Infrared Image Caption Based on Object-Oriented Attention.

With the ongoing development of image technology, the deployment of various intelligent applications on embedded devices has attracted increased attention in the industry. One such application is automatic image captioning for infrared images, which involves converting images into text. This practical task is widely used in night security, as well as for understanding night scenes and other scenarios.

Critical element prediction of tracheal intubation difficulty: Automatic Mallampati classification by jointly using handcrafted and attention-based deep features.

Preoperative assessment of the difficulty of tracheal intubation is of great importance in anesthesia practice because failed intubation can lead to severe complications and even death. The Mallampati score is widely used as a critical assessment criterion in combination with other measures to assess the difficulty of tracheal intubation. The performance of existing methods for Mallampati classification with artificial intelligence (AI) is unreliable to the extent that the current clinical judgment of the Mallampati score relies entirely on doctors' experience.

A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model.

Images captured by sensors in unpleasant environment like low illumination condition are usually degraded, which means low visibility, low brightness, and low contrast. In order to improve this kind of images, in this paper, a low-light sensor image enhancement algorithm based on HSI color model is proposed. At first, we propose a dataset generation method based on the Retinex model to overcome the shortage of sample data.

Biologically Inspired Hierarchical Contour Detection with Surround Modulation and Neural Connection.

Contour is a very important feature in biological visual cognition and has been extensively investigated as a fundamental vision problem. In connection with the limitations of conventional models in detecting image contours in complex scenes, a hierarchical image contour extraction method is proposed based on the biological vision mechanism that draws on the perceptual characteristics of the early vision for features such as edges, shapes, and colours. By simulating the information processing mechanisms of the cells' receptive fields in the early stages of the biological visual system, we put forward a computational model that combines feedforward, lateral, and feedback neural connections to decode and obtain the image contours.

Rapid Airplane Detection in Remote Sensing Images Based on Multilayer Feature Fusion in Fully Convolutional Neural Networks.

To address the issues encountered when using traditional airplane detection methods, including the low accuracy rate, high false alarm rate, and low detection speed due to small object sizes in aerial remote sensing images, we propose a remote sensing image airplane detection method that uses multilayer feature fusion in fully convolutional neural networks. The shallow layer and deep layer features are fused at the same scale after sampling to overcome the problems of low dimensionality in the deep layer and the inadequate expression of small objects. The sizes of candidate regions are modified to fit the size of the actual airplanes in the remote sensing images.