Spike-based dynamic computing with asynchronous sensing

Mimicking high-level abstraction of the brain to achieve energy advantages is a fundamental issue in neuromorphic computing. Here, the authors fabricate an asynchronous chip

HiFiGaze: Improving Eye Tracking Accuracy Using Screen Content

HiFiGaze leverages two pieces of information: 1) crop of the user''s eyes (as captured by increasingly common high-quality, 4K "selfie" cameras), and 2) the contents of the screen (which the

Real-Time Object Detection on Edge Devices: A Fisheye Specific DFINE

Through extensive experiments, the proposed model achieved an F1 score of 0.6268 and over 15 FPS in INT8 mode on the Jetson AGX Orin 32 GB platform, satisfying the requirements for stable real

Edge Computing with Artificial Intelligence: A Machine

Under this background and trend, the traditional cloud computing model may nevertheless encounter many problems in independently tackling the

Review Eye tracking algorithms, techniques, tools, and applications

In addition, they show that eye tracking techniques have more accurate detection results compared with traditional event-detection methods. In addition, various motives and factors in the

Comprehensive review of edge and contour detection: from traditional

Edge and contour detection plays critical roles in computer vision and image processing, with extensive applications in advanced tasks including object recognition, shape matching, visual saliency, image

Using Deep Learning to Increase Eye-Tracking Robustness,

Our general approach involves testing and reporting on the impact of several contemporary eye segmentation networks on the spatial accuracy, precision, and robustness to dropouts of the final

Efficient 2D/3D Gaze Estimation Using TGGNet: A Transformer Graph

This paper presents a novel gaze estimation approach using Graph Neural Networks (GNNs), leveraging the geometric relationship between facial landmarks and gaze direction.

Edge intelligence through in-sensor and near-sensor

We introduce emerging device technologies, circuit architectures, algorithmic frameworks, and applications implementing artificial intelligence of

Prediction of Radiological Diagnostic Errors from Eye

The graph neural network part of the framework also works with radiograph regions that however do not correspond to image patches from the paragraph above. The regions of attention for

Edge-Eye: Rectifying Millimeter-Level Edge Deviation in

In this paper, we propose a camera-enabled approach, called Edge-Eye, to rectify the edge deviation automatically for IXPE production with millimeter-level accuracy.

LB Eye Tracking Accuracy Doc 2

Method Of Data Capture 2. Background 2.1 Machine Learning Web Camera Machine learning applied to eye tracking revolutionizes our understanding of visual attention. By training models on extensive

Eye tracker accuracy and precision

Accuracy and precision are important concepts for understanding and for evaluating the quality of eye tracking data. Accuracy is the offset between the actual gaze position and what the eye tracker

What Is Edge Computing? Everything You Need to

Thus, edge computing is reshaping IT and business computing. Take a comprehensive look at what edge computing is, how it works, the influence of

A systematic review on vision-based gaze estimation: Advance in

Eye gaze estimation is all about figuring out where a person is looking by analyzing eye movements. This is usually done using cameras or eye-tracking devices, a key technique in

Edge-Guided Near-Eye Image Analysis for Head Mounted Displays

Then we feed the edge maps into an Edge-Guided Segmentation and Fitting Network (ESF-Net) for accurate segmentation and el-lipse fitting. Extensive experimental results demonstrate that our

A Quantitative Investigation on the Effect of Edge Enhancement for

Optical flaws and refractive errors of the eye in addition to reducing visual acuity affect contrast sensitivity. Having a high contrast sensitivity and accurate diagnosis, directly depends on

Eye-tracking Technologies in Mobile Devices Using

We present how various computational systems are used in mobile eye-tracking applications and how edge computing and mobile device eye-tracking can be

Edge Intelligence: A Review of Deep Neural Network

Deploying deep neural networks (DNNs) in resource-limited environments—such as smartwatches, IoT nodes, and intelligent

Benchmarking AI Inference at the Edge

Balancing eficiency and power at the edge for Industry 4.0 ta-heavy input from multiple sensors. These robots will intelligently respond to dynamic variables, conditions, and instructions by immediately

A novel real-time eye detection method using edge detection and

This work presents a novel approach to the recognition of eyes and mouths using Euclidean distance and edge detection. By face detection methods, the face region is detected.

Enhancing gaze estimation accuracy in wearable eye-tracking devices

Eye-tracking devices are convenient for interpreting human behaviors and intentions, enabling contactless human–computer interaction, such as in medical image interpretation using eye

Localization of moving edge with sub-pixel accuracy in 1-D images

To simulate the moving edge, the accumulative function is defined and then used in three edge detection algorithms with sub-pixel accuracy in 1-D images: algorithm based on approximation

Performance Analysis of CNN Models for Mobile Device Eye Tracking

We evaluate four lightweight CNN models (LeNet-5, AlexNet, MobileNet, and ShuffleNet) for gaze estimation on mobile devices using a publicly available dataset called GazeCapture.

What is Edge Detection in Image Processing?

Image edge detection is a technique used to locate the boundaries of objects in an image. Instead of processing every pixel value, edge detection

White paper: Tobii Sticky mobile eye tracking accuracy

Summary This paper describes how the performance of Tobii Sticky''s webcam eye tracking algorithm has been validated through live eye tracking studies using online panels. Anonymous respondents

A Mathematical Survey of Image Deep Edge Detection

By synthesizing mathematical formulations, performance metrics, and future directions, this survey equips researchers with a comprehensive