ENHANCEMENTS TO THE MODIFIED COMPOSITE PATTERN METHOD OF STRUCTURED LIGHT 3D CAPTURE

The use of structured light illumination techniques for three-dimensional data acquisition is, in many cases, limited to stationary subjects due to the multiple pattern projections needed for depth analysis. Traditional Composite Pattern (CP) multiplexing utilizes sinusoidal modulation of individual projection patterns to allow numerous patterns to be combined into a single image. However, due to demodulation artifacts, it is often difficult to accurately recover the subject surface contour information. On the other hand, if one were to project an image consisting of many thin, identical stripes onto the surface, one could, by isolating each stripe center, recreate a very accurate representation of surface contour. But in this case, recovery of depth information via triangulation would be quite difficult. The method described herein, Modified Composite Pattern (MCP), is a conjunction of these two concepts. Combining a traditional Composite Pattern multiplexed projection image with a pattern of thin stripes allows for accurate surface representation combined with non-ambiguous identification of projection pattern elements. In this way, it is possible to recover surface depth characteristics using only a single structured light projection. The technique described utilizes a binary structured light projection sequence (consisting of four unique images) modulated according to Composite Pattern methodology. A stripe pattern overlay is then applied to the pattern. Upon projection and imaging of the subject surface, the stripe pattern is isolated, and the composite pattern information demodulated and recovered, allowing for 3D surface representation. In this research, the MCP technique is considered specifically in the context of a Hidden Markov Process Model. Updated processing methodologies explained herein make use of the Viterbi algorithm for the purpose of optimal analysis of MCP encoded images. Additionally, we techniques are introduced which, when implemented, allow fully automated processing of the Modified Composite Pattern image

An in Depth Review Paper on Numerous Image Mosaicing Approaches and Techniques

Image mosaicing is one of the most important subjects of research in computer vision at current. Image mocaicing requires the integration of direct techniques and feature based techniques. Direct techniques are found to be very useful for mosaicing large overlapping regions, small translations and rotations while feature based techniques are useful for small overlapping regions. Feature based image mosaicing is a combination of corner detection, corner matching, motion parameters estimation and image stitching.Furthermore, image mosaicing is considered the process of obtaining a wider field-of-view of a scene from a sequence of partial views, which has been an attractive research area because of its wide range of applications, including motion detection, resolution enhancement, monitoring global land usage, and medical imaging. Numerous algorithms for image mosaicing have been proposed over the last two decades.In this paper the authors present a review on different approaches for image mosaicing and the literature over the past few years in the field of image masaicing methodologies. The authors take an overview on the various methods for image mosaicing.This review paper also provides an in depth survey of the existing image mosaicing algorithms by classifying them into several groups. For each group, the fundamental concepts are first clearly explained. Finally this paper also reviews and discusses the strength and weaknesses of all the mosaicing groups

Tracking interacting targets in multi-modal sensors

PhDObject tracking is one of the fundamental tasks in various applications such as surveillance, sports, video conferencing and activity recognition. Factors such as occlusions, illumination changes and limited field of observance of the sensor make tracking a challenging task. To overcome these challenges the focus of this thesis is on using multiple modalities such as audio and video for multi-target, multi-modal tracking. Particularly, this thesis presents contributions to four related research topics, namely, pre-processing of input signals to reduce noise, multi-modal tracking, simultaneous detection and tracking, and interaction recognition. To improve the performance of detection algorithms, especially in the presence of noise, this thesis investigate filtering of the input data through spatio-temporal feature analysis as well as through frequency band analysis. The pre-processed data from multiple modalities is then fused within Particle filtering (PF). To further minimise the discrepancy between the real and the estimated positions, we propose a strategy that associates the hypotheses and the measurements with a real target, using a Weighted Probabilistic Data Association (WPDA). Since the filtering involved in the detection process reduces the available information and is inapplicable on low signal-to-noise ratio data, we investigate simultaneous detection and tracking approaches and propose a multi-target track-beforedetect Particle filtering (MT-TBD-PF). The proposed MT-TBD-PF algorithm bypasses the detection step and performs tracking in the raw signal. Finally, we apply the proposed multi-modal tracking to recognise interactions between targets in regions within, as well as outside the cameras’ fields of view. The efficiency of the proposed approaches are demonstrated on large uni-modal, multi-modal and multi-sensor scenarios from real world detections, tracking and event recognition datasets and through participation in evaluation campaigns

Real-Time, Multiple Pan/Tilt/Zoom Computer Vision Tracking and 3D Positioning System for Unmanned Aerial System Metrology

The study of structural characteristics of Unmanned Aerial Systems (UASs) continues to be an important field of research for developing state of the art nano/micro systems. Development of a metrology system using computer vision (CV) tracking and 3D point extraction would provide an avenue for making these theoretical developments. This work provides a portable, scalable system capable of real-time tracking, zooming, and 3D position estimation of a UAS using multiple cameras. Current state-of-the-art photogrammetry systems use retro-reflective markers or single point lasers to obtain object poses and/or positions over time. Using a CV pan/tilt/zoom (PTZ) system has the potential to circumvent their limitations. The system developed in this paper exploits parallel-processing and the GPU for CV-tracking, using optical flow and known camera motion, in order to capture a moving object using two PTU cameras. The parallel-processing technique developed in this work is versatile, allowing the ability to test other CV methods with a PTZ system using known camera motion. Utilizing known camera poses, the object\u27s 3D position is estimated and focal lengths are estimated for filling the image to a desired amount. This system is tested against truth data obtained using an industrial system

Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing

3D Face Tracking Using Stereo Cameras with Whole Body View

All visual tracking tasks associated with people tracking are in a great demand for modern applications dedicated to make human life easier and safer. In this thesis, a special case of people tracking - 3D face tracking in whole body view video is explored. Whole body view video means that the tracked face typically occupies not more than 5-10% of the frame area. Currently there is no reliable tracker that can track a face in long-term whole body view videos with luminance cameras in the 3D space. I followed a non-classical approach to designing a 3D tracker: first a 2D face tracking algorithm was developed in one view and then extended into stereo tracking. I recorded and annotated my own extensive dataset specifically for 2D face tracking in whole body view video and evaluated 17 state of the art 2D tracking algorithms. Based on the TLD tracker, I developed a face adapted median flow tracker that shows superior results compared to state of the art generic trackers. I explored different ways of extending 2D tracking into 3D and developed a method of using the epipolar constraint to check consistency of 3D tracking results. This method allows to detect tracking failures early and improves overall 3D tracking accuracy. I demonstrated how a Kinect based method can be compared to visual tracking methods and compared four different visual tracking methods running on low resolution fisheye stereo video and the Kinect face tracking application. My main contributions are: - I developed a face adaptation of generic trackers that improves tracking performance in long-term whole body view videos. - I designed a method of using the epipolar constraint to check consistency of 3D tracking results

Deformable Medical Image Registration: A Survey

Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: i) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; ii) longitudinal studies, where temporal structural or anatomical changes are investigated; and iii) population modeling and statistical atlases used to study normal anatomical variability. In this technical report, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this technical report is to provide an extensive account of registration techniques in a systematic manner.Le recalage déformable d'images est une des tâches les plus fondamentales dans l'imagerie médicale. Parmi ses applications les plus importantes, on compte: i) la fusion d' information provenant des différents types de modalités a n de faciliter le diagnostic et la planification du traitement; ii) les études longitudinales, oú des changements structurels ou anatomiques sont étudiées en fonction du temps; et iii) la modélisation de la variabilité anatomique normale d'une population et les atlas statistiques. Dans ce rapport de recherche, nous essayons de donner un aperçu des différentes méthodes du recalage déformables, en mettant l'accent sur les avancées les plus récentes du domaine. Nous avons particulièrement insisté sur les techniques appliquées aux images médicales. A n d'étudier les méthodes du recalage d'images, leurs composants principales sont d'abord identifiés puis étudiées de manière indépendante, les techniques les plus récentes étant classifiées en suivant un schéma logique déterminé. La contribution de ce rapport de recherche est de fournir un compte rendu détaillé des techniques de recalage d'une manière systématique

Geo-rectification and cloud-cover correction of multi-temporal Earth observation imagery

Over the past decades, improvements in remote sensing technology have led to mass proliferation of aerial imagery. This, in turn, opened vast new possibilities relating to land cover classification, cartography, and so forth. As applications in these fields became increasingly more complex, the amount of data required also rose accordingly and so, to satisfy these new needs, automated systems had to be developed. Geometric distortions in raw imagery must be rectified, otherwise the high accuracy requirements of the newest applications will not be attained. This dissertation proposes an automated solution for the pre-stages of multi-spectral satellite imagery classification, focusing on Fast Fourier Shift theorem based geo-rectification and multi-temporal cloud-cover correction. By automatizing the first stages of image processing, automatic classifiers can take advantage of a larger supply of image data, eventually allowing for the creation of semi-real-time mapping applications

Smart Monitoring and Control in the Future Internet of Things

The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things