Sökning: "low-light vision"

Visar resultat 1 - 5 av 11 uppsatser innehållade orden low-light vision.

  1. 1. Night Vision Goggle Simulation in a Mixed Reality Flight Simulator with Seamless Integrated Real World

    Magister-uppsats, Linköpings universitet/Medie- och Informationsteknik; Linköpings universitet/Tekniska fakulteten

    Författare :Sofia Sproge; [2024]
    Nyckelord :Night vision goggles; NVG; Mixed Reality; Flight simulator; Video see-through; VST; Color transfer algorithm; Seamless integration; Post processing;

    Sammanfattning : Night vision goggles (NVGs) are optical devices used to enhance human vision at low light conditions such as nighttime. The image seen through the goggles is brightened but with the consequence of introduced visual limitations and illusions. LÄS MER

  3. 2. Face Identification Using Eigenfaces and LBPH : A Comparative Study

    Kandidat-uppsats, Blekinge Tekniska Högskola/Institutionen för datavetenskap

    Författare :DEVI DEEPSHIKHA JAMI; NANDA SRIRAAM KAMBHAM; [2023]
    Nyckelord :face identification; LBPH; Eigenfaces;

    Sammanfattning : Background: With the rise of digitalization, there has been an increasing needfor secure and effective identification solutions, particularly in the realm of votingsystems. Facial biometric technology has emerged as a potential solution to combat fraud and improve the transparency and security of the voting process. LÄS MER

  4. 3. Development and Evaluation of a Road Marking Recognition Algorithm implemented on Neuromorphic Hardware

    Kandidat-uppsats, KTH/Datavetenskap

    Författare :Santiago Bou Betran; [2022]
    Nyckelord :SpiNNaker; Neuromorphic Hardware; Spiking Neural Network; Computer vision system; Lane recognition; Autonomous driving; Line Recognition; Neuromorphic Vision Sensors; Third Generation Neural Networks; Algorithm Evaluation.; SpiNNaker; Neuromorfiska hårdvara; neuromorfiska pulserande neuronnät; Datorseende system; läser av körbanans; självkörande bilar; läser av linjer; dynamiska synsensorer; Tredje generation neuronnät; utvärdering av algoritmen.; Hardware Neuromórfico; Sistemas de visión por computador; Reconocimiento de Carril; Conducción autónoma; Sensores de visión neuromórfica; Redes neuronales de Impulsos; Redes Neuronales de Tercera Generación; Evaluación de Algoritmos.;

    Sammanfattning : Driving is one of the most common and preferred forms of transport used in our actual society. However, according to studies, it is also one of the most dangerous. One solution to increase safety on the road is applying technology to automate and prevent avoidable human errors. LÄS MER

  5. 4. Security lighting in horse riding halls: Development of a simulation-led testing methodology in VR

    Master-uppsats, Lunds universitet/Avdelningen för Energi och byggnadsdesign; Lunds universitet/Institutionen för arkitektur och byggd miljö

    Författare :Sheikh Rishad Ahmmad; [2022]
    Nyckelord :Security lighting; horse riding halls; mesopic vision; Virtual Reality; object tracking; Technology and Engineering;

    Sammanfattning : Security lighting plays a vital role for safe evacuation during an emergency such as power outage or a fire related incidence. Installing a security lighting system properly is more crucial for a horse riding facility for two reasons. LÄS MER

  7. 5. RGB-D Deep Learning keypoints and descriptors extraction Network for feature-based Visual Odometry systems

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Federico Bennasciutti; [2022]
    Nyckelord :DeepLearning; Visual Odometry; Computer Vision; RGB-D Camera; Feature Extraction; Interest Point Extraction; Djupinlärning; Visuell Odometri; Datorseende; RGB-D-kamera; Nyckelpunkter; Detektion;

    Sammanfattning : Feature extractors in Visual Odometry pipelines rarely exploit depth signals, even though depth sensors and RGB-D cameras are commonly used in later stages of Visual Odometry systems. Nonetheless, depth sensors from RGB-D cameras function even with no external light and can provide feature extractors with additional structural information otherwise invisible in RGB images. LÄS MER