981 resultados para Change Detection


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A change detection paradigm was used to estimate the role of explicit change detection in the generation of the irrelevant spatial stimulus coding underlying the Simon effect. In one condition, no blank was interposed between two successive displays, which produced efficient change detection. In another condition, the presence of a blank frame produced a robust change blindness effect, which is crucially assumed to occur as the consequence of impaired attentional orienting to the change location. The results showed a strong Simon-like effect under conditions of efficient change detection. By contrast, no Simon-like effect was observed under conditions of change blindness, namely when attention shifting towards the change location was hampered. Experiment 2 supported this pattern by showing that a Simon-like effect could be observed when the blank was present, but only when participants detected the change by means of a cue that was informative as to change location. Overall, our findings show that a Simon-like effect can only be observed under conditions of explicit change detection, likely because a shift of attention towards the change location has occurred.

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Very high-resolution Synthetic Aperture Radar sensors represent an alternative to aerial photography for delineating floods in built-up environments where flood risk is highest. However, even with currently available SAR image resolutions of 3 m and higher, signal returns from man-made structures hamper the accurate mapping of flooded areas. Enhanced image processing algorithms and a better exploitation of image archives are required to facilitate the use of microwave remote sensing data for monitoring flood dynamics in urban areas. In this study a hybrid methodology combining radiometric thresholding, region growing and change detection is introduced as an approach enabling the automated, objective and reliable flood extent extraction from very high-resolution urban SAR images. The method is based on the calibration of a statistical distribution of “open water” backscatter values inferred from SAR images of floods. SAR images acquired during dry conditions enable the identification of areas i) that are not “visible” to the sensor (i.e. regions affected by ‘layover’ and ‘shadow’) and ii) that systematically behave as specular reflectors (e.g. smooth tarmac, permanent water bodies). Change detection with respect to a pre- or post flood reference image thereby reduces over-detection of inundated areas. A case study of the July 2007 Severn River flood (UK) observed by the very high-resolution SAR sensor on board TerraSAR-X as well as airborne photography highlights advantages and limitations of the proposed method. We conclude that even though the fully automated SAR-based flood mapping technique overcomes some limitations of previous methods, further technological and methodological improvements are necessary for SAR-based flood detection in urban areas to match the flood mapping capability of high quality aerial photography.

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There has been recent interest in sensory systems that are able to display a response which is proportional to a fold change in stimulus concentration, a feature referred to as fold-change detection (FCD). Here, we demonstrate FCD in a recent whole-pathway mathematical model of Escherichia coli chemotaxis. FCD is shown to hold for each protein in the signalling cascade and to be robust to kinetic rate and protein concentration variation. Using a sensitivity analysis, we find that only variations in the number of receptors within a signalling team lead to the model not exhibiting FCD. We also discuss the ability of a cell with multiple receptor types to display FCD and explain how a particular receptor configuration may be used to elucidate the two experimentally determined regimes of FCD behaviour. All findings are discussed in respect of the experimental literature.

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The detection of anthropogenic climate change can be improved by recognising the seasonality in the climate change response. This is demonstrated for the North Atlantic jet (zonal wind at 850 hPa, U850) and European precipitation responses projected by the CMIP5 climate models. The U850 future response is characterised by a marked seasonality: an eastward extension of the North Atlantic jet into Europe in November-April, and a poleward shift in May-October. Under the RCP8.5 scenario, the multi-model mean response in U850 in these two extended seasonal means emerges by 2035-2040 for the lower--latitude features and by 2050-2070 for the higher--latitude features, relative to the 1960-1990 climate. This is 5-15 years earlier than when evaluated in the traditional meteorological seasons (December--February, June--August), and it results from an increase in the signal to noise ratio associated with the spatial coherence of the response within the extended seasons. The annual mean response lacks important information on the seasonality of the response without improving the signal to noise ratio. The same two extended seasons are demonstrated to capture the seasonality of the European precipitation response to climate change and to anticipate its emergence by 10-20 years. Furthermore, some of the regional responses, such as the Mediterranean precipitation decline and the U850 response in North Africa in the extended winter, are projected to emerge by 2020-2025, according to the models with a strong response. Therefore, observations might soon be useful to test aspects of the atmospheric circulation response predicted by some of the CMIP5 models.

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This work presents a new approach to detecting the scene change in the successive capture of photographs of a place within equal time interval. This method is based on a gray level histogram of every image. In this method the histogram of an image is processed to modify it for matching with the processed histogram of a reference image. The coefficient of correlation is taken as the measure of matching. As the method does not do any heavy signal processing, and the images are taken successively with a multi-shot digital still camera, it can be applied for real-time processing of such pictures for detection of a scene change. A multi-camera in multi-position approach is also shown to evaluate the change in scene simultaneously from different angles. Both multi-camera and single-camera approaches are compared in detecting a scene change.

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In this paper, we present a novel scene change detection algorithm for mobile camera platforms. Our approach integrates sparse 3D scene background modelling and dense 2D image background modelling into a unified framework. The 3D scene background modelling identifies inconsistent clusters over time in a set of 3D cloud points as the scene changes. The 2D image background modelling further confirms the scene changes by finding inconsistent appearances in a set of aligned images using the classical MRF background subtraction technique. We evaluate the performance of our proposed system on a number of challenging video datasets obtained from a camera placed on a moving vehicle and the experiments show that our proposed method outperforms previous works in scene change detection, which suggested the feasibility of our approach.

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Building on a habitat mapping project completed in 2011, Deakin University was commissioned by Parks Victoria (PV) to apply the same methodology and ground-truth data to a second, more recent and higher resolution satellite image to create habitat maps for areas within the Corner Inlet and Nooramunga Marine and Coastal Park and Ramsar area. A ground-truth data set using in situ video and still photographs was used to develop and assess predictive models of benthic marine habitat distributions incorporating data from both RapidEye satellite imagery (corrected for atmospheric and water column effects by CSIRO) and LiDAR (Light Detection and Ranging) bathymetry. This report describes the results of the mapping effort as well as the methodology used to produce these habitat maps.

Overall accuracies of habitat classifications were good, with error rates similar to or better than the earlier classification (>73 % and kappa values > 0.58 for both study localities). The RapidEye classification failed to accurately detect Pyura and reef habitat classes at the Corner Inlet locality, possibly due to differences in spectral frequencies. For comparison, these categories were combined into a ‘non-seagrass’ category, similar to the one used at the Nooramunga locality in the original classification. Habitats predicted with highest accuracies differed from the earlier classification and were Posidonia in Corner Inlet (89%), and bare sediment (no-visible seagrass class) in Nooramunga (90%). In the Corner Inlet locality reef and Pyura habitat categories were not distinguishable in the repeated classification and so were combined with bare sediments. The majority of remaining classification errors were due to the misclassification of Zosteraceae as bare sediment and vice versa. Dominant habitats were the same as those from the 2011 classification with some differences in extent. For the Corner Inlet study locality the no-visible seagrass category remained the most extensive (9059 ha), followed by Posidonia (5,513 ha) and Zosteraceae (5,504 ha). In Nooramunga no-visible seagrass (6,294 ha), Zosteraceae (3,122 ha) and wet saltmarsh (1,562 ha) habitat classes were most dominant.

Change detection analyses between the 2009 and 2011 imagery were undertaken as part of this project, following the analyses presented in Monk et al. (2011) and incorporating error estimates from both classifications. These analyses indicated some shifts in classification between Posidonia and Zosteraceae as well as a general reduction in the area of Zosteraceae. Issues with classification of mixed beds were apparent, particularly in the main Posidonia bed at Nooramunga where a mosaic of Zosteraceae and Posidonia was seen that was not evident in the ALOS classification. Results of a reanalysis of the 1998-2009 change detection illustrating effects of binning of mixed beds is also provided as an appendix.

This work has been successful in providing baseline maps at an improved level of detail using a repeatable method meaning that any future changes in intertidal and shallow water marine habitats may be assessed in a consistent way with quantitative error assessments. In wider use, these maps should also allow improved conservation planning, advance fisheries and catchment management, and progress infrastructure planning to limit impacts on the Inlet environment.

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Remote sensing is a useful tool for detecting change over time.We introduce a hybrid change-detection method for forest and protected-area vegetation and demonstrate its use with two satellite images of Golestan National Park in northern Iran (1998 and 2010). We report on the advantages and disadvantages of the hybrid method relative to the standard change-detection method. In the proposed hybrid algorithm, the change vector analysis technique was used to determine changes in vegetation. Following this, we used postclassification comparison to determine the nature of the changes observed and their accuracy and to evaluate the effects of different parameters on the performance of the proposed method. We determined 85% accuracy for the proposed hybrid change-detection method, thus demonstrating a method for discovering and assessing environmental threats to natural treasures. © 2014 Society of Photo-Optical Instrumentation Engineers.

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The change detection technique was used in this study to provide preliminary information on the dynamics of land cover in the region over the western basin of the Tiete River. This area is characterized by sequence of reservoirs and intense agricultural activity, triggering series negative effects. One of the impacts is contamination and proliferation of aquatic organisms in these aquatics environments, increased by release of nutrients from human activities. This work was possible to observe a large switching classes and secondary vegetation bare land, probably related to agricultural activity.

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Il telerilevamento rappresenta un efficace strumento per il monitoraggio dell’ambiente e del territorio, grazie alla disponibilità di sensori che riprendono con cadenza temporale fissa porzioni della superficie terrestre. Le immagini multi/iperspettrali acquisite sono in grado di fornire informazioni per differenti campi di applicazione. In questo studio è stato affrontato il tema del consumo di suolo che rappresenta un’importante sfida per una corretta gestione del territorio, poiché direttamente connesso con i fenomeni del runoff urbano, della frammentazione ecosistemica e con la sottrazione di importanti territori agricoli. Ancora non esiste una definizione unica, ed anche una metodologia di misura, del consumo di suolo; in questo studio è stato definito come tale quello che provoca impermeabilizzazione del terreno. L’area scelta è quella della Provincia di Bologna che si estende per 3.702 km2 ed è caratterizzata a nord dalla Pianura Padana e a sud dalla catena appenninica; secondo i dati forniti dall’ISTAT, nel periodo 2001-2011 è stata la quarta provincia in Italia con più consumo di suolo. Tramite classificazione pixel-based è stata fatta una mappatura del fenomeno per cinque immagini Landsat. Anche se a media risoluzione, e quindi non in grado di mappare tutti i dettagli, esse sono particolarmente idonee per aree estese come quella scelta ed inoltre garantiscono una più ampia copertura temporale. Il periodo considerato va dal 1987 al 2013 e, tramite procedure di change detection applicate alle mappe prodotte, si è cercato di quantificare il fenomeno, confrontarlo con i dati esistenti e analizzare la sua distribuzione spaziale.

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By means of fixed-links modeling, the present study identified different processes of visual short-term memory (VSTM) functioning and investigated how these processes are related to intelligence. We conducted an experiment where the participants were presented with a color change detection task. Task complexity was manipulated through varying the number of presented stimuli (set size). We collected hit rate and reaction time (RT) as indicators for the amount of information retained in VSTM and speed of VSTM scanning, respectively. Due to the impurity of these measures, however, the variability in hit rate and RT was assumed to consist not only of genuine variance due to individual differences in VSTM retention and VSTM scanning but also of other, non-experimental portions of variance. Therefore, we identified two qualitatively different types of components for both hit rate and RT: (1) non-experimental components representing processes that remained constant irrespective of set size and (2) experimental components reflecting processes that increased as a function of set size. For RT, intelligence was negatively associated with the non-experimental components, but was unrelated to the experimental components assumed to represent variability in VSTM scanning speed. This finding indicates that individual differences in basic processing speed, rather than in speed of VSTM scanning, differentiates between high- and low-intelligent individuals. For hit rate, the experimental component constituting individual differences in VSTM retention was positively related to intelligence. The non-experimental components of hit rate, representing variability in basal processes, however, were not associated with intelligence. By decomposing VSTM functioning into non-experimental and experimental components, significant associations with intelligence were revealed that otherwise might have been obscured.

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Visual short-term memory (VSTM) is the storage of visual information over a brief time period (usually a few seconds or less). Over the past decade, the most popular task for studying VSTM in humans has been the change detection task. In this task, subjects must remember several visual items per trial in order to identify a change following a brief delay interval. Results from change detection tasks have shown that VSTM is limited; humans are only able to accurately hold a few visual items in mind over a brief delay. However, there has been much debate in regard to the structure or cause of these limitations. The two most popular conceptualizations of VSTM limitations in recent years have been the fixed-capacity model and the continuous-resource model. The fixed-capacity model proposes a discrete limit on the total number of visual items that can be stored in VSTM. The continuous-resource model proposes a continuous-resource that can be allocated among many visual items in VSTM, with noise in item memory increasing as the number of items to be remembered increases. While VSTM is far from being completely understood in humans, even less is known about VSTM in non-human animals, including the rhesus monkey (Macaca mulatta). Given that rhesus monkeys are the premier medical model for humans, it is important to understand their VSTM if they are to contribute to understanding human memory. The primary goals of this study were to train and test rhesus monkeys and humans in change detection in order to directly compare VSTM between the two species and explore the possibility that direct species comparison might shed light on the fixed-capacity vs. continuous-resource models of VSTM. The comparative results suggest qualitatively similar VSTM for the two species through converging evidence supporting the continuous-resource model and thereby establish rhesus monkeys as a good system for exploring neurophysiological correlates of VSTM.

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Introduction: In team sports the ability to use peripheral vision is essential to track a number of players and the ball. By using eye-tracking devices it was found that players either use fixations and saccades to process information on the pitch or use smooth pursuit eye movements (SPEM) to keep track of single objects (Schütz, Braun, & Gegenfurtner, 2011). However, it is assumed that peripheral vision can be used best when the gaze is stable while it is unknown whether motion changes can be equally well detected when SPEM are used especially because contrast sensitivity is reduced during SPEM (Schütz, Delipetkose, Braun, Kerzel, & Gegenfurtner, 2007). Therefore, peripheral motion change detection will be examined by contrasting a fixation condition with a SPEM condition. Methods: 13 participants (7 male, 6 female) were presented with a visual display consisting of 15 white and 1 red square. Participants were instructed to follow the red square with their eyes and press a button as soon as a white square begins to move. White square movements occurred either when the red square was still (fixation condition) or moving in a circular manner with 6 °/s (pursuit condition). The to-be-detected white square movements varied in eccentricity (4 °, 8 °, 16 °) and speed (1 °/s, 2 °/s, 4 °/s) while movement time of white squares was constant at 500 ms. 180 events should be detected in total. A Vicon-integrated eye-tracking system and a button press (1000 Hz) was used to control for eye-movements and measure detection rates and response times. Response times (ms) and missed detections (%) were measured as dependent variables and analysed with a 2 (manipulation) x 3 (eccentricity) x 3 (speed) ANOVA with repeated measures on all factors. Results: Significant response time effects were found for manipulation, F(1,12) = 224.31, p < .01, ηp2 = .95, eccentricity, F(2,24) = 56.43; p < .01, ηp2 = .83, and the interaction between the two factors, F(2,24) = 64.43; p < .01, ηp2 = .84. Response times increased as a function of eccentricity for SPEM only and were overall higher than in the fixation condition. Results further showed missed events effects for manipulation, F(1,12) = 37.14; p < .01, ηp2 = .76, eccentricity, F(2,24) = 44.90; p < .01, ηp2 = .79, the interaction between the two factors, F(2,24) = 39.52; p < .01, ηp2 = .77 and the three-way interaction manipulation x eccentricity x speed, F(2,24) = 3.01; p = .03, ηp2 = .20. While less than 2% of events were missed on average in the fixation condition as well as at 4° and 8° eccentricity in the SPEM condition, missed events increased for SPEM at 16 ° eccentricity with significantly more missed events in the 4 °/s speed condition (1 °/s: M = 34.69, SD = 20.52; 2 °/s: M = 33.34, SD = 19.40; 4 °/s: M = 39.67, SD = 19.40). Discussion: It could be shown that using SPEM impairs the ability to detect peripheral motion changes at the far periphery and that fixations not only help to detect these motion changes but also to respond faster. Due to high temporal constraints especially in team sports like soccer or basketball, fast reaction are necessary for successful anticipation and decision making. Thus, it is advised to anchor gaze at a specific location if peripheral changes (e.g. movements of other players) that require a motor response have to be detected. In contrast, SPEM should only be used if a single object, like the ball in cricket or baseball, is necessary for a successful motor response. References: Schütz, A. C., Braun, D. I., & Gegenfurtner, K. R. (2011). Eye movements and perception: A selective review. Journal of Vision, 11, 1-30. Schütz, A. C., Delipetkose, E., Braun, D. I., Kerzel, D., & Gegenfurtner, K. R. (2007). Temporal contrast sensitivity during smooth pursuit eye movements. Journal of Vision, 7, 1-15.