Hyperspectral Modeling of Material Appearance: General Framework, Challenges and Prospects

The main purpose of this tutorial is to address theoretical and practical issues involved in the development of predictive material appearancemodels for interdisciplinary applications within and outside the visible spectral domain. We examine the specific constraints and pitfalls found in each of the key stages of the model development framework, namely data collection, design and evaluation, and discuss alternatives to enhance the effectiveness of the entire process. Although predictive material appearance models developed by computer graphics researchers are usually aimed at realistic image synthesis applications, they also provide valuable support for a myriad of advanced investigations in related areas, such as computer vision, image processing and pattern recognition, which rely on the accurate analysis and interpretation of material appearance attributes in the hyperspectral domain. In fact, their scope of contributions goes beyond the realm of traditional computer science applications. For example, predictive light transport simulations, which are essential for the development of these models, are also regularly beingused by physical and life science researchers to understand andpredict material appearance changes prompted by mechanisms which cannot be fully studied using standard ``wet'' experimental procedures.For completeness, this tutorial also provides an overview of such synergistic research efforts and in silico investigations, which are illustrated by case studies involving the use of hyperspectral material appearance models

Fair comparison of skin detection approaches on publicly available datasets

Skin detection is the process of discriminating skin and non-skin regions in a digital image and it is widely used in several applications ranging from hand gesture analysis to track body parts and face detection. Skin detection is a challenging problem which has drawn extensive attention from the research community, nevertheless a fair comparison among approaches is very difficult due to the lack of a common benchmark and a unified testing protocol. In this work, we investigate the most recent researches in this field and we propose a fair comparison among approaches using several different datasets. The major contributions of this work are an exhaustive literature review of skin color detection approaches, a framework to evaluate and combine different skin detector approaches, whose source code is made freely available for future research, and an extensive experimental comparison among several recent methods which have also been used to define an ensemble that works well in many different problems. Experiments are carried out in 10 different datasets including more than 10000 labelled images: experimental results confirm that the best method here proposed obtains a very good performance with respect to other stand-alone approaches, without requiring ad hoc parameter tuning. A MATLAB version of the framework for testing and of the methods proposed in this paper will be freely available from https://github.com/LorisNann

Computer Aided Multi-Data Fusion Dismount Modeling

Recent research efforts strive to address the growing need for dismount surveillance, dismount tracking and characterization. Current work in this area utilizes hyperspectral and multispectral imaging systems to exploit spectral properties in order to detect areas of exposed skin and clothing characteristics. Because of the large bandwidth and high resolution, hyperspectral imaging systems pose great ability to characterize and detect dismounts. A multi-data dismount modeling system where the development and manipulation of dismount models is a necessity. This thesis demonstrates a computer aided multi-data fused dismount model, which facilitates studies of dismount detection, characterization and identification. The system is created by fusing: pixel mapping, signature attachment, and pixel mixing algorithms. The developed multi-data dismount model produces simulated hyperspectral images that closely represent an image collected by a hyperspectral imager. The dismount model can be modified to fit the researcher\u27s needs. The multi-data model structure allows the employment of a database of signatures acquired from several sources. The model is flexible enough to allow further exploitation, enhancement and manipulation. The multi-data dismount model developed in this effort fulfills the need for a dismount modeling tool in a hyperspectral imaging environment

Neural Spectro-polarimetric Fields

Modeling the spatial radiance distribution of light rays in a scene has been extensively explored for applications, including view synthesis. Spectrum and polarization, the wave properties of light, are often neglected due to their integration into three RGB spectral bands and their non-perceptibility to human vision. Despite this, these properties encompass substantial material and geometric information about a scene. In this work, we propose to model spectro-polarimetric fields, the spatial Stokes-vector distribution of any light ray at an arbitrary wavelength. We present Neural Spectro-polarimetric Fields (NeSpoF), a neural representation that models the physically-valid Stokes vector at given continuous variables of position, direction, and wavelength. NeSpoF manages inherently noisy raw measurements, showcases memory efficiency, and preserves physically vital signals, factors that are crucial for representing the high-dimensional signal of a spectro-polarimetric field. To validate NeSpoF, we introduce the first multi-view hyperspectral-polarimetric image dataset, comprised of both synthetic and real-world scenes. These were captured using our compact hyperspectral-polarimetric imaging system, which has been calibrated for robustness against system imperfections. We demonstrate the capabilities of NeSpoF on diverse scenes

Biofluorescence in Catsharks (Scyliorhinidae): Fundamental description and relevance for elasmobranch visual ecology

Biofluorescence has recently been found to be widespread in marine fishes, including sharks. Catsharks, such as the Swell Shark (Cephaloscyllium ventriosum) from the eastern Pacific and the Chain Catshark (Scyliorhinus retifer) from the western Atlantic, are known to exhibit bright green fluorescence. We examined the spectral sensitivity and visual characteristics of these reclusive sharks, while also considering the fluorescent properties of their skin. Spectral absorbance of the photoreceptor cells in these sharks revealed the presence of a single visual pigment in each species. Cephaloscyllium ventriosum exhibited a maximum absorbance of 484 +/- 3 nm and an absorbance range at half maximum (lambda(1/2max)) of 440-540 nm, whereas for S. retifer maximum absorbance was 488 +/- 3 nm with the same absorbance range. Using the photoreceptor properties derived here, a "shark eye" camera was designed and developed that yielded contrast information on areas where fluorescence is anatomically distributed on the shark, as seen from other sharks' eyes of these two species. Phylogenetic investigations indicate that biofluorescence has evolved at least three times in cartilaginous fishes. The repeated evolution of biofluorescence in elasmobranchs, coupled with a visual adaptation to detect it; and evidence that biofluorescence creates greater luminosity contrast with the surrounding background, highlights the potential importance of biofluorescence in elasmobranch behavior and biology

Biofluorescence in Catsharks (Scyliorhinidae): Fundamental Description and Relevance for Elasmobranch Visual Ecology

Biofluorescence has recently been found to be widespread in marine fishes, including sharks. Catsharks, such as the Swell Shark (Cephaloscyllium ventriosum) from the eastern Pacific and the Chain Catshark (Scyliorhinus retifer) from the western Atlantic, are known to exhibit bright green fluorescence. We examined the spectral sensitivity and visual characteristics of these reclusive sharks, while also considering the fluorescent properties of their skin. Spectral absorbance of the photoreceptor cells in these sharks revealed the presence of a single visual pigment in each species. Cephaloscyllium ventriosum exhibited a maximum absorbance of 484 ± 3 nm and an absorbance range at half maximum (λ1/2max) of 440–540 nm, whereas for S. retifer maximum absorbance was 488 ± 3 nm with the same absorbance range. Using the photoreceptor properties derived here, a “shark eye” camera was designed and developed that yielded contrast information on areas where fluorescence is anatomically distributed on the shark, as seen from other sharks’ eyes of these two species. Phylogenetic investigations indicate that biofluorescence has evolved at least three times in cartilaginous fishes. The repeated evolution of biofluorescence in elasmobranchs, coupled with a visual adaptation to detect it; and evidence that biofluorescence creates greater luminosity contrast with the surrounding background, highlights the potential importance of biofluorescence in elasmobranch behavior and biology

Infrared face recognition: a comprehensive review of methodologies and databases

Automatic face recognition is an area with immense practical potential which includes a wide range of commercial and law enforcement applications. Hence it is unsurprising that it continues to be one of the most active research areas of computer vision. Even after over three decades of intense research, the state-of-the-art in face recognition continues to improve, benefitting from advances in a range of different research fields such as image processing, pattern recognition, computer graphics, and physiology. Systems based on visible spectrum images, the most researched face recognition modality, have reached a significant level of maturity with some practical success. However, they continue to face challenges in the presence of illumination, pose and expression changes, as well as facial disguises, all of which can significantly decrease recognition accuracy. Amongst various approaches which have been proposed in an attempt to overcome these limitations, the use of infrared (IR) imaging has emerged as a particularly promising research direction. This paper presents a comprehensive and timely review of the literature on this subject. Our key contributions are: (i) a summary of the inherent properties of infrared imaging which makes this modality promising in the context of face recognition, (ii) a systematic review of the most influential approaches, with a focus on emerging common trends as well as key differences between alternative methodologies, (iii) a description of the main databases of infrared facial images available to the researcher, and lastly (iv) a discussion of the most promising avenues for future research.Comment: Pattern Recognition, 2014. arXiv admin note: substantial text overlap with arXiv:1306.160

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe