61,566 research outputs found
Image Segmentation with Multidimensional Refinement Indicators
We transpose an optimal control technique to the image segmentation problem.
The idea is to consider image segmentation as a parameter estimation problem.
The parameter to estimate is the color of the pixels of the image. We use the
adaptive parameterization technique which builds iteratively an optimal
representation of the parameter into uniform regions that form a partition of
the domain, hence corresponding to a segmentation of the image. We minimize an
error function during the iterations, and the partition of the image into
regions is optimally driven by the gradient of this error. The resulting
segmentation algorithm inherits desirable properties from its optimal control
origin: soundness, robustness, and flexibility
Saliency-guided Adaptive Seeding for Supervoxel Segmentation
We propose a new saliency-guided method for generating supervoxels in 3D
space. Rather than using an evenly distributed spatial seeding procedure, our
method uses visual saliency to guide the process of supervoxel generation. This
results in densely distributed, small, and precise supervoxels in salient
regions which often contain objects, and larger supervoxels in less salient
regions that often correspond to background. Our approach largely improves the
quality of the resulting supervoxel segmentation in terms of boundary recall
and under-segmentation error on publicly available benchmarks.Comment: 6 pages, accepted to IROS201
3D environment mapping using the Kinect V2 and path planning based on RRT algorithms
This paper describes a 3D path planning system that is able to provide a solution trajectory for the automatic control of a robot. The proposed system uses a point cloud obtained from the robot workspace, with a Kinect V2 sensor to identify the interest regions and the obstacles of the environment. Our proposal includes a collision-free path planner based on the Rapidly-exploring Random Trees variant (RRT*), for a safe and optimal navigation of robots in 3D spaces. Results on RGB-D segmentation and recognition, point cloud processing, and comparisons between different RRT* algorithms, are presented.Peer ReviewedPostprint (published version
On the Distribution of Salient Objects in Web Images and its Influence on Salient Object Detection
It has become apparent that a Gaussian center bias can serve as an important
prior for visual saliency detection, which has been demonstrated for predicting
human eye fixations and salient object detection. Tseng et al. have shown that
the photographer's tendency to place interesting objects in the center is a
likely cause for the center bias of eye fixations. We investigate the influence
of the photographer's center bias on salient object detection, extending our
previous work. We show that the centroid locations of salient objects in
photographs of Achanta and Liu's data set in fact correlate strongly with a
Gaussian model. This is an important insight, because it provides an empirical
motivation and justification for the integration of such a center bias in
salient object detection algorithms and helps to understand why Gaussian models
are so effective. To assess the influence of the center bias on salient object
detection, we integrate an explicit Gaussian center bias model into two
state-of-the-art salient object detection algorithms. This way, first, we
quantify the influence of the Gaussian center bias on pixel- and segment-based
salient object detection. Second, we improve the performance in terms of F1
score, Fb score, area under the recall-precision curve, area under the receiver
operating characteristic curve, and hit-rate on the well-known data set by
Achanta and Liu. Third, by debiasing Cheng et al.'s region contrast model, we
exemplarily demonstrate that implicit center biases are partially responsible
for the outstanding performance of state-of-the-art algorithms. Last but not
least, as a result of debiasing Cheng et al.'s algorithm, we introduce a
non-biased salient object detection method, which is of interest for
applications in which the image data is not likely to have a photographer's
center bias (e.g., image data of surveillance cameras or autonomous robots)
Robot navigation control based on monocular images: An image processing algorithm for obstacle avoidance decisions
This paper covers the use of monocular vision to control autonomous navigation for a robot in a dynamically changing environment. The solution focused on using colour segmentation against a selected floor plane to distinctly separate obstacles from traversable space, this is then supplemented with canny edge detection to separate similarly coloured boundaries to the floor plane. The resulting binary map (where white identifies an obstacle-free area and black identifies an obstacle) could then be processed by fuzzy logic or neural networks to control the robot’s next movements. Findings shows that the algorithm performed strongly on solid coloured carpets, wooden and concrete floors but had difficulty in separating colours in multi-coloured floor types such as patterned carpets
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