4,268 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
No Spare Parts: Sharing Part Detectors for Image Categorization
This work aims for image categorization using a representation of distinctive
parts. Different from existing part-based work, we argue that parts are
naturally shared between image categories and should be modeled as such. We
motivate our approach with a quantitative and qualitative analysis by
backtracking where selected parts come from. Our analysis shows that in
addition to the category parts defining the class, the parts coming from the
background context and parts from other image categories improve categorization
performance. Part selection should not be done separately for each category,
but instead be shared and optimized over all categories. To incorporate part
sharing between categories, we present an algorithm based on AdaBoost to
jointly optimize part sharing and selection, as well as fusion with the global
image representation. We achieve results competitive to the state-of-the-art on
object, scene, and action categories, further improving over deep convolutional
neural networks
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