Fuzzy component based object detection

Multiple Component Learning for Object Detection

Object detection is one of the key problems in computer vision. In the last decade, discriminative learning approaches have proven effective in detecting rigid objects, achieving very low false positives rates. The field has also seen a resurgence of part-based recognition methods, with impressive results on highly articulated, diverse object categories. In this paper we propose a discriminativ...

Multi-component Models for Object Detection

In this paper, we propose a multi-component approach for object detection. Rather than attempting to represent an object category with a monolithic model, or pre-defining a reduced set of aspects, we form visual clusters from the data that are tight in appearance and configuration spaces. We train individual classifiers for each component, and then learn a second classifier that operates at the...

Component-based Face Detection

We present a component-based, trainable system for detecting frontal and near-frontal views of faces in still gray images. The system consists of a two-level hierarchy of Support Vector Machine (SVM) classifiers. On the first level, component classifiers independently detect components of a face. On the second level, a single classifier checks if the geometrical configuration of the detected co...

Attention-based Object Detection

Contour-based object detection

The arrival of appearance-based image features has dramatically influenced the field of visual object recognition. Previous work has shown, however, that contour curvature and junctions are important for shape representation and detection. We investigate a local representation of contours for object detection that complements appearance-based information, such as texture. We present a non-param...