333 research outputs found
Beyond the Pixel: a Photometrically Calibrated HDR Dataset for Luminance and Color Prediction
Light plays an important role in human well-being. However, most computer
vision tasks treat pixels without considering their relationship to physical
luminance. To address this shortcoming, we introduce the Laval Photometric
Indoor HDR Dataset, the first large-scale photometrically calibrated dataset of
high dynamic range 360{\deg} panoramas. Our key contribution is the calibration
of an existing, uncalibrated HDR Dataset. We do so by accurately capturing RAW
bracketed exposures simultaneously with a professional photometric measurement
device (chroma meter) for multiple scenes across a variety of lighting
conditions. Using the resulting measurements, we establish the calibration
coefficients to be applied to the HDR images. The resulting dataset is a rich
representation of indoor scenes which displays a wide range of illuminance and
color, and varied types of light sources. We exploit the dataset to introduce
three novel tasks, where: per-pixel luminance, per-pixel color and planar
illuminance can be predicted from a single input image. Finally, we also
capture another smaller photometric dataset with a commercial 360{\deg} camera,
to experiment on generalization across cameras. We are optimistic that the
release of our datasets and associated code will spark interest in physically
accurate light estimation within the community. Dataset and code are available
at https://lvsn.github.io/beyondthepixel/
Technologies of information transmission and processing
Сборник содержит статьи, тематика которых посвящена научно-теоретическим разработкам в области сетей телекоммуникаций, информационной безопасности, технологий передачи и обработки информации. Предназначен для научных сотрудников в области инфокоммуникаций, преподавателей, аспирантов, магистрантов и студентов технических вузов
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Anomalous trichromacy : external enhancement of colour signals, individual differences and diagnosis
Anomalous trichromacy is known to conceal a substantial range in perceptual ability, but this is not typically considered in assessments of corrective aids and diagnostic tests. In addition to the diversity caused by genetic polymorphisms, perceptual ability is thought to be influenced by little understood postreceptoral mechanisms, adding to the need for research focusing on this population.
A modelling and behavioural investigation establishes the effectiveness of EnChroma filters in enhancing anomalous colour vision. Paper 1 (not yet published) employs a physiologically accurate model of colour vision to estimate the enhancements in cone-opponent signals conferred by the filters. Paper 2 (not yet published) presents behavioural validation of the model’s predictions, showing that notch filters can result in enhanced perceived saturation for deuteranomalous observers, with effects for suprathreshold perception and partial effects at absolute threshold.
Paper 3 (submitted for publication) uses the physiologically accurate model of colour vision to investigate the impact of variation in edition and lighting conditions on the effectiveness of the Ishihara plates test in identifying those with mild anomalous trichromacy. The model predicts a significant impact of plate and illuminant, but no influence of edition, which is supported by the findings of a behavioural investigation.
This thesis provides the first direct evidence that altering the input to the visual system using filter-based aids can impact the cone-opponent signals available to anomalous trichromats, and that this change in signal is useable by the visual system, resulting in changes to perceived saturatio
Measuring Perceptual Color Differences of Smartphone Photographs
Measuring perceptual color differences (CDs) is of great importance in modern
smartphone photography. Despite the long history, most CD measures have been
constrained by psychophysical data of homogeneous color patches or a limited
number of simplistic natural photographic images. It is thus questionable
whether existing CD measures generalize in the age of smartphone photography
characterized by greater content complexities and learning-based image signal
processors. In this paper, we put together so far the largest image dataset for
perceptual CD assessment, in which the photographic images are 1) captured by
six flagship smartphones, 2) altered by Photoshop, 3) post-processed by
built-in filters of the smartphones, and 4) reproduced with incorrect color
profiles. We then conduct a large-scale psychophysical experiment to gather
perceptual CDs of 30,000 image pairs in a carefully controlled laboratory
environment. Based on the newly established dataset, we make one of the first
attempts to construct an end-to-end learnable CD formula based on a lightweight
neural network, as a generalization of several previous metrics. Extensive
experiments demonstrate that the optimized formula outperforms 33 existing CD
measures by a large margin, offers reasonable local CD maps without the use of
dense supervision, generalizes well to homogeneous color patch data, and
empirically behaves as a proper metric in the mathematical sense. Our dataset
and code are publicly available at https://github.com/hellooks/CDNet.Comment: 10 figures, 8 tables, 14 page
Advanced traffic video analytics for robust traffic accident detection
Automatic traffic accident detection is an important task in traffic video analysis due to its key applications in developing intelligent transportation systems. Reducing the time delay between the occurrence of an accident and the dispatch of the first responders to the scene may help lower the mortality rate and save lives. Since 1980, many approaches have been presented for the automatic detection of incidents in traffic videos. In this dissertation, some challenging problems for accident detection in traffic videos are discussed and a new framework is presented in order to automatically detect single-vehicle and intersection traffic accidents in real-time.
First, a new foreground detection method is applied in order to detect the moving vehicles and subtract the ever-changing background in the traffic video frames captured by static or non-stationary cameras. For the traffic videos captured during day-time, the cast shadows degrade the performance of the foreground detection and road segmentation. A novel cast shadow detection method is therefore presented to detect and remove the shadows cast by moving vehicles and also the shadows cast by static objects on the road.
Second, a new method is presented to detect the region of interest (ROI), which applies the location of the moving vehicles and the initial road samples and extracts the discriminating features to segment the road region. After detecting the ROI, the moving direction of the traffic is estimated based on the rationale that the crashed vehicles often make rapid change of direction. Lastly, single-vehicle traffic accidents and trajectory conflicts are detected using the first-order logic decision-making system.
The experimental results using publicly available videos and a dataset provided by the New Jersey Department of Transportation (NJDOT) demonstrate the feasibility of the proposed methods. Additionally, the main challenges and future directions are discussed regarding (i) improving the performance of the foreground segmentation, (ii) reducing the computational complexity, and (iii) detecting other types of traffic accidents
Developmental changes in colour constancy in a naturalistic object selection task
When the illumination falling on a surface change, so does the reflected light. Despite this, adult observers are good at perceiving surfaces as relatively unchanging-an ability termed colour constancy. Very few studies have investigated colour constancy in infants, and even fewer in children. Here we asked whether there is a difference in colour constancy between children and adults; what the developmental trajectory is between six and 11 years; and whether the pattern of constancy across illuminations and reflectances differs between adults and children. To this end, we developed a novel, child-friendly computer-based object selection task. In this, observers saw a dragon's favourite sweet under a neutral illumination and picked the matching sweet from an array of eight seen under a different illumination (blue, yellow, red, or green). This set contained a reflectance match (colour constant; perfect performance) and a tristimulus match (colour inconstant). We ran two experiments, with two-dimensional scenes in one and three-dimensional renderings in the other. Twenty-six adults and 33 children took part in the first experiment; 26 adults and 40 children took part in the second. Children performed better than adults on this task, and their performance decreased with age in both experiments. We found differences across illuminations and sweets, but a similar pattern across both age groups. This unexpected finding might reflect a real decrease in colour constancy from childhood to adulthood, explained by developmental changes in the perceptual and cognitive mechanisms underpinning colour constancy, or differences in task strategies between children and adults. Highlights Six- to 11-year-old children demonstrated better performance than adults on a colour constancy object selection task. Performance decreased with age over childhood. These findings may indicate development of cognitive strategies used to overcome automatic colour constancy mechanisms.Peer reviewe
Model facial colour appearance and facial attractiveness for human complexions
Human facial complexion has been a subject of great interest in many areas of science and technology including dermatology, cosmetology, computer graphics, and computer vision. Facial colour appearance conveys vital personal information and influences social interactions and mate choices as contributing factors to perceived beauty, health, and age. How various colour characteristics affect facial preference and whether there are cultural differences are not fully understood. On the other hand, facial colour appearance cannot be simply quantified by colour measurement. Facial colour perception is distinctive. The perceptual aspects of facial colour appearance haven’t been precisely investigated.
The present study aims to better understand the human colour perception of facial complexions. Psychophysical experiments were carried out to assess facial colour preference and facial colour appearance, respectively. A set of facial images of real human faces were used and the colour was rigorously controlled in those experiments so that the facial colour appearance could be evaluated based on the realistic skin models.
Experiments on colour preference provided a thorough assessment of the relationships between various facial colour characteristics and preference judgements and meanwhile revealed large cultural differences between Caucasian and Chinese populations. A useful and repeatable analytical framework for facial preference modelling was provided. This work contributes to the growing body of research using realistic skin models and highlights the importance of examining various colour cues utilized in facial preference evaluation.
Experiments on colour appearance for the first time precisely measured the overall colour perception of facial appearance. New indices WIS, RIS, and YIS were developed to accurately quantify perceived facial whiteness, redness, and yellowness. The perceptual difference between the colour appearance of the face stimuli and the nonface stimuli was discovered.
Taken together, the present study shed new light on how our visual system perceives and processes colour information on human faces
A Colour Wheel to Rule them All: Analysing Colour & Geometry in Medical Microscopy
Personalized medicine is a rapidly growing field in healthcare that aims to customize
medical treatments and preventive measures based on each patient’s unique characteristics,
such as their genes, environment, and lifestyle factors. This approach
acknowledges that people with the same medical condition may respond differently
to therapies and seeks to optimize patient outcomes while minimizing the risk
of adverse effects.
To achieve these goals, personalized medicine relies on advanced technologies,
such as genomics, proteomics, metabolomics, and medical imaging. Digital
histopathology, a crucial aspect of medical imaging, provides clinicians with valuable
insights into tissue structure and function at the cellular and molecular levels. By
analyzing small tissue samples obtained through minimally invasive techniques, such
as biopsy or aspirate, doctors can gather extensive data to evaluate potential diagnoses
and clinical decisions. However, digital analysis of histology images presents
unique challenges, including the loss of 3D information and stain variability, which
is further complicated by sample variability. Limited access to data exacerbates
these challenges, making it difficult to develop accurate computational models for
research and clinical use in digital histology.
Deep learning (DL) algorithms have shown significant potential for improving the
accuracy of Computer-Aided Diagnosis (CAD) and personalized treatment models,
particularly in medical microscopy. However, factors such as limited generability,
lack of interpretability, and bias sometimes hinder their clinical impact. Furthermore,
the inherent variability of histology images complicates the development of robust DL
methods. Thus, this thesis focuses on developing new tools to address these issues.
Our essential objective is to create transparent, accessible, and efficient methods
based on classical principles from various disciplines, including histology, medical
imaging, mathematics, and art, to tackle microscopy image registration and colour
analysis successfully. These methods can contribute significantly to the advancement
of personalized medicine, particularly in studying the tumour microenvironment
for diagnosis and therapy research.
First, we introduce a novel automatic method for colour analysis and non-rigid
histology registration, enabling the study of heterogeneity morphology in tumour
biopsies. This method achieves accurate tissue cut registration, drastically reducing
landmark distance and excellent border overlap. Second, we introduce ABANICCO, a novel colour analysis method that combines
geometric analysis, colour theory, fuzzy colour spaces, and multi-label systems
for automatically classifying pixels into a set of conventional colour categories.
ABANICCO outperforms benchmark methods in accuracy and simplicity. It is
computationally straightforward, making it useful in scenarios involving changing
objects, limited data, unclear boundaries, or when users lack prior knowledge of
the image or colour theory. Moreover, results can be modified to match each
particular task.
Third, we apply the acquired knowledge to create a novel pipeline of rigid
histology registration and ABANICCO colour analysis for the in-depth study of
triple-negative breast cancer biopsies. The resulting heterogeneity map and tumour
score provide valuable insights into the composition and behaviour of the tumour,
informing clinical decision-making and guiding treatment strategies.
Finally, we consolidate the developed ideas into an efficient pipeline for tissue
reconstruction and multi-modality data integration on Tuberculosis infection data.
This enables accurate element distribution analysis to understand better interactions
between bacteria, host cells, and the immune system during the course of infection.
The methods proposed in this thesis represent a transparent approach to computational
pathology, addressing the needs of medical microscopy registration and
colour analysis while bridging the gap between clinical practice and computational
research. Moreover, our contributions can help develop and train better, more
robust DL methods.En una época en la que la medicina personalizada está revolucionando la asistencia
sanitaria, cada vez es más importante adaptar los tratamientos y las medidas
preventivas a la composición genética, el entorno y el estilo de vida de cada
paciente. Mediante el empleo de tecnologías avanzadas, como la genómica, la
proteómica, la metabolómica y la imagen médica, la medicina personalizada se
esfuerza por racionalizar el tratamiento para mejorar los resultados y reducir
los efectos secundarios.
La microscopía médica, un aspecto crucial de la medicina personalizada, permite
a los médicos recopilar y analizar grandes cantidades de datos a partir de pequeñas
muestras de tejido. Esto es especialmente relevante en oncología, donde las terapias
contra el cáncer se pueden optimizar en función de la apariencia tisular específica de
cada tumor. La patología computacional, un subcampo de la visión por ordenador,
trata de crear algoritmos para el análisis digital de biopsias. Sin embargo, antes de
que un ordenador pueda analizar imágenes de microscopía médica, hay que seguir
varios pasos para conseguir las imágenes de las muestras.
La primera etapa consiste en recoger y preparar una muestra de tejido del
paciente. Para que esta pueda observarse fácilmente al microscopio, se corta en
secciones ultrafinas. Sin embargo, este delicado procedimiento no está exento de
dificultades. Los frágiles tejidos pueden distorsionarse, desgarrarse o agujerearse,
poniendo en peligro la integridad general de la muestra.
Una vez que el tejido está debidamente preparado, suele tratarse con tintes de
colores característicos. Estos tintes acentúan diferentes tipos de células y tejidos
con colores específicos, lo que facilita a los profesionales médicos la identificación
de características particulares. Sin embargo, esta mejora en visualización tiene
un alto coste. En ocasiones, los tintes pueden dificultar el análisis informático
de las imágenes al mezclarse de forma inadecuada, traspasarse al fondo o alterar
el contraste entre los distintos elementos.
El último paso del proceso consiste en digitalizar la muestra. Se toman imágenes
de alta resolución del tejido con distintos aumentos, lo que permite su análisis por
ordenador. Esta etapa también tiene sus obstáculos. Factores como una calibración
incorrecta de la cámara o unas condiciones de iluminación inadecuadas pueden
distorsionar o hacer borrosas las imágenes. Además, las imágenes de porta completo
obtenidas so de tamaño considerable, complicando aún más el análisis. En general, si bien la preparación, la tinción y la digitalización de las muestras
de microscopía médica son fundamentales para el análisis digital, cada uno de estos
pasos puede introducir retos adicionales que deben abordarse para garantizar un
análisis preciso. Además, convertir un volumen de tejido completo en unas pocas
secciones teñidas reduce drásticamente la información 3D disponible e introduce
una gran incertidumbre.
Las soluciones de aprendizaje profundo (deep learning, DL) son muy prometedoras
en el ámbito de la medicina personalizada, pero su impacto clínico a veces se
ve obstaculizado por factores como la limitada generalizabilidad, el sobreajuste, la
opacidad y la falta de interpretabilidad, además de las preocupaciones éticas y en
algunos casos, los incentivos privados. Por otro lado, la variabilidad de las imágenes
histológicas complica el desarrollo de métodos robustos de DL. Para superar estos
retos, esta tesis presenta una serie de métodos altamente robustos e interpretables
basados en principios clásicos de histología, imagen médica, matemáticas y arte,
para alinear secciones de microscopía y analizar sus colores.
Nuestra primera contribución es ABANICCO, un innovador método de análisis
de color que ofrece una segmentación de colores objectiva y no supervisada y permite
su posterior refinamiento mediante herramientas fáciles de usar. Se ha demostrado
que la precisión y la eficacia de ABANICCO son superiores a las de los métodos
existentes de clasificación y segmentación del color, e incluso destaca en la detección
y segmentación de objetos completos. ABANICCO puede aplicarse a imágenes
de microscopía para detectar áreas teñidas para la cuantificación de biopsias, un
aspecto crucial de la investigación de cáncer.
La segunda contribución es un método automático y no supervisado de segmentación
de tejidos que identifica y elimina el fondo y los artefactos de las
imágenes de microscopía, mejorando así el rendimiento de técnicas más sofisticadas
de análisis de imagen. Este método es robusto frente a diversas imágenes, tinciones
y protocolos de adquisición, y no requiere entrenamiento.
La tercera contribución consiste en el desarrollo de métodos novedosos para
registrar imágenes histopatológicas de forma eficaz, logrando el equilibrio adecuado
entre un registro preciso y la preservación de la morfología local, en función de
la aplicación prevista.
Como cuarta contribución, los tres métodos mencionados se combinan para
crear procedimientos eficientes para la integración completa de datos volumétricos,
creando visualizaciones altamente interpretables de toda la información presente en
secciones consecutivas de biopsia de tejidos. Esta integración de datos puede tener
una gran repercusión en el diagnóstico y el tratamiento de diversas enfermedades,
en particular el cáncer de mama, al permitir la detección precoz, la realización
de pruebas clínicas precisas, la selección eficaz de tratamientos y la mejora en la
comunicación el compromiso con los pacientes. Por último, aplicamos nuestros hallazgos a la integración multimodal de datos y
la reconstrucción de tejidos para el análisis preciso de la distribución de elementos
químicos en tuberculosis, lo que arroja luz sobre las complejas interacciones entre
las bacterias, las células huésped y el sistema inmunitario durante la infección
tuberculosa. Este método también aborda problemas como el daño por adquisición,
típico de muchas modalidades de imagen.
En resumen, esta tesis muestra la aplicación de métodos clásicos de visión por
ordenador en el registro de microscopía médica y el análisis de color para abordar
los retos únicos de este campo, haciendo hincapié en la visualización eficaz y fácil de
datos complejos. Aspiramos a seguir perfeccionando nuestro trabajo con una amplia
validación técnica y un mejor análisis de los datos. Los métodos presentados en esta
tesis se caracterizan por su claridad, accesibilidad, visualización eficaz de los datos,
objetividad y transparencia. Estas características los hacen perfectos para tender
puentes robustos entre los investigadores de inteligencia artificial y los clínicos e
impulsar así la patología computacional en la práctica y la investigación médicas.Programa de Doctorado en Ciencia y Tecnología Biomédica por la Universidad Carlos III de MadridPresidenta: María Jesús Ledesma Carbayo.- Secretario: Gonzalo Ricardo Ríos Muñoz.- Vocal: Estíbaliz Gómez de Marisca
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