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Human-machine interactive systems show increasing demand for analysing fast moving objects in high-frame-rate videos. Robust foreground detection, which is able to reduce large amount of redundant background data from high-frame-rate video, becomes the essence to achieve ultra-high-speed human-machine interactions. This paper proposes a local spatial propagation based background model generation, a local linear illumination correction based background model update, and a regional central coordinates and edge keypoints constrained foreground region reselection. The three proposals make up a robust and hardware-friendly foreground detection method. Experimental results prove that the proposed hardware-friendly algorithm achieves high accuracy and robustness on various kinds of challenging cases. Meanwhile, the hardware implementation utilizes little hardware resources and achieves realtime processing of high-frame-rate (784 frame/second) video with the delay less than 1 ms/frame in image processing core. In addition, a practical system is implemented by combing a PC, a high-speed camera and a field programmable gate array (FPGA) for realworld applications. This work will significatively promote the development and application of high-speed human machine interaction. A demo of the proposed vision system working at 784 FPS is available at https://wcms.waseda.jp/em/5f84f75136a6. Note to Practitioners—This paper was motivated by the problem of high-frame-rate video contains large amount of redundant background pixels which makes ultra-high-speed human-machine interactions inaccessible. Existing approaches are mainly focused on designing complex background models, but processing speed, which is the most important issue for ultra-high-speed human-machine interactions, has received relatively little attention. This paper suggests a robust and hardware-friendly foreground detection algorithm which has been implemented as a hardware system by using an FPGA, a high-frame-rate camera, and a PC. We show that the hardware implementation utilizes less hardware resources and achieves real-time processing speed of 784 FPS with the delay less than 1 ms/frame in the image processing core. This work is a pioneering attempt of ultra-high-speed foreground detection, which will significatively speed up the wide applications of ultra-high-speed human machine interactions. [ABSTRACT FROM AUTHOR]

In the field of image stitching, cases with large camera optical center movement and large parallax have been the virgin territory of research. The goal of image stitching is to overcome the parallax and stitch a natural image. We look into this problem in the context of ultra-low altitude flight of a UAV. We model the 3D world in this scenario and quickly estimate orthographic projection by pairs of homography matrices. Our stitching method can achieve precise alignment since it takes parallax well into consideration. The stitching results are natural and the extra time consumed is short. • We propose a Orthographic Projection Estimation method aided by semantic information. • We propose a image stitching method for low-altitude UAV images. • Low-altitude UAV image stitching based on Orthographic Projection Estimation is proved to be necessary. [ABSTRACT FROM AUTHOR]

Automatic classification of texture images is an important and challenging task in the applications of image analysis and scene understanding. In this paper, we focus on the problem of the classification of texture images acquired under various rotation and illumination conditions and propose a new local image descriptor which is named local spiking pattern (LSP). Specifically, the proposed LSP uses a 2-dimensional neural network, which is made up of a series of interconnected spiking neurons, to generate binary images by iteration. The binary images are then encoded to generate discriminative feature vectors. In classification phase, we use a nearest neighborhood classifier to achieve supervised classification. Finally, LSP is evaluated by comparison with some state-of-the-art local image descriptors. Experimental results on Outex texture database show that LSP outperforms most of the other local image descriptors in the noiseless case and shows high robustness when texture images are distorted by salt & pepper noise. [ABSTRACT FROM AUTHOR]

As a matter of fact, mammalian visual system do not pay an equivalent attention to different regions in an image, the visual cortex is less sensitive to textures than non-textures. Therefore, to obtain the optimal visual quality and the perfect compression ratio simultaneously in image quantization, textures should be quantized coarsely, and non-textures should be quantized finely. The pulse-coupled neural networks (PCNN) is a model of synchronous pulse bursts in mammalian visual cortex, which has been proved to be extremely effective in image processing because of its biological background. In this work, a mammalian visual characteristics inspired perceptual image quantization strategy is proposed. It employs PCNN to extract textures from original image. Then, pixels in textures are quantized into less gray scale layers than pixels in non-textures. After that, quantized textures and quantized non-textures are consolidated. Experimental results prove validity and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]