38,366 research outputs found
Boosting minimalist classifiers for blemish detection in potatoes
This paper introduces novel methods for detecting blemishes in potatoes using machine vision. After segmentation of the potato from the background, a pixel-wise classifier is trained to detect blemishes using features extracted from the image. A very large set of candidate features, based on statistical information relating to the colour and texture of the region surrounding a given pixel, is first extracted. Then an adaptive boosting algorithm (AdaBoost) is used to automatically select the best features for discriminating between blemishes and nonblemishes.
With this approach, different features can be selected
for different potato varieties, while also handling the natural variation in fresh produce due to different seasons, lighting conditions, etc. The results show that the method is able to build “minimalist” classifiers that optimise detection performance at low computational cost. In experiments, minimalist blemish detectors were trained for both white and red potato varieties, achieving 89.6% and 89.5% accuracy respectively
Visual detection of blemishes in potatoes using minimalist boosted classifiers
This paper introduces novel methods for detecting blemishes in potatoes using machine vision. After segmentation of the potato from the background, a pixel-wise classifier is trained to detect blemishes using features extracted from the image.
A very large set of candidate features, based on statistical information relating to the colour and texture of the region surrounding a given pixel, is first extracted.
Then an adaptive boosting algorithm (AdaBoost) is used to automatically select the best features for discriminating between blemishes and non-blemishes.
With this approach, different features can be selected for different potato varieties, while also handling the natural variation in fresh produce due to different seasons, lighting conditions, etc.
The results show that the method is able to build ``minimalist'' classifiers that optimise detection performance at low computational cost.
In experiments, blemish detectors were trained for both white and red potato varieties, achieving 89.6\% and 89.5\% accuracy, respectively
Discriminating small wooded elements in rural landscape from aerial photography: a hybrid pixel/object-based analysis approach
While small, fragmented wooded elements do not represent a large surface area in
agricultural landscape, their role in the sustainability of ecological processes is
recognized widely. Unfortunately, landscape ecology studies suffer from the lack
of methods for automatic detection of these elements. We propose a hybrid
approach using both aerial photographs and ancillary data of coarser resolution
to automatically discriminate small wooded elements. First, a spectral and textural
analysis is performed to identify all the planted-tree areas in the digital photograph.
Secondly, an object-orientated spatial analysis using the two data sources
and including a multi-resolution segmentation is applied to distinguish between
large and small woods, copses, hedgerows and scattered trees. The results show the
usefulness of the hybrid approach and the prospects for future ecological
applications
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