Computer-aided detection systems for lung nodules play an important role in the early diagnosis and treatment process. False positive reduction is a significant component in pulmonary nodule detection. To address the visual similarities between nodules and false positives in CT images and the problem of two-class imbalanced learning, we propose a Central Attention Convolutional Neural Network on Imbalanced Data (CACNNID) to distinguish nodules from a large number of false positive candidates. To solve the imbalanced data problem, we consider density distribution, data augmentation, noise reduction, and balanced sampling for making the network well-learned. During the network training, we design the model to pay high attention to the central information and minimize the influence of irrelevant edge information for extracting the discriminant features. The proposed model has been evaluated on the public dataset LUNA16 and achieved a mean sensitivity of 92.64%, specificity of 98.71%, accuracy of 98.69%, and AUC of 95.67%. The experimental results indicate that our model can achieve satisfactory performance in false positive reduction.
To improve the effectiveness of the infrared image dehazing algorithm, we modify the DCP based on the observation of several outdoor haze-free images: in these images, pixels of most local patches no longer have low intensity but are close to high intensity. The transmission map is estimated through the MDCP, and the guided filter and CLAHE achieve enhancement. The haze infrared images can be recovered completely. Moreover, to thrive the infrared dehazing research, we introduce the IR Dense-haze dataset, which contains 13 pairs of synthetic haze infrared images and corresponding haze-free infrared images and six extra haze infrared images captured in natural scenes. Experiments on the IR Densehaze dataset show that our method achieves dominant performance. The average PSNR of the infrared image is 39.02% higher than the original DCP method, and the average SSIM is 28.65% higher than the original DCP method.
During the capture operation, the hand-eye camera measures the spatial position of the center of the docking ring. However, the center can’t be used as a direct capture point, so we need to find a suitable capture point on the docking ring. This point can be connected with the center of the docking ring while satisfying the capture of the manipulator. This paper presents a method to find the capture point based on the intersection of the circle and a straight line, consisting of the image main point and the center of the circle. Furthermore, an algorithm for calculating the spatial position of the capture point is proposed. The digital and semi-physical simulation experiments verify the effectiveness of the method. The results show the position error of the capture point is within 0.8mm, and the capture angle error is within 0.4°.
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