The characteristics that make up the general identity of engineering technology (ET) degree programs and their graduates are well known;however, the explicit characteristics of ET capstone nationally is unknown. In ot...
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Automated processing, semantic-enrichment and visual analytics methods for point clouds are often use-case specific for a given domain (e.g. for Facility Management (FM) applications). Currently, this means that appli...
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Capturing urban areas and transport infrastructure for automated analysis processes becomes ever more important. Laserscanning and photogrammetry are used for scanning the environment in highly detailed resolution. Th...
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Accurate profiling of microscopy images from small scale to high throughput is an essential procedure in basic and applied biological ***,we present Microsnoop,a novel deep learning–based representation tool trained ...
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Accurate profiling of microscopy images from small scale to high throughput is an essential procedure in basic and applied biological ***,we present Microsnoop,a novel deep learning–based representation tool trained on large-scale microscopy images using masked self-supervised *** can process various complex and heterogeneous images,and we classified images into three categories:single-cell,full-field,and batch-experiment *** benchmark study on 10 high-quality evaluation datasets,containing over 2,230,000 images,demonstrated Microsnoop’s robust and state-ofthe-art microscopy image representation ability,surpassing existing generalist and even several custom *** can be integrated with other pipelines to perform tasks such as superresolution histopathology image and multimodal ***,Microsnoop can be adapted to various hardware and can be easily deployed on local or cloud computing *** will regularly retrain and reevaluate the model using communitycontributed data to consistently improve Microsnoop.
With the continuous advancement of generative models, face morphing attacks have become a significant challenge for existing face verification systems due to their potential use in identity fraud and other malicious a...
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EEG-based neuro-adaptive systems are becoming increasingly popular in the field of learning and education. Their ability to detect changes in brain activity in real-time makes them unique and enables monitoring mental...
EEG-based neuro-adaptive systems are becoming increasingly popular in the field of learning and education. Their ability to detect changes in brain activity in real-time makes them unique and enables monitoring mental workload (MWL) and fatigue. We present a neuro-adaptive system that combines an Augmented Reality (AR) piano tutorial with online EEG measurements of its user, delivered by a passive Brain computer Interface (BCI) system. The MWL was measured by means of EEG and a Filter Bank Common Spatial Patter algorithm (FBCSP) was trained to differentiate between low and high levels of MWL. Low levels were connected to the 0-back task and high levels to the 2-back task. The n-back task was a calibration task to train a binary Machine Learning (ML) classifier to differentiate between low and high MWL. This trained ML algorithm was then used as the central element of the passive BCI, which constantly classified small windows of the EEG during the piano tutorial and adapted the difficulty of the tutorial. 22 Participants were randomly separated into two groups: adaptive and non-adaptive piano tutorial. The results of the non-adaptive group showed significantly higher levels of classified MWL throughout the piano tutorial.
Face recognition (FR) systems are vulnerable to morphing attacks, which refer to face images created by morphing the facial features of two different identities into one face image to create an image that can match bo...
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ISBN:
(数字)9798331536626
ISBN:
(纸本)9798331536633
Face recognition (FR) systems are vulnerable to morphing attacks, which refer to face images created by morphing the facial features of two different identities into one face image to create an image that can match both identities, allowing serious security breaches. In this work, we apply a frequency-based explanation method from the area of explainable face recognition to shine a light on how FR models behave when processing a bona fide or attack pair from a frequency perspective. In extensive experiments, we used two different state-of-the-art FR models and six different morphing attacks to investigate possible differences in behavior. Our results show that FR models rely differently on different frequency bands when making decisions for bona fide pairs and morphing attacks. In the following step, we show that this behavioral difference can be used to detect morphing attacks in an unsupervised setup solely based on the observed frequency-importance differences in a generalizable manner.
This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algori...
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