Tag: Gaussian Mixture Model

Abstract: Imprecise labels or label uncertainty are common problems in many real supervised and semi-supervised learning problems. However, most of the state-of-the-art supervised learning methods in the literature rely on accurate labels. Accurate labels are often either expensive, time-consuming, or even impossible to obtain in many real applications. There are many approaches in the literature […]

Abstract: Sensors which use electromagnetic induction (EMI) to excite a response in conducting bodies have long been investigated for subsurface explosive hazard detection. In particular, EMI sensors have been used to discriminate between different types of objects, and to detect objects with low metal content. One successful, previously investigated approach is the Multiple Instance Adaptive […]

Abstract: Context information is rarely used in the object-based landcover classification. Previous models that attempted to utilize this information usually required the user to input empirical values for critical model parameters, leading to less optimal performance. Multi-view image information is useful for improving classification accuracy, but the methods to assimilate multi-view information to make it […]

Abstract: Background: Root crown phenotyping has linked root properties to shoot mass, nutrient uptake, and yield in the field, which increases the understanding of soil resource acquisition and presents opportunities for breeding. The original methods using manual measurements have been largely supplanted by image-based approaches. However, most image-based systems have been limited to one or […]