Data-Driven Deep Learning-Based Analysis on THz Imaging

Breast cancer affects about 12.5% of women population in the United States. Surgical operations are often needed post diagnosis. Breast conserving surgery can help remove malignant tumors while maximizing the remaining healthy tissues. Due to lacking effective real-time tumor analysis tools and a unified operation standard, re-excision rate could be higher than 30% among breast conserving surgery patients. This results in significant physical, physiological, and financial burdens to those patients. This work designs deep learning-based segmentation algorithms that detect tissue type in excised tissues using pulsed THz technology. This work evaluates the algorithms for tissue type classification task among freshly excised tumor samples. Freshly excised tumor samples are more challenging than formalin-fixed, paraffin-embedded (FFPE) block sample counterparts due to excessive fluid, image registration difficulties, and lacking trustworthy pixelwise labels of each tissue sample. Additionally, evaluating freshly excised tumor samples has profound meaning of potentially applying pulsed THz scan technology to breast conserving cancer surgery in operating room. Recently, deep learning techniques have been heavily researched since GPU based computation power becomes economical and stronger. This dissertation revisits breast cancer tissue segmentation related problems using pulsed terahertz wave scan technique among murine samples and applies recent deep learning frameworks to enhance the performance in various tasks. This study first performs pixelwise classification on terahertz scans with CNN-based neural networks and time-frequency based feature tensors using wavelet transformation. This study then explores the neural network based semantic segmentation strategy performing on terahertz scans considering spatial information and incorporating noisy label handling with label correction techniques. Additionally, this study performs resolution restoration for visual enhancement on terahertz scans using an unsupervised, generative image-to-image translation methodology. This work also proposes a novel data processing pipeline that trains a semantic segmentation network using only neural generated synthetic terahertz scans. The performance is evaluated using various evaluation metrics among different tasks

ComputableViz: Mathematical Operators as a Formalism for Visualization Processing and Analysis

Data visualizations are created and shared on the web at an unprecedented speed, raising new needs and questions for processing and analyzing visualizations after they have been generated and digitized. However, existing formalisms focus on operating on a single visualization instead of multiple visualizations, making it challenging to perform analysis tasks such as sorting and clustering visualizations. Through a systematic analysis of previous work, we abstract visualization-related tasks into mathematical operators such as union and propose a design space of visualization operations. We realize the design by developing ComputableViz, a library that supports operations on multiple visualization specifications. To demonstrate its usefulness and extensibility, we present multiple usage scenarios concerning processing and analyzing visualization, such as generating visualization embeddings and automatically making visualizations accessible. We conclude by discussing research opportunities and challenges for managing and exploiting the massive visualizations on the web.Comment: 15 pages, 12 figures. In the ACM Conference on Human Factors in Computing Systems (CHI) 202

Face Image and Video Analysis in Biometrics and Health Applications

Computer Vision (CV) enables computers and systems to derive meaningful information from acquired visual inputs, such as images and videos, and make decisions based on the extracted information. Its goal is to acquire, process, analyze, and understand the information by developing a theoretical and algorithmic model. Biometrics are distinctive and measurable human characteristics used to label or describe individuals by combining computer vision with knowledge of human physiology (e.g., face, iris, fingerprint) and behavior (e.g., gait, gaze, voice). Face is one of the most informative biometric traits. Many studies have investigated the human face from the perspectives of various different disciplines, ranging from computer vision, deep learning, to neuroscience and biometrics. In this work, we analyze the face characteristics from digital images and videos in the areas of morphing attack and defense, and autism diagnosis. For face morphing attacks generation, we proposed a transformer based generative adversarial network to generate more visually realistic morphing attacks by combining different losses, such as face matching distance, facial landmark based loss, perceptual loss and pixel-wise mean square error. In face morphing attack detection study, we designed a fusion-based few-shot learning (FSL) method to learn discriminative features from face images for few-shot morphing attack detection (FS-MAD), and extend the current binary detection into multiclass classification, namely, few-shot morphing attack fingerprinting (FS-MAF). In the autism diagnosis study, we developed a discriminative few shot learning method to analyze hour-long video data and explored the fusion of facial dynamics for facial trait classification of autism spectrum disorder (ASD) in three severity levels. The results show outstanding performance of the proposed fusion-based few-shot framework on the dataset. Besides, we further explored the possibility of performing face micro- expression spotting and feature analysis on autism video data to classify ASD and control groups. The results indicate the effectiveness of subtle facial expression changes on autism diagnosis

Behavior-based personalization : strategies and Implications

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 53-55).The personalization of services and products offered to customers is becoming crucial for the success of companies. Firms that can maintain a personalized relationship with their customers will not only gain an advantage from competitors but will also benefit from having more loyal and valuable customers. The recent advances in technology and the associated cost reduction are allowing companies to gather information about their customers and their behavior in an easy and inexpensive way. This collection and analysis of behavior-based information increases the companies' knowledge about their customers and allows a more personalized approach. This thesis studies what has been accomplished in the domain of behavior-based personalization and in more detail what are the techniques and strategies being used and how companies can take advantage of its applications. Moreover, this thesis discusses the critical role of personalization in building effective customer relationships management (CRM) strategies.by João G. Violante.S.M

Image and Video Forensics

Nowadays, images and videos have become the main modalities of information being exchanged in everyday life, and their pervasiveness has led the image forensics community to question their reliability, integrity, confidentiality, and security. Multimedia contents are generated in many different ways through the use of consumer electronics and high-quality digital imaging devices, such as smartphones, digital cameras, tablets, and wearable and IoT devices. The ever-increasing convenience of image acquisition has facilitated instant distribution and sharing of digital images on digital social platforms, determining a great amount of exchange data. Moreover, the pervasiveness of powerful image editing tools has allowed the manipulation of digital images for malicious or criminal ends, up to the creation of synthesized images and videos with the use of deep learning techniques. In response to these threats, the multimedia forensics community has produced major research efforts regarding the identification of the source and the detection of manipulation. In all cases (e.g., forensic investigations, fake news debunking, information warfare, and cyberattacks) where images and videos serve as critical evidence, forensic technologies that help to determine the origin, authenticity, and integrity of multimedia content can become essential tools. This book aims to collect a diverse and complementary set of articles that demonstrate new developments and applications in image and video forensics to tackle new and serious challenges to ensure media authenticity

A Survey on Computer Vision based Human Analysis in the COVID-19 Era

The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting face-hand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field. Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given.Comment: Submitted to Image and Vision Computing, 44 pages, 7 figure

VR Lab: User Interaction in Virtual Environments using Space and Time Morphing

Virtual Reality (VR) allows exploring changes in space and time that would otherwise be difficult to simulate in the real world. It becomes possible to transform the virtual world by increasing or diminishing distances or playing with time delays. Analysing the adaptability of users to different space-time conditions allows studying human perception and finding the right combination of interaction paradigms. Different methods have been proposed in the literature to offer users intuitive techniques for navigating wide virtual spaces, even if restricted to small physical play areas. Other studies investigate latency tolerance, suggesting humans’ inability to detect slight discrepancies between visual and proprioceptive sensory information. These studies contribute valuable insights for designing immersive virtual experiences and interaction techniques suitable for each task. This dissertation presents the design, implementation, and evaluation of a tangible VR Lab where spatiotemporal morphing scenarios can be studied. As a case study, we restricted the scope of the research to three spatial morphing scenarios and one temporal morphing scenario. The spatial morphing scenarios compared Euclidean and hyperbolic geometries, studied size discordance between physical and virtual objects, and the representation of hands in VR. The temporal morphing scenario investigated from what visual delay the task performance is affected. The users’ adaptability to the different spatiotemporal conditions was assessed based on task completion time, questionnaires, and observed behaviours. The results revealed significant differences between Euclidean and hyperbolic spaces. They also showed a preference for handling virtual and physical objects with concordant sizes, without any virtual representation of the hands. Although task performance was affected from 200 ms onwards, participants considered the ease of the task to be affected only from 500 ms visual delay onwards.A Realidade Virtual (RV) permite explorar mudanças no espaço e no tempo que de outra forma seriam difíceis de simular no mundo real. Torna-se possível transformar o mundo virtual aumentando ou diminuindo as distâncias ou manipulando os atrasos no tempo. A análise da adaptabilidade dos utilizadores a diferentes condições espaço-temporais permite estudar a perceção humana e encontrar a combinação certa de paradigmas de interação. Diferentes métodos têm sido propostos na literatura para oferecer aos utilizadores técnicas intuitivas de navegação em espaços virtuais amplos, mesmo que restritos a pequenas áreas físicas de jogo. Outros estudos investigam a tolerância à latência, sugerindo a incapacidade do ser humano de detetar ligeiras discrepâncias entre a informação sensorial visual e propriocetiva. Estes estudos contribuem com valiosas informações para conceber experiências virtuais imersivas e técnicas de interação adequadas a cada tarefa. Esta dissertação apresenta o desenho, implementação e avaliação de um Laboratório de RV tangível onde podem ser estudados cenários de distorção espaço-temporal. Como estudo de caso, restringimos o âmbito da investigação a três cenários de distorção espacial e um cenário de distorção temporal. Os cenários de distorção espacial compararam geometrias Euclidianas e hiperbólicas, estudaram a discordância de tamanho entre objetos físicos e virtuais, e a representação das mãos em RV. O cenário de distorção temporal investigou a partir de que atraso visual o desempenho da tarefa é afetado. A adaptabilidade dos utilizadores às diferentes condições espaço-temporais foi avaliada com base no tempo de conclusão da tarefa, questionários, e comportamentos observados. Os resultados revelaram diferenças significativas entre os espaços Euclidiano e hiperbólico. Também mostraram a preferência pelo manuseamento de objetos virtuais e físicos com tamanhos concordantes, sem qualquer representação virtual das mãos. Embora o desempenho da tarefa tenha sido afetado a partir dos 200 ms, os participantes consideraram que a facilidade da tarefa só foi afetada a partir dos 500 ms de atraso visual

Simulating CCDs for the Chandra Advanced CCD Imaging Spectrometer

We have implemented a Monte Carlo algorithm to model and predict the response of various kinds of CCDs to X-ray photons and minimally-ionizing particles and have applied this model to the CCDs in the Chandra X-ray Observatory's Advanced CCD Imaging Spectrometer. This algorithm draws on empirical results and predicts the response of all basic types of X-ray CCD devices. It relies on new solutions of the diffusion equation, including recombination, to predict the radial charge cloud distribution in field-free regions of CCDs. By adjusting the size of the charge clouds, we can reproduce the event grade distribution seen in calibration data. Using a model of the channel stops developed here and an insightful treatment of the insulating layer under the gate structure developed at MIT, we are able to reproduce all notable features in ACIS calibration spectra. The simulator is used to reproduce ground and flight calibration data from ACIS, thus confirming its fidelity. It can then be used for a variety of calibration tasks, such as generating spectral response matrices for spectral fitting of astrophysical sources, quantum efficiency estimation, and modeling of photon pile-up.Comment: 42 pages, 22 figures; accepted for publication in Nuclear Instruments and Methods in Physics Research, Section A; paper with high-quality figures can be found at ftp://ftp.astro.psu.edu/pub/townsley/simulator.p