Motion in depth and/or zooming causes defocus blur. This work presents a solution to the problem of using defocus blur and optical flow information to compute depth at points that defocus when they move. We first formulate a novel algorithm which recovers defocus blur and affine parameters simultaneously. Next we formulate a novel relationship (the blur-depth relationship) between defocus blur, relative object depth and three parameters based on camera motion and intrinsic camera parameters. We can handle the situation where a single image has points which have defocused, got sharper or are focally unperturbed. Moreover, our formulation is valid regardless of whether the defocus is due to the image plane being in front of or behind the point of sharp focus.The blur-depth relationship requires a sequence of at least three images taken with the camera moving either towards or away from the object. It can be used to obtain an initial estimate of relative depth using one of several non-linear methods. We demonstrate a solution based on the Extended Kalman Filter in which the measurement equation is the blur-depth relationship. The estimate of relative depth is then used to compute an initial estimate of camera motion parameters. In order to refine depth values, the values of relative depth and camera motion are then input into a second Extended Kalman Filter in which the measurement equations are the discrete motion equations. This set of cascaded Kalman filters can be employed iteratively over a longer sequence of images in order to further refine depth. We conduct several experiments on real scenery in order to demonstrate the range of object shapes that the algorithm can handle. We show that fairly good estimates of depth can be obtained with just three images.

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Criminals often resort to camera tampering to prevent capture of their actions. Many surveillance systems left unattended and videos surveillance system operators lose their concentration after a short period of time. Many important Real-time automated detection of video camera tampering cases is important for timely warning of the operators. Tampering can be defined as deliberate physical actions on a video surveillance camera and is generally done by obstructing the camera view by a foreign object, displacing the camera and changing the focus of the camera lens. In automated camera tamper detection systems, low false alarm rates are important as reliability of these systems is compromised by unnecessary alarms and consequently the operators start ignoring the warnings. We propose adaptive algorithms to detect and identify such cases with low false alarms rates in typical surveillance scenarios where there is significant activity in the scene. We also give brief information about the camera tampering detection algorithms in the literature. In this thesis we compare performance of the proposed algorithms to the algorithms in the literature by experimenting them with a set of test videos.

<p dir="ltr">Particle tracking velocimetry, also known as PTV, is a technology to measure velocity and study the flow field in fluid by observing change in position of individual tracer particles over time. A laser sheet illuminates a thin layer of the sample, in which particles emit fluorescent light and are visible to the camera. Particles at different distances from the microscope lens focal plane are visible, because particle diameter is much smaller than the thickness of laser sheet in micro-scale. The defocused distance changes the shape of particle seen by the camera. Analyzing particle shapes and obtaining the defocused distance of particles completes the third dimension of PTV with the use of a single camera. One approach to obtain defocused distance from particle shape is by comparing particle shapes with calibration images of known defocused distance. The accuracy of PTV relies on the collection of proper calibration images. There are three methods involved in this work. The first approach is to use synthetic images generated by solving Lommel differential equations, which describe the intensity distribution of particles under the impact of defocusing aberration. It was later discovered that the point source assumption inherent in Lommel function causes inaccuracy in generated calibration images. The second approach captures particle images while manually shifting the microscope stage in the z-direction. This approach causes systematic error by ignoring the refractive index of the immersion medium. The third approach is to use a microscale reference ramp as calibration target. Results are experimentally compared with particle shapes obtained from pressure driven flow with known velocity profile.</p>

During the creation of any concrete floor the concrete needs to be grinded down from it's very rough newly poured form to a more usable floor surface. Concrete floor grinding is very special in that the work area is often immensely large while the height difference on the surface is incredibly small, in-fact the the largest local difference of the surface from a peek to a valley during the grinding process is submillimeter and goes down to micrometer scale. Today's methods for measuring concrete surfaces are very few and all output one dimensional profiles of the surface in very time consuming processes which makes them unsuitable for real-time analysis of the surfaces during the grinding process. Because of this, the effectiveness of the work is dependent on the experience and intuition of the operator of the grinding machine as they have to make the decision of when to move on to the next step in the grinding process. Therefore it is desirable to create a better method for concrete surface measurement that can measure big areas in a short period of time. In this project a structured light method using sinusoidal phase shifting is implemented and evaluated with an easily movable setup that can measure the height of a concrete surface over an area. The method works by encoding the surface with a phase using a projector and analysing how the phase encoding warps when imaging it from an angle. By triangulation this can be made into a height map of the measured area. The end results show that the method is promising for this application and can detect the submillimeter differences. However, more suitable hardware and a more reliable calibration procedure are required to move this prototype towards a more practical measuring device.

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