Self-driving vehicles need to continuously analyse the driving scene, understand the behavior
of other road users and predict their future trajectories in order to plan a safe motion and …

Speeding up the collection and integration of raw botanical observation data is a crucial
step towards a sustainable development of agriculture and the conservation of biodiversity. …

In this paper we introduce a new multiscale shape-based approach for leaf image retrieval.
The leaf is represented by local descriptors associated with margin sample points. Within this …

For an efficient integration of autonomous vehicles on roads, human-like reasoning and
decision making in complex traffic situations are needed. One of the key factors to achieve this …

Plant diseases have recently increased and exacerbated due to several factors such as
climate change, chemicals’ misuse and pollution. They represent a severe threat for both …

Pl@ntNet is an image sharing and retrieval application for the identification of plants,
available on iPhone and iPad devices. Contrary to previous content-based identification …

This paper presents a novel method for leaf species identification combining local and
shape-based features. Our approach extends the shape context model in two ways. First of all, …

Scene understanding and future motion prediction of surrounding vehicles are crucial to
achieve safe and reliable decision-making and motion planning for autonomous driving in a …

The problem of automatic leaf identification is particularly challenging because, in addition to
constraints derived from image processing such as geometric deformations (rotation, scale, …

With the growing interest in identifying plant species and the availability of digital collections,
many automated methods based on bark images have been proposed. Bark identification …