Connected-component labeling based on hypercubes for memory constrained scenarios

Silva, Eduardo Sant’Ana da; Pedrini, Hélio

doi:10.1016/j.eswa.2016.06.002

Cited by 5 publications

(3 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both features are useful in contour extraction by machine vision methods. Here, the image of the scratched gangues is processed by the image segmentation based on run-length connectivity scanning [18][19][20]. The resulting image is displayed in Figure 7, which shows that the scratched gangues were positioned correctly.…”

Section: Figure 6 Image Of Raw Coal After Scratchingmentioning

confidence: 98%

Visual Positioning and Recognition of Gangues Based on Scratch Feature Detection

Zou¹,

Ren²

2019

View full text Add to dashboard Cite

The coals and gangues in a raw coal image have similar visual features, due to the presence of coal ash on the surface. Thus, it is difficult to locate and recognize coals and gangues on the transmission line through visual recognition. To solve the problem, this paper proposes a visual positioning and recognition method for gangues based on scratch feature detection. Firstly, an image acquisition system was designed to capture the clear and suitable images. Next, scratched features were prepared on gangue surface with mechanical tools, laying the basis for visual positioning and recognition. Afterwards, the texture feature recognition method based on grey-level co-occurrence matrix (GLCM) was adopted to identify coal and scratched gangue blocks. The test results show that the GLCM correlation feature parameter is effective for scratch recognition. The parameter and the said method were proved effective through experiments.

show abstract

Section: Figure 6 Image Of Raw Coal After Scratchingmentioning

confidence: 98%

Visual Positioning and Recognition of Gangues Based on Scratch Feature Detection

Zou¹,

Ren²

2019

View full text Add to dashboard Cite

show abstract

“…The morphologic information (such as area, length, and width) of cracks can be quantificated on the CT image after the connected domain-quantifying filtering [4][5]. The number of elements, which are incorporated into the slit region, can be calculated as the area c A of this region.…”

Section: Comprehensive Index Failure Assessment Of Sandstone (1) Morpmentioning

confidence: 99%

Failure warning of hydrous sandstone based on electroencephalogram technique

Tao

Zheng

2018

E3S Web Conf.

View full text Add to dashboard Cite

Sandstone is a type of rock mass that widely exists in nature. Moisture is an important factor that leads to sandstone structural failure. The major failure assessment methods of hydrous sandstone at present cannot satisfy real-time and portability requirements, especially lacks of warning function. In this study, acoustic emission (AE) and computed tomography (CT) techniques are combined for real-time failure assessment of hydrous sandstone. Eight visual colors for warning are screened according to different failure states, and an electroencephalogram (EEG) experiment is conducted to demonstrate their diverse excitations of the human brain’s concentration.

show abstract

“…Then, a spectral unmixing algorithm [20] is used to obtain endmember abundance images, adaptive and automatic cell binaryzation based on morphological operation [21], Otsu's method [22] is performed, and a connected component labeling algorithm [23] is implemented for RBC counting. In addition to the use of spatial and spectral information, the method with a magnification-based parameter setting can achieve full-automatic RBC identification with high precision.…”

Section: Introductionmentioning

confidence: 99%

Adaptive and automatic red blood cell counting method based on microscopic hyperspectral imaging technology

Liu

Zhou

Song

et al. 2017

J. Opt.

View full text Add to dashboard Cite

Red blood cell counting, as a routine examination, plays an important role in medical diagnoses. Although automated hematology analyzers are widely used, manual microscopic examination by a hematologist or pathologist is still unavoidable, which is time-consuming and error-prone. This paper proposes a full-automatic red blood cell counting method which is based on microscopic hyperspectral imaging of blood smears and combines spatial and spectral information to achieve high precision. The acquired hyperspectral image data of the blood smear in the visible and nearinfrared spectral range are firstly preprocessed, and then a quadratic blind linear unmixing algorithm is used to get endmember abundance images. Based on mathematical morphological operation and an adaptive Otsu's method, a binaryzation process is performed on the abundance images. Finally, the connected component labeling algorithm with magnification-based parameter setting is applied to automatically select the binary images of red blood cell cytoplasm. Experimental results show that the proposed method can perform well and has potential for clinical applications.

show abstract

Connected-component labeling based on hypercubes for memory constrained scenarios

Cited by 5 publications

References 30 publications

Visual Positioning and Recognition of Gangues Based on Scratch Feature Detection

Visual Positioning and Recognition of Gangues Based on Scratch Feature Detection

Failure warning of hydrous sandstone based on electroencephalogram technique

Adaptive and automatic red blood cell counting method based on microscopic hyperspectral imaging technology

Contact Info

Product

Resources

About