2014 IEEE Symposium on Computers and Communications (ISCC) 2014
DOI: 10.1109/iscc.2014.6912539
|View full text |Cite
|
Sign up to set email alerts
|

Image segmentation based on complexity mining and mean-shift algorithm

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
4
0

Year Published

2016
2016
2023
2023

Publication Types

Select...
4
1

Relationship

1
4

Authors

Journals

citations
Cited by 6 publications
(4 citation statements)
references
References 14 publications
0
4
0
Order By: Relevance
“…As the final approach, we propose an optimisation of this procedure in order to avoid the identified and mentioned issues (1) and (4). First, we introduce a recognition stage prior to semantic segmentation (known to be computationally expensive [26]) to determine whether or not the tile contains a wind turbine. In other words, we add a binary classification sub operation to recognise whether a tile contains wind turbines and only segment the ones that contain the geospatial element.…”
Section: B End-to-end Methodology For Combining Classification and Semantic Segmentation For A More And Efficient Large-scale Extraction mentioning
confidence: 99%
“…As the final approach, we propose an optimisation of this procedure in order to avoid the identified and mentioned issues (1) and (4). First, we introduce a recognition stage prior to semantic segmentation (known to be computationally expensive [26]) to determine whether or not the tile contains a wind turbine. In other words, we add a binary classification sub operation to recognise whether a tile contains wind turbines and only segment the ones that contain the geospatial element.…”
Section: B End-to-end Methodology For Combining Classification and Semantic Segmentation For A More And Efficient Large-scale Extraction mentioning
confidence: 99%
“…Many techniques including deep and machine learning [44] have been developed for the segmentation [45], reconstruction [46], and registration [47] of images, including medical ones. VanityX supports various volume rendering techniques such as texture-based rendering, ray-cast rendering, and rendering using transfer functions.…”
Section: Rendering Medical Imagingmentioning
confidence: 99%
“…These applications of image processing and analyzing widely used in medical applications [19][20][21], remote sensing [22] and aerial images, which requiring high memory resources [14,16]. Many of presented methods of image segmentation refers to allocating of segments and their characteristics, such as edges, size and borders of segments in homogenous regions, regardless an important calculation factor of memory resources [23][24][25].…”
Section: Introductionmentioning
confidence: 99%