Complete Scene Recovery and Terrain Classification in Textured Terrain Meshes

Abstract:In order to improve the recognition rate of hand gestures a new interactive image segmentation method for hand gesture recognition is presented, and popular methods, e.g., Graph cut, Random walker, Interactive image segmentation using geodesic star convexity, are studied in this article. The Gaussian Mixture Model was employed for image modelling and the iteration of Expectation Maximum algorithm learns the parameters of Gaussian Mixture Model. We apply a Gibbs random field to the image segmentation and minimize the Gibbs Energy using Min-cut theorem to find the optimal segmentation. The segmentation result of our method is tested on an image dataset and compared with other methods by estimating the region accuracy and boundary accuracy. Finally five kinds of hand gestures in different backgrounds are tested on our experimental platform, and the sparse representation algorithm is used, proving that the segmentation of hand gesture images helps to improve the recognition accuracy.

“…T is a constant parameter, whose unit is temperature in physics, and usually its value is 1.

Z false(T false)

is the partition function, and:

Z (T) = \sum_{a \in A} \exp (- \frac{1}{T} E (a)),

where,

E false(bold-italicα false)

is interpreted as the energy function of the state a , to apply GRF in image segmentation, the Gibbs Energy [30] can be defined as follows:

E false(bold-italicα false) = E false(α, normalΘ, bold-italicX false) = E false(α, i, θ, bold-italicX false) = U false(α, i, θ, bold-italicX false) + V false(α, bold-italicX false)

…”

Section: Interactive Image Segmentationmentioning

confidence: 99%

An Interactive Image Segmentation Method in Hand Gesture Recognition

Chen

Sun

et al. 2017

“…Song et al [32] proposed a ground segmentation method in 2D images that combined the GMRF method with a flood-fill algorithm. By segmenting ground pixels in the 2D image, the method detects the ground vertices in the texture mesh by projecting from the ground pixels.…”

Section: Related Workmentioning

confidence: 99%

“…Point interpolation algorithms are also ineffective in representing porous objects, such as vegetation. To solve these problems, Song et al [32] proposed a GMRF based height estimation algorithm by estimating object top pixel in 2D images for each sensed object pixel. He reconstructed the complete terrain from the captured 2D image and the reconstructed terrain mesh.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Intuitive Terrain Reconstruction Using Height Observation-Based Ground Segmentation and 3D Object Boundary Estimation

Song

Cho

et al. 2012

Self Cite

Mobile robot operators must make rapid decisions based on information about the robot’s surrounding environment. This means that terrain modeling and photorealistic visualization are required for the remote operation of mobile robots. We have produced a voxel map and textured mesh from the 2D and 3D datasets collected by a robot’s array of sensors, but some upper parts of objects are beyond the sensors’ measurements and these parts are missing in the terrain reconstruction result. This result is an incomplete terrain model. To solve this problem, we present a new ground segmentation method to detect non-ground data in the reconstructed voxel map. Our method uses height histograms to estimate the ground height range, and a Gibbs-Markov random field model to refine the segmentation results. To reconstruct a complete terrain model of the 3D environment, we develop a 3D boundary estimation method for non-ground objects. We apply a boundary detection technique to the 2D image, before estimating and refining the actual height values of the non-ground vertices in the reconstructed textured mesh. Our proposed methods were tested in an outdoor environment in which trees and buildings were not completely sensed. Our results show that the time required for ground segmentation is faster than that for data sensing, which is necessary for a real-time approach. In addition, those parts of objects that were not sensed are accurately recovered to retrieve their real-world appearances.

“…The first need of 3D data comes from the field of robotics, where exhaustive maps of a robot's surroundings are mandatory for its self-localization and to perform collision avoidance [ 1 – 4 ]. At the same time 3D images, also known as range images, have attracted increasing interest by companies in the field of quality control since the detailed and unsupervised inspection of manufactured goods can speed up industrial processes, especially in those fields, viz.…”

Section: Introductionmentioning

confidence: 99%

A Compact 3D Omnidirectional Range Sensor of High Resolution for Robust Reconstruction of Environments

Marani

Renò

Nitti

et al. 2015

In this paper, an accurate range sensor for the three-dimensional reconstruction of environments is designed and developed. Following the principles of laser profilometry, the device exploits a set of optical transmitters able to project a laser line on the environment. A high-resolution and high-frame-rate camera assisted by a telecentric lens collects the laser light reflected by a parabolic mirror, whose shape is designed ad hoc to achieve a maximum measurement error of 10 mm when the target is placed 3 m away from the laser source. Measurements are derived by means of an analytical model, whose parameters are estimated during a preliminary calibration phase. Geometrical parameters, analytical modeling and image processing steps are validated through several experiments, which indicate the capability of the proposed device to recover the shape of a target with high accuracy. Experimental measurements show Gaussian statistics, having standard deviation of 1.74 mm within the measurable range. Results prove that the presented range sensor is a good candidate for environmental inspections and measurements.