nils-herrmann/clean_scirepeval huggingface dataset:
Fat quantification in MRI-defined lumbar muscles

Some studies suggest fat infiltration in the lumbar muscles (LM) is associated with lower back pain (LBP) in adults. Usually fat in MRI-defined lumbar muscles is qualitatively evaluated by visual grading via a 3 point scale, whereas a quantitative continuous (0-100%) approach may provide a greater insight. In this paper, we propose a method to precisely quantify the fat content / infiltration in a user-defined region of the lumbar muscles, which may aid better diagnosis. The key steps are segmenting the region of interest (ROI) from the lumbar muscles, identifying the fatty regions in the segmented region based on the selected threshold and softness levels, computing the parameters (such as total and region-wise fat content percentage, total-cross sectional area (TCSA), functional cross-sectional area (FCSA)) and exporting the computations and associated patient information from the MRI, into a database. A standalone application using MATLAB R2010a was developed to perform the required computations along with an intuitive GUI.
Wireless Ion-Selective Electrode Autonomous Sensing System

A paradigm shift in sensing methods and principles is required to meet the legislative demands for detecting hazardous substances in the molecular world. This will encompass the development of new sensing technologies capable of performing very selective and sensitive measurements at an acceptable cost, developed by multidisciplinary teams of chemists, engineers and computer scientists to harvest information from a multitude of molecular targets in health, food and within the environment. In this study we present the successful implementation of a low-cost, wireless chemical sensing system that employs a minimum set of components for effective operation. Specifically, our efforts resulted in a wireless, tri-electrode, ISE pH sensor for use in environmental monitoring. Sensor calibration and validated in situ field trials have been carried out and are presented in this paper.
Toward Automatic Activity Classification and Movement Assessment During a Sports Training Session

Motion analysis technologies have been widely used to monitor the potential for injury and enhance athlete performance. However, most of these technologies are expensive, can only be used in laboratory environments, and examine only a few trials of each movement action. In this paper, we present a novel ambulatory motion analysis framework using wearable inertial sensors to accurately assess all of an athlete's activities in real training environment. We first present a system that automatically classifies a large range of training activities using the discrete wavelet transform (DWT) in conjunction with a random forest classifier. The classifier is capable of successfully classifying various activities with up to 98% accuracy. Second, a computationally efficient gradient descent algorithm is used to estimate the relative orientations of the wearable inertial sensors mounted on the shank, thigh, and pelvis of a subject, from which the flexion-extension knee and hip angles are calculated. These angles, along with sacrum impact accelerations, are automatically extracted for each stride during jogging. Finally, normative data are generated and used to determine if a subject's movement technique differed to the normative data in order to identify potential injury-related factors. For the joint angle data, this is achieved using a curve-shift registration technique. It is envisaged that the proposed framework could be utilized for accurate and automatic sports activity classification and reliable movement technique evaluation in various unconstrained environments for both injury management and performance enhancement.
MMHealth 2017: Workshop on Multimedia for Personal Health and Health Care

The goal of the MMHealth 2017 Workshop on Multimedia for Personal Health and Health Care was to explore the relevance and contribution of multimedia in healthcare and personal health. After a successful debut in Amsterdam in 2016, the second workshop at ACM Multimedia 2017 in Mountain View, California, again attracted more than 30 participants, indicating the relevance that this topic has in the multimedia research community.
Real-time head nod and shake detection for continuous human affect recognition

Human affect recognition is the field of study associated with using automatic techniques to identify human emotion or human affective state. A person's affective states is often communicated non-verbally through body language. A large part of human body language communication is the use of head gestures. Almost all cultures use subtle head movements to convey meaning. Two of the most common and distinct head gestures are the head nod and the head shake gestures. In this paper we present a robust system to automatically detect head nod and shakes. We employ the Microsoft Kinect and utilise discrete Hidden Markov Models (HMMs) as the backbone to a machine learning based classifier within the system. The system achieves 86% accuracy on test datasets and results are provided.
ResnetCrowd: A residual deep learning architecture for crowd counting, violent behaviour detection and crowd density level classification

In this paper we propose ResnetCrowd, a deep residual architecture for simultaneous crowd counting, violent behaviour detection and crowd density level classification. To train and evaluate the proposed multi-objective technique, a new 100 image dataset referred to as Multi Task Crowd is constructed. This new dataset is the first computer vision dataset fully annotated for crowd counting, violent behaviour detection and density level classification. Our experiments show that a multi-task approach boosts individual task performance for all tasks and most notably for violent behaviour detection which receives a 9% boost in ROC curve AUC (Area under the curve). The trained ResnetCrowd model is also evaluated on several additional benchmarks highlighting the superior generalisation of crowd analysis models trained for multiple objectives.
Content-based image descriptors for enhanced person annotation in personal digital photo archives

In this paper we investigate the use of content-based image descriptors for enhancing the performance of person annotation in personal photo management applications. The descriptors examined are related to the context of person recognition through face and body-patch feature matching in personal digital photos. In order to identify the best performing content-based descriptors, we first study a number of colour and texture descriptors for body-patch matching and face recognition descriptors for face matching using a suitably chosen data set taken from typical personal photo collections. We then analyse the performance of three different fusion schemes to identify the best combination of colour, texture and face recognition descriptors. Finally, we apply those descriptors to the problem of person annotation and measure their performance using a test data set, which comprises 7 different real-life personal photo collections. The experimental results illustrate that combining body-patch feature matching with face recognition significantly improves the performance of person annotation. We further show that combining colour with texture leads to improved performance of body-patch matching. The content-based image descriptors identified in this paper show great potential for person annotation in personal photo management applications.
Investigation of Image Models for Landmark Classification

One commonly used approach to scene localization and landmark recognition is to match an input image against a large annotated database of images using local image features. However problems exist with these approaches relating to memory constraints and the processing time required to compare high dimensional image feature vectors in a very large scale database.\par We investigate a new landmark classification technique which takes advantage of the fact that there is considerable overlap in visually similar images of landmarks in any large public photo repository. A large number of images containing landmarks are clustered into visually similar clusters. Classification models are then implemented and trained based on global histograms of interest point features from these clusters to create models which can be used for robust real-time accurate classification of images containing these landmarks. We also investigate different techniques for the creation of these classification models to ascertain how best to guarantee a high level of robustness, accuracy and speed.
Identifying an efficient and robust sub-shot segmentation method for home movie summarisation

With the increasing volumes of home video footage and the need for effectively managing such archives, home movie summarisation has become an important and key research topic in the recent past. Despite growing interest from the research community, automatic summarisation remains a challenging research problem due to unrestricted capture and lack of storyline present in the home video content. In this paper, we address a sub-problem of the broad summarisation problem, namely sub-shot segmentation, as an important content analysis step in home movie summarisation. More specifically, we conduct an experimental study to identify an efficient and robust sub-shot segmentation method through a performance comparison of a number of optical flow field computation methods. Formulating the task of sub-shot segmentation as a process of decomposing a raw video into different video segments in line with the change in dominant camera motion, we are interested in detecting the most useful camera motions we have observed in real collections, namely pan, tilt and zoom. To this end, a number of feature-based optical flow field computation methods, including two well-known local feature descriptors called SIFT and SURF, and the Pyramidal Lucas-Kanade (PLK) local feature tracker, are investigated as potentially useful approaches for facilitating efficient and robust sub-shot segmentation in this paper. We then compare the performance of those algorithms with 5 different block matching motion estimation algorithms. Simulation results show that the PLK algorithm outperforms the SIFT and SURF algorithms in terms of both accuracy and efficiency. Furthermore, despite its slightly lower accuracy compared to that of the most optimal block matching algorithms (BMA), we conclude that a local feature tracker like the PLK algorithm is the preferred choice for sub-shot detection in real-life home movie archives due to the trade-off it provides between accuracy and efficiency.
Fischlar-TRECVid-2004: combined text- and image-based searching of video archives

The Fischlar-TRECVid-2004 system was developed for Dublin City University's participation in the 2004 TRECVid video information retrieval benchmarking activity. The system allows search and retrieval of video shots from over 60 hours of content. The shot retrieval engine employed is based on a combination of query text matched against spoken dialogue combined with image-image matching where a still image (sourced externally), or a keyframe (from within the video archive itself), is matched against all keyframes in the video archive. Three separate text retrieval engines are employed for closed caption text, automatic speech recognition and video OCR. Visual shot matching is primarily based on MPEG-7 low-level descriptors. The system supports relevance feedback at the shot level enabling augmentation and refinement using relevant shots located by the user. Two variants of the system were developed, one that supports both text- and image-based searching and one that supports image only search. A user evaluation experiment compared the use of the two systems. Results show that while the system combining text- and image-based searching achieves greater retrieval effectiveness, users make more varied and extensive queries with the image only based searching
Smart Stadium for Smarter Living: Enriching the Fan Experience

Rapid urbanization has led to more people residing in cities than ever before, and projections estimate that 64% of the global population will be urban by 2050. Cities are beginning to explore Smart City initiatives to reduce expenses and complexities while increasing efficiency and quality of life for its citizens. To achieve this goal, advances in technology and policies are needed together with rethinking traditional solutions to transportation, safety, sustainability, among other priority areas. We propose the use of a Smart Stadium as a 'living laboratory' to identify, deploy and test Internet of Things technologies and Smart City solutions in an environment small enough to practically trial but large enough to evaluate effectiveness and scalability. The Smart Stadium for Smarter Living initiative brings together Arizona State University, Dublin City University, Intel Corporation, Gaelic Athletic Association, Sun Devil Stadium and Croke Park to explore smart environment solutions.
Low Computational Complexity Variable Block Size (VBS) Partitioning for Motion Estimation Using the Walsh Hadamard Transform (WHT)

Variable Block Size (VBS) based motion estimation has been adapted in state of the art video coding, such asH.264/AVC, VC-1. However, a low complexity H.264/AVCencoder cannot take advantage of VBS due to its power consumption requirements. In this paper, we present a VBS partition algorithm based on a binary motion edge map without either initial motion estimation or Rate-Distortion (R-D)optimization for selecting modes. The proposed algorithm uses the Walsh Hadamard Transform (WHT) to create a binary edge map, which provides a computational complexity cost effectiveness compared to other light segmentation methods typically used to detect the required region.
MPEG audio bitstream processing towards the automatic generation of sports programme summaries

The frequency subband scale-factors are fundamental components of MPEG-1 audio encoded bitstreams. Examination of scale-factor weights is sufficient for the establishment of an audio amplitude profile of an audio track. If, for sports programme TV broadcasts, the audio amplitude is assumed to primarily reflect the noise level exhibited by the commentator (and/or attending spectators), then, this vocal reaction to the significance of unfolding events may be used as a basis for summarisation. i.e. by relying on the exhilaration, or otherwise, expressed by the commentator/spectators, individual clips of the programme (e.g. camera shots), may be ranked according to their relative significance. A summary may then be produced by amalgamating (chronologically) any number of these clips corresponding to selected audio peaks.
Enhancing the Functionality of Interactive TV with Content-Based Multimedia Analysis

In this paper we describe how content-based analysis techniques can be used to provide much greater functionality to the users of an interactive TV (iTV) device. We describe several content-based multimedia analysis techniques and how some of these can be exploited in the iTV domain, resulting in the provision of a set of powerful functions for iTV users. To validate our ideas, we introduce an iTV application we developed which incorporates some of these techniques into a simple set of user features, in order to demonstrate the usefulness of content-based techniques for iTV. The contribution of this paper is not to provide an in-depth discussion on each of the individual content-based techniques, but rather to show how many of these powerful technologies can be incorporated into an interactive TV system.
3D Estimation and Visualization of Motion in a Multicamera Network for Sports

In this work, we develop image processing and computer vision techniques for visually tracking a tennis ball, in 3D, on a court instrumented with multiple low-cost IP cameras. The technique first obtains 2D ball tracking data from each camera view using 2D object tracking methods. Next, an automatic feature-based video synchronization method is applied. This technique uses the extracted 2D ball information from two or more camera views, plus camera calibration information. In order to find 3D trajectory, the temporal 3D locations of the ball is estimated using triangulation of correspondent 2D locations obtained from automatically synchronized videos. Furthermore, in order to improve the continuity of the tracked 3D ball during times when no two cameras have overlapping views of the ball location, we incorporate a physics-based trajectory model into the system. The resultant 3D ball tracks are then visualized in a virtual 3D graphical environment. Finally, we quantify the accuracy of our system in terms of reprojection error.
A Sensing Platform for Physiological and Contextual Feedback to Tennis Athletes

In this paper we describe our work on creating a multimodal sensing platform for providing feedback to tennis coaches and players. The platform includes a fixed installation around a tennis court consisting of a video camera network and a localisation system as well as wearable sensing technology deployed to individual athletes. We describe the various components of this platform and explain how we can capture synchronised multi-modal sensor data streams for games or training sessions. We then describe the content-based retrieval system we are building to facilitate the development of novel coaching tools. We provide some examples of the queries that the system can support, where these queries are chosen to be suitably expressive so as to reflect a coach’s complex information needs regarding tennis-related performance factors.
Temporal video segmentation for real-time key frame extraction

The extensive amount of media coverage today, generates difficulties in identifying and selecting desired information. Browsing and retrieval systems become more and more necessary in order to support users with powerful and easy-to-use tools for searching, browsing and summarization of information content. The starting point for these tasks in video browsing and retrieval systems is the low level analysis of video content, especially the segmentation of video content into shots. This paper presents a fast and efficient way to detect shot changes using only the temporal distribution of macroblock types in MPEG compressed video. The notion of a dominant reference frame is introduced here. A dominant frame denotes the reference frame (I or P) used as prediction reference for most of the macroblocks from a subsequent B frame.
TennisSense: A platform for extracting semantic information from multi-camera tennis data

In this paper, we introduce TennisSense, a technology platform for the digital capture, analysis and retrieval of tennis training and matches. Our algorithms for extracting useful metadata from the overhead court camera are described and evaluated. We track the tennis ball using motion images for ball candidate detection and then link ball candidates into locally linear tracks. From these tracks we can infer when serves and rallies take place. Using background subtraction and hysteresis-type blob tracking, we track the tennis players positions. The performance of both modules is evaluated using ground-truthed data. The extracted metadata provides valuable information for indexing and efficient browsing of hours of multi-camera tennis footage and we briefly illustrative how this data is used by our tennis-coach playback interface.
Coastal fog detection using visual sensing

Use of visual sensing techniques to detect low visibility conditions may have a number of advantages when combined with other methods, such as satellite based remote sensing, as data can be collected and processed in real or near real time. Camera-enabled visual sensing can provide direct confirmation of modelling and forecasting results. Indeed, fog detection, modelling and prediction are a priority for maritime communities and coastal cities due to economic impacts of fog on aviation, marine, and land transportation. Canadian and Irish coasts are particularly vulnerable to dense fog under certain environmental conditions, and offshore installations related to oil and gas production on the Grand Banks (off the Canadian East Coast) for example can be adversely affected by weather and sea state conditions. In particular, fog can disrupt the transfer of equipment and people to/from the production platforms by helicopter. Such disruptions create delays and the delays cost money. According to offshore oil and gas industry representatives at a recent workshop on metocean monitoring and forecasting for the Newfoundland and Labrador (NL) offshore, there is a real need for improved forecasting of visibility (fog) out to 3 days. The ability to accurately forecast future fog conditions would improve industry's ability to adjust its schedule of operations accordingly. In addition, it was recognised by workshop participants that the physics of the Grand Banks fog formation is not well understood, and that more and better data are needed. In Europe, an EU COST action fog project, with objectives of reducing economic loss and fatalities, has also been created to develop advanced methods for very short-range forecasts of fog and low clouds. Key research objectives of the project included methods for determining the optimal combination of satellite and ground-based observations and measurement techniques of fog. These data were then used to develop estimates of fog risk at high spatial and temporal resolutions, however the focus was on remote satellite observations rather than local visual sensing systems .
Background Modelling in Infrared and Visible Spectrum Video for People Tracking

In this paper, we present our approach to robust background modelling which combines visible and thermal infrared spectrum data. Our work is based on the non-parametric background model describe in 1. We use a pedestrian detection module to prevent erroneous data from becoming part of the background model and this allows us to initialise our bacjground model, even in the presence of foreground objects. Visible and infrared features are use to remove incorrectly detected foreground regions. Allowing our model to quickly recover from ghost regions and rapid lighting changes. An object-based shadow detector also improves our algorithm's performance.
Performance of video processing at the edge for crowd-monitoring applications

Video analytics has a key role to play in smart cities and connected community applications such as crowd counting, activity detection, event classification, traffic counting etc. Using a cloud-centric approach where data is funnelled to a central processor presents a number of key problems such as available bandwidth, real-time responsiveness and personal data privacy issues. With the development of edge computing, a new paradigm for smart data management is emerging. Raw video feeds can be pre-processed at the point of capture while integration and deeper analytics is performed in the cloud. In this paper we explore the capacity of video processing at the edge and shown that basic image processing can be achieved in near real-time on low-powered gateway devices. We have also investigated deep learning model capabilities for crowd counting in this context showing that its performance is highly dependent on the input size and rescaling video frames can optimise processing and performance. Increased edge processing resolves a number of issues in video analytics for crowd monitoring applications.