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Statistical Hypothesis Detector for Abnormal Event Detection in Crowded Scenes
IEEE Transactions on Cybernetics, 2017 Abnormal event detection is now a challenging task, especially for crowded scenes. Many existing methods learn a normal event model in the training phase, and events which cannot be well represented are treated as abnormalities.
Yachuang Feng, Xiaoqiang Lu
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The 2nd Canadian Conference on Computer and Robot Vision (CRV'05), 2005
Analyzing human gait has become popular in computer vision. So far, however, contributions to this topic almost exclusively considered the problem of person identification. In this paper, we view gait analysis from a different angle and shall examine its use as a means to deduce the physical condition of people.
Christian Bauckhage +2 more
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Analyzing human gait has become popular in computer vision. So far, however, contributions to this topic almost exclusively considered the problem of person identification. In this paper, we view gait analysis from a different angle and shall examine its use as a means to deduce the physical condition of people.
Christian Bauckhage +2 more
openaire +2 more sources
Detection of Abnormalities in ECG
2012The neural networks have many applications in technical fields. They have been applied successfully to speech recognition, image analysis and adaptive control, in order to construct software agents or autonomous robots. The electrocardiography (ECG) signal is periodical and therefore is quite predictable. In this paper we describe use of neural network
Branko Babusiak, Michal Gála
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Detection of Abnormal Crowd Distribution
2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing, 2010With the application of GPS and popularity of intelligent cell phones, the physical location of a person can be easily obtained. Thus, we attempt to analyze the spatial distribution of crowd to facilitate the swift response to the emergency of public security. The states of crowd can be represented as the spatial distribution of moving points.
Zhenmei Liao, Su Yang, Jianning Liang
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On detecting abnormalities in digital mammography
2010 IEEE 39th Applied Imagery Pattern Recognition Workshop (AIPR), 2010Breast cancer is the most common cancer in many countries all over the world. Early detection of cancer, in either diagnosis or screening programs, decreases the mortality rates. Computer Aided Detection (CAD) is software that aids radiologists in detecting abnormalities in medical images.
Waleed A. Yousef +4 more
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Contactless abnormal gait detection
2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011We present a new method to detect abnormal gait based on the symmetry verification of the two-leg movement. Unlike other methods requiring special motion captors, the proposed method uses image processing techniques to correctly track leg movement. Our method first divides each leg into upper and lower parts using anatomical knowledge.
Anh-Tuan Nghiem +3 more
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Antenatal detection of renal abnormalities
Irish Journal of Medical Science, 1992The aim of this retrospective study is to assess the value of routine ultrasonography in the detection of renal abnormalities. Twenty-nine pregnancies (one set of twins) with suspected renal abnormalities (i.e. renal cystic spaces, oligohydramnios or hyperechoic kidneys) were delivered over a two year period (1.8.1987-31-7-1989) in a unit where 90% of ...
R G, Ashe, N, Campbell, J C, Dornan
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The Limits of Detecting Abnormalities with Scanning
Australasian Radiology, 1967SUMMARYA theoretical comparison is made between two different criteria of statistical significance for detecting abnormalities in a scan. Criterion I utilizes as much of the available information content as possible, while Criterion II is applicable to visual reading of a photoscan or colour‐scan. The limits of detecting abnormalities with Criterion II
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Real-time detection and tracking of fish abnormal behavior based on improved YOLOV5 and SiamRPN++
Computers and Electronics in Agriculture, 2022Daoliang Li, Shili Zhao
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