Muzammil Khan

Nitish Kumar Mahala, Muzammil Khan, Pushpendra Kumar

Fire outbreaks pose critical threats to human life and infrastructure, necessitating high-fidelity early-warning systems that detect combustion precursors such as smoke. However, smoke plumes exhibit complex spatiotemporal dynamics influenced by illumination variability, flow kinematics, and environmental noise, undermining the reliability of traditional detectors. To address these challenges without the logistical complexity of multi-sensor arrays, we propose an information-fusion framework by integrating smoke feature representations extracted from monocular imagery. Specifically, a Two-Phase Uncertainty-Aware Shifted Windows Transformer for robust and reliable smoke detection, leveraging a novel smoke segmentation dataset, constructed via optical flow-based motion encoding, is proposed. The optical flow estimation is performed with a four-color-theorem-inspired dual-phase level-set fractional-order variational model, which preserves motion discontinuities. The resulting color-encoded optical flow maps are fused with appearance cues via a Gaussian Mixture Model to generate binary segmentation masks of the smoke regions. These fused representations are fed into the novel Shifted-Windows Transformer, which is augmented with a multi-scale uncertainty estimation head and trained under a two-phase learning regimen. First learning phase optimizes smoke detection accuracy, while during the second phase, the model learns to estimate plausibility confidence in its predictions by jointly modeling aleatoric and epistemic uncertainties. Extensive experiments using multiple evaluation metrics and comparative analysis with state-of-the-art approaches demonstrate superior generalization and robustness, offering a reliable solution for early fire detection in surveillance, industrial safety, and autonomous monitoring applications.

Muzammil Khan, Enzo Kerkhof, Matteo Fusaglia et al.

Accurate endoscope pose estimation and 3D tissue surface reconstruction significantly enhances monocular minimally invasive surgical procedures by enabling accurate navigation and improved spatial awareness. However, monocular endoscope pose estimation and tissue reconstruction face persistent challenges, including depth ambiguity, physiological tissue deformation, inconsistent endoscope motion, limited texture fidelity, and a restricted field of view. To overcome these limitations, a unified framework for monocular endoscopic tissue reconstruction that integrates scale-aware depth prediction with temporally-constrained perceptual refinement is presented. This framework incorporates a novel MAPIS-Depth module, which leverages Depth Pro for robust initialisation and Depth Anything for efficient per-frame depth prediction, in conjunction with L-BFGS-B optimisation, to generate pseudo-metric depth estimates. These estimates are temporally refined by computing pixel correspondences using RAFT and adaptively blending flow-warped frames based on LPIPS perceptual similarity, thereby reducing artefacts arising from physiological tissue deformation and motion. To ensure accurate registration of the synthesised pseudo-RGBD frames from MAPIS-Depth, a novel WEMA-RTDL module is integrated, optimising both rotation and translation. Finally, truncated signed distance function-based volumetric fusion and marching cubes are applied to extract a comprehensive 3D surface mesh. Evaluations on HEVD and SCARED, with ablation and comparative analyses, demonstrate the framework's robustness and superiority over state-of-the-art methods.