arXiv subjects

Mohammad Ali Armin

Publications and source records attributed to Mohammad Ali Armin.

At least 19 recordsLinked to original sources

arXiv2026

GeoCond: A Conditioning-Aware Reliability Adapter for Feed-Forward 3D Reconstruction

Feed-forward 3D foundation models such as VGGT predict cameras, depth, and point maps in a single pass, but can fail silently under low overlap, low parallax, and extreme relative rotation. Stratified analyses over these factors show that these failures are governed by geometric conditioning and are poorly captured by native aleatoric confidence. We introduce GeoCond, a lightweight reliability adapter for frozen feed-forward 3D backbones. GeoCond reads the backbone's predicted geometry and outputs pose-level uncertainty and a refinement gate. During training, it can be supervised by frame-permutation orbit variance, ground-truth pose error when labels are available, or cycle residuals from unlabelled independent pose graphs. At inference, the default head requires only one backbone pass and a small MLP. On VGGT, GeoCond improves out-of-distribution (OOD) AUSE (area under the sparsification-error curve; lower is better) from $0.32$ to $0.20$ over native confidence, transfers zero-shot to outdoor extreme-view scenes, and avoids the collapse caused by applying bundle adjustment uniformly. Across multiple backbones, cycle-distilled variants provide a ground-truth-free adaptation route, including cases where permutation variance vanishes on equivariant models. The same reliability signal supports gated refinement, pose-graph weighting, calibration, curation, and capture decisions. Reliable feed-forward 3D reconstruction requires not only predicting geometry, but also knowing when that geometry should be trusted.

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arXiv2026

InfoTaxa: Information-Calibrated Label-Free Clustering for Fine-Grained Visual Taxonomy

Label-free clustering of frozen pretrained visual embeddings offers a scalable route to biodiversity monitoring, but image-only fine-grained taxonomy exhibits a consistent coarse-to-fine failure mode: clusters recover broad taxonomic structure yet plateau at species level. We study this behaviour on BIOSCAN-5M through an information-calibrated clustering analysis. BioCLIP~2 features with UMAP and HDBSCAN reach $0.79$ AMI at family and $0.67$ at genus, substantially improving over the prior image baseline and remaining competitive with oracle-$K$, graph-based, and learned clustering heads on the same frozen features. To diagnose whether the remaining plateau is method-limited or information-limited, we introduce InfoTaxa, which combines clustering efficiency---the fraction of probe-estimated image information recovered by an unsupervised partition---with paired DNA as an audit signal only, not an inference input. The density pipeline recovers approximately $0.90$ and $0.81$ of the image-available information at order and family, respectively. Held-out late-fusion probes show that adding DNA to the image embedding reduces species-level prediction error by approximately two bits. Robustness analyses cover multiple image encoders, described-species and rare-class subsets, probe diagnostics, and held-out-species coarse-rank generalisation and same-species retrieval. Thus, in the tested setting, species-level label-free clustering is both clustering-limited and representation-limited: improved clustering may recover additional image-exposed structure, but cannot close the DNA-audited information gap alone.

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arXiv2026

Wound3DAssist: A Practical Framework for 3D Wound Assessment

Managing chronic wounds remains a major healthcare challenge, with clinical assessment often relying on subjective and time-consuming manual documentation methods. Although 2D digital videometry frameworks have aided wound measurement, these approaches struggle with perspective distortion, a limited field of view, and an inability to capture wound depth, especially in anatomically complex or curved regions. To overcome these limitations, we present Wound3DAssist, a practical framework for 3D wound assessment using monocular consumer-grade videos. Our framework generates 3D wound models from short handheld recordings captured using consumer-grade devices, enabling non-contact, automatic measurements from reconstructed multi-view surfaces. We integrate 3D reconstruction, wound segmentation, tissue classification, and periwound analysis into a modular workflow. We evaluate Wound3DAssist across digital models with known geometry, silicone phantoms, and real patients. Results show that the framework supports high-quality wound bed visualization, approximately millimeter-scale surface reconstruction accuracy in the evaluated clinical cases, and multi-view wound-tissue composition analysis. Full assessments are completed in under 20 minutes, demonstrating feasibility for a research framework intended for future clinical workflow evaluation.

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arXiv2026

Facial Spatiotemporal Graphs: Leveraging the 3D Facial Surface for Remote Physiological Measurement

Facial remote photoplethysmography (rPPG) methods estimate physiological signals by modeling subtle color changes on the 3D facial surface over time. However, existing methods fail to explicitly align their receptive fields with the 3D facial surface-the spatial support of the rPPG signal. To address this, we propose the Facial Spatiotemporal Graph (STGraph), a novel representation that encodes facial color and structure using 3D facial mesh sequences-enabling surface-aligned spatiotemporal processing. We introduce MeshPhys, a lightweight spatiotemporal graph convolutional network that operates on the STGraph to estimate physiological signals. Across four benchmark datasets, MeshPhys achieves state-of-the-art or competitive performance in both intra- and cross-dataset settings. Ablation studies show that constraining the model's receptive field to the facial surface acts as a strong structural prior, and that surface-aligned, 3D-aware node features are critical for robustly encoding facial surface color. Together, the STGraph and MeshPhys constitute a novel, principled modeling paradigm for facial rPPG, enabling robust, interpretable, and generalizable estimation. Code is available at https://samcantrill.github.io/facial-stgraph-rppg/ .

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arXiv2025

Quality-Driven and Diversity-Aware Sample Expansion for Robust Marine Obstacle Segmentation

Marine obstacle detection demands robust segmentation under challenging conditions, such as sun glitter, fog, and rapidly changing wave patterns. These factors degrade image quality, while the scarcity and structural repetition of marine datasets limit the diversity of available training data. Although mask-conditioned diffusion models can synthesize layout-aligned samples, they often produce low-diversity outputs when conditioned on low-entropy masks and prompts, limiting their utility for improving robustness. In this paper, we propose a quality-driven and diversity-aware sample expansion pipeline that generates training data entirely at inference time, without retraining the diffusion model. The framework combines two key components:(i) a class-aware style bank that constructs high-entropy, semantically grounded prompts, and (ii) an adaptive annealing sampler that perturbs early conditioning, while a COD-guided proportional controller regulates this perturbation to boost diversity without compromising layout fidelity. Across marine obstacle benchmarks, augmenting training data with these controlled synthetic samples consistently improves segmentation performance across multiple backbones and increases visual variation in rare and texture-sensitive classes.

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arXiv2024

Developing Normative Gait Cycle Parameters for Clinical Analysis Using Human Pose Estimation

Gait analysis using computer vision is an emerging field in AI, offering clinicians an objective, multi-feature approach to analyse complex movements. Despite its promise, current applications using RGB video data alone are limited in measuring clinically relevant spatial and temporal kinematics and establishing normative parameters essential for identifying movement abnormalities within a gait cycle. This paper presents a data-driven method using RGB video data and 2D human pose estimation for developing normative kinematic gait parameters. By analysing joint angles, an established kinematic measure in biomechanics and clinical practice, we aim to enhance gait analysis capabilities and improve explainability. Our cycle-wise kinematic analysis enables clinicians to simultaneously measure and compare multiple joint angles, assessing individuals against a normative population using just monocular RGB video. This approach expands clinical capacity, supports objective decision-making, and automates the identification of specific spatial and temporal deviations and abnormalities within the gait cycle.

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arXiv2024

Computer Vision for Clinical Gait Analysis: A Gait Abnormality Video Dataset

Clinical gait analysis (CGA) using computer vision is an emerging field in artificial intelligence that faces barriers of accessible, real-world data, and clear task objectives. This paper lays the foundation for current developments in CGA as well as vision-based methods and datasets suitable for gait analysis. We introduce The Gait Abnormality in Video Dataset (GAVD) in response to our review of over 150 current gait-related computer vision datasets, which highlighted the need for a large and accessible gait dataset clinically annotated for CGA. GAVD stands out as the largest video gait dataset, comprising 1874 sequences of normal, abnormal and pathological gaits. Additionally, GAVD includes clinically annotated RGB data sourced from publicly available content on online platforms. It also encompasses over 400 subjects who have undergone clinical grade visual screening to represent a diverse range of abnormal gait patterns, captured in various settings, including hospital clinics and urban uncontrolled outdoor environments. We demonstrate the validity of the dataset and utility of action recognition models for CGA using pretrained models Temporal Segment Networks(TSN) and SlowFast network to achieve video abnormality detection of 94% and 92% respectively when tested on GAVD dataset. A GitHub repository https://github.com/Rahmyyy/GAVD consisting of convenient URL links, and clinically relevant annotation for CGA is provided for over 450 online videos, featuring diverse subjects performing a range of normal, pathological, and abnormal gait patterns.

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arXiv2024

Orientation-conditioned Facial Texture Mapping for Video-based Facial Remote Photoplethysmography Estimation

Camera-based remote photoplethysmography (rPPG) enables contactless measurement of important physiological signals such as pulse rate (PR). However, dynamic and unconstrained subject motion introduces significant variability into the facial appearance in video, confounding the ability of video-based methods to accurately extract the rPPG signal. In this study, we leverage the 3D facial surface to construct a novel orientation-conditioned facial texture video representation which improves the motion robustness of existing video-based facial rPPG estimation methods. Our proposed method achieves a significant 18.2% performance improvement in cross-dataset testing on MMPD over our baseline using the PhysNet model trained on PURE, highlighting the efficacy and generalization benefits of our designed video representation. We demonstrate significant performance improvements of up to 29.6% in all tested motion scenarios in cross-dataset testing on MMPD, even in the presence of dynamic and unconstrained subject motion, emphasizing the benefits of disentangling motion through modeling the 3D facial surface for motion robust facial rPPG estimation. We validate the efficacy of our design decisions and the impact of different video processing steps through an ablation study. Our findings illustrate the potential strengths of exploiting the 3D facial surface as a general strategy for addressing dynamic and unconstrained subject motion in videos. The code is available at https://samcantrill.github.io/orientation-uv-rppg/.

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arXiv2024

Deep Learning Approaches for Seizure Video Analysis: A Review

Seizure events can manifest as transient disruptions in the control of movements which may be organized in distinct behavioral sequences, accompanied or not by other observable features such as altered facial expressions. The analysis of these clinical signs, referred to as semiology, is subject to observer variations when specialists evaluate video-recorded events in the clinical setting. To enhance the accuracy and consistency of evaluations, computer-aided video analysis of seizures has emerged as a natural avenue. In the field of medical applications, deep learning and computer vision approaches have driven substantial advancements. Historically, these approaches have been used for disease detection, classification, and prediction using diagnostic data; however, there has been limited exploration of their application in evaluating video-based motion detection in the clinical epileptology setting. While vision-based technologies do not aim to replace clinical expertise, they can significantly contribute to medical decision-making and patient care by providing quantitative evidence and decision support. Behavior monitoring tools offer several advantages such as providing objective information, detecting challenging-to-observe events, reducing documentation efforts, and extending assessment capabilities to areas with limited expertise. The main applications of these could be (1) improved seizure detection methods; (2) refined semiology analysis for predicting seizure type and cerebral localization. In this paper, we detail the foundation technologies used in vision-based systems in the analysis of seizure videos, highlighting their success in semiology detection and analysis, focusing on work published in the last 7 years. Additionally, we illustrate how existing technologies can be interconnected through an integrated system for video-based semiology analysis.

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arXiv2024

Syn3DWound: A Synthetic Dataset for 3D Wound Bed Analysis

Wound management poses a significant challenge, particularly for bedridden patients and the elderly. Accurate diagnostic and healing monitoring can significantly benefit from modern image analysis, providing accurate and precise measurements of wounds. Despite several existing techniques, the shortage of expansive and diverse training datasets remains a significant obstacle to constructing machine learning-based frameworks. This paper introduces Syn3DWound, an open-source dataset of high-fidelity simulated wounds with 2D and 3D annotations. We propose baseline methods and a benchmarking framework for automated 3D morphometry analysis and 2D/3D wound segmentation.

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arXiv2022

A Hyperspectral and RGB Dataset for Building Facade Segmentation

Hyperspectral Imaging (HSI) provides detailed spectral information and has been utilised in many real-world applications. This work introduces an HSI dataset of building facades in a light industry environment with the aim of classifying different building materials in a scene. The dataset is called the Light Industrial Building HSI (LIB-HSI) dataset. This dataset consists of nine categories and 44 classes. In this study, we investigated deep learning based semantic segmentation algorithms on RGB and hyperspectral images to classify various building materials, such as timber, brick and concrete.

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arXiv2022

Automated Coronary Arteries Labeling Via Geometric Deep Learning

Automatic labelling of anatomical structures, such as coronary arteries, is critical for diagnosis, yet existing (non-deep learning) methods are limited by a reliance on prior topological knowledge of the expected tree-like structures. As the structure such vascular systems is often difficult to conceptualize, graph-based representations have become popular due to their ability to capture the geometric and topological properties of the morphology in an orientation-independent and abstract manner. However, graph-based learning for automated labeling of tree-like anatomical structures has received limited attention in the literature. The majority of prior studies have limitations in the entity graph construction, are dependent on topological structures, and have limited accuracy due to the anatomical variability between subjects. In this paper, we propose an intuitive graph representation method, well suited to use with 3D coordinate data obtained from angiography scans. We subsequently seek to analyze subject-specific graphs using geometric deep learning. The proposed models leverage expert annotated labels from 141 patients to learn representations of each coronary segment, while capturing the effects of anatomical variability within the training data. We investigate different variants of so-called message passing neural networks. Through extensive evaluations, our pipeline achieves a promising weighted F1-score of 0.805 for labeling coronary artery (13 classes) for a five-fold cross-validation. Considering the ability of graph models in dealing with irregular data, and their scalability for data segmentation, this work highlights the potential of such methods to provide quantitative evidence to support the decisions of medical experts.

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arXiv2022

Vision-Based Activity Recognition in Children with Autism-Related Behaviors

Advances in machine learning and contactless sensors have enabled the understanding complex human behaviors in a healthcare setting. In particular, several deep learning systems have been introduced to enable comprehensive analysis of neuro-developmental conditions such as Autism Spectrum Disorder (ASD). This condition affects children from their early developmental stages onwards, and diagnosis relies entirely on observing the child's behavior and detecting behavioral cues. However, the diagnosis process is time-consuming as it requires long-term behavior observation, and the scarce availability of specialists. We demonstrate the effect of a region-based computer vision system to help clinicians and parents analyze a child's behavior. For this purpose, we adopt and enhance a dataset for analyzing autism-related actions using videos of children captured in uncontrolled environments (e.g. videos collected with consumer-grade cameras, in varied environments). The data is pre-processed by detecting the target child in the video to reduce the impact of background noise. Motivated by the effectiveness of temporal convolutional models, we propose both light-weight and conventional models capable of extracting action features from video frames and classifying autism-related behaviors by analyzing the relationships between frames in a video. Through extensive evaluations on the feature extraction and learning strategies, we demonstrate that the best performance is achieved with an Inflated 3D Convnet and Multi-Stage Temporal Convolutional Networks, achieving a 0.83 Weighted F1-score for classification of the three autism-related actions, outperforming existing methods. We also propose a light-weight solution by employing the ESNet backbone within the same system, achieving competitive results of 0.71 Weighted F1-score, and enabling potential deployment on embedded systems.

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arXiv2022

Blind Image Decomposition

We propose and study a novel task named Blind Image Decomposition (BID), which requires separating a superimposed image into constituent underlying images in a blind setting, that is, both the source components involved in mixing as well as the mixing mechanism are unknown. For example, rain may consist of multiple components, such as rain streaks, raindrops, snow, and haze. Rainy images can be treated as an arbitrary combination of these components, some of them or all of them. How to decompose superimposed images, like rainy images, into distinct source components is a crucial step toward real-world vision systems. To facilitate research on this new task, we construct multiple benchmark datasets, including mixed image decomposition across multiple domains, real-scenario deraining, and joint shadow/reflection/watermark removal. Moreover, we propose a simple yet general Blind Image Decomposition Network (BIDeN) to serve as a strong baseline for future work. Experimental results demonstrate the tenability of our benchmarks and the effectiveness of BIDeN.

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arXiv2022

You Only Cut Once: Boosting Data Augmentation with a Single Cut

We present You Only Cut Once (YOCO) for performing data augmentations. YOCO cuts one image into two pieces and performs data augmentations individually within each piece. Applying YOCO improves the diversity of the augmentation per sample and encourages neural networks to recognize objects from partial information. YOCO enjoys the properties of parameter-free, easy usage, and boosting almost all augmentations for free. Thorough experiments are conducted to evaluate its effectiveness. We first demonstrate that YOCO can be seamlessly applied to varying data augmentations, neural network architectures, and brings performance gains on CIFAR and ImageNet classification tasks, sometimes surpassing conventional image-level augmentation by large margins. Moreover, we show YOCO benefits contrastive pre-training toward a more powerful representation that can be better transferred to multiple downstream tasks. Finally, we study a number of variants of YOCO and empirically analyze the performance for respective settings. Code is available at GitHub.

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arXiv2022

In-Bed Human Pose Estimation from Unseen and Privacy-Preserving Image Domains

Medical applications have benefited greatly from the rapid advancement in computer vision. Considering patient monitoring in particular, in-bed human posture estimation offers important health-related metrics with potential value in medical condition assessments. Despite great progress in this domain, it remains challenging due to substantial ambiguity during occlusions, and the lack of large corpora of manually labeled data for model training, particularly with domains such as thermal infrared imaging which are privacy-preserving, and thus of great interest. Motivated by the effectiveness of self-supervised methods in learning features directly from data, we propose a multi-modal conditional variational autoencoder (MC-VAE) capable of reconstructing features from missing modalities seen during training. This approach is used with HRNet to enable single modality inference for in-bed pose estimation. Through extensive evaluations, we demonstrate that body positions can be effectively recognized from the available modality, achieving on par results with baseline models that are highly dependent on having access to multiple modes at inference time. The proposed framework supports future research towards self-supervised learning that generates a robust model from a single source, and expects it to generalize over many unknown distributions in clinical environments.

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arXiv2021

The CSIRO Crown-of-Thorn Starfish Detection Dataset

Crown-of-Thorn Starfish (COTS) outbreaks are a major cause of coral loss on the Great Barrier Reef (GBR) and substantial surveillance and control programs are underway in an attempt to manage COTS populations to ecologically sustainable levels. We release a large-scale, annotated underwater image dataset from a COTS outbreak area on the GBR, to encourage research on Machine Learning and AI-driven technologies to improve the detection, monitoring, and management of COTS populations at reef scale. The dataset is released and hosted in a Kaggle competition that challenges the international Machine Learning community with the task of COTS detection from these underwater images.

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arXiv2021

Learning To Segment Dominant Object Motion From Watching Videos

Existing deep learning based unsupervised video object segmentation methods still rely on ground-truth segmentation masks to train. Unsupervised in this context only means that no annotated frames are used during inference. As obtaining ground-truth segmentation masks for real image scenes is a laborious task, we envision a simple framework for dominant moving object segmentation that neither requires annotated data to train nor relies on saliency priors or pre-trained optical flow maps. Inspired by a layered image representation, we introduce a technique to group pixel regions according to their affine parametric motion. This enables our network to learn segmentation of the dominant foreground object using only RGB image pairs as input for both training and inference. We establish a baseline for this novel task using a new MovingCars dataset and show competitive performance against recent methods that require annotated masks to train.

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