22 research outputs found
A novel computer vision based neutrosophic approach for leaf disease identification and classification
The natural products are inexpensive, non-toxic, and have fewer side effects. Thus, their demand especially
herbs based medical products, health products, nutritional supplements, cosmetics etc. are increasing. The quality of leafs defines the degree of excellence or a state of being free from defects, deficits, and substantial variations. Also, the diseases in leafs possess threats to the economic, and production status in the agricultural industry worldwide
Effects of resolution of lighting control systems
Advances in lighting technologies have spurred sophisticated lighting control systems (LCSs). To conserve energy and improve occupants’ wellbeing, LCSs have been integrated into sustainable buildings. However, the complexity of LCSs may lead to negative experiences and reduce the frequency of their use. One fundamental issue, which has not been systematically investigated, is the impact of control resolution (the smallest change produced by an LCS). In an ideal LCS, the resolution would be sufficiently fine for users to specify their desired lighting conditions, but the smallest change would be detectable. Thus, the design of optimal control systems requires a thorough understanding of the detectability and acceptability of differences in illuminance, luminance and colour. The control of colour is complicated by the range of interfaces that can be used to facilitate colour mixing. Four psychophysical experiments investigated the effect of LCS resolution. The first two experiments explored the effect of resolution in white light LCSs on usability and energy conservation. The results suggest that, in different applications, LCSs with resolutions between 14.8 % and 17.7 % (of illuminance) or 26.0 % and 32.5 % (of luminance) have the highest usability. The third experiment evaluated the usability of three colour channel control interfaces based on red, green, blue (RGB), hue, saturation, brightness (HSB) and opponent colour mixing systems. Although commonly used, the RGB interface was found to have the lowest usability. The fourth experiment explored the effect of hue resolution, saturation resolution and luminance resolution on the usability. Generally, middle range resolutions, which are approximately between three and five times the magnitude of the just noticeable difference (JND), for both hue and saturation were found to yield the greatest usability. The interaction between these three variables was characterised. Findings from this research provide a deeper understanding of the fundamental attribute of control resolution and can guide the development of useful and efficient lighting control systems
Understanding and modeling of aesthetic response to shape and color in car body design
This study explored the phenomenon that a consumer's preference on color of car body may vary depending on shape of the car body. First, the study attempted to establish a theoretical framework that can account for this phenomenon. This framework is based on the (modern-) Darwinism approach to the so-called evolutionary psychology and aesthetics. It assumes that human's aesthetic sense works like an agent that seeks for environmental patterns that potentially afford to benefit the underlying needs of the agent, and this seeking process is evolutionary fitting. Second, by adopting the framework, a pattern called “fundamental aesthetic dimensions” was developed for identifying and modeling consumer’s aesthetic response to car body shape and color. Next, this study developed an effective tool that is capable in capturing and accommodating consumer’s color preference on a given car body shape. This tool was implemented by incorporating classic color theories and advanced digital technologies; it was named “Color-Shape Synthesizer”. Finally, an experiment was conducted to verify some of the theoretical developments.
This study concluded (1) the fundamental aesthetics dimensions can be used for describing aesthetics in terms of shape and color; (2) the Color-Shape Synthesizer tool can be well applied in practicing car body designs; and (3) mapping between semantic representations of aesthetic response to the fundamental aesthetics dimensions can likely be a multiple-network structure
Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)
The book attempts to introduce a gentle introduction to the field of Facial
Micro Expressions Recognition (FMER) using Color and Depth images, with the aid
of MATLAB programming environment. FMER is a subset of image processing and it
is a multidisciplinary topic to analysis. So, it requires familiarity with
other topics of Artifactual Intelligence (AI) such as machine learning, digital
image processing, psychology and more. So, it is a great opportunity to write a
book which covers all of these topics for beginner to professional readers in
the field of AI and even without having background of AI. Our goal is to
provide a standalone introduction in the field of MFER analysis in the form of
theorical descriptions for readers with no background in image processing with
reproducible Matlab practical examples. Also, we describe any basic definitions
for FMER analysis and MATLAB library which is used in the text, that helps
final reader to apply the experiments in the real-world applications. We
believe that this book is suitable for students, researchers, and professionals
alike, who need to develop practical skills, along with a basic understanding
of the field. We expect that, after reading this book, the reader feels
comfortable with different key stages such as color and depth image processing,
color and depth image representation, classification, machine learning, facial
micro-expressions recognition, feature extraction and dimensionality reduction.
The book attempts to introduce a gentle introduction to the field of Facial
Micro Expressions Recognition (FMER) using Color and Depth images, with the aid
of MATLAB programming environment.Comment: This is the second edition of the boo
Recent Developments in Smart Healthcare
Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine
Instrumentation and Analysis Miscellanea Regarding the Cosmology Large Angular Scale Surveyor
The Cosmology Large Angular Scale Surveyor (CLASS) is an array of polarization-sensitive millimeter-wave telescopes that observes ~70% of the sky in frequency bands centered near 40 GHz, 90 GHz, 150 GHz, and 220 GHz from a high-altitude site in the Atacama desert of northern Chile. It seeks to measure polarization anisotropy in the cosmic microwave background (CMB), with a particular emphasis on measuring the optical depth due to reionization via large-angular-scale polarization E-modes, as well as searching for primordial polarization B-modes, a detection of which would provide strong evidence for cosmological inflation. This dissertation starts by providing an overview of physical cosmology, before describing the science goals and instrument design of CLASS. It then describes various instrument components that were developed, describes a novel 3D-printed millimeter-wave absorber, and describes the control and systems software used to operate the telescopes. Analysis efforts are then covered, specifically the modeling and detection of atmospheric circular polarization due to Zeeman-splitting of molecular oxygen emission lines in the geomagnetic field and a method of cleaning CMB foregrounds from full-sky maps that utilizes machine learning techniques
Integrating colour correction algorithms
Digital cameras sense colour different than the human visual system (HVS). Digital cameras sense colour using imaging sensor, whereas the HVS senses colour using the cone photoreceptors in our retina. Each digital camera model has its own device specific spectral sensitivity function. It is therefore necessary to convert the device specific colour responses of an imaging sensor to values that are related to the HVS. This process is typically referred to as colour correction, and it is common to the image processing pipeline across all cameras.
In this thesis, we explore the topic of colour correction for digital cameras. Colour correction algorithms establish the mapping between device specific responses of the camera with HVS related colour responses. Colour correction algorithms typically need to be trained with datasets. During the training process, we adjust the parameters of the colour correction algorithm, in order to minimise the fitting error between the device specific responses and the corresponding HVS responses.
In this thesis, we first show that the choice of the training dataset affects the performance of the colour correction algorithm. Then, we propose to circumvent this problem by considering a reflectance dataset as a set of samples of a much larger reflectance space. We approximate the convex closure of the reflectance dataset in the reflectance space using a hypercube. Finally we integrate over this hypercube in order to calculate a matrix for linear colour correction. By computing the linear colour correction matrix this way, we are able to fill in the gap within a reflectance dataset.
We then expand upon the idea of reflectance space further, by allowing all possible reflectances. We explore an alternative formulation of Maximum Ignorance with Positivity (MIP) colour correction. Our alternative formulation allows us to develop a polynomial variant of the concept. Polynomial MIP colour correction is far more complex thant MIP colour correction in terms of formulation. Our contribution is theoretically interesting, however practically, it delivers poorer performance
Analysis of Temporal Variations in Dermoscopy Images of Pigmented Skin Lesions by Machine Learning Techniques
Each year more people are diagnosed with skin cancer all over the world. The large incidence in populations is causing a huge concern to the scientific community, which leads the development of multiple studies related to diagnose this type of cancer.Therefore computer-aided systems are becoming more important in this field due to the challenging task of discriminate benign from malignant skin lesions. These systems can process several images and are intended to make a decision based on the diagnosis achieved by the processing of the images which will reduce the dependency on the experience of the dermatologist and the time consumed in the visual interpretation of each lesion.The main goal of this thesis is the study of the evolution of pigmented skin lesions. Starting from two images of the same lesion at different moments of evaluation, that is the identification of changes that may lead to the intervention of the specialist. These possible alterations may be evidenced through image processing techniques implemented using MATLAB which may help the physician to make a decision. This work addresses three main steps in image processing namely pre-processing, segmentation and feature extraction and aims to obtain results based on the temporal analysis of the lesion
Imaging White Blood Cells using a Snapshot Hyper-Spectral Imaging System
Automated white blood cell (WBC) counting systems process an extracted whole blood sample and provide a cell count. A step that would not be ideal for onsite screening of individuals in triage or at a security gate. Snapshot Hyper-Spectral imaging systems are capable of capturing several spectral bands simultaneously, offering co-registered images of a target. With appropriate optics, these systems are potentially able to image blood cells in vivo as they flow through a vessel, eliminating the need for a blood draw and sample staining. Our group has evaluated the capability of a commercial Snapshot Hyper-Spectral imaging system, specifically the Arrow system from Rebellion Photonics, in differentiating between white and red blood cells on unstained and sealed blood smear slides. We evaluated the imaging capabilities of this hyperspectral camera as a platform to build an automated blood cell counting system. Hyperspectral data consisting of 25, 443x313 hyperspectral bands with ~3nm spacing were captured over the range of 419 to 494nm. Open-source hyperspectral datacube analysis tools, used primarily in Geographic Information Systems (GIS) applications, indicate that white blood cells\u27 features are most prominent in the 428-442nm band for blood samples viewed under 20x and 50x magnification over a varying range of illumination intensities. The system has shown to successfully segment blood cells based on their spectral-spatial information. These images could potentially be used in subsequent automated white blood cell segmentation and counting algorithms for performing in vivo white blood cell counting