Results 261 to 270 of about 48,049 (318)

The role of ultrasound texture analysis in the discrimination of pleomorphic adenoma and Warthin tumor in subjects with well-defined tumor borders. [PDF]

Gland Surg
Hu Q, Li S, Mo C, Ouyang S, Wen X, Li Z.
europepmc   +1 more source

Accurate Asthma-COPD Overlap Classification via Deep Transfer Learning in Medical Image Segmentation. [PDF]

Int J Chron Obstruct Pulmon Dis
Ye W, Mo D, Yang Y.
europepmc   +1 more source

SkinFormer: a hybrid vision transformer and ConvNeXtV2 approach for skin cancer detection and segmentation. [PDF]

Sci Rep
Fu Y, Guo C.
europepmc   +1 more source

Deep Learning-Based Semantic Segmentation and Classification of Otoscopic Images for Otitis Media Diagnosis and Health Promotion. [PDF]

Diagnostics (Basel)
Yang CY, Lee CJ, Lai WS, Chen KY, Kuo CF, Liu CH, Liu SC.   +6 more
europepmc   +1 more source

Hypothalamic Atrophy and Textural Changes in Polyglutamine Ataxias. [PDF]

Cerebellum
Rodrigues L, Rezende TJR, Martinez ARM, Massuyama B, Pedroso JL, Barsottini OGP, Iglesias JE, Appenzeller S, Rittner L, França MC.   +9 more
europepmc   +1 more source

Application of Information Theory to Computer Vision and Image Processing II. [PDF]

Entropy (Basel)
Flores-Fuentes W, Sergiyenko O, Rodríguez-Quiñonez JC, Miranda-Vega JE.   +3 more
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Texture segmentation benchmark

2008 19th International Conference on Pattern Recognition, 2008
The Prague texture segmentation data-generator and benchmark (mosaic.utia.cas.cz) is a web-based service designed to mutually compare and rank (recently nearly 200) different static and dynamic texture and image segmenters, to find optimal parametrization of a segmenter and support the development of new segmentation and classification methods.
Stanislav Mikes, Michal Haindl
openaire   +3 more sources

Segmentation of textured images

Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003
The authors present a method for texture segmentation that does not assume any prior knowledge about either the type of textures or the number of textured regions present in the image. Local orientation and spatial frequencies are used as the key parameters for classifying texture.
Adi Perry, David G. Lowe
openaire   +1 more source

Blind texture segmentation

[1988 Proceedings] 9th International Conference on Pattern Recognition, 2003
The technique presented solves the problem of texture segmentation in two steps. In the first, a textured image is divided into small squares (20*20 in this case) and a hierarchical clustering algorithm related to a choice of ultrametric distances is used to obtain an initial segmentation. In the second step, the texture boundaries are improved using a
André Gagalowicz, Christine Graffigne
openaire   +1 more source