The rail train number is the only sign information of the vehicle. It is very important to use the image processing method to locate the train number to improve the efficiency of rail train management. Therefore, an automatic image positioning framework for rail train numbers is developed. First, aiming at the problem of low contrast between the rail train number and vehicle body caused by insufficient illumination of the rail train in a tunnel and indoor, the improved single-scale Retinex algorithm based on brightness control is used to enhance the image, which provides the basis for subsequent train number image positioning. Then, a train number location algorithm based on speed-up robust features and stroke width transform is proposed to locate the train number accurately. The experimental results show that the accuracy of the developed method is 96.91% in the complex environment of uneven illumination and large distortion of rail train numbers.

Automatic image positioning of a rail train number using speed-up robust features and stroke width transform

Despite years of work, a robust, widely applicable generic “symmetry detector” that can paral-lel other kinds of computer vision/image processing tools for the more basic structural charac-teristics, such as a “edge” or “corner” detector, remains a computational challenge. A new symmetry feature detector with a descriptor is proposed in this paper, namely the Simple Robust Features (SRF) algorithm. A performance comparison is made among SRF with SRF, Speeded-up Robust Features (SURF) with SURF, Maximally Stable Extremal Regions (MSER) with SURF, Harris with Fast Retina Keypoint (FREAK), Minimum Eigenvalue with FREAK, Features from Accelerated Segment Test (FAST) with FREAK, and Binary Robust Invariant Scalable Keypoints (BRISK) with FREAK. A visual tracking dataset is used in this performance evaluation in terms of accuracy and computational cost. The results have shown that combining the SRF detector with the SRF descriptor is preferable, as it has on average the highest accuracy. Additionally, the computational cost of SRF with SRF is much lower than the others.

https://www.mdpi.com/2073-8994/13/8/1337/pdf

A Descriptor-Based Advanced Feature Detector for Improved Visual Tracking

Field carrier robot can help people carry a certain amount of necessary materials in mountaineering, exploration and other fields, and even help field soldiers carry a certain amount of armaments 

Autonomous recognition technology of carrier robot on various terrain environment

Retinal image registration is a critical task in the diagnosis and treatment of various eye diseases. And as a relatively new imaging method, optical coherence tomography (OCT) has been widely used in the diagnosis of retinal diseases. This paper is devoted to retinal OCT image registration methods and their clinical applications. Registration methods including volumetric transformation-based registration methods and image features-based registration methods are systematically reviewed. Furthermore, to better understanding these methods, their applications in correcting scanning artifacts, reducing speckle noise, fusing and splicing images and evaluating longitudinal disease progression are studied as well. At the end of this paper, registration of retina with serious pathology and registration with deep learning technique are also discussed. 

Retinal OCT Image Registration: Methods and Applications

Unified approach for detecting traffic signs and potholes on Indian roads

For the speed of traditional SIFT algorithm in the feature extraction and matching is slow, the article proposes an improved RANSAC features image matching method based on speeded up robust features (SURF). First of all, detect images features and extract with SURF method, use the fast library for approximate nearest neighbours-based matcher method to perform initial matching on image feature points. Improve the RANSAC algorithm to increase the probability of correct matching points being sampled. Experimental results show that the improved RANSAC algorithm has high matching accuracy, good robustness, and short running time. It lays the foundation for the subsequent fast image stitching.

FanLiang Bu

Papers

Improved RANSAC features image-matching method based on SURF