To get a better fused performance in the multi-focus image fusion based on a transform domain, a new multi-focus image algorithm combined with the adaptive dual-channel spiking cortical model (SCM) in non-subsampled shearlet (NSST) domain and the difference images is proposed in this paper. First, a basic fused image is constructed in the NSST domain by registering the source image and adaptive dual channel SCM (dual-channel SCM). Next, the focus areas of the sources input images based on the difference images between the basic fused image and the sources images are detected. In the end, the final fused image generated in this paper is realized by combining the focal regions. Because of the global coupling of the dual-SCM, the synchronization characteristics of the pulse, and the multi-resolution and direction of the NSST, the proposed algorithm can preserve the information of the source's image well and present a clear image more in line with the human visual effects. In summary, the image fusion algorithm that we have designed is superior to the most advanced algorithms. INDEX TERMS Difference images, multi-focus image fusion, non-subsampled shearlet, spiking cortical model.
In order to obtain a panoramic image which is clearer, and has more layers and texture features, we propose an innovative multi-focus image fusion algorithm by combining with non-subsampled shearlet transform (NSST) and residual network (ResNet). First, NSST decomposes a pair of input images to produce subband coefficients of different frequencies for subsequent feature processing. Then, ResNet is applied to fuse the low frequency subband coefficients, and improved gradient sum of Laplace energy (IGSML) perform high frequency feature information processing. Finally, the inverse NSST is performed on the fused coefficients of different frequencies to obtain the final fused image. In our method, we fully consider the low frequency global features and high frequency detail information in image by using NSST. For low-frequency coefficients fusion, we can also obtain the spatial information features of low-frequency coefficient images by using ResNet, which has a deep network structure. IGSML can use different directional gradients to process high-frequency subband coefficients of different levels and directions, which is more conducive to the fusion of the coefficients. The experiment results show that the proposed method has been improved in the structural features and edge texture in the fusion images. INDEX TERMS Image fusion, multi-focus image fusion, NSST, ResNet. YIFEI WU received the B.S. degree from Xidian University, Xi'an, China, in 2018. He is currently pursuing the M.S. degree with the
The development of a system for automatically locating and tracking a human in the vicinity of a robot is described. The system consists of multiple passive infrared (PIR) sensors, two color cameras, a pair of microwave sensors and a pair of PCs for data collection, signal processing and data fusion. The cameras are treated as individual sensors rather than a stereo pair to minimize the affect of occlusion by the robot. The area around the robot is subdivided into an occupancy grid with 0.5m by 0.5m cells. A data fusion algorithm, based on Dempster-Shafer evidence theory, is used to estimate the probability of human occupancy for each cell. This information is used to estimate the human's location. A novel concept termed a "protective cell" is introduced to further increase the human's safety in the presence of sensor uncertainty. Experimental results are included demonstrating the system's effectiveness.Index Terms -data fusion, human-robot interaction, human tracking, multisensor system, robot safety. 0-7803-8914-X/05/$20.00 ©2005 IEEE.
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