Motion Segmentation Aided Super Resolution Image Reconstruction

This dissertation addresses Super Resolution (SR) Image Reconstruction focusing on motion segmentation. The main thrust is Information Complexity guided Gaussian Mixture Models (GMMs) for Statistical Background Modeling. In the process of developing our framework we also focus on two other topics; motion trajectories estimation toward global and local scene change detections and image reconstruction to have high resolution (HR) representations of the moving regions. Such a framework is used for dynamic scene understanding and recognition of individuals and threats with the help of the image sequences recorded with either stationary or non-stationary camera systems. We introduce a new technique called Information Complexity guided Statistical Background Modeling. Thus, we successfully employ GMMs, which are optimal with respect to information complexity criteria. Moving objects are segmented out through background subtraction which utilizes the computed background model. This technique produces superior results to competing background modeling strategies. The state-of-the-art SR Image Reconstruction studies combine the information from a set of unremarkably different low resolution (LR) images of static scene to construct an HR representation. The crucial challenge not handled in these studies is accumulating the corresponding information from highly displaced moving objects. In this aspect, a framework of SR Image Reconstruction of the moving objects with such high level of displacements is developed. Our assumption is that LR images are different from each other due to local motion of the objects and the global motion of the scene imposed by non-stationary imaging system. Contrary to traditional SR approaches, we employed several steps. These steps are; the suppression of the global motion, motion segmentation accompanied by background subtraction to extract moving objects, suppression of the local motion of the segmented out regions, and super-resolving accumulated information coming from moving objects rather than the whole scene. This results in a reliable offline SR Image Reconstruction tool which handles several types of dynamic scene changes, compensates the impacts of camera systems, and provides data redundancy through removing the background. The framework proved to be superior to the state-of-the-art algorithms which put no significant effort toward dynamic scene representation of non-stationary camera systems

The cooperative effects of channel length-bias, width asymmetry, gradient steepness, and contact-guidance on fibroblasts’ directional decision making

Cell migration in complex micro-environments, that are similar to tissue pores, is important for predicting locations of tissue nucleation and optimizing scaffold architectures. Firstly, how fibroblast cells - relevant to tissue engineering, affect each other’s directional decisions when encountered with a bifurcation of different channel lengths was investigated. It was found that cell sequence and cell mitosis influence the directional choices that the cells made while chemotaxing. Specifically, the fibroblasts chose to alternate between two possible paths - one longer and the other shorter - at a bifurcation. This finding was counter-intuitive given that the shorter path had a steeper chemoattractant gradient, and would thus be expected to be the preferred path, according to classical chemotaxis theory. Hence, a multiscale image-based modeling was performed in order to explain this behavior. It showed that consumption of the chemotactic signals by the neighboring cells led to the sequence-dependent directional decisions. Furthermore, it was also found that cellular division led to daughter cells making opposite directional choices from each other; even it meant that one of the daughter cells had to move against the chemotactic gradient, and overcome oncoming traffic of other cells. Secondly, a comparison of the effects of the various directional cues on the migration of individual fibroblast cells: including the chemoattractant concentration gradient, the channel width, and the contact-guidance was provided. Simple bifurcated mazes with two branches of different widths were created and fibroblasts were allowed to travel across these geometries by introducing a gradient of PDGF-BB at the ‘exit’ of the device. By incorporating image-based modeling methodology into the experimental approach, an insight into (i) how individual cells make directional decisions in the presence of complex migration cues and (ii) how the cell-cell interaction influences it was provided. It was found that a larger width ratio between the two bifurcated branches outdoes a gradient difference in attracting the cells. Also, when cells encounter a symmetric bifurcation (i.e., no difference between the branch widths), the gradient is predominant in deciding which path the cell will take. Then, in a symmetrical gradient field (i.e., inside a bifurcation of similar branch widths, and in the absence of any leading cells), the contact guidance is important for guiding the cells in making directional choices. Finally, these directional cues were ranked according to the order from the most importance to the least: vast gradient difference between the two branches, channel width bias, mild gradient difference, and contact-guidance

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

Multi-signal gesture recognition using body and hand poses

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 147-154).We present a vision-based multi-signal gesture recognition system that integrates information from body and hand poses. Unlike previous approaches to gesture recognition, which concentrated mainly on making it a signal signal, our system allows a richer gesture vocabulary and more natural human-computer interaction. The system consists of three parts: 3D body pose estimation, hand pose classification, and gesture recognition. 3D body pose estimation is performed following a generative model-based approach, using a particle filtering estimation framework. Hand pose classification is performed by extracting Histogram of Oriented Gradients features and using a multi-class Support Vector Machine classifier. Finally, gesture recognition is performed using a novel statistical inference framework that we developed for multi-signal pattern recognition, extending previous work on a discriminative hidden-state graphical model (HCRF) to consider multi-signal input data, which we refer to Multi Information-Channel Hidden Conditional Random Fields (MIC-HCRFs). One advantage of MIC-HCRF is that it allows us to capture complex dependencies of multiple information channels more precisely than conventional approaches to the task. Our system was evaluated on the scenario of an aircraft carrier flight deck environment, where humans interact with unmanned vehicles using existing body and hand gesture vocabulary. When tested on 10 gestures recorded from 20 participants, the average recognition accuracy of our system was 88.41%.by Yale Song.S.M

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Spatiotemporal analysis of human actions using RGB-D cameras

Markerless human motion analysis has strong potential to provide cost-efficient solution for action recognition and body pose estimation. Many applications including humancomputer interaction, video surveillance, content-based video indexing, and automatic annotation among others will benefit from a robust solution to these problems. Depth sensing technologies in recent years have positively changed the climate of the automated vision-based human action recognition problem, deemed to be very difficult due to the various ambiguities inherent to conventional video. In this work, first a large set of invariant spatiotemporal features is extracted from skeleton joints (retrieved from depth sensor) in motion and evaluated as baseline performance. Next we introduce a discriminative Random Decision Forest-based feature selection framework capable of reaching impressive action recognition performance when combined with a linear SVM classifier. This approach improves upon the baseline performance obtained using the whole feature set with a significantly less number of features (one tenth of the original). The approach can also be used to provide insights on the spatiotemporal dynamics of human actions. A novel therapeutic action recognition dataset (WorkoutSU-10) is presented. We took advantage of this dataset as a benchmark in our tests to evaluate the reliability of our proposed methods. Recently the dataset has been published publically as a contribution to the action recognition community. In addition, an interactive action evaluation application is developed by utilizing the proposed methods to help with real life problems such as 'fall detection' in the elderly people or automated therapy program for patients with motor disabilities

Computational Video Enhancement

During a video, each scene element is often imaged many times by the sensor. I propose that by combining information from each captured frame throughout the video it is possible to enhance the entire video. This concept is the basis of computational video enhancement. In this dissertation, the viability of computational video processing is explored in addition to presenting applications where this processing method can be leveraged. Spatio-temporal volumes are employed as a framework for efficient computational video processing, and I extend them by introducing sheared volumes. Shearing provides spatial frame warping for alignment between frames, allowing temporally-adjacent samples to be processed using traditional editing and filtering approaches. An efficient filter-graph framework is presented to support this processing along with a prototype video editing and manipulation tool utilizing that framework. To demonstrate the integration of samples from multiple frames, I introduce methods for improving poorly exposed low-light videos to achieve improved results. This integration is guided by a tone-mapping process to determine spatially-varying optimal exposures and an adaptive spatio-temporal filter to integrate the samples. Low-light video enhancement is also addressed in the multispectral domain by combining visible and infrared samples. This is facilitated by the use of a novel multispectral edge-preserving filter to enhance only the visible spectrum video. Finally, the temporal characteristics of videos are altered by a computational video resampling process. By resampling the video-rate footage, novel time-lapse sequences are found that optimize for user-specified characteristics. Each resulting shorter video is a more faithful summary of the original source than a traditional time-lapse video. Simultaneously, new synthetic exposures are generated to alter the output video's aliasing characteristics

FREQUENCY DOMAIN CHARACTERIZATION OF OPTIC FLOW AND VISION-BASED OCELLAR SENSING FOR ROTATIONAL MOTION

The structure of an animal’s eye is determined by the tasks it must perform. While vertebrates rely on their two eyes for all visual functions, insects have evolved a wide range of specialized visual organs to support behaviors such as prey capture, predator evasion, mate pursuit, flight stabilization, and navigation. Compound eyes and ocelli constitute the vision forming and sensing mechanisms of some flying insects. They provide signals useful for flight stabilization and navigation. In contrast to the well-studied compound eye, the ocelli, seen as the second visual system, sense fast luminance changes and allows for fast visual processing. Using a luminance-based sensor that mimics the insect ocelli and a camera-based motion detection system, a frequency-domain characterization of an ocellar sensor and optic flow (due to rotational motion) are analyzed. Inspired by the insect neurons that make use of signals from both vision sensing mechanisms, advantages, disadvantages and complementary properties of ocellar and optic flow estimates are discussed