3D ear shape reconstruction and recognition for biometric applications

This paper presents a new method based on a generalized neural reflectance (GNR) model for enhancing ear recognition under variations in illumination. It is based on training a number of synthesis images of each ear taken at single lighting direction with a single view. The way of synthesizing images can be used to build training cases for each ear under different known illumination conditions from which ear recognition can be significantly improved. Our training algorithm assigns to recognize the ear by similarity measure on ear features extracting firstly by the principal component analysis method and then further processing by the Fisher’s discriminant analysis to acquire lower-dimensional patterns. Experimental results conducted on our collected ear database show that lower error rates of individual and symmetry are achieved under different variations in lighting. The recognition performance of using our proposed GRN model significantly outperforms the performance that without using the proposed GNR model

Depth Enhancement and Surface Reconstruction with RGB/D Sequence

Surface reconstruction and 3D modeling is a challenging task, which has been explored for decades by the computer vision, computer graphics, and machine learning communities. It is fundamental to many applications such as robot navigation, animation and scene understanding, industrial control and medical diagnosis. In this dissertation, I take advantage of the consumer depth sensors for surface reconstruction. Considering its limited performance on capturing detailed surface geometry, a depth enhancement approach is proposed in the first place to recovery small and rich geometric details with captured depth and color sequence. In addition to enhancing its spatial resolution, I present a hybrid camera to improve the temporal resolution of consumer depth sensor and propose an optimization framework to capture high speed motion and generate high speed depth streams. Given the partial scans from the depth sensor, we also develop a novel fusion approach to build up complete and watertight human models with a template guided registration method. Finally, the problem of surface reconstruction for non-Lambertian objects, on which the current depth sensor fails, is addressed by exploiting multi-view images captured with a hand-held color camera and we propose a visual hull based approach to recovery the 3D model

LightDepth: Single-View Depth Self-Supervision from Illumination Decline

Single-view depth estimation can be remarkably effective if there is enough ground-truth depth data for supervised training. However, there are scenarios, especially in medicine in the case of endoscopies, where such data cannot be obtained. In such cases, multi-view self-supervision and synthetic-to-real transfer serve as alternative approaches, however, with a considerable performance reduction in comparison to supervised case. Instead, we propose a single-view self-supervised method that achieves a performance similar to the supervised case. In some medical devices, such as endoscopes, the camera and light sources are co-located at a small distance from the target surfaces. Thus, we can exploit that, for any given albedo and surface orientation, pixel brightness is inversely proportional to the square of the distance to the surface, providing a strong single-view self-supervisory signal. In our experiments, our self-supervised models deliver accuracies comparable to those of fully supervised ones, while being applicable without depth ground-truth data

Depth Structure from Asymmetric Shading Supports Face Discrimination

To examine the effect of illumination direction on the ability of observers to discriminate between faces, we manipulated the direction of illumination on scanned 3D face models. In order to dissociate the surface reflectance and illumination components of front-view face images, we introduce a symmetry algorithm that can separate the symmetric and asymmetric components of the face in both low and high spatial frequency bands. Based on this approach, hybrid faces stimuli were constructed with different combinations of symmetric and asymmetric spatial content. Discrimination results with these images showed that asymmetric illumination information biased face perception toward the structure of the shading component, while the symmetric illumination information had little, if any, effect. Measures of perceived depth showed that this property increased systematically with the asymmetric but not the symmetric low spatial frequency component. Together, these results suggest that (1) the asymmetric 3D shading information dramatically affects both the perceived facial information and the perceived depth of the facial structure; and (2) these effects both increase as the illumination direction is shifted to the side. Thus, our results support the hypothesis that face processing has a strong 3D component

Single image super resolution for spatial enhancement of hyperspectral remote sensing imagery

Hyperspectral Imaging (HSI) has emerged as a powerful tool for capturing detailed spectral information across various applications, such as remote sensing, medical imaging, and material identification. However, the limited spatial resolution of acquired HSI data poses a challenge due to hardware and acquisition constraints. Enhancing the spatial resolution of HSI is crucial for improving image processing tasks, such as object detection and classification. This research focuses on utilizing Single Image Super Resolution (SISR) techniques to enhance HSI, addressing four key challenges: the efficiency of 3D Deep Convolutional Neural Networks (3D-DCNNs) in HSI enhancement, minimizing spectral distortions, tackling data scarcity, and improving state-of-the-art performance. The thesis establishes a solid theoretical foundation and conducts an in-depth literature review to identify trends, gaps, and future directions in the field of HSI enhancement. Four chapters present novel research targeting each of the aforementioned challenges. All experiments are performed using publicly available datasets, and the results are evaluated both qualitatively and quantitatively using various commonly used metrics. The findings of this research contribute to the development of a novel 3D-CNN architecture known as 3D Super Resolution CNN 333 (3D-SRCNN333). This architecture demonstrates the capability to enhance HSI with minimal spectral distortions while maintaining acceptable computational cost and training time. Furthermore, a Bayesian-optimized hybrid spectral spatial loss function is devised to improve the spatial quality and minimize spectral distortions, combining the best characteristics of both domains. Addressing the challenge of data scarcity, this thesis conducts a thorough study on Data Augmentation techniques and their impact on the spectral signature of HSI. A new Data Augmentation technique called CutMixBlur is proposed, and various combinations of Data Augmentation techniques are evaluated to address the data scarcity challenge, leading to notable enhancements in performance. Lastly, the 3D-SRCNN333 architecture is extended to the frequency domain and wavelet domain to explore their advantages over the spatial domain. The experiments reveal promising results with the 3D Complex Residual SRCNN (3D-CRSRCNN), surpassing the performance of 3D-SRCNN333. The findings presented in this thesis have been published in reputable conferences and journals, indicating their contribution to the field of HSI enhancement. Overall, this thesis provides valuable insights into the field of HSI-SISR, offering a thorough understanding of the advancements, challenges, and potential applications. The developed algorithms and methodologies contribute to the broader goal of improving the spatial resolution and spectral fidelity of HSI, paving the way for further advancements in scientific research and practical implementations.Hyperspectral Imaging (HSI) has emerged as a powerful tool for capturing detailed spectral information across various applications, such as remote sensing, medical imaging, and material identification. However, the limited spatial resolution of acquired HSI data poses a challenge due to hardware and acquisition constraints. Enhancing the spatial resolution of HSI is crucial for improving image processing tasks, such as object detection and classification. This research focuses on utilizing Single Image Super Resolution (SISR) techniques to enhance HSI, addressing four key challenges: the efficiency of 3D Deep Convolutional Neural Networks (3D-DCNNs) in HSI enhancement, minimizing spectral distortions, tackling data scarcity, and improving state-of-the-art performance. The thesis establishes a solid theoretical foundation and conducts an in-depth literature review to identify trends, gaps, and future directions in the field of HSI enhancement. Four chapters present novel research targeting each of the aforementioned challenges. All experiments are performed using publicly available datasets, and the results are evaluated both qualitatively and quantitatively using various commonly used metrics. The findings of this research contribute to the development of a novel 3D-CNN architecture known as 3D Super Resolution CNN 333 (3D-SRCNN333). This architecture demonstrates the capability to enhance HSI with minimal spectral distortions while maintaining acceptable computational cost and training time. Furthermore, a Bayesian-optimized hybrid spectral spatial loss function is devised to improve the spatial quality and minimize spectral distortions, combining the best characteristics of both domains. Addressing the challenge of data scarcity, this thesis conducts a thorough study on Data Augmentation techniques and their impact on the spectral signature of HSI. A new Data Augmentation technique called CutMixBlur is proposed, and various combinations of Data Augmentation techniques are evaluated to address the data scarcity challenge, leading to notable enhancements in performance. Lastly, the 3D-SRCNN333 architecture is extended to the frequency domain and wavelet domain to explore their advantages over the spatial domain. The experiments reveal promising results with the 3D Complex Residual SRCNN (3D-CRSRCNN), surpassing the performance of 3D-SRCNN333. The findings presented in this thesis have been published in reputable conferences and journals, indicating their contribution to the field of HSI enhancement. Overall, this thesis provides valuable insights into the field of HSI-SISR, offering a thorough understanding of the advancements, challenges, and potential applications. The developed algorithms and methodologies contribute to the broader goal of improving the spatial resolution and spectral fidelity of HSI, paving the way for further advancements in scientific research and practical implementations