Visual 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. / text

Change detection is amongst the most effective critical examination methods used in remote sensing technology. In this research, new methods are proposed for building and vegetation change detection using only LiDAR data without using any other remotely sensed data. Two LiDAR datasets from 2009 and 2013 will be used in this research. These datasets are provided by the City of Surrey. A Parcel map which shows parcels in the study area will be also used in this research because the objective of this research is detecting changes based on parcels. Different methods are applied to detect changes in buildings and vegetation respectively. Three attributes of object –slope, building volume, and building height are derived and used in this study. Changes in buildings are not only detected but also categorized based on their attributes. In addition, vegetation change detection is performed based on parcels. The output shows parcels with a change of vegetation. Accuracy assessment is done by using measures of completeness, correctness, and quality of extracted regions. Accuracy assessments suggest that building change detection is performed with better results.

No description available.

Abstract—This paper describes the application of the abrupt change detection technologies to detect the abrupt changes in the signals recorded during disturbances in the electrical power network of South Africa for disturbance analysis and relay performance monitoring. The aim is to estimate the time instants of the changes in the signal model parameters during the prefault condition, after initiation of fault, after the circuit-breaker opening and autoreclosure, etc. After these event-specific segmentations, the synchronization of the different digital fault recorder recordings are done based on the fault inception timings. The synchronized signals are segmented again. This synchronized segmentation is the first step toward automatic disturbance recognition, facilitating further complex feature vector analysis and pattern recognition. Besides, the synchronized, segmented recordings can be directly used to analyze certain kinds of disturbances and monitor the relay performance. This paper presents many practical examples from the power network in South Africa.

Due to the relatively complex land cover configuration and a series of significant ecological implications, the issue of land cover changes in the Big Creek area are of critical value to environmental conservation groups, policy makers, and relevant stakeholders. In consultation with the Carolinian Canada Coalition (CCC), the potential of IKONOS imagery as a high spatial resolution remote sensing product is assessed for significant habitat mapping, and a change detection methodology is developed and implemented for the Big Creek area that will be of value to decision makers and policy analysts. In order to take advantage of the synergistic strengths of multiple change detection techniques, a hybrid approach is adopted in this study, aiming to detect and stratify land cover changes over the time span from 2004 to 2012. On the basis of an assessment of the capability of differentiating changed from unchanged areas, the image differencing method based on Normalized Difference Vegetation Index (NDVI) was found to be the most accurate among the three change detection techniques employed in this study. As an attempt to incorporate local spatial autocorrelation information into the change detection analysis, the Getis statistic was used as a spatial filter in conjunction with the image differencing technique, and it showed great promise for improving the change/no change maps both qualitatively and quantitatively. In particular, the extreme Getis statistic proposed in this study demonstrated strong potential for automatically determining the optimal scale for spatial smoothing, which could greatly improve the efficiency and accuracy of change detection practices. In addition, the performance of the post-classification comparison approach was found to be highly dependent on the intrinsic characteristics of the individual classified maps, rather than simply the accuracy scores of the classifications subject to the comparison. Therefore, it is recommended that a benchmark approach be taken to compensate for this uncertainty of the post-classification comparison method, such that the negative impact of the misclassification errors in the individual classified maps could be reduced to an acceptable level. The findings of this research will contribute to a better understanding of the usefulness of some widely used change detection techniques in a relatively complex physical environment with abundant vegetation cover. In addition, the application of the Getis statistic as a spatial filter is proven useful for suppressing potential "salt and pepper" effects in the context of change detection analysis, especially if high spatial resolution imagery is employed. With minor modifications, the workflow proposed in this study is likely to reliably fulfill the purpose of monitoring land cover dynamics in other environments as well. However, it should be noted that clear awareness of the characteristics of the study area and needs of information is a premise to the successful application of any change detection approach in different environments.

Change detection is one of the most essential processing steps for monitoring urban and forest areas using remote sensing data. Even though 2D data obtained from satellite images from different dates can already provide plenty of useful information, it is usually insufficient when dealing with changes in the vertical direction. Moreover, if only one class of changes, such as buildings or forest, is of interest, it is often difficult to distinguish between relevant and irrelevant changes. In such cases, the information provided by Digital Surface Models (DSMs) is crucial, as it provides additional height information, which can be indispensable when analyzing changes. This dissertation addresses the challenge of using DSMs generated by satellite stereo images for 3D change detection. DSM generation techniques based on stereo imagery from space have been improved continuously in recent years, enhancing the quality of the generated DSMs considerably. Nevertheless, up to now these DSMs have not been widely adopted for change detection methods. Available 3D change detection approaches prefer LiDAR data, which are more accurate but have the drawback of being more expensive and exhibit a comparatively low temporal repetition rate. The characteristic and quality of DSMs based on satellite stereo imagery have so far hardly been considered within 3D change detection procedures. Therefore, more in-depth investigations concerning the adoption of these DSMs for 3D change detection should be performed. In this dissertation, DSMs based on stereo imagery have been visually and numerically evaluated and subsequently analyzed for various land cover types. Taking into account the quality of DSMs generated with the described methods, three DSM-assisted change detection approaches are developed. The first method, called “DSM-assisted change localization”, describes a robust change difference map generation method followed by DSM denoising. The generated change map is refined using vegetation and shadow masks and finally shape feature are used to consolidate the results through distinguishing relevant from irrelevant objects. Concerning fusion-based change detection, two methods, feature fusion and decision fusion, are proposed. The proposed feature fusion methods make use of the fact that panchromatic images feature much sharper contours than DSMs. To alleviate the shortcomings of the DSMs, the designed region-based change detection framework extracts the original regions from the ortho-images. For this approach, a new robust region-merging strategy is proposed to combine segmentation maps from two dates. Regarding the uncertain information contained in the DSMs and spectral images, a decision fusion method is proposed as the second fusion-based change detection method. The extracted features are classified as change indicators and no-change indicators, while two steps of the Dempster-Shafer fusion model are implemented for the final change detection. Post-classification is a common DSM-assisted change detection method, since the DSM can be very helpful for building extraction. In this third approach, the changed building’s location is obtained by comparing the new building mask with existing (often outdated) building footprint information, e.g. from GIS databases. To extract the boundaries of newly built buildings, a robust building extraction method has been developed by also considering the quality of the DSM. These three approaches are evaluated experimentally using four representative data sets. Quantitative and qualitative experimental results obtained from each data set are analyzed in detail. The experimental results show that all of the proposed approaches are able to determine the change status of the objects of interest. The results achieved vary according to the DSM quality, the object of interest and the test area. Furthermore, it is shown experimentally that, by making proper use of DSMs in complex decision frameworks, both the efficiency and the accuracy of the change detection are improve in comparison to 2D change detection approaches. In addition, the developed approaches enable the rapid localization of changes concerning objects of interest, such as buildings and forest, which is valuable for many applications such as fast response systems after earthquake or other disasters.

In this study, participants performed a change detection task. Specifically we examined whether participants had to fixate on a difference between two images before they could detect it. Thirty-six participants performed a change detection task in either a 3 minute or a 1.5 minute condition. We found a significant interaction between task duration and fixation type (whether the participant had fixated on the difference in both, one, or neither image). Participants found a greater number of differences given more time only when they fixated on the difference in both images. The number of differences which were detected by participants with a fixation on only one image or on neither image did not increase with a corresponding increase in time, indicating that some mechanical error may be involved. This suggests that participants need to fixate on a difference before being able to detect it.

Activity Recognition is concerned with identifying the physical state of a user at a particular point in time. Activity recognition task requires the training of classification algorithm using the processed sensor data from the representative population of users. The accuracy of the generated model often reduces during classification of new instances due to the non-stationary sensor data and variations in user characteristics. Thus, there is a need to adapt the classification model to new user haracteristics. However, the existing approaches to model adaptation in activity recognition are blind. They continuously adapt a classification model at a regular interval without specific and precise detection of the indicator of the degrading performance of the model. This approach can lead to wastage of system resources dedicated to continuous adaptation. This thesis addresses the problem of detecting changes in the accuracy of activity recognition model. The thesis developed a classifier for activity recognition. The classifier uses three statistical summaries data that can be generated from any dataset for similarity based classification of new samples. The weighted ensemble combination of the classification decision from each statistical summary data results in a better performance than three existing benchmarked classification algorithms. The thesis also presents change detection approaches that can detect the changes in the accuracy of the underlying recognition model without having access to the ground truth label of each activity being recognised. The first approach called `UDetect' computes the change statistics from the window of classified data and employed statistical process control method to detect variations between the classified data and the reference data of a class. Evaluation of the approach indicates a consistent detection that correlates with the error rate of the model. The second approach is a distance based change detection technique that relies on the developed statistical summaries data for comparing new classified samples and detects any drift in the original class of the activity. The implemented approach uses distance function and a threshold parameter to detect the accuracy change in the classifier that is classifying new instances. Evaluation of the approach yields above 90% detection accuracy. Finally, a layered framework for activity recognition is proposed to make model adaptation in activity recognition informed using the developed techniques in this thesis.

The manipulation contact forces convey substantial information about the manipulation state. This paper address the fundamental problem of interpreting the force signals without any additional manipulation context. Techniques based on forms of the generalized sequential likelihood ratio test are used to segment individual strain signals into statistically equivalent pieces. We report on our experimental development of the segmentation algorithm and on its results for contact states. The sequential likelihood ratio test is reviewed and some of its special cases and optimal properties are discussed. Finally, we conclude by discussing extensions to the techniques and a contact interpretation framework.

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. / text