arXiv subjects

Petteri Teikari

Publications and source records attributed to Petteri Teikari.

5 recordsLinked to original sources

arXiv2026

Textile IR: A Bidirectional Intermediate Representation for Physics-Aware Fashion CAD

We introduce Textile IR, a bidirectional intermediate representation that connects manufacturing-valid CAD, physics-based simulation, and lifecycle assessment for fashion design. Unlike existing siloed tools where pattern software guarantees sewable outputs but understands nothing about drape, and physics simulation predicts behaviour but cannot automatically fix patterns, Textile IR provides the semantic glue for integration through a seven-layer Verification Ladder -- from cheap syntactic checks (pattern closure, seam compatibility) to expensive physics validation (drape simulation, stress analysis). The architecture enables bidirectional feedback: simulation failures suggest pattern modifications; material substitutions update sustainability estimates in real time; uncertainty propagates across the pipeline with explicit confidence bounds. We formalise fashion engineering as constraint satisfaction over three domains and demonstrate how Textile IR's scene-graph representation enables AI systems to manipulate garments as structured programs rather than pixel arrays. The framework addresses the compound uncertainty problem: when measurement errors in material testing, simulation approximations, and LCA database gaps combine, sustainability claims become unreliable without explicit uncertainty tracking. We propose six research priorities and discuss deployment considerations for fashion SMEs where integrated workflows reduce specialised engineering requirements. Key contribution: a formal representation that makes engineering constraints perceptible, manipulable, and immediately consequential -- enabling designers to navigate sustainability, manufacturability, and aesthetic tradeoffs simultaneously rather than discovering conflicts after costly physical prototyping.

cs.CV↗

arXiv2025

From Photons to Physics: Autonomous Indoor Drones and the Future of Objective Property Assessment

The convergence of autonomous indoor drones with physics-aware sensing technologies promises to transform property assessment from subjective visual inspection to objective, quantitative measurement. This comprehensive review examines the technical foundations enabling this paradigm shift across four critical domains: (1) platform architectures optimized for indoor navigation, where weight constraints drive innovations in heterogeneous computing, collision-tolerant design, and hierarchical control systems; (2) advanced sensing modalities that extend perception beyond human vision, including hyperspectral imaging for material identification, polarimetric sensing for surface characterization, and computational imaging with metaphotonics enabling radical miniaturization; (3) intelligent autonomy through active reconstruction algorithms, where drones equipped with 3D Gaussian Splatting make strategic decisions about viewpoint selection to maximize information gain within battery constraints; and (4) integration pathways with existing property workflows, including Building Information Modeling (BIM) systems and industry standards like Uniform Appraisal Dataset (UAD) 3.6.

cs.RO↗

arXiv2025

The Architecture of Trust: A Framework for AI-Augmented Real Estate Valuation in the Era of Structured Data

The Uniform Appraisal Dataset (UAD) 3.6's mandatory 2026 implementation transforms residential property valuation from narrative reporting to structured, machine-readable formats. This paper provides the first comprehensive analysis of this regulatory shift alongside concurrent AI advances in computer vision, natural language processing, and autonomous systems. We develop a three-layer framework for AI-augmented valuation addressing technical implementation and institutional trust requirements. Our analysis reveals how regulatory standardization converging with AI capabilities enables fundamental market restructuring with profound implications for professional practice, efficiency, and systemic risk. We make four key contributions: (1) documenting institutional failures including inter-appraiser variability and systematic biases undermining valuation reliability; (2) developing an architectural framework spanning physical data acquisition, semantic understanding, and cognitive reasoning that integrates emerging technologies while maintaining professional oversight; (3) addressing trust requirements for high-stakes financial applications including regulatory compliance, algorithmic fairness, and uncertainty quantification; (4) proposing evaluation methodologies beyond generic AI benchmarks toward domain-specific protocols. Our findings indicate successful transformation requires not merely technological sophistication but careful human-AI collaboration, creating systems that augment rather than replace professional expertise while addressing historical biases and information asymmetries in real estate markets.

cs.CY↗

arXiv2019

Embedded deep learning in ophthalmology: Making ophthalmic imaging smarter

Deep learning has recently gained high interest in ophthalmology, due to its ability to detect clinically significant features for diagnosis and prognosis. Despite these significant advances, little is known about the ability of various deep learning systems to be embedded within ophthalmic imaging devices, allowing automated image acquisition. In this work, we will review the existing and future directions for "active acquisition" embedded deep learning, leading to as high quality images with little intervention by the human operator. In clinical practice, the improved image quality should translate into more robust deep learning-based clinical diagnostics. Embedded deep learning will be enabled by the constantly improving hardware performance with low cost. We will briefly review possible computation methods in larger clinical systems. Briefly, they can be included in a three-layer framework composed of edge, fog and cloud layers, the former being performed at a device-level. Improved edge layer performance via "active acquisition" serves as an automatic data curation operator translating to better quality data in electronic health records (EHRs), as well as on the cloud layer, for improved deep learning-based clinical data mining.

cs.CV↗

arXiv2016

Deep Learning Convolutional Networks for Multiphoton Microscopy Vasculature Segmentation

Recently there has been an increasing trend to use deep learning frameworks for both 2D consumer images and for 3D medical images. However, there has been little effort to use deep frameworks for volumetric vascular segmentation. We wanted to address this by providing a freely available dataset of 12 annotated two-photon vasculature microscopy stacks. We demonstrated the use of deep learning framework consisting both 2D and 3D convolutional filters (ConvNet). Our hybrid 2D-3D architecture produced promising segmentation result. We derived the architectures from Lee et al. who used the ZNN framework initially designed for electron microscope image segmentation. We hope that by sharing our volumetric vasculature datasets, we will inspire other researchers to experiment with vasculature dataset and improve the used network architectures.

cs.CV↗