Path planning is of essential importance for Unmanned Surface Vessels (USV). Lots of path planning algorithms have been proposed in the last few years, however these algorithms have high computational complexity. Therefore, these algorithms are time consuming and not suitable for online path planning. In this paper, a rapid path planning algorithm for USVs is developed. The proposed algorithm segments the searching space into three subspaces: starting subspace, end subspace and passing subspace. With consider the performances of USVs, our algorithm plans a path from the edge of the starting subspace to the end subspace. Therefore the computational complexity is dramatically decreased. The experiment results show that the proposed scheme is efficient to fulfill the path planning task.
Obstacle detection is of essential importance for Unmanned Surface Vehicles (USV). Although some obstacles (e.g., ships, islands) can be detected by Radar, there are many other obstacles (e.g., floating pieces of woods, swimmers) which are difficult to be detected via Radar because these obstacles have low radar cross section. Therefore, detecting obstacle from images taken onboard is an effective supplement. In this paper, a robust vision-based obstacle detection method for USVs is developed. The proposed method employs the monocular image sequence captured by the camera on the USVs and detects obstacles on the sea surface from the image sequence. The experiment results show that the proposed scheme is efficient to fulfill the obstacle detection task.
An object detection for vision-aided inventory counting is developed. The propose approach is simple to use and reduce the workload remarkably. Meanwhile, the approach count the items of interest almost in real time with an acceptable precision, which is desirable in inventory counting. The experiment results show that the proposed approach is efficient to fulfill the counting task.
The detection of shadow is the first step to reduce the imaging effect that is caused by the interactions of the light
source with surfaces, and then shadow removal can recover the vein information from the dark region. In this paper, we
have presented a new method to detect the shadow in a single nature image with the saliency map and to remove the
shadow. Firstly, RGB image is transferred to 2D module in order to improve the blue component. Secondly, saliency
map of blue component is extracted via graph-based manifold ranking. Then the edge of the shadow can be detected in
order to recover the transitional region between the shadow and non-shadow region. Finally, shadow is compensated by
enhancing the image in RGB space. Experimental results show the effectiveness of the proposed method.
In this paper, a novel difficult prediction scheme for infrared building target recognition is developed. Our scheme can predict the difficulty of recognizing a designated target in advance, which is desirable in infrared building recognition. The experiment results show that our scheme is efficient to fulfill the prediction task and the prediction is consistent with the real recognition results.
This paper proposes a new approach to localize buildings from forward looking infrared (FLIR) images. The proposed approach can localize not only large buildings, but also small buildings. Furthermore, the proposed approach is also robust with those FLIR images degraded by clouds. This breakthrough is due to the following improvements: (1) the Histogram of Oriented Gradients approach is improved to match FLIR images with our templates; (2) a new kind of feature image is presented to reduce the difference between template and target; (3) we project 3D building models into images, with different colors on different sides, distinguishing those sides apart; (4) we generate templates which contain all buildings in the visual field. As a result, the FLIR images can be matched with the big templates at a high correct rate, and then target buildings can be localized. The experimental results show the superior performance of the proposed approach.
This paper firstly improves D.L.Plillips's representation about image restoration and then points out
that image restoration is just a 'partial ill-posed' problem rather than a 'total ill-posed'
problem---amplitude restoration is ill-posed but phase restoration is well-posed. Basing on the
viewpoints, the paper proposes a restoration method, which cuts down phase-pollution caused by
traditional regularization methods, that amplitude restoration is realized by regularization and phase
restoration is achieved by algebraic method. Experimental results indicate that the proposed method
performs well. It can efficiently restore image phase and elaborately preserve image details.
In this paper, a novel automatic DSM and remote sensing images registration scheme using template matching technique
is developed. Due to the heterogeneity of DSM and remote sensing images, the emphases of our scheme are to describe
the common feature between DSM and remote sensing images, and to generate a suitable template for template
matching. Based on the sparse representation theory, we present a new feature descriptor, which can highlight the
similarities of DSM and remote sensing images, and can be used to form a new kind of feature image. Meanwhile we
present a criterion to choose the proper region from the feature image as the template which will ensure perfect template
matching performance. The experiment results show that our scheme is efficient to fulfill the task of registration.
Due to the infection by heat transfer at the junction between different materials, edges in infrared(IR) images are usually
blurred. The phenomenon named the edge effect almost always ignored by the existing methods for infrared images
simulation. In this paper, we develop a simulation algorithm based on heat transfer model to obtain more perfect
simulation results for infrared images. Meanwhile, to further enhance the fidelity of simulated IR images, a novel scheme
to generate IR texture is presented. The experiment results show that our method can yield higher fidelity IR image than
the existing simulation method.