Articles tagged with Image Processing
Researchers at MSU have developed a state-of-the-art imaging system that can capture brain activity in real-time, revealing the intricate processes of memory formation. The system has the potential to image 10,000 to 20,000 neurons simultaneously, offering unprecedented insights into how memories are created and recalled.
Researchers developed a fast and convenient method called polarized imaging dynamic light scattering (PIDLS) to evaluate nanoparticle size, morphology and distributions. The method uses optical sphericity to describe the degree of deviation from spheres and provides statistical morphological distribution.
Researchers have implemented Orbital Angular Momentum (OAM) as an independent information carrier for optical holography, leading to OAM multiplexed holography. The new design approach, MHC-OAM, uses spatial light modulators to achieve multiramp helical conical beams with different parameters serving as information encryption or decryp...
Engineers at Rice University and the University of Maryland developed NeuWS, a technology that can undo light scattering effects, enabling full-motion video through various media. The technology measures wavefronts to rapidly decipher phase information, overcoming the 'holy grail problem' in optical imaging.
DragGAN enables non-professionals to perform complex image edits with AI support, adjusting pose, gaze direction, and viewing angle. The method uses Generative Adversarial Networks to generate new images, promising simplified post-processing for AI-generated content.
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.
Researchers at the Beckman Institute for Advanced Science and Technology have developed a new framework for super-resolution ultrasound using deep learning, reducing processing speeds from minutes to seconds. The new technology enables real-time blood flow visualization, overcoming challenges faced by conventional methods.
Online radiologists choose studies based on financial attractiveness, leading to delays in high-priority cases. This study found that expedited priority class contained the highest percentage of delayed studies.
Researchers develop a new composite strategy to produce clean Landsat images with reduced cloud and shadow errors. The approach uses an algorithm to select pixels from multiple dates to create a virtual median-value point, detecting and replacing clouds and shadows in the process.
Researchers developed a new probe to measure pH levels in cells, revealing a constant conversion rate from endosomes to lysosomes. The probe's ability to track pH changes enables faster diagnosis and potential treatments for lysosomal diseases.
Dr. Miaomiao Zhang's research aims to automate late mechanical activation detection from cardiac magnetic resonance images using machine learning and AI techniques. This could lead to more accurate placement of CRT electrodes and improved patient outcomes.
Researchers have developed a quantum lidar system that uses single-photon detection to acquire high-resolution 3D images underwater. The technology has the potential to inspect underwater installations, monitor submerged archaeology sites, and enhance security applications.
A mobile application utilizing Python and a single-element ultrasound transducer has been developed for photoacoustic tomography (PAT) image reconstruction. The application successfully reconstructs high-quality images with signal-to-noise ratio values above 30 decibels, making it suitable for point-of-care diagnosis in low-resource se...
Researchers have developed a non-invasive technique using laser speckle imaging to visualize microvasculature in donor hearts and detect abnormal blood flow. The method enables precise visualization of blood circulation, potentially identifying hearts suitable for transplantation.
A team from Nanjing University and Sun Yat-Sen University developed a two-facing Janus OPO scheme for generating high-efficiency, high-purity broadband LG modes with tunable topological charge. The output LG mode has a tunable wavelength between 1.5 μm and 1.6 μm, with a conversion efficiency above 15 percent.
Researchers at EPFL have developed a novel imaging technique using cryogenic transmission electron tomography and deep learning to visualize the nanostructure of platinum catalyst layers in fuel cells. This breakthrough reveals the heterogenous thickness of ionomer, a crucial component that influences catalyst performance.
A new review by Pieter Roelfsema sheds light on how the brain creates a coherent image when looking at different objects. The binding problem, which refers to how fragmented representations of objects across multiple brain regions can lead to a unified perception, is tackled in the study.
Researchers developed AIDEDNet, a novel dehazing network that enhances image details while removing haze interference. The model is trained on haze-free images to learn the patterns of haze and enhance image quality.
Researchers at the University of Missouri are acquiring a new transmission electron microscope (TEM) with a $800,000 grant from the National Science Foundation. The TEM will allow them to conduct experiments in real-time and gain a greater understanding of material structure at an atomic level.
The UTSA ScooterLab will collect data on riders' mobility, context and environment to improve sustainable transportation solutions. The project aims to transform the way we think about micro-mobility.
Researchers used MRI scans and texture analysis to differentiate between two types of benign oral bone lesions, ameloblastomas and odontogenic keratocysts. The study found that certain pixel and voxel organizational parameters in the MRI images were statistically significant, enabling more accurate diagnosis and treatment planning.
The JIPipe software enables automated analysis of images generated in research without requiring programming skills. Users can create flowcharts and perform automatic image analyses using artificial intelligence.
Researchers developed a novel, calibration-free, and reconstruction-free imaging technique that directly obtains a clear image from a single shot of speckle images. The method uses real-time video imaging to build a three-dimensional image, taking slices through the speckles.
RIT Professor Roger Easton and his team used multispectral imaging to reveal detailed drawings on an ancient Greek palimpsest, reconstructing Hipparchus' Star Catalogue. The discovery showcases the technique's growing role in historical preservation, with RIT supporting locations across US and Europe.
Researchers use drone technology to detect changes in building facades and assess technical condition, enabling accurate classification and identification. The system can also be used for urban heritage site monitoring and planning urban development.
This study proposes DSD-MatchingNet, a novel network leveraging sparse-to-dense hypercolumn matching and deformable convolutional networks for robust local feature matching. The proposed framework generates multilevel dense feature maps and incorporates pixel-level correspondence estimation error to improve accuracy.
A new AI tool developed by Brigham and Women's Hospital improves the accuracy of time-critical pathology diagnostics during surgery. The tool, which leverages deep-learning technology, translates frozen tissue samples into high-quality images, increasing diagnostic accuracy and reducing the need for lengthy laboratory tests.
A team led by Abhinav Jha is developing a novel low-count quantitative single photon emission computed tomography (LC-QSPECT) method to measure the concentration of radiopharmaceutical material in tumors and vital organs. This technique aims to provide accurate measurements of absorbed dose from radiation therapy, helping plan treatmen...
Researchers developed a laser-based approach to perform microbiopsies, enabling fast, painless tissue sampling with minimal damage. The novel technique uses laser ablation to extract tiny tissue volumes, which can be analyzed using virtual H&E imaging and other techniques in minutes, not hours.
Researchers at Incheon National University have developed an IoT-enabled, real-time object detection system for autonomous vehicles. The YOLOv3-based model achieved high accuracy (>96%) in detecting 2D and 3D objects, outperforming other state-of-the-art detection models.
Researchers developed a new 3D imaging model to analyze cellular geometry and mechanics, helping biologists quickly see how plants respond to environmental changes. The model identified unexpected guard cell behavior, shedding light on plant adaptation to drought.
Researchers from the University of Johannesburg deployed Few Shot Learning (FSL) for NIALM, a non-intrusive appliance load monitoring system. FSL requires only 7 test images to recognize appliances with 97.83% accuracy, making it faster and more cost-effective than traditional Machine Learning.
Researchers are developing a comprehensive cell atlas for the human brain, which would cover billions of cells and provide a holistic description of its properties. Advanced visualization techniques have been developed to catalog brain regions and cell types, enabling better resolution and accuracy in studying neural circuits.
A novel multi-modal image retrieval system, DenseBert4Ret, has been developed by researchers from Gwangju Institute of Science and Technology (GIST) using deep learning algorithms. The system outperforms state-of-the-art models in retrieving images based on both image and text features.
Researchers develop hybrid brightfield-darkfield transport of intensity approach, expanding accessible sample spatial frequencies and achieving 5-fold resolution increase. This method enables precise detection and quantitative analysis of subcellular features in large-scale cell studies.
Researchers from North Carolina State University have developed a new method for identifying genes relevant to the aging process in the C. elegans roundworm model. By exposing thousands of worms to random genetic mutations, they can pinpoint which genes are associated with protein aggregation and reduced lifespan.
Researchers from the University of Kassel developed an approach to extend the limits of interferometric topography measurements for optical resolution below small structures. Microsphere assistance enables fast and label-free imaging without requiring extensive sample preparation.
Researchers from the University of Tokyo's Interactive Intelligent Systems Laboratory developed a new system called LookHere that incorporates natural hand gestures into the teaching process. This approach eliminates extraneous details and provides better input data for machines to create models, resulting in improved efficiency and ac...
The Keck School of Medicine's Stevens INI has received a nearly $2 million grant to upgrade its storage system, increasing its capacity to handle vast amounts of brain imaging data. This will enable the lab to maximize scientific productivity and fulfill its promise to collaborate with researchers globally.
Researchers at UTHealth Houston will create a coordinating unit for biostatistics, informatics, and engagement to advance knowledge about human brain neurons. The project aims to produce an open-access digital brain cell reference atlas to improve understanding of neurological functions and disorders.
A team of international scientists found that many densely populated coastal cities worldwide are at risk from sea level rise due to land subsidence. The study used satellite images to estimate the rate of land sinking in 48 cities, with median speeds ranging from 16.2mm/year to 43mm/year.
Researchers developed a lensless camera that captures 3D information with a single exposure using a thin microlens array and new image processing algorithms. The camera can produce 3D images in real-time, enabling applications such as industrial part inspection, gesture recognition, and 3D display systems.
Researchers at George Washington University found that people perceive objects differently based on their prior knowledge and experience, with manipulable objects perceived faster but with less detail, while non-manipulable objects are perceived slower but with higher detail.
Researchers from Singapore University of Technology and Design (SUTD) have developed a new Brain-Inspired Replay model that enables continual learning in edge computing systems without storing data. This approach achieves state-of-the-art accuracy and high energy efficiency, overcoming the stability-plasticity issue in traditional models.
Researchers have developed a novel computational imaging framework, Compact Light-field Photography (CLIP), allowing the camera system to acquire wide and deep panoramic views. The technology enables the detection of hidden objects around occlusions and has potential applications in autonomous vehicles and medical imaging.
Researchers found that 7-month-old infants quickly discriminate between symmetrical and asymmetrical mosaics, indicating a robust ability to detect structural symmetry. This skill coincides with those found in studies using other stimuli, suggesting babies are good at extracting structure and rules from various media.
Researchers at MIT have developed a machine-learning system that uses computer vision to monitor the 3D printing process and correct errors in real-time. The system successfully printed objects more accurately than other 3D printing controllers, enabling engineers to incorporate novel materials into their prints with ease.
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.
A new study suggests that voluntary imagination, rather than speech, is crucial for combinatorial language acquisition in children with language deficiencies. The research found that children who engaged in a voluntary imagination intervention showed significant improvement in language comprehension.
Researchers at Duke University have developed a new design for plasmonic metasurfaces that greatly expands their frequency range while also making them more robust against the elements. The new fabrication process allows for the use of a wide variety of shapes, opening up new possibilities for applications such as super cameras.
Researchers developed a novel convolutional neural network for facial expression recognition, outperforming conventional models while being computationally less expensive. The new model achieved an accuracy of 72.4% using only 58,000 parameters.
Researchers developed a lidar-based system for smart cars to recognize objects more accurately than cameras. The system uses a grid map to divide the field of view into regions containing individual objects.
A research team reanalyzed M87 data using standard tools, revealing a core at the galactic center instead of the ring structure observed by the Event Horizon Telescope. The study validates EHT results and provides new insights into the astrophysical jet extending from the core.
By using the brain's visual response as feedback, researchers can reconstruct images of simple objects in real-time. The technique has potential applications in augmenting human capabilities and could one day be used to bring together human and artificial intelligence.
Researchers at the University of Tokyo have developed a new method to detect deepfakes, using self-blended images that improve detection accuracy by 5-12%. The team created novel synthesized images with controlled artifacts to train algorithms and found significant improvements in detecting deepfake images and videos.
Researchers have discovered the process of incorporating selenium into 25 specialized proteins, essential for various cellular and metabolic processes. The study provides critical insights into the workings of these vital mechanisms, which could lead to the development of new medical therapies.
The device can instantly recognize what it sees, like automatic descriptions of pictures taken by a camera or phone, and outperforms the eye in the number of wavelengths it can see. It's uniqueness comes from its ability to integrate three different operations into one, making it many times faster than current technology.
Researchers found that individual variation in primary visual cortex size and brain tissue can predict contrast sensitivity, with larger V1 areas and more cortical tissue leading to better vision at specific locations. The study reveals a new link between brain structure and behavior.
Researchers at Carnegie Mellon University developed an AI-powered method for robots to recognize and pour transparent liquids like water. The technique uses contrastive learning for unpaired image-to-image translation, enabling robots to see through different backgrounds and pour accurately.
Scientists create genetically engineered mouse model that changes color in response to light, allowing them to isolate background noise from blood flow and enhance imaging techniques. This breakthrough enables researchers to observe internal physiology with unprecedented accuracy, paving the way for new treatments and therapies.