1,539 research outputs found
Network Uncertainty Informed Semantic Feature Selection for Visual SLAM
In order to facilitate long-term localization using a visual simultaneous
localization and mapping (SLAM) algorithm, careful feature selection can help
ensure that reference points persist over long durations and the runtime and
storage complexity of the algorithm remain consistent. We present SIVO
(Semantically Informed Visual Odometry and Mapping), a novel
information-theoretic feature selection method for visual SLAM which
incorporates semantic segmentation and neural network uncertainty into the
feature selection pipeline. Our algorithm selects points which provide the
highest reduction in Shannon entropy between the entropy of the current state
and the joint entropy of the state, given the addition of the new feature with
the classification entropy of the feature from a Bayesian neural network. Each
selected feature significantly reduces the uncertainty of the vehicle state and
has been detected to be a static object (building, traffic sign, etc.)
repeatedly with a high confidence. This selection strategy generates a sparse
map which can facilitate long-term localization. The KITTI odometry dataset is
used to evaluate our method, and we also compare our results against ORB_SLAM2.
Overall, SIVO performs comparably to the baseline method while reducing the map
size by almost 70%.Comment: Published in: 2019 16th Conference on Computer and Robot Vision (CRV
A Replica Inference Approach to Unsupervised Multi-Scale Image Segmentation
We apply a replica inference based Potts model method to unsupervised image
segmentation on multiple scales. This approach was inspired by the statistical
mechanics problem of "community detection" and its phase diagram. Specifically,
the problem is cast as identifying tightly bound clusters ("communities" or
"solutes") against a background or "solvent". Within our multiresolution
approach, we compute information theory based correlations among multiple
solutions ("replicas") of the same graph over a range of resolutions.
Significant multiresolution structures are identified by replica correlations
as manifest in information theory overlaps. With the aid of these correlations
as well as thermodynamic measures, the phase diagram of the corresponding Potts
model is analyzed both at zero and finite temperatures. Optimal parameters
corresponding to a sensible unsupervised segmentation correspond to the "easy
phase" of the Potts model. Our algorithm is fast and shown to be at least as
accurate as the best algorithms to date and to be especially suited to the
detection of camouflaged images.Comment: 26 pages, 22 figure
Automatic Crack Detection in Built Infrastructure Using Unmanned Aerial Vehicles
This paper addresses the problem of crack detection which is essential for
health monitoring of built infrastructure. Our approach includes two stages,
data collection using unmanned aerial vehicles (UAVs) and crack detection using
histogram analysis. For the data collection, a 3D model of the structure is
first created by using laser scanners. Based on the model, geometric properties
are extracted to generate way points necessary for navigating the UAV to take
images of the structure. Then, our next step is to stick together those
obtained images from the overlapped field of view. The resulting image is then
clustered by histogram analysis and peak detection. Potential cracks are
finally identified by using locally adaptive thresholds. The whole process is
automatically carried out so that the inspection time is significantly improved
while safety hazards can be minimised. A prototypical system has been developed
for evaluation and experimental results are included.Comment: In proceeding of The 34th International Symposium on Automation and
Robotics in Construction (ISARC), pp. 823-829, Taipei, Taiwan, 201
Artificial Neural Network based Cancer Cell Classification
This paper addresses the system which achieves auto-segmentation and cell characterization for prediction of percentage of carcinoma (cancerous) cells in the given image with high accuracy. The system has been designed and developed for analysis of medical pathological images based on hybridization of syntactic and statistical approaches, using Artificial Neural Network as a classifier tool (ANN) [2]. This system performs segmentation and classification as is done in human vision system [1] [9] [10] [12], which recognize objects; perceives depth; identifies different textures, curved surfaces, or a surface inclination by texture information and brightness. In this paper, an attempt has been made to present an approach for soft tissue characterization utilizing texture-primitive features and segmentation with Artificial Neural Network (ANN) classifier tool. The present approach directly combines second, third, and fourth steps into one algorithm. This is a semi-supervised approach in which supervision is involved only at the level of defining structure of Artificial Neural Network; afterwards, algorithm itself scans the whole image and performs the segmentation and classification in unsupervised mode. Finally, algorithm was applied to selected pathological images for segmentation and classification. Results were in agreement with those with manual segmentation and were clinically correlated [18] [21]. Keywords: Grey scale images, Histogram equalization, Gausian filtering, Haris corner detector, Threshold, Seed point, Region growing segmentation, Tamura texture feature extraction, Artificial Neural Network(ANN), Artificial Neuron, Synapses, Weights, Activation function, Learning function, Classification matrix
- …