The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bott...
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Vertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for d...
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In this paper, we present a novel open-source pipeline for face registration based on Gaussian processes as well as an application to face image analysis. Non-rigid registration of faces is significant for many applic...
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In this paper, we present a novel open-source pipeline for face registration based on Gaussian processes as well as an application to face image analysis. Non-rigid registration of faces is significant for many applications in computer vision, such as the construction of 3D Morphable face models (3DMMs). Gaussian Process Morphable Models (GPMMs) unify a variety of non-rigid deformation models with B-splines and PCA models as examples. GPMM separate problem specific requirements from the registration algorithm by incorporating domain-specific adaptions as a prior model. The novelties of this paper are the following: (i) We present a strategy and modeling technique for face registration that considers symmetry, multi-scale and spatially-varying details. The registration is applied to neutral faces and facial expressions. (ii) We release an open-source software framework for registration model-building demonstrated on the publicly available BU3D-FE database. The released pipeline also contains an implementation of an Analysis-by-Synthesis model adaption of 2D face images, tested on the Multi-PIE and LFW database. This enables the community to reproduce, evaluate and compare the individual steps of registration to model-building and 3D/2D model fitting. (iii) Along with the framework release, we publish a new version of the Basel Face Model (BFM-2017) with an improved age distribution and an additional facial expression model.
Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been considerable progress in developing...
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Registration of longitudinal brain Magnetic Resonance Imaging (MRI) scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been considerable progress in developing general-purpose medical image registration techniques, they have not yet attained the requisite precision and reliability for this task, highlighting its inherent complexity. Here we describe the Brain Tumor Sequence Registration (BraTS-Reg) challenge, as the first public benchmark environment for deformable registration algorithms focusing on estimating correspondences between pre-operative and follow-up scans of the same patient diagnosed with a diffuse brain glioma. The challenge was conducted in conjunction with both the IEEE International Symposium on Biomedical Imaging (ISBI) 2022 and the International Conference on Medical Image Computing and computer-Assisted Intervention (MICCAI) 2022. The BraTS-Reg data comprise de-identified multi-institutional multi-parametric MRI (mpMRI) scans, curated for size and resolution according to a canonical anatomical template, and divided into training, validation, and testing sets. Clinical experts annotated ground truth (GT) landmark points of anatomical locations distinct across the temporal domain. The training data with their GT annotations, were publicly released to enable the development of registration algorithms. The validation data, without their GT annotations, were also released to allow for algorithmic evaluation prior to the testing phase, which only allowed submission of containerized algorithms for evaluation on hidden hold-out testing data. Quantitative evaluation and ranking was based on the Median Euclidean Error (MEE), Robustness, and the determinant of the Jacobian of the displacement field. The top-ranked methodologies yielded similar performance across all evaluation metrics and shared several methodological commonalities, including pre-alignment, deep neural networks, inverse consistency
Gamification is the use of game elements in a non-gaming environments. The aims of implementing game elements in gamifying educational environment is to make the learners engaged and motivated in the learning process....
Gamification is the use of game elements in a non-gaming environments. The aims of implementing game elements in gamifying educational environment is to make the learners engaged and motivated in the learning process. But, some of the research result giving bad impacts in implementing gamification in education. The objective of this research is to study the impact of implementing game elements in education. The aim of this research is to study which game elements is most commonly used in gamifying educational environment. The study is done by reviewing previous research on impacts of implementing game element in education from 2008 until 2018 to see whether the elements giving positive or negative impacts on education. The results shows the most common game elements used in gamification is rewards, feedback, challenge, quest/mission/goal, level/stage, point/score, avatars/players, task, character, time-limit, narrative/dialogue, leaderboards, progress bars, and badges. Majority of the research on gamification in education reports positive impacts by introducing gamification in education. In conclusion, by implementing game elements in the gamifying educational environment is a good method to create a good adjustment in learners' behavior and assertiveness in the learning process, it can develop engagement and motivation of the learners'.
Face alignment has witnessed substantial progress in the last decade. One of the recent focuses has been aligning a dense 3D face shape to face images with large head poses. The dominant technology used is based on th...
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It is well known that distance estimation in immersive virtual reality environments suffers from compression when viewed from an egocentric perspective with a head mounted display. While previous research indicates th...
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ISBN:
(纸本)9781509008377
It is well known that distance estimation in immersive virtual reality environments suffers from compression when viewed from an egocentric perspective with a head mounted display. While previous research indicates that providing the user with a virtual avatar with high geometric and motion fidelity may alleviate this problem, little work has been done to investigate which properties of the avatar's representation influence the distance estimation and how. In this poster we report the results of an evaluation of the user's distance perception with different avatar representations. Our results indicate that anthropometric fidelity of the avatar has stronger effect on the distance perception than its visual fidelity.
There can be no debate that Information Mining Projects cause processing tools to arise with the sole purpose of converting available organization data into useful knowledge on account of decision-making. Considering ...
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In the orthognathic surgery, dental splints are important and necessary to help the surgeon reposition the maxilla or mandible. However, the traditional methods of manual design of dental splints are difficult and tim...
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Structural properties of molecules are of primary concern in many fields. This report provides a comprehensive overview on techniques that have been developed in the fields of molecular graphics and visualization with...
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