Related references
Note: Only part of the references are listed.Segmentation of blood vessels using rule-based and machine-learning-based methods: a review
Fengjun Zhao et al.
MULTIMEDIA SYSTEMS (2019)
Plant disease leaf image segmentation based on superpixel clustering and EM algorithm
Shanwen Zhang et al.
NEURAL COMPUTING & APPLICATIONS (2019)
A new image classification method using CNN transfer learning and web data augmentation
Dongmei Han et al.
EXPERT SYSTEMS WITH APPLICATIONS (2018)
An adaptive-scale active contour model for inhomogeneous image segmentation and bias field estimation
Qing Cai et al.
PATTERN RECOGNITION (2018)
Learning domain-shared group-sparse representation for unsupervised domain adaptation
Baoyao Yang et al.
PATTERN RECOGNITION (2018)
Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truth
Vanya V. Valindria et al.
IEEE TRANSACTIONS ON MEDICAL IMAGING (2017)
Data augmentation for unbalanced face recognition training sets
Biao Leng et al.
NEUROCOMPUTING (2017)
Weed segmentation using texture features extracted from wavelet sub-images
Adel Bakhshipour et al.
BIOSYSTEMS ENGINEERING (2017)
A survey of image processing techniques for plant extraction and segmentation in the field
Esmael Hamuda et al.
COMPUTERS AND ELECTRONICS IN AGRICULTURE (2016)
Semantic segmentation of images exploiting DCT based features and random forest
D. Ravi et al.
PATTERN RECOGNITION (2016)
Principal pixel analysis and SVM for automatic image segmentation
Xuefei Bai et al.
NEURAL COMPUTING & APPLICATIONS (2016)
Future Scenarios for Plant Phenotyping
Fabio Fiorani et al.
ANNUAL REVIEW OF PLANT BIOLOGY, VOL 64 (2013)
Combining color and depth for enhanced image segmentation and retargeting
Meir Johnathan Dahan et al.
VISUAL COMPUTER (2012)
Genomic disorders: Molecular mechanisms for rearrangements and conveyed phenotypes
JR Lupski et al.
PLOS GENETICS (2005)
Color texture measurement and segmentation
MA Hoang et al.
SIGNAL PROCESSING (2005)
Share Paper
