Lane detection is challenging under varied light conditions (e.g., night, shadow, and dazzling light) because a lane becomes blurred and extracting features becomes more difficult. Some researchers have proposed methods based on multitask learning and contextual information to solve this problem; however, these methods result in additional computing. A data enhancement method based on retinex theory is proposed. This method improves the adaptability of a lane model under varied light conditions. In particular, we design an image enhancement network for calculating the reflectivity of images, modifying their exposure, and then generating images with consistent exposure. These images are fed to the lane detection model for training and detection. Our network consists of two parts: exposure-consistent image generation and lane detection. We validate our method on the CULane dataset, and results show that it can improve lane detection performance, particularly on light-related datasets.
As an instance-level recognition problem, the key to effective vehicle re-identification (Re-ID) is to carefully reason the discriminative and viewpoint-invariant features of vehicle parts at high-level and low-level semantics. However, learning part-based features requires a laborious human annotation of some factors as attributes. To address this issue, we propose a region-aware multi-resolution (RAMR) Re-ID framework that can extract features from a series of local regions without extra manual annotations. Technically, the proposed method improves the discriminative ability of the local features through parallel high-to-low resolution convolutions. We also introduce a position attention module to focus on the prominent regions that can provide effective information. Given that the vehicle Re-ID performance can be affected by background clutters, we use the image obtained through foreground segmentation to extract local features. Results show that using original and foreground images can enhance the Re-ID performance compared with using either the original or foreground images alone. In other words, the original and foreground images complement each other in the vehicle Re-ID process. Finally, we aggregate the global appearance and local features to improve the system performance. Extensive experiments on two publicly available vehicle Re-ID datasets, namely, VeRi-776 and VehicleID, are conducted to validate the effectiveness of each proposed strategy. The findings indicate that the RAMR model achieves significant improvement in comparison with other state-of-the-art methods.
KEYWORDS: Medicine, Medical imaging, Picture Archiving and Communication System, Databases, Standards development, Information technology, Image transmission, Data storage, Broadband telecommunications, Image storage
In this paper we propose the way to form a kind of medicine Image information sharing platform, which is based on PACS and broadband network. And we also discuss some key technologies used in building up the platform, such as sharing information between heterogeneous data sources based on HL7, storing and transmission the medical images based on DICOM. The study result shows that it can make full use of those heterogeneous data resources currently in different hospitals, and give them a good way to share the data.
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