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....
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....
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...
A method is presented to quantitatively measure structural changes induced by milk processing and storage, such as fat aggregation and milk fat globule membrane disruption, by analysing confocal micrographs. ...
The objective of this study was to develop an automatic method to reproduce human-like discriminative capabilities in the context of microstructure evaluation....
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...
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....
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...