202 research outputs found
Real-Time Monitoring and Assessment System with Facial Landmark Estimation for Emotional Recognition in Work
The Model for Monitoring and Regulating Emotional States in the Work Environment based on Neural Networks and Emotion Recognition Algorithms presents an innovative approach to enhancing employee well-being and productivity by leveraging advanced technologies. This paper on the development of a system that utilizes neural networks and emotion recognition algorithms to monitor and interpret emotional cues exhibited by individuals in real-time within the work environment. With the uses of novel Directional Marker Controlled Facial Landmark (DMCFL) Emotion recognition algorithms are employed to analyze facial expressions, speech patterns, physiological data, and text-based communication to infer the emotional state of employees. Neural networks are then utilized to process this data and provide more sophisticated emotion classification and prediction. The emotional states are classified with the integrated Regression Logistics Classifier (RLC) model for classification. The analysis of the findings expressed that the real-time monitoring enables employers and supervisors to gain insights into the emotional well-being of employees, identifying patterns and potential issues. The system facilitates feedback and regulation mechanisms, allowing for personalized interventions and support tailored to individual emotional needs
Hidden Markov Model Deep Learning Architecture for Virtual Reality Assessment to Compute Human–Machine Interaction-Based Optimization Model
Virtual Reality (VR) is a technology that immerses users in a simulated, computer-generated environment. It creates a sense of presence, allowing individuals to interact with and experience virtual worlds. Human-Machine Interaction (HMI) refers to the communication and interaction between humans and machines. Optimization plays a crucial role in Virtual Reality (VR) and Human-Machine Interaction (HMI) to enhance the overall user experience and system performance. This paper proposed an architecture of the Hidden Markov Model with Grey Relational Analysis (GRA) integrated with Salp Swarm Algorithm (SSA) for the automated Human-Machine Interaction. The proposed architecture is stated as a Hidden Markov model Grey Relational Salp Swarm (HMM_ GRSS). The proposed HMM_GRSS model estimates the feature vector of the variables in the virtual reality platform and compute the feature spaces. The HMM_GRSS architecture aims to estimate the feature vector of variables within the VR platform and compute the feature spaces. Hidden Markov Models are used to model the temporal behavior and dynamics of the system, allowing for predictions and understanding of the interactions. Grey Relational Analysis is employed to evaluate the relationship and relevance between variables, aiding in feature selection and optimization. The SSA helps optimize the feature spaces by simulating the collective behavior of salp swarms, improving the efficiency and effectiveness of the HMI system. The proposed HMM_GRSS architecture aims to enhance the automated HMI process in a VR platform, allowing for improved interaction and communication between humans and machines. Simulation analysis provides a significant outcome for the proposed HMM_GRSS model for the estimation Human-Machine Interaction
Two-Factor Biometric Identity Verification System for the Human-Machine System Integrated Deep Learning Model
The Human-Machine Identity Verification System based on Deep Learning offers a robust and automated approach to identity verification, leveraging the power of deep learning algorithms to enhance accuracy and security. This paper focused on the biometric-based authentical scheme with Biometric Recognition for the Huma-Machinary Identification System. The proposed model is stated as the Two-Factor Biometric Authentication Deep Learning (TBAuthDL). The proposed TBAuthDL model uses the iris and fingerprint biometric data for authentication. TBAuthDL uses the Weighted Hashing Cryptographic (WHC) model for the data security. The TBAuthDL model computes the hashing factors and biometric details of the person with WHC and updates to the TBAuthDL. Upon the verification of the details of the assessment is verified in the Human-Machinary identity. The simulation analysis of TBAuthDL model achieves a higher accuracy of 99% with a minimal error rate of 1% which is significantly higher than the existing techniques. The performance also minimizes the computation and processing time with reduced complexity
Learning Depth from Monocular Videos using Direct Methods
The ability to predict depth from a single image - using recent advances in
CNNs - is of increasing interest to the vision community. Unsupervised
strategies to learning are particularly appealing as they can utilize much
larger and varied monocular video datasets during learning without the need for
ground truth depth or stereo. In previous works, separate pose and depth CNN
predictors had to be determined such that their joint outputs minimized the
photometric error. Inspired by recent advances in direct visual odometry (DVO),
we argue that the depth CNN predictor can be learned without a pose CNN
predictor. Further, we demonstrate empirically that incorporation of a
differentiable implementation of DVO, along with a novel depth normalization
strategy - substantially improves performance over state of the art that use
monocular videos for training
A Survey on Open-Vocabulary Detection and Segmentation: Past, Present, and Future
As the most fundamental tasks of computer vision, object detection and
segmentation have made tremendous progress in the deep learning era. Due to the
expensive manual labeling, the annotated categories in existing datasets are
often small-scale and pre-defined, i.e., state-of-the-art detectors and
segmentors fail to generalize beyond the closed-vocabulary. To resolve this
limitation, the last few years have witnessed increasing attention toward
Open-Vocabulary Detection (OVD) and Segmentation (OVS). In this survey, we
provide a comprehensive review on the past and recent development of OVD and
OVS. To this end, we develop a taxonomy according to the type of task and
methodology. We find that the permission and usage of weak supervision signals
can well discriminate different methodologies, including: visual-semantic space
mapping, novel visual feature synthesis, region-aware training,
pseudo-labeling, knowledge distillation-based, and transfer learning-based. The
proposed taxonomy is universal across different tasks, covering object
detection, semantic/instance/panoptic segmentation, 3D scene and video
understanding. In each category, its main principles, key challenges,
development routes, strengths, and weaknesses are thoroughly discussed. In
addition, we benchmark each task along with the vital components of each
method. Finally, several promising directions are provided to stimulate future
research
Place recognition: An Overview of Vision Perspective
Place recognition is one of the most fundamental topics in computer vision
and robotics communities, where the task is to accurately and efficiently
recognize the location of a given query image. Despite years of wisdom
accumulated in this field, place recognition still remains an open problem due
to the various ways in which the appearance of real-world places may differ.
This paper presents an overview of the place recognition literature. Since
condition invariant and viewpoint invariant features are essential factors to
long-term robust visual place recognition system, We start with traditional
image description methodology developed in the past, which exploit techniques
from image retrieval field. Recently, the rapid advances of related fields such
as object detection and image classification have inspired a new technique to
improve visual place recognition system, i.e., convolutional neural networks
(CNNs). Thus we then introduce recent progress of visual place recognition
system based on CNNs to automatically learn better image representations for
places. Eventually, we close with discussions and future work of place
recognition.Comment: Applied Sciences (2018
CoupleNet: Coupling Global Structure with Local Parts for Object Detection
The region-based Convolutional Neural Network (CNN) detectors such as Faster
R-CNN or R-FCN have already shown promising results for object detection by
combining the region proposal subnetwork and the classification subnetwork
together. Although R-FCN has achieved higher detection speed while keeping the
detection performance, the global structure information is ignored by the
position-sensitive score maps. To fully explore the local and global
properties, in this paper, we propose a novel fully convolutional network,
named as CoupleNet, to couple the global structure with local parts for object
detection. Specifically, the object proposals obtained by the Region Proposal
Network (RPN) are fed into the the coupling module which consists of two
branches. One branch adopts the position-sensitive RoI (PSRoI) pooling to
capture the local part information of the object, while the other employs the
RoI pooling to encode the global and context information. Next, we design
different coupling strategies and normalization ways to make full use of the
complementary advantages between the global and local branches. Extensive
experiments demonstrate the effectiveness of our approach. We achieve
state-of-the-art results on all three challenging datasets, i.e. a mAP of 82.7%
on VOC07, 80.4% on VOC12, and 34.4% on COCO. Codes will be made publicly
available.Comment: Accepted by ICCV 201
Neutralizing antibody response in the patients with hand, foot and mouth disease to enterovirus 71 and its clinical implications
Enterovirus 71 (EV71) has emerged as a significant pathogen causing large outbreaks in China for the past 3 years. Developing an EV71 vaccine is urgently needed to stop the spread of the disease; however, the adaptive immune response of humans to EV71 infection remains unclear. We examined the neutralizing antibody titers in HFMD patients and compared them to those of asymptomatic healthy children and young adults. We found that 80% of HFMD patients became positive for neutralizing antibodies against EV71 (GMT = 24.3) one day after the onset of illness. The antibody titers in the patients peaked two days (GMT = 79.5) after the illness appeared and were comparable to the level of adults (GMT = 45.2). Noticeably, the antibody response was not correlated with disease severity, suggesting that cellular immune response, besides neutralizing antibodies, could play critical role in controlling the outcome of EV71 infection in humans
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