Blob Detection Based on Soft Morphological Filter

Tong, Weiqing; Li, Haisheng; Chen, Guoyue

doi:10.1587/transinf.2018edp7385

Cited by 4 publications

(2 citation statements)

References 22 publications

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“…To help the system determine the perspective transformation coordinates automatically, a blob detection algorithm is used. Blobs usually show areas with different colour levels from the environmental domain [20]. This algorithm will find auxiliary circles that have been positioned in the parking lot as needed.…”

Section: A Blob Detectionmentioning

confidence: 99%

Perspective Transformation Automation In Identification Of Parking Lot Status With Blob Detection

Mubin,

Kusuma,

Rivai

2023

JAREE

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Implementation of automation greatly facilitates the work of a system. This research automates the search for perspective transformation coordinates. In previous study, the process was done manually and was considered time-consuming and costly. The search for these coordinates is carried out with the help of red circles at several points in the parking area to be identified. There are two cases of images to be automated, namely the image of the parking area without obstacles and with obstacles. In the unobstructed images, the identification of transformation coordinates is carried out by identifying the coordinates of the auxiliary circle. Whereas in the images with obstructions, the identification of the transformation coordinates also involves the intersection equations of lines. The process of identifying the coordinates is done with the condition of the parking lot without a single vehicle. Once the coordinates are obtained, all coordinates are stored and will be used in the perspective transformation process in status parking slot identification stage. The identification stage is same with previous study. The proposed system 100% able to identify the transformation coordinates and carry out the perspective transformation process as expected. Of the 900 samples in each case, we acquire 100% recall, and most of the parking slot identification status being above 85% precision and accuracy. Compared to previous studies, the proposed system is more effective, with recall, precision, and accuracy values at 100%. The effectiveness of the proposed system is even more evident with average data automation time is 31.689 seconds.

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Section: A Blob Detectionmentioning

confidence: 99%

Perspective Transformation Automation In Identification Of Parking Lot Status With Blob Detection

Mubin,

Kusuma,

Rivai

2023

JAREE

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“…extract the characteristic signal. Cyclostationary signal analysis [21,22] relies on the cyclostationary characteristics of the bearing fault impulse signal to design a filter to eliminate random noise. A Wiener filter [23,24] is used to eliminate the stationary random noise in the fault impulse signal.…”

mentioning

confidence: 99%

A New Fault Feature Extraction Method for Rotating Machinery Based on Multiple Sensors

Miao

Zhao

Wang

et al. 2020

Sensors

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During the operation of rotating machinery, the vibration signals measured by sensors are the aliasing signals of various vibration sources, and they contain strong noises. Conventional signal processing methods have difficulty separating the aliasing signals, which causes great difficulties in the condition monitoring and fault diagnosis of the equipment. The principle and method of blind source separation are introduced, and it is pointed out that the blind source separation algorithm is invalid in strong pulse noise environments. In these environments, the vibration signals are first de-noised with the median filter (MF) method and the de-noised signals are separated with an improved joint approximate diagonalization of eigenmatrices (JADE) algorithm. The simulation results found here verify the effectiveness of the proposed method. Finally, the vibration signal of the hybrid rotor is effectively separated by the proposed method. A new separation approach is thus provided for vibration signals in strong pulse noise environments. extract the characteristic signal. Cyclostationary signal analysis [21,22] relies on the cyclostationary characteristics of the bearing fault impulse signal to design a filter to eliminate random noise. A Wiener filter [23,24] is used to eliminate the stationary random noise in the fault impulse signal. A wavelet transform [25,26] first decomposes the vibration signal into different frequency bands, then defines the threshold to eliminate the noise components and reconstructs the characteristic signal. A Kalman filter [27,28] first establishes the vibration state model and signal observation model of rolling bearing, then iteratively eliminates the noise in the signal. Stochastic resonance [29,30] uses noise to enhance the fault characteristic component of the bearing vibration signal. All of the above mentioned methods are universal, can effectively eliminate background noise and interference components, and extract fault signals in specific environment, but they are not suitable for complex interference situations, and especially when the interference components in vibration signals are similar to fault signals, it is difficult to distinguish them by the above method, let alone eliminating interference components and extract fault signals. Blind source separation (BSS) technology can realize the separation of multiple aliased signals [1,31]. Meanwhile, blind source separation is not affected by the time and frequency overlap of source signals, and the separated output signal will not lose the weak feature information in the source signal.So far, many effective and distinctive blind source separation algorithms have been constructed. Typical algorithms include fast fixed-point [32] algorithms, natural gradient [28] algorithms, second-order blind identification (SOBI) [33] algorithms, equivalation adaptive separation via independence (EASI) [34] algorithms, and joint approximate diagonalization of eigenmatrices (JADE) [35] algorithms. When separating the noiseless mixed signals, these ...

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Welcome to Heshan: An Installation to Create Immersive and Entertaining Experiences with Local Art Through Interactive Media Technologies

Zhou

Xie

2022

Lecture Notes in Computer Science

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Blob Detection Based on Soft Morphological Filter

Cited by 4 publications

References 22 publications

Perspective Transformation Automation In Identification Of Parking Lot Status With Blob Detection

Perspective Transformation Automation In Identification Of Parking Lot Status With Blob Detection

A New Fault Feature Extraction Method for Rotating Machinery Based on Multiple Sensors

Welcome to Heshan: An Installation to Create Immersive and Entertaining Experiences with Local Art Through Interactive Media Technologies

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