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This study aims to establish a novel solar air collection-cascade storage system. Flat-plate micro-heat pipe arrays are applied in collector and heat storage units for heat transfer enhancement. To increase energy utilization ratio and improve system stability, a cascade storage configuration using paraffin and lauric acid is adopted. In the charging process, the cascade heat storage units store the solar irradiance collected by the collector in the phase change materials through the heat exchange fluid (air). During the discharging process, air takes away the heat stored in the phase change material. The heat transfer process and thermal performance of the system was analyzed experimentally in different volume flow rates from the perspectives of energy and exergy. The experimental results show that the air flow rate affects the thermal performance of the system significantly: as the volume flow rate increases, the heat collection power and efficiency show an upward trend, and the heat storage power and heat storage efficiency also show an increasing trend. Contrary to the increasing trend of exergy storage efficiency, exergy extraction efficiency decreases with increasing volume flow rate. An airflow rate of 220 m3/h obtains the highest average heat collection power and average heat collection efficiency, which are 542.0 W and 35.8%, respectively. In this condition, the average heat storage power and average heat storage efficiency of latent heat storage unit I are 175.1 W and 67.5%, respectively, and those of unit II are 136.0 W and 87.5%, respectively.

Laser active imaging is widely used in many fields. The intensity image quality of laser active imaging is affected by various degradations, such as speckle effect and noise. Most existing objective image quality assessment (IQA) methods that consider only a single distorted image are not suitable for an intensity image. A multiscale full-reference intensity IQA method is presented. The proposed method is based on an improved gradient magnitude similarity deviation (GMSD) in the nonsubsampled contourlet transform (NSCT) domain. The reference and distorted images are decomposed by NSCT to emulate the multichannel structure of the human visual system. Then, the GMSD of each sub-band is computed to capture the intensity image quality. At last, the contrast sensitivity function implementation is employed in the sub-bands of the NSCT domain. All sub-bands’ GMSD is evaluated and pooled together to yield the objective quality index of a distorted intensity image. Experimental results show that the proposed method can effectively and accurately evaluate the quality of intensity images, and it is highly consistent with subjective perception.

Purpose The purpose of this paper is to improve the robustness of the traditional Bhattacharyya metric for the effect of histogram quantization in the histogram-based visual tracking. However, the traditional Bhattacharyya metric neglects the correlation of crossing-bin and is not robust for the effect of histogram quantization. Design/methodology/approach In this paper, the authors propose a visual tracking method via crossing-bin histogram Bhattacharyya similarity in the particle filter. Findings A crossing-bin matrix is introduced into the traditional Bhattacharyya similarity for measuring the reference histogram and the candidate histogram, and the basic tasks of measure such as maximum similarity of self and the triangle inequality are proven. The authors use the proposed measure in the particle filter visual tracking framework and address a model update strategy based on the crossing-bin histogram Bhattacharyya similarity to improve the robustness of visual tracking. Originality/value In the experiments using the famous challenging benchmark sequences, precision of the proposed method increases by 12.8 per cent comparing the traditional Bhattacharyya similarity and the cost time decreases by 38 times comparing the incremental Bhattacharyya similarity. The experimental results show that the proposed method can track the object robustly and rapidly under illumination change and occlusion.

Road markings are important information of transport systems for drivers or intelligent vehicle. Efficient road markings feature extraction is pre-requisite to road markings detection, recognition and visual localization. However, most of previous lane markings feature extractors are operating on conventional images, the feature extraction methods for omnidirectional images are rarely considered in road markings detection research. In this paper, three road markings feature extractors for omni-directional images are presented based on different characteristics of road markings. Those lane markings feature extractors are evaluated on real world panoramic image dataset. Experimental results show the feature extractor combing the edge and intensity distribution perform best.

In this paper, a fast algorithm is proposed to tackle the constrained total generalized variation (TGV) based image restoration problem. The proposed algorithm proceeds by splitting: the nonsmooth constrained TGV model is first decomposed into several subproblems easier to solve; then the linear gradient or proximity operators, including projections and shrinkages, of the subproblems are individually called without inner iteration. The algorithm is highly parallel since most of its steps can be executed simultaneously. Image restoration experiments demonstrate that the proposed algorithm outperforms several state-of-the-art methods both in speed and accuracy, while efficiently suppressing staircase effects and presenting better visual impression.

In view that the noise of image in monocular visual navigation, a Neigh-Shrink de-noising method based on Bayes-Shrink is proposed. First, the method uses Bayes-Shrink adaptive threshold replace common threshold as the contraction threshold of Neigh-Shrink, and then eliminate the high frequency sub-band of image obtained by monocular visual system after non-subsample Contourlet transform (NSCT) decomposition. Experiment results show that the improved Neigh-Shrink method in de-noising performance and visual effects have been improved, with better anti-noise performance and excellent edge protection, and can effectively be used in monocular visual navigation.

Aiming at the problem of the geometric distortion of the real-time image under the condition of multi- aircrafts cooperative detection, an effective method for the geometric correction is proposed. First multi-aircrafts cooperative terminal guidance model is established, especially the relationship between terminal detection angle and the initial position. Second the detecting angle model combined with the actual parameters, is analyzed, then put forward a real-time image geometric correction method; Simulation of the detection angle geometric distortion is applied to real-time image. Last using normalized product correlation algorithm for a large number of matching simulation, then get matching threshold, a real-time image geometric correction experiment is carried out. The simulation results show that the proposed method is effective,which provides theoretical basis for the reformation of reference image and the further improvement of the algorithm.