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In this paper, we introduce a non-verbal multimodal joint visual attention model for human-robot interaction in household scenarios. Our model combines the bottom-up saliency and depth-based segmentation with the top-down cues such as pointing and gaze to detect the objects of interest according to the user. For generation of the top-down saliency maps, we have introduced novel methods for object...
Frequent and more accurate water level measurement will allow for a more efficient distribution of water, resulting in less water loss. Therefore in this paper we propose a novel method for accurate water level detection and measurement applied on images of staff gauges, retrieved from mobile device camera. In the first step, we propose fast segmentation of the staff gauge using a 2-class random forest...
In this paper we tackle the challenges of visual tracking for personal robots. We have proposed a novel track-by-detection method that combines a semantic object model with depth properties to obtain target contours. The tracking can be initialized by either 2D or 3D inputs, which are further refined using clustering based background removal to obtain an initial object model. During tracking, we propose...
In this paper we focus on a perception system for cognitive interaction between robots and humans especially for learning to recognize objects in household environments. Therefore we propose a novel three layered framework for object learning to bridge the gap between the robot's recognition capabilities at lower neural level to the higher cognitive level of humans using the weighted fusion of multimodal...
For human-robot interaction users have to be robustly identified and their appearances learned online. Existing state of the art methods for face recognition do not support online learning of faces and lack the recognition performance required to be used in real-world situations. Hence a novel method is introduced in this paper as a descriptor, which provides the required performance by increasing...
Extensive research has been conducted in the domain of object tracking. Among the existing tracking methods, most of them mainly focus on using various cues such as color, texture, contour, features, motion as well as depth information to achieve a robust tracking performance. The tracking methods themselves are highly emphasized while properties of the objects to be tracked are usually not exploited...
In this paper, we present a unifying approach for learning and recognition of objects in unstructured environments through exploration. Taking inspiration from how young infants learn objects, we establish four principles for object learning. First, early object detection is based on an attention mechanism detecting salient parts in the scene. Second, motion of the object allows more accurate object...
In this paper we present a scene exploration method for the identification of interest regions in unknown indoor environments and the position estimation of the objects located in those regions. Our method consists of two stages: First, we generate a saliency map of the scene based on the spectral residual of three color channels and interest points are detected in this map. Second, we propose and...
Content-based image retrieval (CBIR) system with relevance feedback, which uses the algorithm for feature-vector (FV) dimension reduction, is described. Feature-vector reduction (FVR) exploits the clustering of FV components for a given query. Clustering is based on the comparison of magnitudes of FV components of a query. Instead of all FV components describing color, line directions, and texture,...
A content-based image retrieval system with query image classification prior to retrieving procedure is proposed. Query image is compared to representative patterns of image classes, not to all images from database, accelerating thus initial retrieving step. Such procedure is possible when images from database are grouped into classes with similar content. Classification is performed using minor component...
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