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Статті в журналах з теми "Changes detection"

Автор: Grafiati

Опубліковано: 7 грудня 2024

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1

Kouda, Takaharu. "Detection of Blink and Facial Expression Changes using DCT Signs." Journal of the Institute of Industrial Applications Engineers 2, no. 2 (April 25, 2014): 70–73. http://dx.doi.org/10.12792/jiiae.2.70.

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2

Yang, Le, Yiming Chen, Shiji Song, Fan Li, and Gao Huang. "Deep Siamese Networks Based Change Detection with Remote Sensing Images." Remote Sensing 13, no. 17 (August 26, 2021): 3394. http://dx.doi.org/10.3390/rs13173394.

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Анотація:

Although considerable success has been achieved in change detection on optical remote sensing images, accurate detection of specific changes is still challenging. Due to the diversity and complexity of the ground surface changes and the increasing demand for detecting changes that require high-level semantics, we have to resort to deep learning techniques to extract the intrinsic representations of changed areas. However, one key problem for developing deep learning metho for detecting specific change areas is the limitation of annotated data. In this paper, we collect a change detection dataset with 862 labeled image pairs, where the urban construction-related changes are labeled. Further, we propose a supervised change detection method based on a deep siamese semantic segmentation network to handle the proposed data effectively. The novelty of the method is that the proposed siamese network treats the change detection problem as a binary semantic segmentation task and learns to extract features from the image pairs directly. The siamese architecture as well as the elaborately designed semantic segmentation networks significantly improve the performance on change detection tasks. Experimental results demonstrate the promising performance of the proposed network compared to existing approaches.

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3

Guépié, Blaise Kévin, Lionel Fillatre, and Igor Nikiforov. "Sequential Detection of Transient Changes." Sequential Analysis 31, no. 4 (October 2012): 528–47. http://dx.doi.org/10.1080/07474946.2012.719443.

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4

Kashiwagi, Nobuhisa. "Bayesian detection of structural changes." Annals of the Institute of Statistical Mathematics 43, no. 1 (March 1991): 77–93. http://dx.doi.org/10.1007/bf00116470.

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5

Gałkowski, Tomasz, Adam Krzyżak, Zofia Patora-Wysocka, Zbigniew Filutowicz, and Lipo Wang. "A New Approach to Detection of Changes in Multidimensional Patterns." Journal of Artificial Intelligence and Soft Computing Research 11, no. 3 (May 29, 2021): 217–27. http://dx.doi.org/10.2478/jaiscr-2021-0013.

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Abstract In the paper we develop an algorithm based on the Parzen kernel estimate for detection of sudden changes in 3-dimensional shapes which happen along the edge curves. Such problems commonly arise in various areas of computer vision, e.g., in edge detection, bioinformatics and processing of satellite imagery. In many engineering problems abrupt change detection may help in fault protection e.g. the jump detection in functions describing the static and dynamic properties of the objects in mechanical systems. We developed an algorithm for detecting abrupt changes which is nonparametric in nature and utilizes Parzen regression estimates of multivariate functions and their derivatives. In tests we apply this method, particularly but not exclusively, to the functions of two variables.

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6

KUROHARA, Akira, Kensuke TERAI, Hiromi TAKEUCHI, and Akio UMEZAWA. "Respiratory changes during detection of deception." Japanese Journal of Physiological Psychology and Psychophysiology 19, no. 2 (2001): 75–86. http://dx.doi.org/10.5674/jjppp1983.19.75.

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7

Lee, T. Y., and D. H. Brainard. "Detection of changes in luminance distributions." Journal of Vision 11, no. 13 (November 15, 2011): 14. http://dx.doi.org/10.1167/11.13.14.

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8

Croy, I., F. Krone, S. Walker, and T. Hummel. "Olfactory Processing: Detection of Rapid Changes." Chemical Senses 40, no. 5 (April 24, 2015): 351–55. http://dx.doi.org/10.1093/chemse/bjv020.

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9

Kramer, David. "DHS changes tack on radiation detection." Physics Today 64, no. 9 (September 2011): 32–33. http://dx.doi.org/10.1063/pt.3.1253.

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10

Nikiforov, Igor V. "Sequential detection/isolation of abrupt changes." Sequential Analysis 35, no. 3 (July 2, 2016): 268–301. http://dx.doi.org/10.1080/07474946.2016.1206354.

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11

Eismann, Michael T., Joseph Meola, Alan D. Stocker, Scott G. Beaven, and Alan P. Schaum. "Airborne hyperspectral detection of small changes." Applied Optics 47, no. 28 (July 15, 2008): F27. http://dx.doi.org/10.1364/ao.47.000f27.

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12

Zheng, Bin, Xianta Jiang, and M. Stella Atkins. "Detection of Changes in Surgical Difficulty." Surgical Innovation 22, no. 6 (March 9, 2015): 629–35. http://dx.doi.org/10.1177/1553350615573582.

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13

Horváth, Lajos. "Detection of Changes in Linear Sequences." Annals of the Institute of Statistical Mathematics 49, no. 2 (June 1997): 271–83. http://dx.doi.org/10.1023/a:1003110912735.

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14

SAHEL, JA. "Perspectives for detection of photoreceptor changes." Acta Ophthalmologica 86 (September 4, 2008): 0. http://dx.doi.org/10.1111/j.1755-3768.2008.4113.x.

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15

Rodway, Paul, Karen Gillies, and Astrid Schepman. "Vivid Imagers Are Better at Detecting Salient Changes." Journal of Individual Differences 27, no. 4 (January 2006): 218–28. http://dx.doi.org/10.1027/1614-0001.27.4.218.

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Анотація:

This study examined whether individual differences in the vividness of visual imagery influenced performance on a novel long-term change detection task. Participants were presented with a sequence of pictures, with each picture and its title displayed for 17 s, and then presented with changed or unchanged versions of those pictures and asked to detect whether the picture had been changed. Cuing the retrieval of the picture's image, by presenting the picture's title before the arrival of the changed picture, facilitated change detection accuracy. This suggests that the retrieval of the picture's representation immunizes it against overwriting by the arrival of the changed picture. The high and low vividness participants did not differ in overall levels of change detection accuracy. However, in replication of Gur and Hilgard (1975) , high vividness participants were significantly more accurate at detecting salient changes to pictures compared to low vividness participants. The results suggest that vivid images are not characterised by a high level of detail and that vivid imagery enhances memory for the salient aspects of a scene but not all of the details of a scene. Possible causes of this difference, and how they may lead to an understanding of individual differences in change detection, are considered.

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16

Perry, Kimberly, Matthew Pacailler, and Mark W. Scerbo. "The Impact of Natural Visual Interruptions and Cueing on Detecting Changes in Dynamic Scenes." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 66, no. 1 (September 2022): 1240–44. http://dx.doi.org/10.1177/1071181322661398.

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The goal of the present study was to examine how naturalistic interruptions (head turns) and cueing affect change detection within dynamic scenes. Based on the memory for goals (Altmann & Trafton, 2002) and visual memory theories (Hollingsworth & Henderson, 2001), participants monitoring videos were expected to detect fewer target changes when interrupted than without interruptions. Additionally, reliable cues that provided information about the target were expected to improve target detection compared to neutral cues (Logan, 1996; Posner, Snyder, & Davidson, 1980). Undergraduate students were assigned to one of two cueing conditions (reliable or neutral) and watched twenty videos. Ten of the videos were interrupted by having participants turn their heads to attend to a secondary display while an object changed in the video on the primary monitor. Eight videos (half with and without interruptions) had a single object that underwent a perceptual feature change in color, brightness, appearance, or disappearance. Overall, participants were very poor at detecting target changes. However, participants detected more object changes during uninterrupted versus interrupted trials. Providing a cue related to the object change did not improve detection performance. Overall, these results support other dynamic change detection findings that show the difficulty of detecting targets within dynamic environments even when provided reliable information.

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17

Shamul, Naomi, and Leo Joskowicz. "Change detection in sparse repeat CT scans with non-rigid deformations." Journal of X-Ray Science and Technology 29, no. 6 (October 29, 2021): 987–1007. http://dx.doi.org/10.3233/xst-211040.

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BACKGROUND: Detecting and interpreting changes in the images of follow-up CT scans by the clinicians is often time-consuming and error-prone due to changes in patient position and non-rigid anatomy deformations. Thus, reconstructed repeat scan images are required, precluding reduced dose sparse-view repeat scanning. OBJECTIVE: A method to automatically detect changes in a region of interest of sparse-view repeat CT scans in the presence of non-rigid deformations of the patient’s anatomy without reconstructing the original images. METHODS: The proposed method uses the sparse sinogram data of two CT scans to distinguish between genuine changes in the repeat scan and differences due to non-rigid anatomic deformations. First, size and contrast level of the changed regions are estimated from the difference between the scans’ sinogram data. The estimated types of changes in the repeat scan help optimize the method’s parameter values. Two scans are then aligned using Radon space non-rigid registration. Rays which crossed changes in the ROI are detected and back-projected onto image space in a two-phase procedure. These rays form a likelihood map from which the binary changed region map is computed. RESULTS: Experimental studies on four pairs of clinical lung and liver CT scans with simulated changed regions yield a mean changed region recall rate > 86%and a mean precision rate > 83%when detecting large changes with low contrast, and high contrast changes, even when small. The new method outperforms image space methods using prior image constrained compressed sensing (PICCS) reconstruction, particularly for small, low contrast changes (recall = 15.8%, precision = 94.7%). CONCLUSION: Our method for automatic change detection in sparse-view repeat CT scans with non-rigid deformations may assist radiologists by highlighting the changed regions and may obviate the need for a high-quality repeat scan image when no changes are detected.

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18

Mulahusić, Admir, and Nedim Tuno. "Methods for Change Detection in Remote Sensing." Geodetski glasnik, no. 40 (March 31, 2011): 3–13. http://dx.doi.org/10.58817/2233-1786.2011.45.40.3.

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Анотація:

In this paper, the different ways to identify changes in remote sensing are given. Various authors have presented different methods of detecting changes on the Earth's surface. Detection of changes, among other things, are very important for tracking changes, as well as assessment and evaluation of changes and interrelations of natural and artificial objects. All this leads to better understanding of potential causes of change.

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19

Yadav, R., A. Nascetti, and Y. Ban. "BUILDING CHANGE DETECTION USING MULTI-TEMPORAL AIRBORNE LIDAR DATA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 31, 2022): 1377–83. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-1377-2022.

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Abstract. Building change detection is essential for monitoring urbanization, disaster assessment, urban planning and frequently updating the maps. 3D structure information from airborne light detection and ranging (LiDAR) is very effective for detecting urban changes. But the 3D point cloud from airborne LiDAR(ALS) holds an enormous amount of unordered and irregularly sparse information. Handling such data is tricky and consumes large memory for processing. Most of this information is not necessary when we are looking for a particular type of urban change. In this study, we propose an automatic method that reduces the 3D point clouds into a much smaller representation without losing necessary information required for detecting Building changes. The method utilizes the Deep Learning(DL) model U-Net for segmenting the buildings from the background. Produced segmentation maps are then processed further for detecting changes and the results are refined using morphological methods. For the change detection task, we used multi-temporal airborne LiDAR data. The data is acquired over Stockholm in the years 2017 and 2019. The changes in buildings are classified into four types: ‘newly built’, ‘demolished’, ‘taller’ and ’shorter’. The detected changes are visualized in one map for better interpretation.

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20

Marx, Cyril, Elem Güzel Kalayci, and Peter Moertl. "Temporal Dashboard Gaze Variance (TDGV) Changes for Measuring Cognitive Distraction While Driving." Sensors 22, no. 23 (December 6, 2022): 9556. http://dx.doi.org/10.3390/s22239556.

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A difficult challenge for today’s driver monitoring systems is the detection of cognitive distraction. The present research presents the development of a theory-driven approach for cognitive distraction detection during manual driving based on temporal control theories. It is based solely on changes in the temporal variance of driving-relevant gaze behavior, such as gazes onto the dashboard (TDGV). Validation of the detection method happened in a field and in a simulator study by letting participants drive, alternating with and without a secondary task inducing external cognitive distraction (auditory continuous performance task). The general accuracy of the distraction detection method varies between 68% and 81% based on the quality of an individual prerecorded baseline measurement. As a theory-driven system, it represents not only a step towards a sophisticated cognitive distraction detection method, but also explains that changes in temporal dashboard gaze variance (TDGV) are a useful behavioral indicator for detecting cognitive distraction.

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21

Politz, Florian, Monika Sester, and Claus Brenner. "Building Change Detection of Airborne Laser Scanning and Dense Image Matching Point Clouds using Height and Class Information." AGILE: GIScience Series 2 (June 4, 2021): 1–14. http://dx.doi.org/10.5194/agile-giss-2-10-2021.

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Abstract. Detecting changes is an important task to update databases and find irregularities in spatial data. Every couple of years, national mapping agencies (NMAs) acquire nation-wide point cloud data from Airborne Laser Scanning (ALS) as well as from Dense Image Matching (DIM) using aerial images. Besides deriving several other products such as Digital Elevation Models (DEMs) from them, those point clouds also offer the chance to detect changes between two points in time on a large scale. Buildings are an important object class in the context of change detection to update cadastre data. As detecting changes manually is very time consuming, the aim of this study is to provide reliable change detections for different building sizes in order to support NMAs in their task to update their databases. As datasets of different times may have varying point densities due to technological advancements or different sensors, we propose a raster-based approach, which is independent of the point density altogether. Within a raster cell, our approach considers the height distribution of all points for two points in time by exploiting the Jensen-Shannon distance to measure their similarity. Our proposed method outperforms simple threshold methods on detecting building changes with respect to the same or different point cloud types. In combination with our proposed class change detection approach, we achieve a change detection performance measured by the mean F1-Score of about 71% between two ALS and about 60% between ALS and DIM point clouds acquired at different times.

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22

Rishbeth, Henry. "Chances and changes: The detection of long-term trends in the ionosphere." Eos, Transactions American Geophysical Union 80, no. 49 (1999): 590. http://dx.doi.org/10.1029/99eo00401.

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23

Oron-Gilad, Tal, Joachim Meyer, and Daniel Gopher. "Detecting Changes in Dynamic Functions with Tables and Graphs." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 21 (July 2000): 3–435. http://dx.doi.org/10.1177/154193120004402115.

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The relative efficiency of graphic and tabular displays for detecting changes in the amplitude of periodic sine functions, simulating a dynamic process, was assessed. Line graphs had an advantage over tables for the detection of changes and for the correct identification of the changed function. However, the advantage depended on the type of change that could occur. A large difference between the displays was evident when both increases and decreases in amplitude were possible, and differences were much smaller when amplitudes could only increase. These results indicate that participants adapted their detection methods to the types of possible changes. The findings demonstrate the utility of graphic displays for process control and substantiate the claim that graphic displays have an advantage when the displayed information has inherent structure and when the task requires the use of this structure. In addition, task performance was shown to be the subject of adaptive changes, which depend on the context in which the task is performed.

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24

CHETOUANI, YAHYA. "USE OF CUMULATIVE SUM (CUSUM) TEST FOR DETECTING ABRUPT CHANGES IN THE PROCESS DYNAMICS." International Journal of Reliability, Quality and Safety Engineering 14, no. 01 (February 2007): 65–80. http://dx.doi.org/10.1142/s0218539307002519.

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In order to operate a successful plant or process, continuous improvement must be made in the areas of safety, quality and reliability. Central to this continuous improvement is the early or proactive detection of process faults. Even though fault detection algorithms are designed and implemented for quickly detecting incidents, most these algorithms do not have an optimal property in terms of detection delay with respect to false alarm rate. Based on this optimization property, a real-time system for detecting changes in dynamic systems is designed in this paper. This work is motivated by combining two fault detection (FD) methods; a simplified procedure of the incident detection problem is formulated by using the combination of the Artificial Neural Networks (ANN) and the cumulative sum (CUSUM) or Page-Hinkley test. It is intended to reveal any drift from the normal behavior of the process. The process behavior under its normal operating conditions is established by a reliable model. In order to obtain this reliable model for the process dynamics, the black-box identification by means of a NARX (Nonlinear Auto-Regressive with eXogenous input) model is chosen in this study. The purpose is to develop and test the fault detection method on a real incident data, to detect the change presence, and pinpoint the moment it occurred. The experimental results demonstrate the robustness of the FD method.

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25

Johnson, P., J. Moriarty, and G. Peskir. "Detecting changes in real-time data: a user’s guide to optimal detection." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 375, no. 2100 (July 10, 2017): 20160298. http://dx.doi.org/10.1098/rsta.2016.0298.

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The real-time detection of changes in a noisily observed signal is an important problem in applied science and engineering. The study of parametric optimal detection theory began in the 1930s, motivated by applications in production and defence. Today this theory, which aims to minimize a given measure of detection delay under accuracy constraints, finds applications in domains including radar, sonar, seismic activity, global positioning, psychological testing, quality control, communications and power systems engineering. This paper reviews developments in optimal detection theory and sequential analysis, including sequential hypothesis testing and change-point detection, in both Bayesian and classical (non-Bayesian) settings. For clarity of exposition, we work in discrete time and provide a brief discussion of the continuous time setting, including recent developments using stochastic calculus. Different measures of detection delay are presented, together with the corresponding optimal solutions. We emphasize the important role of the signal-to-noise ratio and discuss both the underlying assumptions and some typical applications for each formulation. This article is part of the themed issue ‘Energy management: flexibility, risk and optimization’.

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26

Jung, Sejung, Won Hee Lee, and Youkyung Han. "Change Detection of Building Objects in High-Resolution Single-Sensor and Multi-Sensor Imagery Considering the Sun and Sensor’s Elevation and Azimuth Angles." Remote Sensing 13, no. 18 (September 13, 2021): 3660. http://dx.doi.org/10.3390/rs13183660.

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Building change detection is a critical field for monitoring artificial structures using high-resolution multitemporal images. However, relief displacement depending on the azimuth and elevation angles of the sensor causes numerous false alarms and misdetections of building changes. Therefore, this study proposes an effective object-based building change detection method that considers azimuth and elevation angles of sensors in high-resolution images. To this end, segmentation images were generated using a multiresolution technique from high-resolution images after which object-based building detection was performed. For detecting building candidates, we calculated feature information that could describe building objects, such as rectangular fit, gray-level co-occurrence matrix (GLCM) homogeneity, and area. Final building detection was then performed considering the location relationship between building objects and their shadows using the Sun’s azimuth angle. Subsequently, building change detection of final building objects was performed based on three methods considering the relationship of the building object properties between the images. First, only overlaying objects between images were considered to detect changes. Second, the size difference between objects according to the sensor’s elevation angle was considered to detect the building changes. Third, the direction between objects according to the sensor’s azimuth angle was analyzed to identify the building changes. To confirm the effectiveness of the proposed object-based building change detection performance, two building density areas were selected as study sites. Site 1 was constructed using a single sensor of KOMPSAT-3 bitemporal images, whereas Site 2 consisted of multi-sensor images of KOMPSAT-3 and unmanned aerial vehicle (UAV). The results from both sites revealed that considering additional shadow information showed more accurate building detection than using feature information only. Furthermore, the results of the three object-based change detections were compared and analyzed according to the characteristics of the study area and the sensors. Accuracy of the proposed object-based change detection results was achieved over the existing building detection methods.

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27

Javed, Aisha, Sejung Jung, Won Hee Lee, and Youkyung Han. "Object-Based Building Change Detection by Fusing Pixel-Level Change Detection Results Generated from Morphological Building Index." Remote Sensing 12, no. 18 (September 11, 2020): 2952. http://dx.doi.org/10.3390/rs12182952.

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Change detection (CD) is an important tool in remote sensing. CD can be categorized into pixel-based change detection (PBCD) and object-based change detection (OBCD). PBCD is traditionally used because of its simple and straightforward algorithms. However, with increasing interest in very-high-resolution (VHR) imagery and determining changes in small and complex objects such as buildings or roads, traditional methods showed limitations, for example, the large number of false alarms or noise in the results. Thus, researchers have focused on extending PBCD to OBCD. In this study, we proposed a method for detecting the newly built-up areas by extending PBCD results into an OBCD result through the Dempster–Shafer (D–S) theory. To this end, the morphological building index (MBI) was used to extract built-up areas in multitemporal VHR imagery. Then, three PBCD algorithms, change vector analysis, principal component analysis, and iteratively reweighted multivariate alteration detection, were applied to the MBI images. For the final CD result, the three binary change images were fused with the segmented image using the D–S theory. The results obtained from the proposed method were compared with those of PBCD, OBCD, and OBCD results generated by fusing the three binary change images using the major voting technique. Based on the accuracy assessment, the proposed method produced the highest F1-score and kappa values compared with other CD results. The proposed method can be used for detecting new buildings in built-up areas as well as changes related to demolished buildings with a low rate of false alarms and missed detections compared with other existing CD methods.

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28

Chen, Xiao Yu, Kun Ma, Jia Quan Wu, and Xiang Guo. "Damage Detection through Changes in Frequency Base on Reinforced Concrete Beam." Advanced Materials Research 255-260 (May 2011): 188–92. http://dx.doi.org/10.4028/www.scientific.net/amr.255-260.188.

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Анотація:

The detection of the structur[1]al damage by the method of changes in frequency is limited in detecting single location of the structural damage. This paper try to solve the problems of how to detect the multi-location of the structural damages and the corresponding severity. Therefore, a simple supported large-size reinforced concrete beam in different damage conditions is simulated by the finite element software-ANSYS. Cruves of frequency changes ratio can be maped by the date of the simulation, the locations of damages and corresponding severity can be detection by judging the superposition of the intersections of many curves of the frequent changes ratio. The simulation results demonstrate that the method proposed in this paper cannot only detection the multi-locations of the structural damages accurately, but also analyze the severity of the structural damages qualitatively. Corresponding author: Makun, School of Science, Kunming University of Science and Technology, makun_box@sina.com

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29

Glumov, N. I., and A. V. Kuznetsov. "Detection of local artificial changes in images." Optoelectronics, Instrumentation and Data Processing 47, no. 3 (June 2011): 207–14. http://dx.doi.org/10.3103/s8756699011030010.

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30

Peach, Nigel, M. Basseville, and I. V. Nikiforov. "Detection of Abrupt Changes: Theory and Applications." Journal of the Royal Statistical Society. Series A (Statistics in Society) 158, no. 1 (1995): 185. http://dx.doi.org/10.2307/2983416.

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31

MORI, Toshio. "Real-Time Detection of Anomalous Geoelectric Changes." Zisin (Journal of the Seismological Society of Japan. 2nd ser.) 44, no. 1 (1991): 29–37. http://dx.doi.org/10.4294/zisin1948.44.1_29.

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32

Snell, Karen B. "Age-related changes in temporal gap detection." Journal of the Acoustical Society of America 101, no. 4 (April 1997): 2214–20. http://dx.doi.org/10.1121/1.418205.

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33

Jansen, A. A. I., J. J. de Gier, and J. L. Slangen. "Diazepam-Induced Changes in Signal Detection Performance." Neuropsychobiology 16, no. 4 (1986): 193–97. http://dx.doi.org/10.1159/000118325.

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34

Romero, Daniel, Juan Pablo Martínez, Pablo Laguna, and Esther Pueyo. "Ischemia detection from morphological QRS angle changes." Physiological Measurement 37, no. 7 (May 31, 2016): 1004–23. http://dx.doi.org/10.1088/0967-3334/37/7/1004.

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