How do situational variables, related to shooting, in elite Gaelic football impact shot success?

Analysis of 3926 shots from the 2019 Senior inter-county football championship aims to establish the impact of distance, angle, shot type, method and pressure on shot success. Findings demonstrate that shots from free kicks contribute 20.5% of the total attempts in Gaelic football, with a success rate of 75%, in contrast to 50% success of shots from open play. Moreover, the range from which free kick success is >57.6% accuracy extends to 40 m, while from open play this is passed at a range of 28 m. There were almost twice as many right foot shots (64.4%) compared with the left foot (32.4%), with right foot attempts marginally more accurate. Shots under low pressure were most successful, while those under medium pressure were less successful than those under high pressure, albeit taken from an average distance of 7.5 m closer to the target. A logistic regression model to explore the impact of all variables on shot outcome demonstrates the significance of shot distance, angle and pressure on the kicker, as well as whether shots are taken with the hand or foot. This research provides an important step in understanding the scale of the impact of a range of variables on shot success in Gaelic football while simultaneously providing an initial model to predict the shot outcome based on these variables.

Automatic coverage measurement of shot peened panels

Shot peening is the process of treating metallic surfaces with a regulated blast of shots to increase material strength and durability. Determining the coverage level of the shots is an important parameter in the assessment of the quality of treatment. Traditionally, coverage measurement is performed manually using a magnifying glass, which leads to inefficiency. Despite the proposal for the use of image segmentation techniques for determining the coverage measurement, literature on this topic is not extensively developed. In this thesis, various relevant image segmentation techniques are investigated including thresholding, edge detection, watershed segmentation, active contour, graph cut and neural network. The aim is to develop a generic coverage measurement algorithm, which is accurate and robust to variations in illumination, shot type, coverage level and has real-time capabilities using a simple experimental setup. The results obtained from each method are discussed and compared against a set of relevant performance criteria.

Automatic coverage measurement of shot peened panels

Shot peening is the process of treating metallic surfaces with a regulated blast of shots to increase material strength and durability. Determining the coverage level of the shots is an important parameter in the assessment of the quality of treatment. Traditionally, coverage measurement is performed manually using a magnifying glass, which leads to inefficiency. Despite the proposal for the use of image segmentation techniques for determining the coverage measurement, literature on this topic is not extensively developed. In this thesis, various relevant image segmentation techniques are investigated including thresholding, edge detection, watershed segmentation, active contour, graph cut and neural network. The aim is to develop a generic coverage measurement algorithm, which is accurate and robust to variations in illumination, shot type, coverage level and has real-time capabilities using a simple experimental setup. The results obtained from each method are discussed and compared against a set of relevant performance criteria.

Moving beyond the ‘shot-type list’ towards the ‘Meaning Model’: Placing meaning at the centre of film education

In 1990 Kress and Van Leeuwen’s Reading Images began a conversation based upon the practice of teaching image-orientated texts in Australian classrooms. Since then, however, little of this important conversation has been translated into meaningful pedagogical change for the teaching of kineikonic (moving image) texts in Australia. From state-run primary schools to national postgraduate film education institutions, the primary tool used to initiate students into the potential to create meaning through film – the shot-type list – has remained relatively unchanged. This article proposes an updated pedagogical tool – identified as the ‘Meaning Model’ – which draws from contemporary discourses around how films make meaning in seeking to bring understandings of the kineikonic mode into the classroom, in a practical and accessible way.

Comparative Study of Movie Shot Classification Based on Semantic Segmentation

The shot-type decision is a very important pre-task in movie analysis due to the vast information, such as the emotion, psychology of the characters, and space information, from the shot type chosen. In order to analyze a variety of movies, a technique that automatically classifies shot types is required. Previous shot type classification studies have classified shot types by the proportion of the face on-screen or using a convolutional neural network (CNN). Studies that have classified shot types by the proportion of the face on-screen have not classified the shot if a person is not on the screen. A CNN classifies shot types even in the absence of a person on the screen, but there are certain shots that cannot be classified because instead of semantically analyzing the image, the method classifies them only by the characteristics and patterns of the image. Therefore, additional information is needed to access the image semantically, which can be done through semantic segmentation. Consequently, in the present study, the performance of shot type classification was improved by preprocessing the semantic segmentation of the frame extracted from the movie. Semantic segmentation approaches the images semantically and distinguishes the boundary relationships among objects. The representative technologies of semantic segmentation include Mask R-CNN and Yolact. A study was conducted to compare and evaluate performance using these as pretreatments for shot type classification. As a result, the average accuracy of shot type classification using a frame preprocessed with semantic segmentation increased by 1.9%, from 93% to 94.9%, when compared with shot type classification using the frame without such preprocessing. In particular, when using ResNet-50 and Yolact, the classification of shot type showed a 3% performance improvement (to 96% accuracy from 93%).