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.
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.
Incremental Learning to Segment Micrographs
The main problem of classi cation approaches is the long, tedious, human-intensive training phase. In our experience, an accurate image labelling can take several hours to an expert operator.
An Interactive Level Set Approach to Semi-automatic Detection of Features in Food Micrographs
In this paper, we propose a fast approximate level set algorithm for the detection of microstructural features in food micrographs. Our approximation method is in the spirit of. Hence, we chose a user-assisted approach. Approximating the exact evolution equation allows for interactive computations.
