Articles tagged with Edge Detection
Researchers developed a compact sensor system with infrared imaging capabilities that can be fitted to drones for remote crop monitoring. The system can rapidly switch between edge detection and detailed infrared imaging, allowing farmers to pinpoint specific crop needs and boost harvests.
A new machine learning approach combines computer vision with deep-learning algorithms to pinpoint problem areas in concrete structures. The system enables efficient identification and inspection of cracks using autonomous robots, reducing the overall inspection workload.
Researchers have developed a noise-resistant method for detecting object edges without prior imaging. The new method, called edge-sensitive single-pixel imaging, proves highly effective in accurately detecting object edges despite the presence of noise.
Researchers at North Carolina State University have developed a new methodology called Patch-to-Cluster attention (PaCa) that addresses the challenges of vision transformers. PaCa improves ViT's ability to identify, classify, and segment objects in images while reducing computational demands and enhancing model interpretability.
An edge-based digital twin service was evaluated for remote control of robotic arms, revealing that inverse kinematics computation and movement trajectory planning are critical functions. The service profile also shows potential for computational resource savings with low-abstraction level commands.
A team of researchers at Osaka University has created a machine learning system that can virtually remove buildings from a live view, streaming in real-time on a mobile device. This technology can help accelerate the process of urban renewal based on community agreement, reducing conflicts and delays.
The National Institute for Materials Science in Japan has developed an ionic artificial vision device capable of increasing edge contrast between dark and light areas like human vision. This device uses ionic migration and interaction within solids to mimic human optical illusions without software.
A team of researchers at Osaka University created a custom dataset to train an AI algorithm to digitally remove unwanted objects from building façade images. The algorithm achieved high accuracy in inpainting occluded regions with digital inpainting.
A team of scientists from Osaka University developed a machine learning method for classifying the type of building and its primary façade color using deep learning models applied to street-level images. This work may assist in fostering neighborhood cohesion and support urban renewal by providing tailored street-view datasets.
Researchers have developed a new method for two-dimensional optical spatial differentiation, enabling efficient broadband imaging with high contrast. The proposed dielectric metasurface device outperforms current methods in efficiency, compactness, and power consumption.