MCE detects focal lesions in the upper and lower stomach with comparable accuracy with conventional gastroscopy. MCE is preferred by almost all patients, compared with gastroscopy, and can be used to screen gastric diseases without sedation. Clinicaltrials.gov number: NCT02219529.

This paper presents a robust and efficient method for license plate detection with the purpose of accurately localizing vehicle license plates from complex scenes in real time. A simple yet effective image downscaling method is first proposed to substantially accelerate license plate localization without sacrificing detection performance compared with that achieved using the original image. Furthermore, a novel line density filter approach is proposed to extract candidate regions, thereby significantly reducing the area to be analyzed for license plate localization. Moreover, a cascaded license plate classifier based on linear support vector machines using color saliency features is introduced to identify the true license plate from among the candidate regions. For performance evaluation, a data set consisting of 3977 images captured from diverse scenes under different conditions is also presented. Extensive experiments on the widely used Caltech license plate data set and our newly introduced data set demonstrate that the proposed approach substantially outperforms state-of-the-art methods in terms of both detection accuracy and run-time efficiency, increasing the detection ratio from 91.09% to 96.62% while decreasing the run time from 672 to 42 ms for processing an image with a resolution of 1082×728 . The executable code and our collected data set are publicly available.

Capsule network is a novel architecture to encode the properties and spatial relationships of the feature in the images, which shows encouraging results on image classification. However, the original capsule network is not suitable for some classification tasks that the detected object has complex internal representations. Hence, we propose Multi-Scale Capsule Network, a novel variation of capsule network to enhance the computational efficiency and representation capacity of capsule network. The proposed Multi-Scale Capsule Network consists of two stages. In the first stage the structural and semantic information are obtained by the multi-scale feature extraction. The second stage, we encode the hierarchy of features to multi-dimensional primary capsule. Moreover, we propose an improved dropout to enhance the robustness of capsule network. Experimental results show that our method has competitive performance on FashionMNIST and CIFAR10 datasets.

MCE showed a diagnostic accuracy similar to that of standard gastroscopy. These results suggest that MCE is a promising alternative to gastroscopy for noninvasive screening of gastric diseases.Clinical trial registration number: NCT01903629.