Publication: Developing Hopfield Neural Networks Using Gaussian Distributed Small World Topology For Visual Object Tracking
| dc.contributor.author | Sun, Jun | |
| dc.date.accessioned | 2025-07-23T07:35:11Z | |
| dc.date.available | 2025-07-23T07:35:11Z | |
| dc.date.issued | 2024-06 | |
| dc.description.abstract | Visual object tracking (vot) is considered a challenging research topic in artificial intelligence. Today, many industries rely on object tracking technologies to identify errors, monitor environments, and make timely decisions based on tracking results. Visual object tracking has enabled many innovations, such as autonomous vehicles, traffic monitoring systems, remote medical diagnostic systems, and more cutting-edge applications are on the horizon. However, among these notable achievements, it is worth noting that, unlike these object-tracking techniques, a human brain is more efficient for object tracking tasks and requires fewer resources. Recent neuroscience studies have shown that artificial neural networks organized as real cortical connectivity may perform more efficiently in complex recognition tasks. Therefore, a novel visual object tracking method based on hopfield neural networks is proposed in this study. A small-world network is employed as the topology of the neural network model. However, a biological feature is integrated into the small-world network model: the exponential decay rule, which may mimic some characteristics of the structure of the cerebral cortex. In the neural network, each pixel of video frames is assigned to a neuron at the corresponding position. Pixel strength is characterized as the state of a neuron. The video frame is memorized after all neurons in the neural network have been trained to a stable state. A bionic mechanism utilizing the associative memory property of a bionic hopfield neural network is proposed to track objects in video frames. | |
| dc.identifier.uri | https://erepo.usm.my/handle/123456789/22350 | |
| dc.subject | Developing Hopfield Neural Networks Using Gaussian Distributed | |
| dc.title | Developing Hopfield Neural Networks Using Gaussian Distributed Small World Topology For Visual Object Tracking | |
| dc.type | Resource Types::text::thesis::doctoral thesis | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | Universiti Sains Malaysia |