Browsing by Subject "Change detection"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
Item Change detection models for mobile cameras(2012-05) Kit, Dmitry Mark; Ballard, Dana Harry; Hayhoe, Mary; Miikkulainen, Risto; Stone, Peter; Grauman, KristenChange detection is an ability that allows intelligent agents to react to unexpected situations. This mechanism is fundamental in providing more autonomy to robots. It has been used in many different fields including quality control and network intrusion. In the visual domain, however, most research has been confined to stationary cameras and only recently have researchers started to shift to mobile cameras. \ We propose a general framework for building internal spatial models of the visual experiences. These models are used to retrieve expectations about visual inputs which can be compared to the actual observation in order to identify the presence of changes. Our framework leverages the tolerance to small view changes of optic flow and color histogram representations and a self-organizing map to build a compact memory of camera observations. The effectiveness of the approach is demonstrated in a walking simulation, where spatial information and color histograms are combined to detect changes in a room. The location signal allows the algorithm to query the self-organizing map for the expected color histogram and compare it to the current input. Any deviations can be considered changes and are then localized on the input image. Furthermore, we show how detecting a vehicle entering or leaving the camera's lane can be reduced to a change detection problem. This simplifies the problem by removing the need to track or even know about other vehicles. Matching Pursuit is used to learn a compact dictionary to describe the observed experiences. Using this approach, changes are detected when the learned dictionary is unable to reconstruct the current input. The human experiments presented in this dissertation support the idea that humans build statistical models that evolve with experience. We provide evidence that not only does this experience improve people's behavior in 3D environments, but also enables them to detect chromatic changes. Mobile cameras are now part of our everyday lives, ranging from built-in laptop cameras to cell phone cameras. The vision of this research is to enable these devices with change detection mechanisms to solve a large class of problems. Beyond presenting a foundation that effectively detects changes in environments, we also show that the algorithms employed are computationally inexpensive. The practicality of this approach is demonstrated by a partial implementation of the algorithm on commodity hardware such as Android mobile devices.Item Detection and transient dynamics modeling of experimental hypersonic inlet unstart(2011-12) Hutchins, Kelley Elizabeth; Akella, Maruthi Ram, 1972-; Clemens, Noel T.During unstart, the rapid upstream propagation of a hypersonic engine's inlet shock system can be clearly seen through inlet pressure measurements. Specifically, the magnitude of the pressure readings suddenly and dramatically increases as soon as the leading edge of the shock system passes the measurement location. A change detection algorithm can monitor the pressure time history at a given sensing location and determine when an abrupt pressure rise occurs. If this kind of information can be obtained at various sensing locations distributed throughout the inlet then a feedback control scheme has an improved basis upon which to make actuation decisions for preventing unstart. In this thesis a variety of change detection algorithms have been implemented and tested on multiple sources of experimental high-speed pressure transducer data. The performance of these algorithms is compared and suitability of each algorithm for the general unstart problem is discussed. Attempts to model the transient dynamics governing the unstart process have also been made through the use of system identification techniques. The result of these system identification efforts is a partially nonlinear mathematical model that describes shock motion through pressure signals. The process reveals that the nonlinear behavior can be separated from the linear with relative ease. Related attempts are then made to create a model where the nonlinear portion has been specified leaving only the linear portion to be determined by system identification. The modeling and identification process specific to the unstart data used is discussed and successful models are presented for both cases.Item Eye movements, visual search and scene memory in an immersive virtual environment(2014-08) Snyder, Katherine Lorraine; Hayhoe, MaryVisual memory has been demonstrated to play a role in both visual search and attentional prioritization in natural scenes. However, it has been studied predominantly in experimental paradigms using multiple two-dimensional images. Natural experience, however, entails prolonged immersion in a limited number of three-dimensional environments. The goal of the present experiment was to recreate circumstances comparable to natural visual experience in order to evaluate the role of scene memory in guiding eye movements in a natural environment. Subjects performed a continuous visual-search task within an immersive virtual-reality environment over three days. We found that, similar to two-dimensional contexts, viewers rapidly learn the location of objects in the environment over time, and use spatial memory to guide search. Incidental fixations did not provide obvious benefit to subsequent search, suggesting that semantic contextual cues may often be just as efficient, or that many incidentally fixated items are not held in memory in the absence of a specific task. On the third day of the experience in the environment, previous search items changed in color. These items were fixated upon with increased probability relative to control objects, suggesting that memory-guided prioritization (or Surprise) may be a robust mechanisms for attracting gaze to novel features of natural environments, in addition to task factors and simple spatial saliency.