Generating Text Sequence Images for Recognition

Recently, methods based on deep learning have dominated the field of text recognition. With a large number of training data, most of them can achieve the state-of-the-art performances. However, it is hard to harvest and label sufficient text sequence images from the real scenes. To mitigate this issue, several methods to synthesize text sequence images were proposed, yet they usually need complicated preceding or follow-up steps. In this work, we present a method which is able to generate infinite training data without any auxiliary pre/post-process. We tackle the generation task as an image-to-image translation one and utilize conditional adversarial networks to produce realistic text sequence images in the light of the semantic ones. Some evaluation metrics are involved to assess our method and the results demonstrate that the caliber of the data is satisfactory. The code and dataset will be publicly available soon

Patient specific training: development of a CT-based mixed reality fibreoptic intubation simulator

Fibreoptic intubation training has traditionally been performed using real fibreoptic scopes and manikins or improvised airway ‘boxes’, recently progressing to virtual reality training devices [1]. The latter are populated with computer generated images, represented 2 dimensionally on screens without depth perception and fail to reproduce the natural variation. We aimed to address these issues by producing a simulator that utilises a real patient’s anatomy, in a mixed reality platform, without the need for additional hardware

Focus-Enhanced Scene Text Recognition with Deformable Convolutions

Recently, scene text recognition methods based on deep learning have sprung up in computer vision area. The existing methods achieved great performances, but the recognition of irregular text is still challenging due to the various shapes and distorted patterns. Consider that at the time of reading words in the real world, normally we will not rectify it in our mind but adjust our focus and visual fields. Similarly, through utilizing deformable convolutional layers whose geometric structures are adjustable, we present an enhanced recognition network without the steps of rectification to deal with irregular text in this work. A number of experiments have been applied, where the results on public benchmarks demonstrate the effectiveness of our proposed components and shows that our method has reached satisfactory performances. The code will be publicly available at https://github.com/Alpaca07/dtr soon

OR-048 Effects and Mechanism of myokines in exercise mediated improving obesity rats skeletal muscle remodeling

Objective Aims From the year 2000, many experimental research data have indicated that skeletal muscle could express, synthesis and secrete multiple cytokines and polypeptides. The cytokines and polypeptides, not only regulate skeletal muscle growth, metabolism and motor function by paracrine/autocrine pathway, but also regulate functions of peripheral tissue and organs by endocrine pathway. Further researches proposed muscle as a secretory organ played a key role in mediating the health-promoting effects of physical activity and proteins expressed and released by skeletal muscle have been termed as myokines. Disorders of skeletal muscle endocrine function have related to the occurrence and development of multiple metabolic diseases, and myokines participate in obesity skeletal muscle remodeling. This study aims to investigate the expression changes of myokines and its effects in exercise mediated improving skeletal muscle remodeling on obesity mice, and explore the underlying mechanism of its functions. Methods Methods Five-week-old male Sprague-Dawley(SD) rats were randomly divided into a control group of 8 and a high-fat diet（HFD）group of 16. The control group was given normal food，while the HFD group were provided with high-fat diet for eight weeks and further divided into a sedentary HFD group and a treadmill running HFD group，each of 8. The exercise mice underwent 60 min treadmill running at 26 m/min each day，5 days/week for 8 weeks. Biochemical analyses, immune-histochemical, ELISA, RT-PCR and Western Blot methods were used to investigate multiple myokines expression changes and its mechanism. Results Results 1) Exercise significantly upregulated the expression of IL-15 in soleus and gastrocnemius muscle of obesity rats, indicating IL-15 could inhibit skeletal muscle endoplasmic reticulum stress and improve insulin sensitivity. 2) Exercise significantly inhibited the expression of myostatin (MSTN) in gastrocnemius muscle and mediated the changes of muscle fiber types. 3) Exercise markedly promoted the expression of apelin/APJ and angiogenesis function in obesity skeletal muscle. 4) Exercise upregulated skeletal muscle vascular endothelial growth factor B receptor expression and improved skeletal muscle ectopic lipid accumulation. Conclusions Conclusion Exercise regulates skeletal muscle myokines expression and secretion and have the effects on skeletal muscle fiber type changes, myofiber capillary density, glucose and lipid metabolism, thus improves the skeletal muscle remodeling and maintain body homeostasis

Unified Chinese License Plate Detection and Recognition with High Efficiency

Recently, deep learning-based methods have reached an excellent performance on License Plate (LP) detection and recognition tasks. However, it is still challenging to build a robust model for Chinese LPs since there are not enough large and representative datasets. In this work, we propose a new dataset named Chinese Road Plate Dataset (CRPD) that contains multi-objective Chinese LP images as a supplement to the existing public benchmarks. The images are mainly captured with electronic monitoring systems with detailed annotations. To our knowledge, CRPD is the largest public multi-objective Chinese LP dataset with annotations of vertices. With CRPD, a unified detection and recognition network with high efficiency is presented as the baseline. The network is end-to-end trainable with totally real-time inference efficiency (30 fps with 640p). The experiments on several public benchmarks demonstrate that our method has reached competitive performance. The code and dataset will be publicly available at https://github.com/yxgong0/CRPD