Multifocus Image Fusion Using a Sparse and Low-Rank Matrix Decomposition for Aviator’s Night Vision Goggle

This study proposed the concept of sparse and low-rank matrix decomposition to address the need for aviator’s night vision goggles (NVG) automated inspection processes when inspecting equipment availability. First, the automation requirements include machinery and motor-driven focus knob of NVGs and image capture using cameras to achieve autofocus. Traditionally, passive autofocus involves first computing of sharpness of each frame and then use of a search algorithm to quickly find the sharpest focus. In this study, the concept of sparse and low-rank matrix decomposition was adopted to achieve autofocus calculation and image fusion. Image fusion can solve the multifocus problem caused by mechanism errors. Experimental results showed that the sharpest image frame and its nearby frame can be image-fused to resolve minor errors possibly arising from the image-capture mechanism. In this study, seven samples and 12 image-fusing indicators were employed to verify the image fusion based on variance calculated in a discrete cosine transform domain without consistency verification, with consistency verification, structure-aware image fusion, and the proposed image fusion method. Experimental results showed that the proposed method was superior to other methods and compared the autofocus put forth in this paper and the normalized gray-level variance sharpness results in the documents to verify accuracy.

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Foreground Detection via Robust Low Rank Matrix Decomposition Including Spatio-Temporal Constraint

Computer Vision - ACCV 2012 Workshops - Lecture Notes in Computer Science ◽

10.1007/978-3-642-37410-4_28 ◽

2013 ◽

pp. 315-320 ◽

Cited By ~ 15

Author(s):

Charles Guyon ◽

Thierry Bouwmans ◽

El-Hadi Zahzah

Keyword(s):

Matrix Decomposition ◽

Low Rank ◽

Foreground Detection ◽

Temporal Constraint ◽

Rank Matrix ◽

Spatio Temporal ◽

Low Rank Matrix

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A Boosting Framework of Factorization Machine

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001421590369 ◽

2021 ◽

pp. 2159036

Author(s):

Jun Zhou ◽

Longfei Li ◽

Ziqi Liu ◽

Chaochao Chen

Keyword(s):

Large Scale ◽

State Of The Art ◽

Experimental Results ◽

Low Rank ◽

Inner Product ◽

Adaptive Boosting ◽

Rank Matrix ◽

Factorization Machine ◽

Fixed Rank ◽

Low Rank Matrix

Recently, Factorization Machine (FM) has become more and more popular for recommendation systems due to its effectiveness in finding informative interactions between features. Usually, the weights for the interactions are learned as a low rank weight matrix, which is formulated as an inner product of two low rank matrices. This low rank matrix can help improve the generalization ability of Factorization Machine. However, to choose the rank properly, it usually needs to run the algorithm for many times using different ranks, which clearly is inefficient for some large-scale datasets. To alleviate this issue, we propose an Adaptive Boosting framework of Factorization Machine (AdaFM), which can adaptively search for proper ranks for different datasets without re-training. Instead of using a fixed rank for FM, the proposed algorithm will gradually increase its rank according to its performance until the performance does not grow. Extensive experiments are conducted to validate the proposed method on multiple large-scale datasets. The experimental results demonstrate that the proposed method can be more effective than the state-of-the-art Factorization Machines.

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