Principal Component Analysis-Based Image Fusion Routine with Application to Automotive Stamping Split Detection

Zhou, Yi; Mayyas, Ahmad; Omar, Mohammed

doi:10.1080/09349847.2011.553348

Cited by 29 publications

(6 citation statements)

References 49 publications

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“…The three remaining metrics, i.e. mutual information (

) [ 49 ], structural similarity index measure (

) [ 50 ] and Peak signal-to-noise ratio (

) [ 51 ], are determined by the fused and source images. In this paper, if the resolution of the fused image

is higher than that of the source image

(either the infrared image

or the visible image

), then we must downsample the fused image first and then calculate the metrics.…”

Section: Resultsmentioning

confidence: 99%

Fusing Infrared and Visible Images of Different Resolutions via Total Variation Model

et al. 2018

Sensors

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In infrared and visible image fusion, existing methods typically have a prerequisite that the source images share the same resolution. However, due to limitations of hardware devices and application environments, infrared images constantly suffer from markedly lower resolution compared with the corresponding visible images. In this case, current fusion methods inevitably cause texture information loss in visible images or blur thermal radiation information in infrared images. Moreover, the principle of existing fusion rules typically focuses on preserving texture details in source images, which may be inappropriate for fusing infrared thermal radiation information because it is characterized by pixel intensities, possibly neglecting the prominence of targets in fused images. Faced with such difficulties and challenges, we propose a novel method to fuse infrared and visible images of different resolutions and generate high-resolution resulting images to obtain clear and accurate fused images. Specifically, the fusion problem is formulated as a total variation (TV) minimization problem. The data fidelity term constrains the pixel intensity similarity of the downsampled fused image with respect to the infrared image, and the regularization term compels the gradient similarity of the fused image with respect to the visible image. The fast iterative shrinkage-thresholding algorithm (FISTA) framework is applied to improve the convergence rate. Our resulting fused images are similar to super-resolved infrared images, which are sharpened by the texture information from visible images. Advantages and innovations of our method are demonstrated by the qualitative and quantitative comparisons with six state-of-the-art methods on publicly available datasets.

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“…The three remaining metrics, i.e. mutual information (

) [ 49 ], structural similarity index measure (

) [ 50 ] and Peak signal-to-noise ratio (

) [ 51 ], are determined by the fused and source images. In this paper, if the resolution of the fused image

is higher than that of the source image

(either the infrared image

or the visible image

), then we must downsample the fused image first and then calculate the metrics.…”

Section: Resultsmentioning

confidence: 99%

Fusing Infrared and Visible Images of Different Resolutions via Total Variation Model

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…PCA [18] is a standard technique for dimensionality-reduction and has been applied to a broad class of computer vision problems, including feature selection, object recognition and face recognition.…”

Section: Face Recognition Systemmentioning

confidence: 99%

“…This can have various practical applications, such as automated person identification and the recognition of race, gender, emotions, etc. [18]. The procedure for the face recognition system is as follows.…”

Section: Face Recognition Systemmentioning

confidence: 99%

2D-3D Face Recognition Method Basedon a Modified CCA-PCA Algorithm

Kamencay

Hudec

Benčo

et al. 2014

International Journal of Advanced Robotic Systems

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This paper presents a proposed methodology for face recognition based on an information theory approach to coding and decoding face images. In this paper, we propose a 2D-3D face-matching method based on a principal component analysis (PCA) algorithm using canonical correlation analysis (CCA) to learn the mapping between a 2D face image and 3D face data. This method makes it possible to match a 2D face image with enrolled 3D face data. Our proposed fusion algorithm is based on the PCA method, which is applied to extract base features. PCA feature-level fusion requires the extraction of different features from the source data before features are merged together. Experimental results on the TEXAS face image database have shown that the classification and recognition results based on the modified CCA-PCA method are superior to those based on the CCA method. Testing the 2D-3D face match results gave a recognition rate for the CCA method of a quite poor 55% while the modified CCA method based on PCA-level fusion achieved a very good recognition score of 85%.

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“…The different levels at which image fusion can be done are the signal level, pixel level, feature level and at decision level [27][28][29]. Image fusion methodologies are mainly based on pixel-level techniques [6,[30][31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Small Multispectral UAV Sensor and Its Image Fusion Capability in Cultural Heritage Applications

Kaimaris

Kandylas

2020

Heritage

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For many decades the multispectral images of the earth’s surface and its objects were taken from multispectral sensors placed on satellites. In recent years, the technological evolution produced similar sensors (much smaller in size and weight) which can be placed on Unmanned Aerial Vehicles (UAVs), thereby allowing the collection of higher spatial resolution multispectral images. In this paper, Parrot’s small Multispectral (MS) camera Sequoia+ is used, and its images are evaluated at two archaeological sites, on the Byzantine wall (ground application) of Thessaloniki city (Greece) and on a mosaic floor (aerial application) at the archaeological site of Dion (Greece). The camera receives RGB and MS images simultaneously, a fact which does not allow image fusion to be performed, as in the standard utilization procedure of Panchromatic (PAN) and MS image of satellite passive systems. In this direction, that is, utilizing the image fusion processes of satellite PAN and MS images, this paper demonstrates that with proper digital processing the images (RGB and MS) of small MS cameras can lead to a fused image with a high spatial resolution, which retains a large percentage of the spectral information of the original MS image. The high percentage of spectral fidelity of the fused images makes it possible to perform high-precision digital measurements in archaeological sites such as the accurate digital separation of the objects, area measurements and retrieval of information not so visible with common RGB sensors via the MS and RGB data of small MS sensors.

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Principal Component Analysis-Based Image Fusion Routine with Application to Automotive Stamping Split Detection

Cited by 29 publications

References 49 publications

Fusing Infrared and Visible Images of Different Resolutions via Total Variation Model

Fusing Infrared and Visible Images of Different Resolutions via Total Variation Model

2D-3D Face Recognition Method Basedon a Modified CCA-PCA Algorithm

Small Multispectral UAV Sensor and Its Image Fusion Capability in Cultural Heritage Applications

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