We propose a closely coupled object detection and segmentation algorithm for enhancing both processes in a cooperative and iterative manner. Figure-ground segmentation reduces the...
Object detection in aerial imagery has been well studied in computer vision for years. However, given the complexity of large variations of the appearance of the object and the ba...
We present a novel categorical object detection scheme that uses only local contour-based features. A two-stage, partially supervised learning architecture is proposed: a rudiment...
We present a latent hierarchical structural learning method for object detection. An object is represented by a mixture of hierarchical tree models where the nodes represent objec...
Leo Zhu, Yuanhao Chen, Antonio Torralba, Alan Yuil...
A new learning strategy for object detection is presented.
The proposed scheme forgoes the need to train a collection
of detectors dedicated to homogeneous families of poses,
an...
Karim Ali, Francois Fleuret, David Hasler and Pasc...
Nowadays, the use of machine learning methods for visual object detection has become widespread. Those methods are robust. They require an important processing power and a high mem...
- Robot companions need to be able to constantly acquire knowledge about new objects for instance in order to detect them in the environment. This ability is necessary since it is ...
— Classical position-based visual servoing approaches rely on the presence of distinctive features in the image such as corners and edges. In this contribution we exploit a hiera...
Abstract. Context plays an important role in general scene perception. In particular, it can provide cues about an object’s location within an image. In computer vision, object d...
In this paper, we present a real-time algorithm for 3D object detection in images. Our method relies on the Ullman and Basri [13] theory which claims that the same object under di...