Binocular Examination in Children

This chapter introduces the reader to tests commonly performed in a binocular vision examination to determine a child's binocular status. The testings introduced in this chapter are all chairside techniques that do not involve a significant amount of extra equipment or time. The binocular examination consists of determining the ocular position and then accommodation, vergence, and ocular motor statuses. The ocular position consists of determining where the eyes point in space and if they are correctly aligned. Evaluation of ocular alignment and the ability to move the eyes with coordination can be determined with various techniques, including Hirschberg, Krimsky, cover test, as well as Modified Thorington, motor testing, and vergence ranges. This chapter will familiarize the clinician with the basics of binocular vision testing and improve the reader's comfort with the various elements of the binocular vision examination.

Prismatic correction in the presence of acutely acquired esotropia with diplopia

Purpose. To analyze changes in binocular vision functions in patients with acutely acquired esotropia after preoperative correction by prisms. Material and methods. The study included 16 patients with acutely acquired esotropia with diplopia. Depending on the tactics of preoperative case management, two groups were distinguished. Eight patients with acutely acquired esotropia of the Bielshowsky type at the age from 12 to 35 years old (mean age, M±sd – 17.5±1.9 years). This group underwent preoperative prismatic correction of diplopia and the deviation angle, as well as eight patients with acutely acquired esotropia of the Bielshowsky type at the age from 12 to 35 years (18.5±1.9 years). Patients in this group underwent surgery without prior prismatic correction. Fresnel prisms were used to select and assign prismatic correction. The patients were monitored dynamically. Results. In the course of treatment 1 group (prismatic correction followed by surgical correction) according to the indicator of Worth's four-dot test color test, revealed a statistically significant increase in the frequency of value of the binocular single vision (p=0.041), the frequency of the presence of stereovision according to the Fly-test (p=0.013), fusion amplitude in the distance by 1.11 times (p=0.033) and near fusion amplitude by 1.15 times (p=0.003). Significant differences between the groups were observed only in the frequency of stereovision presence according to the Fly-test after treatment, which was 2.0 times higher in group 1 (p=0.041). Conclusion. Prismatic correction in combination with surgical treatment had better curative benefits than surgery itself in improving both binocular and stereoscopic visual functions. Key words: acutely acquired esotropia, diplopia, binocular vision, stereoscopic vision, Fresnel prisms

Research on Measurement Method of Yak Body Size and Weight Based on Convolutional Neural Network and Binocular Vision

In order to solve the labor-intensive and time-consuming problem in the process of measuring yak body size and weight in yak breeding industry in Qinghai Province, a non-contact method for measuring yak body size and weight was proposed in this experiment, and key technologies based on semantic segmentation, binocular ranging and neural network algorithm were studied to boost the development of yak breeding industry in Qinghai Province. Main conclusions: (1) Study yak foreground image extraction, and implement yak foreground image extraction model based on U-net algorithm; select 2263 yak images for experiment, and verify that the accuracy of the model in yak image extraction is over 97%. (2) Develop an algorithm for estimating yak body size based on binocular vision, and use the extraction algorithm of yak body size related measurement points combined with depth image to estimate yak body size. The final test shows that the average estimation error of body height and body oblique length is 2.6%, and the average estimation error of chest depth is 5.94%. (3) Study the yak weight prediction model; select the body height, body oblique length and chest depth obtained by binocular vision to estimate the yak weight; use two algorithms to establish the yak weight prediction model, and verify that the average estimation error of the model for yak weight is 10.78% and 13.01% respectively.

Research on Dynamic Obstacle Avoidance Method of Manipulator Based on Binocular Vision

Background: With the development of robotics, more and more robots are used in manufacturing. However, in actual work, safety accidents happen to robots from time to time. How to ensure the safe operation of robots in a limited and complex working environment is the key to improve robot technology. Therefore, it is of great significance to study the dynamic obstacle avoidance of robots in complex environment for improving the intelligence and safety of robots, and the application of human-robot collaboration. Objective: The primary purpose of this paper is to improve the traditional artificial potential field method, including he disadvantages that the improved target is inaccessible and easily plunged into local optimal solution of the drawback of the improved method, second. Secondly, the background difference method based on binocular vision and Kalman filtering algorithm, and the environmental map containing the static and dynamic obstacles is obtained. After obtaining the position information of static and dynamic obstacles, the robot arm can make good use of the improved artificial potential field method to plan its own trajectory, thus realizing the dynamic obstacle avoidance of the robot arm in complex environment. Methodology: The background difference method and the Kalman filtering algorithm based on binocular vision were introduced to track the dynamic obstacles, and the improved artificial potential field method for path planning was applied to the dynamic obstacle avoidance path planning of the manipulator. Finally, the simulation and experimental results show that under the complex environment with dynamic obstacles exist, robot arm can realize independent dynamic obstacle avoidance. Results: By using background difference method and Kalman filtering algorithm to track the target in real time, the result showed that the target could be detected and tracked well. By improving the defect that the traditional artificial potential field method is easy to fall into local optimum, the improved algorithm can well realize the dynamic obstacle avoidance of the manipulator. Conclusions: For the development requirements of the industrial robots in the future, this paper based on binocular vision, which can make the manipulator realize more intelligent industrial production activities in complex working environment, meet the needs of future industrial development, and make this technology play an important role in production activities.