Babak Rasolzadeh

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The ability to autonomously acquire new knowledge through interaction with the environment is an important research topic in the field of robotics. The knowledge can only be acquired if suitable perception– action capabilities are present: a robotic system has to be able to detect, attend to and manipulate objects in its surrounding. In this paper, we(More)
Our work is oriented towards the idea of developing cognitive capabilities in artificial systems through Object Action Complexes (OACs) [7]. The theory comes up with the claim that objects and actions are inseparably intertwined. Categories of objects are not built by visual appearance only, as very common in computer vision, but by the actions an agent can(More)
A distinct property of robot vision systems is that they are embodied. Visual information is extracted for the purpose of moving in and interacting with the environment. Thus, different types of perception-action cycles need to be implemented and evaluated. In this paper, we study the problem of designing a vision system for the purpose of object grasping(More)
The ability to autonomously acquire new knowledge through interaction with the environment is an important research topic in the field of robotics. The knowledge can be acquired only if suitable perception-action capabilities are present: a robotic system has to be able to detect, attend to and manipulate objects in its surrounding. In this paper, we(More)
This paper demonstrates the value of improving the discriminating strength of weak classifiers in the context of boosting by using response binning. The reasoning is centered around, but not limited to, the well known Haar-features used by Viola and Jones (2001) in their face detection/pedestrian detection systems. It is shown that using a weak classifier(More)
Attention plays an important role in human processing of sensory information as a mean of focusing resources towards the most important inputs at the moment. In vision it has been argued attentional processes are crucial for dealing with the complexity of real world scenes. The problem has often been posed in terms of visual search tasks. It has been shown(More)
In this paper we address the problem of using boosting (e.g. AdaBoost [7]) to classify a target class with significant intra-class variation against a large background class. This situation occurs for example when we want to recognize a visual object class against all other image patches. The boosting algorithm produces a strong classifier, which is a(More)
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