Humans perceive the three-dimensional structure of the world with apparent ease. However,
despite all of the recent advances in computer vision research, the dream of having a …

Object detection and recognition are important problems in computer vision. Since these
problems are meta-heuristic, despite a lot of research, practically usable, intelligent, real …

Multiple-instance problems arise from the situations where training class labels are attached
to sets of samples (named bags), instead of individual samples within each bag (called …

Software reliability engineering is focused on engineering techniques for developing and
maintaining software systems whose reliability can be quantitatively evaluated. In order to …

The objective of this work is the detection of object classes, such as airplanes or horses.
Instead of using a model based on salient image fragments, we show that object class …

Automatically detecting objects with complex appearance and arbitrary orientations in
remote sensing imagery (RSI) is a big challenge. To explore a possible solution to the …

This paper develops a novel statistical approach for automatic vehicle detection based on
local features that are located within three significant subregions of the image. In the …

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 …

In video surveillance, classification of visual data can be very hard, due to the scarce
resolution and the noise characterizing the sensors' data. In this paper, we propose a novel …

Abstract Multiple Instance Learning (MIL) provides a framework for training a discriminative
classifier from data with ambiguous labels. This framework is well suited for the task of …