We propose a fast method for salient region detection which aims at providing a computationally efficient method for online image processing. It is scalable and can be adjusted on the run to adapt to different computational requirements, which makes it a perfect candidate for time crucial applications. In our approach, we apply a template sampling over the image and compare these templates with each other by calculating a dissimilarity score. Templates with a low overall response are therefore likely to be part of a salient region in the image. This conceptually easy method is simple to implement and still outperforms state-of-the-art salient region detection systems (Our model's AUC(ROC) Score 0.794-AIM 0.772).

In this paper, we present an biologically-motivated object recognition system for robots and vision tasks in general. Our approach is based on a hierarchical model of the visual cortex for feature extraction and rapid scene categorization. We modify this static model to be usable in time-crucial real-world scenarios by applying methods for optimization from signal detection theory, information theory, signal processing and linear algebra. Our system is more robust to clutter and supports object localization by approaching the binding problem in contrast to previous models. We show that our model outperforms the preceding model and that by our modifications we created a robust and fast system which integrates the capabilities of biological-inspired object recognition in a technical application.

In this paper we present a new approach for labeling 3D points with different geometric surface primitives using a novel feature descriptor - the Fast Point Feature Histograms, and discriminative graphical models. To build informative and robust 3D feature point representations, our descriptors encode the underlying surface geometry around a point p using multi-value histograms. This highly dimensional feature space copes well with noisy sensor data and is not dependent on pose or sampling density. By defining classes of 3D geometric surfaces and making use of contextual information using Conditional Random Fields (CRFs), our system is able to successfully segment and label 3D point clouds, based on the type of surfaces the points are lying on. We validate and demonstrate the method's efficiency by comparing it against similar initiatives as well as present results for table setting datasets acquired in indoor environments.

We report on our experiences regarding the acquisition of hybrid Semantic 3D Object Maps for indoor household environments, in particular kitchens, out of sensed 3D point cloud data. Our proposed approach includes a processing pipeline, including geometric mapping and learning, for processing large input datasets and for extracting relevant objects useful for a personal robotic assistant to perform complex manipulation tasks. The type of objects modeled are objects which perform utilitarian functions in the environment such as kitchen appliances, cupboards, tables, and drawers. The resulted model is accurate enough to use it in physics-based simulations, where doors of 3D containers can be opened based on their hinge position. The resulted map is represented as a hybrid concept and is comprised of both the hierarchically classified objects and triangular meshes used for collision avoidance in manipulation routines.