Object detection and features extraction in video frames using direct thresholding

Al-Salih, Asaad A. M.; Ahson, S. I.

doi:10.1109/mspct.2009.5164215

Cited by 7 publications

(1 citation statement)

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“…Additional image classification tasks have been demonstrated on neuromorphic systems, which include the classifying of real-world images such as traffic signs [212][213][214][215], face recognition or detection [216][217][218][219][220][221], car recognition or detection [221][222][223][224][225], identifying air pollution in images [221,226,227], identifying manufacturing defects or defaults [228,229], hand gesture recognition [221,230,231], object texture analysis [232,233], and other real-world image recognition tasks [221,234]. The employment of neuromorphic systems in video-based applications has also been common [32]; video frames are analyzed as images and object recognition is done without necessarily taking into consideration the time component [235][236][237][238]. Nevertheless, a temporal component is necessary for several additional video applications, and further works have investigated this for applications such as activity recognition [239][240][241], motion tracking [242,243], motion estimation [244][245][246] and motion detection…”

Section: Neuromorphic Computing In Slammentioning

confidence: 99%

Application of Event Cameras and Neuromorphic Computing to VSLAM: A Survey

Tenzin,

Rassau,

Chai

2024

Preprint

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Simultaneous Localization and Mapping (SLAM) is a crucial function for most autonomous systems, allowing them to both navigate through and create maps of unfamiliar surroundings. Traditional Visual SLAM, also commonly known as VSLAM, relies on frame-based cameras and structured processing pipelines, which face challenges in dynamic or low-light environments. However, recent advancements in event camera technology and neuromorphic processing offer promising opportunities to overcome these limitations. Event cameras inspired by biological vision systems capture the scenes asynchronously consuming minimal power but with higher temporal resolution. Neuromorphic processors, which are designed to mimic the parallel processing capabilities of the human brain, offer efficient computation for real-time data processing of event-based data streams. This paper provides a comprehensive overview of recent research efforts in integrating event cameras and neuromorphic processors into VSLAM systems. It discusses the principles behind event cameras and neuromorphic processors, highlighting their advantages over traditional sensing and processing methods. Furthermore, an in-depth survey was conducted on state-of-the-art approaches in event-based SLAM, including feature extraction, motion estimation, and map reconstruction techniques. Additionally, the integration of event cameras with neuromorphic processors, focusing on their synergistic benefits in terms of energy efficiency, robustness, and real-time performance was explored. The paper also discusses the challenges and open research questions in this emerging field, such as sensor calibration, data fusion, and algorithmic development. Finally, the potential applications and future directions for event-based SLAM systems are outlined, ranging from robotics and autonomous vehicles to augmented reality.

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