AUC(P D , P F ) and AUC(P D , τ ) indicates a higher detection performance.…”

Section: A Ti Experimentsmentioning

confidence: 99%

“…Conversely, a lower value of AUC(P F , τ ) indicates a better BKG suppression, and thus a better detection performance. As a result, the AUC OD defined in (43), indeed, combines the values calculated by the AUC values produced by these three 2-D ROC curves to produce a quantitative value of the overall detection performance for each of the test anomaly detectors where a higher value of an AUC OD produced by an anomaly detector is a better and more effective the anomaly detector.…”

Section: A Ti Experimentsmentioning

confidence: 99%

“…Table IV tabulates the AUC values of the 2-D ROC curves of (P D , P F ), the 2-D ROC curves of (P D , τ ), the 2-D ROC curves of (P F , τ ) produced in Fig. 8, and their AUC OD calculated by (43).…”

Section: A Ti Experimentsmentioning

confidence: 99%

See 2 more Smart Citations

Orthogonal Subspace Projection-Based Go-Decomposition Approach to Finding Low-Rank and Sparsity Matrices for Hyperspectral Anomaly Detection

Chang

Cao

Chen

et al. 2021

IEEE Trans. Geosci. Remote Sensing

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Section: A Ti Experimentsmentioning

confidence: 99%

Section: A Ti Experimentsmentioning

confidence: 99%

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Orthogonal Subspace Projection-Based Go-Decomposition Approach to Finding Low-Rank and Sparsity Matrices for Hyperspectral Anomaly Detection

Chang

Cao

Chen

et al. 2021

IEEE Trans. Geosci. Remote Sensing

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“…The technique has been applied to video surveillance [1], [10] and face recognition [11] successfully. There are also some attempts to apply PCP to latent sematic indexing [12].…”

Section: Introductionmentioning

confidence: 99%

Application of principal component pursuit to process fault detection and diagnosis

Cheng

Chen

2013

2013 American Control Conference

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Data-driven process monitoring has been extensively discussed in both academia and industry because of its applicability and effectiveness. One of the most applied techniques is the principal component analysis (PCA). Recently a new technique called principal component pursuit (PCP) is introduced. Compared to PCA, PCP is more robust to outliers. In this paper, the application of the PCP technique to process monitoring is thoroughly discussed from training data preprocessing to residual signal post-filtering. A new scaling preprocessing step is proposed to improve quality of data matrices in the sense of low coherence. A residual generator and a post-filter suitable for PCP generated process models are also provided. The post-filtered residual represents the fault signal, which makes the fault detection, isolation and reconstruction procedures simple and straightforward. A numerical example is provided to describe and illustrate the PCP-based process modeling and monitoring procedures.

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“…The spectral video has many applications in the industry and the academy, such as surveillance, moving targets recognition, security, and classification, where the discrimination of the features is performed over the different spectral bands instead of use only three channels (RGB) as in traditional approaches [7,8,9,10]. Figure 2 shows the sensing process in the video C-CASSI system for a spectral video.…”

Section: Introductionmentioning

confidence: 99%

Multiresolution-based reconstruction for compressive spectral video sensing using a spectral multiplexing sensor

León-López¹,

Galvis-Carreño²,

Arguello-Fuentes³

2018

revuin

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Este artículo puede compartirse bajo la licencia CC BY-ND 4.0 y se referencia usando el siguiente formato: K. León, L. Galvis, H. Arguello, "Multiresolution-based reconstruction for compressive spectral video sensing using a spectral multiplexing sensor," Rev. UIS Ing., vol. 17, no. 1, pp. 209-216, 2018. Doi: https://doi.org/10.18273/revuin.v17n1-2018020 Vol. 17, no. 1, pp. 209-216, enero-junio 2018 Revista UIS Ingenierías ABSTRACTSpectral multiplexing sensors based on compressive sensing attempt to break the Nyquist barrier to acquire high spectral resolution scenes. Particularly, the colored coded aperture-based compressive spectral imager extended to video, or video C-CASSI, is a spectral multiplexing sensor that allows capturing spectral dynamic scenes by projecting each spectral frame onto a bidimensional detector using a 3D coded aperture. Afterwards, the compressed signal reconstruction is performed iteratively by finding a sparse solution to an undetermined linear system of equations. Even though the acquired signal can be recovered from much fewer observations by an ℓ 2 − ℓ 1 -norm recovery algorithm than using conventional sensors, the reconstruction exhibits diverse challenges originated by the temporal variable or motion. The motion during the reconstruction produces artifacts that damages the entire data. In this work, a multiresolution-based reconstruction method for compressive spectral video sensing is proposed. In this way, it obtains the temporal information from the measurements at a low computational cost. Thereby, the optimization problem to recover the signal is extended by adding temporal information in order to correct the errors originated by the scene motion. Computational experiments performed over four different spectral videos show an improvement up to 4dB in terms of peak-signal to noise ratio (PSNR) in the reconstruction quality using the multiresolution approach applied to the spectral video reconstruction with respect to the traditional inverse problem. KEYWORDS:Multiresolution reconstruction; compressive spectral video; optimization. RESUMENLos sensores de multiplexación espectral basados en muestreo compresivo intentan romper la barrera de Nyquist para adquirir escenas de alta resolución espectral. Particularmente, el sistema de imágenes espectrales de única captura basado en aperturas codificadas de color extendido a vídeo, o video -CCASSI, es un sensor de multiplexación espectral que permite la adquisición de imágenes espectrales dinámicas proyectando cada fotograma espectral sobre un detector bidimensional usando un apertura de codificación 3D. Posteriormente, la reconstrucción de la señal 210 K. León, L. Galvis, H. Arguello comprimida se realiza iterativamente encontrando una solución escasa a un sistema lineal de ecuaciones indeterminado. Si bien la señal adquirida puede ser recuperada desde un algoritmo basado en la norma l_2 − l_1, con muchas menos observaciones en comparación a los sistemas convencionales, dicha reconstrucción presenta diversos desafíos originados p...

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Video anomaly recovery from compressed spectral imaging

Cited by 5 publications

References 12 publications

Orthogonal Subspace Projection-Based Go-Decomposition Approach to Finding Low-Rank and Sparsity Matrices for Hyperspectral Anomaly Detection

Orthogonal Subspace Projection-Based Go-Decomposition Approach to Finding Low-Rank and Sparsity Matrices for Hyperspectral Anomaly Detection

Application of principal component pursuit to process fault detection and diagnosis

Multiresolution-based reconstruction for compressive spectral video sensing using a spectral multiplexing sensor

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