Pixel-Level Fusion for Infrared and Visible Acquisitions

Abstract

This article presents a unique combined routine to fuse Long Wave Infrared (7.5-13 micron) with visible (0.38-0.78 micron) acquisitions, and to track pedestrians and road information in night or low light driving scenarios. Three fusion levels are presented and discussed: pixellevel, feature-level and decision-level. A pixel-level fusion is then used, through a novel combination of an adaptive weighting algorithm for un-saturated data points, and a PCA statistics for saturated pixels. The registration is done at the hardware level with Gaussian smoothing, while the smoothed histograms provide initial threshold values. The proposed routine is then applied for different night-time driving scenarios, further compared with the tracking results for non-fused thermal imagery from a commercial night-vision module. The fused imagery coupled with the proposed pre and post-processing provide complete detection of reflective and emitting objects with better shape retrieval and orientation prediction, computed and judged through objects morphology. The result of current work is an enhanced passive display for: passing vehicles with glare, pedestrians and non-emitting road features, with its motion predicted.

Multi-Sensor Fusion in Automated Driving: A Survey

“…Fusion strategy based on discernible units (FSBDU), or datalevel fusion, refers to the fusion process in which the data of distinguishable units of different sensors are directly fused, and then the data after fusion is further processed. FSBDU [93], [94] is abundantly adopted in multi-source image fusion for image enhancement, especially in the application of remote sensing imaging by fusing infrared images and RGB images. Because of the longer wavelength, the raw data of MMW-Radar is not conducive to imaging immediately.…”

Image processing analysis of sigmoidal Hadamard wavelet with PCA to detect hidden object

“…However, in our real-time system, the commercial, computational and the type of enhancement it becomes important in commercial surveillance systems. Consequently, multiple-sensor image fusion of visual and infrared images has been tested extensively [9,10]. Earlier research studies also explored psuedocoloring of X-ray images, clutter removal with latency time responses, neural networks, MIMO-SAR based UWB imaging, fast LTR Analysis [11][12][13].…”

“…The DWT can perform N level decomposition depending on the application requirements. 2D-DWT is defined by (9).…”

Registration of AFM and SEM Scans Using Local Features

“…Nevertheless, no superior method for all image registration tasks could be identified as of yet. This is mainly due to the different assumptions made for specific registration problems, such as natural photograph stitching, multimodal medical image fusion, or multispectral image fusion (Zhou and Omar 2009). Although there is no generally valid solution for all image registration tasks, a procedure for image registration can be designed systematically for a given problem (Goshtasby 2005).…”