Cross-modal face recognition with illumination-invariant local discrete cosine transform binary pattern (LDCTBP)

Koley, Subhadeep; Roy, Hiranmoy; Dhar, Soumyadip; Bhattacharjee, Debotosh

doi:10.1007/s10044-023-01139-x

Cited by 3 publications

(3 citation statements)

References 47 publications

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“…In image sparse decomposition, it is very crucial to design a suitable dictionary to approximate the sparse components. For expressing the smooth area, the most commonly used dictionaries are curvelet transform dictionary [32], local ridgelet transform dictionary [33,34], and porous algorithm dictionary; for texture regions, wavelet dictionary [35,36], Gabor dictionary [37], local discrete cosine transform dictionary [38], etc. In this article, we compared the advantages and disadvantages of these dictionaries, and considered their applicability to medical images before choosing the curvelet transform dictionary and the local discrete cosine transform dictionary to approximate the sparse components decomposed by MCA.…”

Section: Dictionary Selectionmentioning

confidence: 99%

Research and Implementation of Denoising Algorithm for Brain MRIs via Morphological Component Analysis and Adaptive Threshold Estimation

Awudong,

Yakupu,

Yan

et al. 2024

Mathematics

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The inevitable noise generated in the acquisition and transmission process of MRIs seriously affects the reliability and accuracy of medical research and diagnosis. The denoising effect for Rician noise, whose distribution is related to MR image signal, is not good enough. Furthermore, the brain has a complex texture structure and a small density difference between different parts, which leads to higher quality requirements for brain MR images. To upgrade the reliability and accuracy of brain MRIs application and analysis, we designed a new and dedicated denoising algorithm (named VST–MCAATE), based on their inherent characteristics. Comparative experiments were performed on the same simulated and real brain MR datasets. The peak signal-to-noise ratio (PSNR), and mean structural similarity index measure (MSSIM) were used as objective image quality evaluation. The one-way ANOVA was used to compare the effects of denoising between different approaches. p < 0.01 was considered statistically significant. The experimental results show that the PSNR and MSSIM values of VST–MCAATE are significantly higher than state-of-the-art methods (p < 0.01), and also that residual images have no anatomical structure. The proposed denoising method has advantages in improving the quality of brain MRIs, while effectively removing the noise with a wide range of unknown noise levels without damaging texture details, and has potential clinical promise.

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Section: Dictionary Selectionmentioning

confidence: 99%

Research and Implementation of Denoising Algorithm for Brain MRIs via Morphological Component Analysis and Adaptive Threshold Estimation

Awudong,

Yakupu,

Yan

et al. 2024

Mathematics

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show abstract

“…Subsequently, the test images were cooperatively represented using the training images of different resolutions. Koley et al 28 . proposed a new face image descriptor named local discrete cosine transform binary pattern (LDCTBP) and incorporated a multi-resolution approach to equip LDCTBP to extract micro- and macro-level features.…”

Section: Introductionmentioning

confidence: 99%

“…Subsequently, the test images were cooperatively represented using the training images of different resolutions. Koley et al 28 proposed a new face image descriptor named local discrete cosine transform binary pattern (LDCTBP) and incorporated a multi-resolution approach to equip LDCTBP to extract micro-and macro-level features. The LDCTBP descriptor can efficiently tackle illumination-and modality-variations existent in the face samples, and the effectiveness of this method was verified on multiple face datasets.…”

Section: Introductionmentioning

confidence: 99%

Face recognition algorithm based on sample expansion and label embedding for multi-resolution dictionary learning

Yan

Cui

2023

J. Electron. Imag.

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.Face recognition, being a widely utilized and profoundly challenging task within the field of computer vision, has witnessed the emergence of numerous practical methodologies in recent years. Among these, dictionary learning-based methods have garnered extensive attention and have found widespread application. Nevertheless, it is worth noting that the majority of dictionary learning methods suffer from certain limitations. On the one hand, their primary focus lies solely on the resolution of the original images, thereby rendering them susceptible to the impact of resolution changes encountered in real-world scenarios. On the other hand, their ability to acquire a robust dictionary is hindered by the inadequacy of available training samples. We propose a multi-resolution dictionary learning method based on sample extension and label embedding. We generate virtual samples for the original training samples and convert all samples into different resolutions. In addition, noise constraints are applied to the virtual samples to enhance robustness. Then, we construct the label embedding term using the label information of the atoms and embed it on a multi-resolution dictionary learning model for training. The recognition rates of the method on the ORL, extended Yale B, CMU PIE, and GT datasets are 93.72%, 92.57%, 97.79%, and 73.91%, respectively.

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The fusion feature wavelet pyramid based on FCIS and GLCM for texture classification

Su,

Chen,

et al. 2023

Int. J. Mach. Learn. & Cyber.

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Cross-modal face recognition with illumination-invariant local discrete cosine transform binary pattern (LDCTBP)

Cited by 3 publications

References 47 publications

Research and Implementation of Denoising Algorithm for Brain MRIs via Morphological Component Analysis and Adaptive Threshold Estimation

Research and Implementation of Denoising Algorithm for Brain MRIs via Morphological Component Analysis and Adaptive Threshold Estimation

Face recognition algorithm based on sample expansion and label embedding for multi-resolution dictionary learning

The fusion feature wavelet pyramid based on FCIS and GLCM for texture classification

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