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Interactive Perception For Cluttered Environments, Robert Willimon
Interactive Perception For Cluttered Environments, Robert Willimon
All Theses
Robotics research tends to focus upon either non-contact sensing or machine manipulation, but not both. This paper explores the benefits of combining the two by addressing the problem of extracting and classifying unknown objects within a cluttered environment, such as found in recycling and service robot applications. In the proposed approach, a pile of objects lies on a flat background, and the goal of the robot is to sift through the pile and classify each object so that it can be studied further. One object should be removed at a time with minimal disturbance to the other objects. We propose …
Electronic Image Stabilization For Mobile Robotic Vision Systems, Michael John Smith
Electronic Image Stabilization For Mobile Robotic Vision Systems, Michael John Smith
Theses and Dissertations
When a camera is affixed on a dynamic mobile robot, image stabilization is the first step towards more complex analysis on the video feed. This thesis presents a novel electronic image stabilization (EIS) algorithm for small inexpensive highly dynamic mobile robotic platforms with onboard camera systems. The algorithm combines optical flow motion parameter estimation with angular rate data provided by a strapdown inertial measurement unit (IMU). A discrete Kalman filter in feedforward configuration is used for optimal fusion of the two data sources. Performance evaluations are conducted by a simulated video truth model (capturing the effects of image translation, rotation, …
Artificial Intelligence – Ii: Image Normalization For Face Recognition Using 3d Model, Zahid Riaz, Michael Beetz, Bernd Radig
Artificial Intelligence – Ii: Image Normalization For Face Recognition Using 3d Model, Zahid Riaz, Michael Beetz, Bernd Radig
International Conference on Information and Communication Technologies
This paper describes an image segmentation and normalization technique using 3D point distribution model and its counterpart in 2D space. This segmentation is efficient to work for holistic image recognition algorithm. The results have been tested with face recognition application using Cohn Kanade facial expressions database (CKFED). The approach follows by fitting a model to face image and registering it to a standard template. The models consist of distribution of points in 2D and 3D. We extract a set of feature vectors from normalized images using principal components analysis and using them for a binary decision tree for classification. A …
Accurate Tracking Of Objects Using Level Sets, Nalin Senthamil
Accurate Tracking Of Objects Using Level Sets, Nalin Senthamil
All Theses
Our current work presents an approach to tackle the challenging task of tracking objects in Internet videos taken from large web repositories such as YouTube. Such videos more often than not, are captured by users using their personal hand-held cameras and cellphones and hence suffer from problems such as poor quality, camera jitter and unconstrained lighting and environmental settings. Also, it has been observed that events being recorded by such videos usually contain objects moving in an unconstrained fashion. Hence, tracking objects in Internet videos is a very challenging task in the field of computer vision since there is no …
Recognition Of Human Interactions Using Limb-Level Feature Points, Michael David Dudley
Recognition Of Human Interactions Using Limb-Level Feature Points, Michael David Dudley
Theses
Human activity recognition is an emerging area of research in computer vision with applications in video surveillance, human-computer interaction, robotics, and video annotation. Despite a number of recent advances, there are still many opportunities for new developments, especially in the area of person-person and person-object interaction. Many proposed algorithms focus on recognizing solely single person, person-person or person-object activities. An algorithm which can recognize all three types would be a significant step toward the real-world application of this technology. This thesis investigates the design and implementation of such an algorithm. It utilizes background subtraction to extract the subjects in the …
Recognition Of Human Activities And Expressions In Video Sequences Using Shape Context Descriptor, Natasha Prashant Kholgade
Recognition Of Human Activities And Expressions In Video Sequences Using Shape Context Descriptor, Natasha Prashant Kholgade
Theses
The recognition of objects and classes of objects is of importance in the field of computer vision due to its applicability in areas such as video surveillance, medical imaging and retrieval of images and videos from large databases on the Internet. Effective recognition of object classes is still a challenge in vision; hence, there is much interest to improve the rate of recognition in order to keep up with the rising demands of the fields where these techniques are being applied. This thesis investigates the recognition of activities and expressions in video sequences using a new descriptor called the spatiotemporal …
Real-Time Optical Flow Sensor Design And Its Application On Obstacle Detection, Zhaoyi Wei
Real-Time Optical Flow Sensor Design And Its Application On Obstacle Detection, Zhaoyi Wei
Theses and Dissertations
Motion is one of the most important features describing an image sequence. Motion estimation has been widely applied in structure from motion, vision-based navigation and many other fields. However, real-time motion estimation remains a challenge because of its high computational expense. The traditional CPU-based scheme cannot satisfy the power, size and computation requirements in many applications. With the availability of new parallel architectures such as FPGAs and GPUs, applying these new technologies to computer vision tasks such as motion estimation has been an active research field in recent years. In this dissertation, FPGAs have been applied to real-time motion estimation …
Communication Framework For Distributed Computer Vision On Stationary And Mobile Platforms, Christopher Armenio
Communication Framework For Distributed Computer Vision On Stationary And Mobile Platforms, Christopher Armenio
Theses
Recent advances in the complexity and manufacturability of digital video cameras coupled with the ubiquity of high speed computers and communication networks have led to burgeoning research in the fields of computer vision and image understanding. As the generated vision algorithms become increasingly complex, a need arises for robust communication between remote cameras on mobile units and their associated distributed vision algorithms. A communication framework would provide a basis for modularization and abstraction of a collection of computer vision algorithms; the resulting system would allow for straightforward image capture, simplified communication between algorithms, and easy replacement or upgrade of existing …
Automatic Detection Of Child Pornography, Glen Thompson
Automatic Detection Of Child Pornography, Glen Thompson
Australian Digital Forensics Conference
Before the introduction of the internet, the availability of child pornography was reported as on the decline (Jenkins 2001). Since its emergence, however, the internet has made child pornography a much more accessible and available means of trafficking across borders (Biegel 2001; Jenkins 2001; Wells, Finkelhor et al. 2007). The internet as it is at present is made up of a vast array of protocols and networks where traffickers can anonymously share large volumes of illegal material amongst each other from locations with relaxed or non-existent laws that prohibit the possession or trafficking of illegal material. Likewise the internet is …
An Optical Flow Implementation Comparison Study, John M. Bodily
An Optical Flow Implementation Comparison Study, John M. Bodily
Theses and Dissertations
Optical flow is the apparent motion of brightness patterns within an image scene. Algorithms used to calculate the optical flow for a sequence of images are useful in a variety of applications, including motion detection and obstacle avoidance. Typical optical flow algorithms are computationally intense and run slowly when implemented in software, which is problematic since many potential applications of the algorithm require real-time calculation in order to be useful. To increase performance of the calculation, optical flow has recently been implemented on FPGA and GPU platforms. These devices are able to process optical flow in real-time, but are generally …
Deeply-Integrated Feature Tracking For Embedded Navigation, Jeffery R. Gray
Deeply-Integrated Feature Tracking For Embedded Navigation, Jeffery R. Gray
Theses and Dissertations
The Air Force Institute of Technology (AFIT) is investigating techniques to improve aircraft navigation using low-cost imaging and inertial sensors. Stationary features tracked within the image are used to improve the inertial navigation estimate. These features are tracked using a correspondence search between frames. Previous research investigated aiding these correspondence searches using inertial measurements (i.e., stochastic projection). While this research demonstrated the benefits of further sensor integration, it still relied on robust feature descriptors (e.g., SIFT or SURF) to obtain a reliable correspondence match in the presence of rotation and scale changes. Unfortunately, these robust feature extraction algorithms are computationally …
Model-Based Control Using Model And Mechanization Fusion Techniques For Image-Aided Navigation, Constance D. Hendrix
Model-Based Control Using Model And Mechanization Fusion Techniques For Image-Aided Navigation, Constance D. Hendrix
Theses and Dissertations
Unmanned aerial vehicles are no longer used for just reconnaissance. Current requirements call for smaller autonomous vehicles that replace the human in high-risk activities. Many times these activities are performed in GPS-degraded environments. Without GPS providing today's most accurate navigation solution, autonomous navigation in tight areas is more difficult. Today, image-aided navigation is used and other methods are explored to more accurately navigate in such areas (e.g., indoors). This thesis explores the use of inertial measurements and navigation solution updates using cameras with a model-based Linear Quadratic Gaussian controller. To demonstrate the methods behind this research, the controller will provide …
Using Predictive Rendering As A Vision-Aided Technique For Autonomous Aerial Refueling, Adam D. Weaver
Using Predictive Rendering As A Vision-Aided Technique For Autonomous Aerial Refueling, Adam D. Weaver
Theses and Dissertations
This research effort seeks to characterize a vision-aided approach for an Unmanned Aerial System (UAS) to autonomously determine relative position to another aircraft in a formation, specifically to address the autonomous aerial refueling problem. A system consisting of a monocular digital camera coupled with inertial sensors onboard the UAS is analyzed for feasibility of using this vision-aided approach. A three-dimensional rendering of the tanker aircraft is used to generate predicted images of the tanker as seen by the receiver aircraft. A rigorous error model is developed to model the relative dynamics between an INS-equipped receiver and the tanker aircraft. A …
Communication Free Robot Swarming, Zachary C. Gray
Communication Free Robot Swarming, Zachary C. Gray
Theses and Dissertations
As the military use of unmanned aerial vehicles increases, a growing need for novel strategies to control these systems exists. One such method for controlling many unmanned aerial vehicles simultaneously is the through the use of swarm algorithms. This research explores a swarm robotic algorithm developed by Kadrovach implemented on Pioneer Robots in a real-world environment. An adaptation of his visual sensor is implemented using stereo vision as the primary method of sensing the environment. The swarm members are prohibited from explicitly communicating other than passively through the environment. The resulting implementation produces a communication free swarming algorithm. The algorithm …
Locating And Tracking Objects By Efficient Comparison Of Real And Predicted Synthetic Video Imagery, Damian M. Lyons, D. Paul Benjamin
Locating And Tracking Objects By Efficient Comparison Of Real And Predicted Synthetic Video Imagery, Damian M. Lyons, D. Paul Benjamin
Faculty Publications
A mobile robot moving in an environment in which there are other moving objects and active agents, some of which may represent threats and some of which may represent collaborators, needs to be able to reason about the potential future behaviors of those objects and agents. In this paper we present an approach to tracking targets with complex behavior, leveraging a 3D simulation engine to generate predicted imagery and comparing that against real imagery. We introduce an approach to compare real and simulated imagery and present results using this approach to locate and track objects with complex behaviors. In this …
Texture Classification Using Fractal Geometry For The Diagnosis Of Skin Cancers, Jonathan Blackledge, Dymitiy A. Dubovitskiy
Texture Classification Using Fractal Geometry For The Diagnosis Of Skin Cancers, Jonathan Blackledge, Dymitiy A. Dubovitskiy
Conference papers
We present an approach to object detection and recognition in a digital image using a classification method that is based on the application of a set of features that include fractal parameters such as the Lacunarity and Fractal Dimension. The principal issues associated with object recognition are presented and a self-learning procedure for designing a decision making engine using fuzzy logic and membership function theory considered. The methods discussed, and the ‘system’ developed, have a range of applications in ‘machine vision’ and in this publication, we focus on the development and implementation of a skin cancer screening system that can …
Object Detection And Texture Classification With Applications To The Diagnosis Of Skin Cancer, Jonathan Blackledge, D. A. Dubovitskiy
Object Detection And Texture Classification With Applications To The Diagnosis Of Skin Cancer, Jonathan Blackledge, D. A. Dubovitskiy
Books/Book chapters
We present an approach to object detection and recognition in a digital image using a classification method that is based on the application of a set of features that include fractal parameters such as the Lacunarity and Fractal Dimension. The principal issues associated with object recognition are presented and a self-learning procedure for designing a decision making engine using fuzzy logic and membership function theory considered. The methods discussed, and the ‘system’ developed, have a range of applications in ‘machine vision’ and in this publication, we focus on the development and implementation of a skin cancer screening system that can …
Real-Time Image-Based Motion Detection Using Color And Structure, Manali Chakraborty
Real-Time Image-Based Motion Detection Using Color And Structure, Manali Chakraborty
Open Access Theses & Dissertations
Motion detection is an important problem in computer vision and has multiple applications in the real world, including surveillance-related activities, gaming, and human-robot interactions. These systems need to be robust enough to handle fluctuations in light intensity and other external factors like noise and compression artifacts. In this thesis a method is proposed for detecting the regions of motion from a video sequence in real time. The main idea of this work is to detect motion based on both structure and color. Structure-based detection is carried out using information from the Census Transform computed on gradient images obtained with Sobel …
A Subspace Projection Methodology For Nonlinear Manifold Based Face Recognition, Praveen Sankaran
A Subspace Projection Methodology For Nonlinear Manifold Based Face Recognition, Praveen Sankaran
Electrical & Computer Engineering Theses & Dissertations
A novel feature extraction method that utilizes nonlinear mapping from the original data space to the feature space is presented in this dissertation. Feature extraction methods aim to find compact representations of data that are easy to classify. Measurements with similar values are grouped to same category, while those with differing values are deemed to be of separate categories. For most practical systems, the meaningful features of a pattern class lie in a low dimensional nonlinear constraint region (manifold) within the high dimensional data space. A learning algorithm to model this nonlinear region and to project patterns to this feature …