Blunted angiogenesis and hypertrophy are associated with increased fatigue resistance and unchanged aerobic capacity in old overloaded mouse muscle.

We hypothesize that the attenuated hypertrophic response in old mouse muscle is (1) partly due to a reduced capillarization and angiogenesis, which is (2) accompanied by a reduced oxidative capacity and fatigue resistance in old control and overloaded muscles, that (3) can be rescued by the antioxidant resveratrol. To investigate this, the hypertrophic response, capillarization, oxidative capacity, and fatigue resistance of m. plantaris were compared in 9- and 25-month-old non-treated and 25-month-old resveratrol-treated mice. Overload increased the local capillary-to-fiber ratio less in old (15 %) than in adult (59 %) muscle (P < 0.05). Although muscles of old mice had a higher succinate dehydrogenase (SDH) activity (P < 0.05) and a slower fiber type profile (P < 0.05), the isometric fatigue resistance was similar in 9- and 25-month-old mice. In both age groups, the fatigue resistance was increased to the same extent after overload (P < 0.01), without a significant change in SDH activity, but an increased capillary density (P < 0.05). Attenuated angiogenesis during overload may contribute to the attenuated hypertrophic response in old age. Neither was rescued by resveratrol supplementation. Changes in fatigue resistance with overload and aging were dissociated from changes in SDH activity, but paralleled those in capillarization. This suggests that capillarization plays a more important role in fatigue resistance than oxidative capacity

Facial image processing in computer vision

NoThe application of computer vision in face processing remains an important research field. The aim of this chapter is to provide an up-to-date review of research efforts of computer vision scientist in facial image processing, especially in the areas of entertainment industry, surveillance, and other human computer interaction applications. To be more specific, this chapter reviews and demonstrates the techniques of visible facial analysis, regardless of specific application areas. First, the chapter makes a thorough survey and comparison of face detection techniques. It provides some demonstrations on the effect of computer vision algorithms and colour segmentation on face images. Then, it reviews the facial expression recognition from the psychological aspect (Facial Action Coding System, FACS) and from the computer animation aspect (MPEG-4 Standard). The chapter also discusses two popular existing facial feature detection techniques: Gabor feature based boosted classifiers and Active Appearance Models, and demonstrate the performance on our in-house dataset. Finally, the chapter concludes with the future challenges and future research direction of facial image processing

Facial micro-expressions grand challenge 2018 summary

Abstract This paper summarises the Facial Micro-Expression Grand Challenge (MEGC 2018) held in conjunction with the 13th IEEE Conference on Automatic Face and Gesture Recognition (FG) 2018. In this workshop, we aim to stimulate new ideas and techniques for facial micro-expression analysis by proposing a new cross-database challenge. Two state-of-the-art datasets, CASME II and SAMM, are used to validate the performance of existing and new algorithms. Also, the challenge advocates the recognition of micro-expressions based on AU-centric objective classes rather than emotional classes. We present a summary and analysis of the baseline results using LBP-TOP, HOOF and 3DHOG, together with results from the challenge submissions

MEGC 2019:the second facial micro-expressions grand challenge

Abstract Automatic facial micro-expression (ME) analysis is a growing field of research that has gained much attention in the last five years. With many recent works testing on limited data, there is a need to spur better approaches that are both robust and effective. This paper summarises the 2nd Facial Micro-Expression Grand Challenge (MEGC 2019) held in conjunction with the 14th IEEE Conference on Automatic Face and Gesture Recognition (FG) 2019. In this workshop, we proposed challenges for two micro-expression (ME) tasks- spotting and recognition, with the aim of encouraging rigorous evaluation and development of new robust techniques that can accommodate data captured across a variety of settings. In this paper, we outline the evaluation protocols for the two challenge tasks, the datasets involved, and an analysis of the best performing works from the participating teams, together with a summary of results. Finally, we highlight some possible future directions

MEGC2020:the third facial micro-expression grand challenge

Abstract The recent emergence of automatic facial micro-expression analysis has attracted a lot of attention in the last five years. Compared to the advances made in micro-expression recognition, the task of micro-expression spotting from long videos is tremendously in need of more effective methods. This paper summarises the 3rd Facial Micro-Expression Grand Challenge (MEGC 2020) held in conjunction with the 15th IEEE Conference on Automatic Face and Gesture Recognition (FG) 2020. In this workshop, we propose a new challenge of spotting both macro- and micro-expressions from long videos, to spur the community to develop new techniques for micro-expression spotting and also to extend facial micro-expression analysis to more complex real-world scenarios where micro-expressions are likely to be intertwined among normal expressions. In this paper, we outline the evaluation protocols for the challenge task, and describe the datasets involved. Then, we summarize the methods from the accepted challenge papers, present the comparison and analysis of results, as well as future directions

Why is the winner the best?

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi-center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and postprocessing (66%). The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work