Dietary assessment and obesity aviodance system based on vision: A review

Using technology for food objects recognition and estimation of its calories is very useful to spread food culture and awareness among people in the age of obesity due to the bad habits of food consumption and wide range of inappropriate food products.Image based sensing of such system is very promising with the large expanding of camera embedded portable devices such as smartphones, PC tablets, and laptops.In the past decade, researchers have been working on developing a reliable image based system for food recognition and calories estimation.Different approaches have tackled the system from different aspects.This paper reviews the state of the art of this interesting application, and presents its experimental results.Future work of research is presented in order to guide new researchers toward potential tracks to create more maturity and reliability to this application

Effectiveness of specularity removal from hyperspectral images on the quality of spectral signatures of food products

Specularity or highlight problem exists widely in hyperspectral images, provokes reflectance deviation from its true value, and can hide major defects in food objects or detecting spurious false defects causing failure of inspection and detection processes. In this study, a new non-iterative method based on the dichromatic reflection model and principle component analysis (PCA) was proposed to detect and remove specular highlight components from hyperspectral images acquired by various imaging modes and under different configurations for numerous agro-food products. To demonstrate the effectiveness of this approach, the details of the proposed method were described and the experimental results on various spectral images were presented. The results revealed that the method worked well on all hyperspectral and multispectral images examined in this study, effectively reduced the specularity and significantly improves the quality of the extracted spectral data. Besides the spectral images from available databases, the robustness of this approach was further validated with real captured hyperspectral images of different food materials. By using qualitative and quantitative evaluation based on running time and peak signal to noise ratio (PSNR), the experimental results showed that the proposed method outperforms other specularity removal methods over the datasets of hyperspectral and multispectral images.info:eu-repo/semantics/acceptedVersio

Optimisation of corneal biomechanical characteristics in orthokeratology for myopia control

The rapid increase in myopia prevalence has escalated a wealth of research interest in the prevention mechanisms of myopia. Orthokeratology (ortho-k) is among the most promising approaches. A reluctance to employ this modality has been observed, owing to the selective treatment outcome and the long-term effects to the corneal tissue. This thesis investigates the attitudes of clinicians towards various myopia control interventions, including ortho-k within a cross-sectional internet-based survey; long-term effects of ortho-k lens wear on corneal biomechanical properties in myopic school-children over a two year period; short-term corneal biomechanical changes over the first 7 nights of lens wear; and the influence of factors (age, ethnicity, eye/body size and nutrition) on corneal biomechanical properties in healthy adults. The aim of this thesis is to aid a deeper understanding of the role of corneal biomechanical properties in ortho-k lens wear,specifically for myopia control. The findings within the thesis demonstrate that surveyed eye-care practitioners are aware of the scientific findings within the field of myopia control; two thirds would still prescribe single vision glasses to their patients, owing to a lack of clear guidelines and the selective treatment outcome. Results of the ortho-k studies suggest that the corneal biomechanical characteristics are affected by long term ortho-k wear, having a stabilising effect to the components of the anterior eye in progressing myopia. Short term ortho- k lens wear study reveals marked changes in corneal biomechanical parameters within the first seven nights of lens wear. Ortho-k itself and the anterior segment changes observed cannot explain all the variation in treatment response. The final study demonstrates the relationship between corneal biomechanical parameters and nutrition, ocular biometry and body size, suggesting that individual factors, although non-substantially, contribute towards the treatment outcome. It, is therefore, suggested to establish an internationally acknowledged guideline for myopia control. Further studies should be designed to understand the complex mechanisms underlying ortho-k in myopia control

Mutual Illumination Photometric Stereo

Many techniques have been developed in computer vision to recover three-dimensional shape from two-dimensional images. These techniques impose various combinations of assumptions/restrictions of conditions to produce a representation of shape (e.g. surface normals or a height map). Although great progress has been made it is a problem which remains far from solved. In this thesis we propose a new approach to shape recovery - namely `mutual illumination photometric stereo'. We exploit the presence of colourful mutual illumination in an environment to recover the shape of objects from a single image

Mechanical characterisation of biological cells and biofunctional interfaces

Biological cells sense the mechanical properties of their surrounding environment and adapt their shape and function. Moreover, the mechanical properties of cells and tissues tightly correlate with their functions. The main thrust of this thesis is to quantitatively determine the mechanical proper- ties of cells and cell-repellent coating materials by the combination of unique experimental techniques by covering different spatio-temporal domains. In chapter 7 the viscoelastic shape relaxation of malaria-infected human red blood cells with a di- ameter of about 10 μm was monitored by the combination of a custom-designed microfluidic device and a high-speed imaging platform under collaboration with Prof. Dr. M. Lanzer (Center for Inte- grative Infectious Diseases, Heidelberg University). Using the binarised cell rims extracted from the live-cell images, the shape recovery of red blood cells upon the ejection from the narrow constriction was monitored with a time resolution of 30 μs per frame. The mechanical responses of the malaria- infected red blood cells were monitored through the entire life cycle of parasites. The systematic comparison of the red blood cells with genetically mutated hemoglobin (hemoglobinopathie) with normal red blood cells indicated a less pronounced change in the relaxation time in hemoglobinopa- thetic red blood cells, which might correlate with delayed protein synthesis in hemoglobinopathetic red blood cells. In chapter 8 the film elastic properties and internal structures of the monolayers of oligoethylene glycol-based dendrons for the coating of iron-oxide nanoparticles were studied by the combination of high energy X-ray reflectivity and high-speed atomic force microscopy. To achieve higher film sta- bility in blood stream, the dendrons, synthesized by the group of Prof. Dr. Felder-Flesch (Institut de Physique et Chimie des Materiaux , Univ. Strasbourg) were coupled to the oxide surface via two phosphonate groups. The interfacial force measurements were performed on planar silicon dioxide surfaces instead of iron oxide nanoparticle surfaces due to the technical limitations. The internal structures of dendron monolayers in water were probed by high energy specular X-ray reflectivity. An analytical model considering the transition from a soft layer to a hard layer was introduced to cal- culate the Young’s modulus from nm-thick monolayers. To gain deeper insights into the interfacial force interactions, the coarse-scale surface force-distance curves were measured by a cell-sized particle attached to an atomic force cantilever cantilever, while the size and distribution of nanoscopic pin- ning centers were monitored by fast force mapping with a pixel rate of 200 Hz. The capability of the dendron coating to prevent the platelet aggregation was assessed by observing the non-specific adhesion of human platelets on dendron-coated substrates. The dynamic uptake and localisation of fluorescent dendron-coated iron oxide nanoparticles into hypoxic mouse breast cancer cells was tracked using fluorescence imaging and cryo-transmission electron microscopy. Together, these meth- ods revealed a continuous uptake of iron oxide nanoparticles into in intracellular compartments such as endosomes via endocytosis. The iron oxide particles were found either agglomerated or as single nanoparticles

Colored Apparel - Relevance to Attraction in Humans

There are numerous different dyes available, many varied fashion trends, and various different ways to change/enhance physical aesthetics. Predicting color preferences and how colors and color combinations, in a shape context, stimulate certain emotions, represents a challenging prospect. Color is a critical cue for sexual signaling, but what the preferred colors actually are in humans, is difficult to predict. Understanding color preferences and perception of color within a context such as attraction, is essential for improving color forecasting and gaining a deeper understanding of color perception. The appearance of color can change based on lighting, shape, texture, and the surrounding environment and associated colors. While these provide physical color characteristics, human vision and perception contributes to how a color appears to the individual. Perception is unique to each individual and is constantly changing due to the influence of a range of variables. This can cause someone to appear visually attractive or visually unattractive. By taking into account all the variables that contribute to human studies in color perception, tailored research can continue to be undertaken to further develop a deeper understanding of color perception and human attraction regarding visual stimulation

Development of deep learning methods for head and neck cancer detection in hyperspectral imaging and digital pathology for surgical guidance

Surgeons performing routine cancer resections utilize palpation and visual inspection, along with time-consuming microscopic tissue analysis, to ensure removal of cancer. Despite this, inadequate surgical cancer margins are reported for up to 10-20% of head and neck squamous cell carcinoma (SCC) operations. There exists a need for surgical guidance with optical imaging to ensure complete cancer resection in the operating room. The objective of this dissertation is to evaluate hyperspectral imaging (HSI) as a non-contact, label-free optical imaging modality to provide intraoperative diagnostic information. For comparison of different optical methods, autofluorescence, RGB composite images synthesized from HSI, and two fluorescent dyes are also acquired and investigated for head and neck cancer detection. A novel and comprehensive dataset was obtained of 585 excised tissue specimens from 204 patients undergoing routine head and neck cancer surgeries. The first aim was to use SCC tissue specimens to determine the potential of HSI for surgical guidance in the challenging task of head and neck SCC detection. It is hypothesized that HSI could reduce time and provide quantitative cancer predictions. State-of-the-art deep learning algorithms were developed for SCC detection in 102 patients and compared to other optical methods. HSI detected SCC with a median AUC score of 85%, and several anatomical locations demonstrated good SCC detection, such as the larynx, oropharynx, hypopharynx, and nasal cavity. To understand the ability of HSI for SCC detection, the most important spectral features were calculated and correlated with known cancer physiology signals, notably oxygenated and deoxygenated hemoglobin. The second aim was to evaluate HSI for tumor detection in thyroid and salivary glands, and RGB images were synthesized using the spectral response curves of the human eye for comparison. Using deep learning, HSI detected thyroid tumors with 86% average AUC score, which outperformed fluorescent dyes and autofluorescence, but HSI-synthesized RGB imagery performed with 90% AUC score. The last aim was to develop deep learning algorithms for head and neck cancer detection in hundreds of digitized histology slides. Slides containing SCC or thyroid carcinoma can be distinguished from normal slides with 94% and 99% AUC scores, respectively, and SCC and thyroid carcinoma can be localized within whole-slide images with 92% and 95% AUC scores, respectively. In conclusion, the outcomes of this thesis work demonstrate that HSI and deep learning methods could aid surgeons and pathologists in detecting head and neck cancers.Ph.D

Detection of Fusarium Head Blight in Wheat Grains Using Hyperspectral and RGB Imaging

In modern agriculture, it is imperative to ensure that crops are healthy and safe for consumption. Fusarium Head Blight (FHB) can cause significant damage to wheat grains by reducing essential components such as moisture, protein, and starch, while also introducing dangerous toxins. Therefore, accurately distinguishing between healthy and FHB-infected wheat grains is essential to guarantee stable and reliable wheat production while limiting financial losses and ensuring food safety. This thesis proposes effective methods to classify healthy and FHB infected wheat grains using Hyperspectral Imaging (HSI) and Red Green Blue (RGB) images. The approach includes a combination of Principal Component Analysis (PCA) with morphology, in addition to dark and white reference correction, to create masks for grains in each image. The classification for the hyperspectral images was achieved using a Partial Least Squares Discriminant Analysis (PLS-DA) model for hyperspectral images and a Convolutional Neural Network (CNN) model for RGB images. Both object-based and pixel-based approaches were compared for the PLS-DA model. The results indicated that the object-based approach outperformed the pixel-based approach and other well-known machine learning algorithms, including Random Forest (RF), linear Support Vector Machine (SVM), Stochastic Gradient Descent (SGD) calibrated one-vs-all and DecisionTree. The PLS-DA model using the object-based method yielded better results when tested on all wheat varieties, achieving an F1-score of 99.4%. Specific wavelengths were investigated based on a loading plot, and four effective wavelengths were identified, 953 nm, 1373 nm, 1923 nm and 2493 nm, with classification accuracy found to be similar to the full spectral range. Moreover, the moisture and water content in the grains were analyzed using hyperspectral images through an aquagram, which demonstrated that healthy grains exhibited higher absorbance values than infected grains for all Water Matrix Coordinates (WAMACS). Furthermore, the CNN model was trained on cropped individual grains, and the classification accuracy was similar to the PLS-DA model, with an F1- score of 98.1%. These findings suggest that HSI is suitable for identifying FHB-infected wheat grains, while RGB images may provide a cost-effective alternative to hyperspectral images for this specific classification task. Further research should consider to explore the potential benefits of HSI for deeper investigations into how water absorption affects spectral measurements and moisture content in grains, in addition to user-friendly interfaces for deep learning based image classification