Search Results for author: Muhammad Zeeshan Arshad

Found 4 papers, 0 papers with code

Structure-from-Sherds++: Robust Incremental 3D Reassembly of Axially Symmetric Pots from Unordered and Mixed Fragment Collections

no code implementations19 Feb 2025 Seong Jong Yoo, Sisung Liu, Muhammad Zeeshan Arshad, Jinhyeok Kim, Young Min Kim, Yiannis Aloimonos, Cornelia Fermuller, Kyungdon Joo, Jinwook Kim, Je Hyeong Hong

Reassembling multiple axially symmetric pots from fragmentary sherds is crucial for cultural heritage preservation, yet it poses significant challenges due to thin and sharp fracture surfaces that generate numerous false positive matches and hinder large-scale puzzle solving.

3D Reconstruction

Gait Events Prediction using Hybrid CNN-RNN-based Deep Learning models through a Single Waist-worn Wearable Sensor

no code implementations28 Feb 2022 Muhammad Zeeshan Arshad, Ankhzaya Jamsrandorj, Jinwook Kim, Kyung-Ryoul Mun

The results showed that the use of CNN-RNN hybrid models with Attention and Bidirectional mechanisms is promising for accurate gait event detection using a single waist sensor.

Event Detection

Gait-based Frailty Assessment using Image Representation of IMU Signals and Deep CNN

no code implementations15 Oct 2021 Muhammad Zeeshan Arshad, Dawoon Jung, Mina Park, Hyungeun Shin, Jinwook Kim, Kyung-Ryoul Mun

In this paper, it is shown that by encoding gait signals as images, deep learning-based models can be utilized for the classification of gait type.

Deep Learning

Gait-based Human Identification through Minimum Gait-phases and Sensors

no code implementations15 Oct 2021 Muhammad Zeeshan Arshad, Dawoon Jung, Mina Park, Kyung-Ryoul Mun, Jinwook Kim

It was shown that it is possible to achieve high accuracy of over 95. 5 percent by monitoring a single phase of the whole gait cycle through only a single sensor.

Descriptive Gait Identification

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