1,884 research outputs found
Exploiting surroundedness for saliency detection: a boolean map approach
We demonstrate the usefulness of surroundedness for eye fixation prediction by proposing a Boolean Map based Saliency model (BMS). In our formulation, an image is characterized by a set of binary images, which are generated by randomly thresholding the image's feature maps in a whitened feature space. Based on a Gestalt principle of figure-ground segregation, BMS computes a saliency map by discovering surrounded regions via topological analysis of Boolean maps. Furthermore, we draw a connection between BMS and the Minimum Barrier Distance to provide insight into why and how BMS can properly captures the surroundedness cue via Boolean maps. The strength of BMS is verified by its simplicity, efficiency and superior performance compared with 10 state-of-the-art methods on seven eye tracking benchmark datasets.US National Science Foundation; 1059218; 1029430http://cs-people.bu.edu/jmzhang/BMS/BMS_iccv13_preprint.pdfAccepted manuscrip
Highlighting objects of interest in an image by integrating saliency and depth
Stereo images have been captured primarily for 3D reconstruction in the past.
However, the depth information acquired from stereo can also be used along with
saliency to highlight certain objects in a scene. This approach can be used to
make still images more interesting to look at, and highlight objects of
interest in the scene. We introduce this novel direction in this paper, and
discuss the theoretical framework behind the approach. Even though we use depth
from stereo in this work, our approach is applicable to depth data acquired
from any sensor modality. Experimental results on both indoor and outdoor
scenes demonstrate the benefits of our algorithm
Hierarchical Salient Object Detection for Assisted Grasping
Visual scene decomposition into semantic entities is one of the major
challenges when creating a reliable object grasping system. Recently, we
introduced a bottom-up hierarchical clustering approach which is able to
segment objects and parts in a scene. In this paper, we introduce a transform
from such a segmentation into a corresponding, hierarchical saliency function.
In comprehensive experiments we demonstrate its ability to detect salient
objects in a scene. Furthermore, this hierarchical saliency defines a most
salient corresponding region (scale) for every point in an image. Based on
this, an easy-to-use pick and place manipulation system was developed and
tested exemplarily.Comment: Accepted for ICRA 201
Segmenting salient objects in 3D point clouds of indoor scenes using geodesic distances
Visual attention mechanisms allow humans to extract relevant and important information from raw input percepts. Many applications in robotics and computer vision have modeled human visual attention mechanisms using a bottom-up data centric approach. In contrast, recent studies in cognitive science highlight advantages of a top-down approach to the attention mechanisms, especially in applications involving goal-directed search. In this paper, we propose a top-down approach for extracting salient objects/regions of space. The top-down methodology first isolates different objects in an unorganized point cloud, and compares each object for uniqueness. A measure of saliency using the properties of geodesic distance on the object’s surface is defined. Our method works on 3D point cloud data, and identifies salient objects of high curvature and unique silhouette. These being the most unique features of a scene, are robust to clutter, occlusions and view point changes. We provide the details of the proposed method and initial experimental results
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