Incremental learning to segment micrographs
We propose a simple segmentation framework based on classification and supervised incremental learning. A statistical model of pixel classes is learnt by incrementally adding new sample image patches to automatically-learned probability functions.
Image Analysis and Microscopy in Food Science: Computer Vision and Visual Inspection
When computer vision experts evaluate algorithms, they often focus on segmentation quality and optimisation of resources. On the other hand, they often neglect simplicity and interactivity.
Association of total mixed ration particle fractions retained on the Penn State Particle Separator with milk, fat, and protein yield lactation curves at the cow level
The objective of this study was to assess the association of particle size in TMR estimated using the Penn State Particle Separator with production of milk, fat, and protein on a representative sample of Ragusa dairy farms, while controlling for nutrient content.
Segmentation of structural features in cheese micrographs using pixel statistics
In this paper, a machine learning method was employed based on a statistical model of image pixels, learned from sample images by human training. The observation underlying to this class of segmentation
methods is that image pixels can be considered as a statistical population.
Measurement of Gas Holes and Mechanical Openness in Cheese by Image Analysis
The objective of the present study was to develop an image analysis method to measure the area of the surface of cheese slices occupied by gas holes (or other types of mechanical openness) for use in measurement of gas production in research studies and for quality control in cheese manufacturing.
