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In this study, we propose a method for object matching between visible and infrared images. We consider object matching between visible and infrared images as a computational patch-matching problem, and the main solution to this problem involves calculating the similarities of the relationships between the objects in the target and search images. Therefore, in this study, we propose a Siamese neural network, which takes a pair of visible and infrared images as the input. Our proposed Siamese network comprises a convolutional neural network (CNN) to ensure the effective extraction of features from visible and infrared images. The CNN comprises convolutional and pooling layers without padding. By calculating the cross-correlation of the objects in the visible image and those in the entire infrared image, we regard the parts with the highest similarity as the matched targets. During the training process, we use focal loss to solve the problem of the imbalance between the positive and negative samples in the dataset, after which we use interpolation to determine the locations of the target patches in the infrared images. We then conduct experiments on different classes of targets, and the results demonstrate that our proposed approach achieves greater accuracy and precision than other methods.

Feature point matching has been widely applied in image registration, image fusion, remote sensing and other fields. The relation between the pixels of infrared images and pixels of visible images is complex due to the images were taken by different sensors. Different sensors images also contain some common information which can been depended to achieve point matching. Scale Invariant Feature Transform (SIFT) algorithm is an effective and popular feature extraction algorithm. SIFT algorithm can be used in point matching, it can get feature descriptor vectors of the feature points which extracted from the images. But in some scenes, SIFT algorithm can’t achieve the accurate feature point matching. Different from SIFT algorithm, Edge-Oriented-Histogram (EOH) algorithm characters the orientation information of the edge and EOH feature descriptor can integrate the boundary information in different directions around the feature points, so that we can realize the describe of the edge direction and amplitude by EOH algorithm. To achieve the accurate feature point matching of infrared image and visible image, we propose a feature point matching method based on SIFT algorithm and EOH algorithm. Firstly, we use image enhancement to increase contrast of the image and then we use SIFT algorithm to extract feature points. Next, we utilize the EOH algorithm to get the 80 bins descriptors of the feature points which detected by SIFT algorithm. We calculate Euclidean distance to get the similarity of the descriptors to achieve point matching. In order to improve the matching accuracy, we adopt Random Sample Consensus (RANSAC) algorithm to eliminate the mismatching points. Nevertheless, RANSAC can’t get all correct points, we use another measure of Euclidean to select correct pairs, then we combine the two parts to match the feature points again. Experimental results demonstrate that our method can effectively improve the accuracy of matching and find more correct matching point pairs.

The formation mechanism of regularly arranged tubular pores during an acid-based electrophoretic deposition (EPD) process was explored by studying the influence of gauze electrodes and suspension properties on the pore structures. The gauze electrodes can change the intensity of electrical field on the electrode surface, and thus control the pore locations. The mesh size not only restricts the ultimate pore size, but also determines the regularity of the pores. Under specific experimental conditions, a critical value of mesh size for attaining the regularity of the pores arrangement can be determined. Meanwhile, the pore structures can be controlled by a combination of pH value and zeta potential of the suspensions. The strength of the acidity is also one of the determinants to the final structures.