Infrared target detection and tracking technology has been widely used in the fields of transportation, medical, safety and military affairs, etc. However, there stills exists some challenges in infrared target detection and tracking, such as dim small target, complex background, target occlusion and appearance changes, etc. On the other hand, as the most effective bio-intelligence system, Human Visual System (HVS) has significant advantages in image processing. In this paper, several brain-inspired models (including lateral inhibition, receptive field, synchronous burst, visual attention, and cognitive memory) and Deep Neural Networks (DNNs) have been studied. Furthermore, the relevant mathematical models are established, the corresponding algorithms are proposed, and the comparison experiments are conducted. In summary, applying the brain-inspired models and DNNs to the infrared target detection and tracking is beneficial to achieve the accurate infrared target detection and robust tracking under complex conditions.
Quantification of image clutter plays an important role in predicting target acquisition performances of a photoelectric imaging system due to the strong effect of optoelectronic image clutter. Accuracy in predicting the targeting performance of previous reported clutter metrics was relatively low because of disadvantages, such as lack of ability to accurately quantify the image with complex clutters and threshold selection problem. To address this problem, a novel multidirectionaldifference-Hash-based image clutter metric is proposed in this paper. Initially, an image similarity measure method based on multidirectional difference hash is established. Then, this method is applied to the quantification of image clutter, and an MDHash-based image clutter metric is obtained. Experimental results show that the proposed clutter metric correlates effectively with probability of detection, false alarm rate, and search time of observers.
Pulse Coupled Neural Network (PCNN) is improved by Adaptive Lateral Inhibition (ALI), while a method of infrared (IR) dim small target segmentation based on ALI-PCNN model is proposed in this paper. Firstly, the feeding input signal is modulated by lateral inhibition network to suppress background. Then, the linking input is modulated by ALI, and linking weight matrix is generated adaptively by calculating ALI coefficient of each pixel. Finally, the binary image is generated through the nonlinear modulation and the pulse generator in PCNN. The experimental results show that the segmentation effect as well as the values of contrast across region and uniformity across region of the proposed method are better than the OTSU method, maximum entropy method, the methods based on conventional PCNN and visual attention, and the proposed method has excellent performance in extracting IR dim small target from complex background.
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