Mathematical Modelling of Ground Truth Image for 3D Microscopic Objects Using Cascade of Convolutional Neural Networks Optimized with Parameters' Combinations Generators.
Omar BilalovicZikrija AvdagicSamir OmanovicIngmar BesicVedad LeticChristophe TatoutPublished in: Symmetry (2020)
Keyphrases
- convolutional network
- convolutional neural networks
- ground truth
- image analysis
- test images
- image data
- image regions
- image pixels
- image content
- image features
- bounding box
- input image
- image classification
- multiple objects
- spatial relationships
- image retrieval
- complex scenes
- target object
- object models
- multiscale
- single image
- camera positions
- segmented images
- image segmentation
- edge detection
- three dimensional objects
- segmentation method
- image representation
- maximum likelihood
- lighting conditions
- object detectors
- similar objects
- image segments
- moving objects
- high quality
- object segmentation
- object classes
- partial occlusion
- spatial relations
- probability density function
- parameter space
- expectation maximization
- feature points