| 1. |
- Ringaby, Erik, 1984-
(författare)
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Geometric Models for Rolling-shutter and Push-broom Sensors
- 2014
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Almost all cell-phones and camcorders sold today are equipped with a CMOS (Complementary Metal Oxide Semiconductor) image sensor and there is also a general trend to incorporate CMOS sensors in other types of cameras. The CMOS sensor has many advantages over the more conventional CCD (Charge-Coupled Device) sensor such as lower power consumption, cheaper manufacturing and the potential for onchip processing. Nearly all CMOS sensors make use of what is called a rolling shutter readout. Unlike a global shutter readout, which images all the pixels at the same time, a rolling-shutter exposes the image row-by-row. If a mechanical shutter is not used this will lead to geometric distortions in the image when either the camera or the objects in the scene are moving. Smaller cameras, like those in cell-phones, do not have mechanical shutters and systems that do have them will not use them when recording video. The result will look wobbly (jello eect), skewed or otherwise strange and this is often not desirable. In addition, many computer vision algorithms assume that the camera used has a global shutter and will break down if the distortions are too severe.In airborne remote sensing it is common to use push-broom sensors. These sensors exhibit a similar kind of distortion as that of a rolling-shutter camera, due to the motion of the aircraft. If the acquired images are to be registered to maps or other images, the distortions need to be suppressed.The main contributions in this thesis are the development of the three-dimensional models for rolling-shutter distortion correction. Previous attempts modelled the distortions as taking place in the image plane, and we have shown that our techniques give better results for hand-held camera motions. The basic idea is to estimate the camera motion, not only between frames, but also the motion during frame capture. The motion is estimated using image correspondences and with these a non-linear optimisation problem is formulated and solved. All rows in the rollingshutter image are imaged at dierent times, and when the motion is known, each row can be transformed to its rectied position. The same is true when using depth sensors such as the Microsoft Kinect, and the thesis describes how to estimate its 3D motion and how to rectify 3D point clouds.In the thesis it has also been explored how to use similar techniques as for the rolling-shutter case, to correct push-broom images. When a transformation has been found, the images need to be resampled to a regular grid in order to be visualised. This can be done in many ways and dierent methods have been tested and adapted to the push-broom setup.In addition to rolling-shutter distortions, hand-held footage often has shaky camera motion. It is possible to do ecient video stabilisation in combination with the rectication using rotation smoothing. Apart from these distortions, motion blur is a big problem for hand-held photography. The images will be blurry due to the camera motion and also noisy if taken in low light conditions. One of the contributions in the thesis is a method which uses gyroscope measurements and feature tracking to combine several images, taken with a smartphone, into one resulting image with less blur and noise. This enables the user to take photos which would have otherwise required a tripod.
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| 2. |
- Grelsson, Bertil
(författare)
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Global Pose Estimation from Aerial Images : Registration with Elevation Models
- 2014
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Over the last decade, the use of unmanned aerial vehicles (UAVs) has increased drastically. Originally, the use of these aircraft was mainly military, but today many civil applications have emerged. UAVs are frequently the preferred choice for surveillance missions in disaster areas, after earthquakes or hurricanes, and in hazardous environments, e.g. for detection of nuclear radiation. The UAVs employed in these missions are often relatively small in size which implies payload restrictions.For navigation of the UAVs, continuous global pose (position and attitude) estimation is mandatory. Cameras can be fabricated both small in size and light in weight. This makes vision-based methods well suited for pose estimation onboard these vehicles. It is obvious that no single method can be used for pose estimation in all dierent phases throughout a ight. The image content will be very dierent on the runway, during ascent, during ight at low or high altitude, above urban or rural areas, etc. In total, a multitude of pose estimation methods is required to handle all these situations. Over the years, a large number of vision-based pose estimation methods for aerial images have been developed. But there are still open research areas within this eld, e.g. the use of omnidirectional images for pose estimation is relatively unexplored.The contributions of this thesis are three vision-based methods for global egopositioning and/or attitude estimation from aerial images. The rst method for full 6DoF (degrees of freedom) pose estimation is based on registration of local height information with a geo-referenced 3D model. A dense local height map is computed using motion stereo. A pose estimate from navigation sensors is used as an initialization. The global pose is inferred from the 3D similarity transform between the local height map and the 3D model. Aligning height information is assumed to be more robust to season variations than feature matching in a single-view based approach.The second contribution is a method for attitude (pitch and roll angle) estimation via horizon detection. It is one of only a few methods in the literature that use an omnidirectional (sheye) camera for horizon detection in aerial images. The method is based on edge detection and a probabilistic Hough voting scheme. In a ight scenario, there is often some knowledge on the probability density for the altitude and the attitude angles. The proposed method allows this prior information to be used to make the attitude estimation more robust.The third contribution is a further development of method two. It is the very rst method presented where the attitude estimates from the detected horizon in omnidirectional images is rened through registration with the geometrically expected horizon from a digital elevation model. It is one of few methods where the ray refraction in the atmosphere is taken into account, which contributes to the highly accurate pose estimates. The attitude errors obtained are about one order of magnitude smaller than for any previous vision-based method for attitude estimation from horizon detection in aerial images.
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| 3. |
- Hedborg, Johan, 1876-
(författare)
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Motion and Structure Estimation From Video
- 2012
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Digital camera equipped cell phones were introduced in Japan in 2001, they quickly became popular and by 2003 outsold the entire stand-alone digital camera market. In 2010 sales passed one billion units and the market is still growing. Another trend is the rising popularity of smartphones which has led to a rapid development of the processing power on a phone, and many units sold today bear close resemblance to a personal computer. The combination of a powerful processor and a camera which is easily carried in your pocket, opens up a large eld of interesting computer vision applications.The core contribution of this thesis is the development of methods that allow an imaging device such as the cell phone camera to estimates its own motion and to capture the observed scene structure. One of the main focuses of this thesis is real-time performance, where a real-time constraint does not only result in shorter processing times, but also allows for user interaction.In computer vision, structure from motion refers to the process of estimating camera motion and 3D structure by exploring the motion in the image plane caused by the moving camera. This thesis presents several methods for estimating camera motion. Given the assumption that a set of images has known camera poses associated to them, we train a system to solve the camera pose very fast for a new image. For the cases where no a priory information is available a fast minimal case solver is developed. The solver uses ve points in two camera views to estimate the cameras relative position and orientation. This type of minimal case solver is usually used within a RANSAC framework. In order to increase accuracy and performance a renement to the random sampling strategy of RANSAC is proposed. It is shown that the new scheme doubles the performance for the ve point solver used on video data. For larger systems of cameras a new Bundle Adjustment method is developed which are able to handle video from cell phones.Demands for reduction in size, power consumption and price has led to a redesign of the image sensor. As a consequence the sensors have changed from a global shutter to a rolling shutter, where a rolling shutter image is acquired row by row. Classical structure from motion methods are modeled on the assumption of a global shutter and a rolling shutter can severely degrade their performance. One of the main contributions of this thesis is a new Bundle Adjustment method for cameras with a rolling shutter. The method accurately models the camera motion during image exposure with an interpolation scheme for both position and orientation.The developed methods are not restricted to cellphones only, but is rather applicable to any type of mobile platform that is equipped with cameras, such as a autonomous car or a robot. The domestic robot comes in many avors, everything from vacuum cleaners to service and pet robots. A robot equipped with a camera that is capable of estimating its own motion while sensing its environment, like the human eye, can provide an eective means of navigation for the robot. Many of the presented methods are well suited of robots, where low latency and real-time constraints are crucial in order to allow them to interact with their environment.
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| 4. |
- Larsson, Fredrik, 1980-
(författare)
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Methods for Visually Guided Robotic Systems : Matching, Tracking and Servoing
- 2009
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This thesis deals with three topics; Bayesian tracking, shape matching and visual servoing. These topics are bound together by the goal of visual control of robotic systems. The work leading to this thesis was conducted within two European projects, COSPAL and DIPLECS, both with the stated goal of developing artificial cognitive systems. Thus, the ultimate goal of my research is to contribute to the development of artificial cognitive systems.The contribution to the field of Bayesian tracking is in the form of a framework called Channel Based Tracking (CBT). CBT has been proven to perform competitively with particle filter based approaches but with the added advantage of not having to specify the observation or system models. CBT uses channel representation and correspondence free learning in order to acquire the observation and system models from unordered sets of observations and states. We demonstrate how this has been used for tracking cars in the presence of clutter and noise.The shape matching part of this thesis presents a new way to match Fourier Descriptors (FDs). We show that it is possible to take rotation and index shift into account while matching FDs without explicitly de-rotate the contours or neglecting the phase. We also propose to use FDs for matching locally extracted shapes in contrast to the traditional way of using FDs to match the global outline of an object. We have in this context evaluated our matching scheme against the popular Affine Invariant FDs and shown that our method is clearly superior.In the visual servoing part we present a visual servoing method that is based on an action precedes perception approach. By applying random action with a system, e.g. a robotic arm, it is possible to learn a mapping between action space and percept space. In experiments we show that it is possible to achieve high precision positioning of a robotic arm without knowing beforehand how the robotic arm looks like or how it is controlled.
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| 5. |
- Larsson, Fredrik, 1980-
(författare)
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Shape Based Recognition – Cognitive Vision Systems in Traffic Safety Applications
- 2011
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Traffic accidents are globally the number one cause of death for people 15-29 years old and is among the top three causes for all age groups 5-44 years. Much of the work within this thesis has been carried out in projects aiming for (cognitive) driver assistance systems and hopefully represents a step towards improving traffic safety.The main contributions are within the area of Computer Vision, and more specifically, within the areas of shape matching, Bayesian tracking, and visual servoing with the main focus being on shape matching and applications thereof. The different methods have been demonstrated in traffic safety applications, such as bicycle tracking, car tracking, and traffic sign recognition, as well as for pose estimation and robot control.One of the core contributions is a new method for recognizing closed contours, based on complex correlation of Fourier descriptors. It is shown that keeping the phase of Fourier descriptors is important. Neglecting the phase can result in perfect matches between intrinsically different shapes. Another benefit of keeping the phase is that rotation covariant or invariant matching is achieved in the same way. The only difference is to either consider the magnitude, for rotation invariant matching, or just the real value, for rotation covariant matching, of the complex valued correlation.The shape matching method has further been used in combination with an implicit star-shaped object model for traffic sign recognition. The presented method works fully automatically on query images with no need for regions-of-interests. It is shown that the presented method performs well for traffic signs that contain multiple distinct contours, while some improvement still is needed for signs defined by a single contour. The presented methodology is general enough to be used for arbitrary objects, as long as they can be defined by a number of regions.Another contribution has been the extension of a framework for learning based Bayesian tracking called channel based tracking. Compared to earlier work, the multi-dimensional case has been reformulated in a sound probabilistic way and the learning algorithm itself has been extended. The framework is evaluated in car tracking scenarios and is shown to give competitive tracking performance, compared to standard approaches, but with the advantage of being fully learnable.The last contribution has been in the field of (cognitive) robot control. The presented method achieves sufficient accuracy for simple assembly tasks by combining autonomous recognition with visual servoing, based on a learned mapping between percepts and actions. The method demonstrates that limitations of inexpensive hardware, such as web cameras and low-cost robotic arms, can be overcome using powerful algorithms.All in all, the methods developed and presented in this thesis can all be used for different components in a system guided by visual information, and hopefully represents a step towards improving traffic safety.
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| 6. |
- Wallenberg, Marcus, 1984-
(författare)
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Components of Embodied Visual Object Recognition : Object Perception and Learning on a Robotic Platform
- 2013
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Object recognition is a skill we as humans often take for granted. Due to our formidable object learning, recognition and generalisation skills, it is sometimes hard to see the multitude of obstacles that need to be overcome in order to replicate this skill in an artificial system. Object recognition is also one of the classical areas of computer vision, and many ways of approaching the problem have been proposed. Recently, visually capable robots and autonomous vehicles have increased the focus on embodied recognition systems and active visual search. These applications demand that systems can learn and adapt to their surroundings, and arrive at decisions in a reasonable amount of time, while maintaining high object recognition performance. Active visual search also means that mechanisms for attention and gaze control are integral to the object recognition procedure. This thesis describes work done on the components necessary for creating an embodied recognition system, specifically in the areas of decision uncertainty estimation, object segmentation from multiple cues, adaptation of stereo vision to a specific platform and setting, and the implementation of the system itself. Contributions include the evaluation of methods and measures for predicting the potential uncertainty reduction that can be obtained from additional views of an object, allowing for adaptive target observations. Also, in order to separate a specific object from other parts of a scene, it is often necessary to combine multiple cues such as colour and depth in order to obtain satisfactory results. Therefore, a method for combining these using channel coding has been evaluated. Finally, in order to make use of three-dimensional spatial structure in recognition, a novel stereo vision algorithm extension along with a framework for automatic stereo tuning have also been investigated. All of these components have been tested and evaluated on a purpose-built embodied recognition platform known as Eddie the Embodied.
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