Color Image Segmentation Based on HSI Model

he objective of the research is to develop a fast procedure for segmenting typical videophone images. In this paper, a new approach to color image segmentation based on HSI(Hue, Saturation, Intensity) color model is reported. It is in contrast to the conventional approaches by using the three components of HSI color model in succession. This strategy makes the segmentation procedure much fast and effective. Experimental results with typical “headandshoulders” real images taken from videophone sequences show that the new appproach can fulfill the application requirements.

Adaptive Maxwell’s Equations Derived Optimization and Its Application in Antenna Array Synthesis

In this paper,a self-adaptive method for the Maxwell’s Equations Derived Optimization(MEDO)is proposed.It is implemented by applying the Sequential Model-Based Optimization(SMBO)algorithm to the iterations of the MEDO,and achieves the automatic adjustment of the parameters.The proposed method is named as adaptive Maxwell’s equations derived optimization(AMEDO).In order to evaluate the performance of AMEDO,eight benchmarks are used and the results are compared with the original MEDO method.The results show that AMEDO can greatly reduce the workload of manual adjustment of parameters,and at the same time can keep the accuracy and stability.Moreover,the convergence of the optimization can be accelerated due to the dynamical adjustment of the parameters.In the end,the proposed AMEDO is applied to the side lobe level suppression and array failure correction of a linear antenna array,and shows great potential in antenna array synthesis.

UAV navigation system using line-based sensor pose estimation

This work presents a mapping and tracking system based on images to enable a small Unmanned Aerial Vehicle(UAV)to accurately navigate in indoor and GPS-denied outdoor environments.A method is proposed to estimate the UAV’s pose(i.e.,the 3D position and orientation of the camera sensor)in real-time using only the on-board RGB camera as the UAV travels through a known 3D environment(i.e.,a 3D CAD model).Linear features are extracted and automatically matched between images collected by the UAV’s onboard RGB camera and the 3D object model.The matched lines from the 3D model serve as ground control to estimate the camera pose in real-time via line-based space resection.The results demonstrate that the proposed modelbased pose estimation algorithm provides sub-meter positioning accuracies in both indoor and outdoor environments.It is also that shown the proposed method can provide sparse updates to correct the drift from complementary simultaneous localization and mapping(SLAM)-derived pose estimates.