1,633 research outputs found
Reverse engineering of CAD models via clustering and approximate implicitization
In applications like computer aided design, geometric models are often
represented numerically as polynomial splines or NURBS, even when they
originate from primitive geometry. For purposes such as redesign and
isogeometric analysis, it is of interest to extract information about the
underlying geometry through reverse engineering. In this work we develop a
novel method to determine these primitive shapes by combining clustering
analysis with approximate implicitization. The proposed method is automatic and
can recover algebraic hypersurfaces of any degree in any dimension. In exact
arithmetic, the algorithm returns exact results. All the required parameters,
such as the implicit degree of the patches and the number of clusters of the
model, are inferred using numerical approaches in order to obtain an algorithm
that requires as little manual input as possible. The effectiveness, efficiency
and robustness of the method are shown both in a theoretical analysis and in
numerical examples implemented in Python
State of the Art in Face Recognition
Notwithstanding the tremendous effort to solve the face recognition problem, it is not possible yet to design a face recognition system with a potential close to human performance. New computer vision and pattern recognition approaches need to be investigated. Even new knowledge and perspectives from different fields like, psychology and neuroscience must be incorporated into the current field of face recognition to design a robust face recognition system. Indeed, many more efforts are required to end up with a human like face recognition system. This book tries to make an effort to reduce the gap between the previous face recognition research state and the future state
NDDepth: Normal-Distance Assisted Monocular Depth Estimation and Completion
Over the past few years, monocular depth estimation and completion have been
paid more and more attention from the computer vision community because of
their widespread applications. In this paper, we introduce novel physics
(geometry)-driven deep learning frameworks for these two tasks by assuming that
3D scenes are constituted with piece-wise planes. Instead of directly
estimating the depth map or completing the sparse depth map, we propose to
estimate the surface normal and plane-to-origin distance maps or complete the
sparse surface normal and distance maps as intermediate outputs. To this end,
we develop a normal-distance head that outputs pixel-level surface normal and
distance. Meanwhile, the surface normal and distance maps are regularized by a
developed plane-aware consistency constraint, which are then transformed into
depth maps. Furthermore, we integrate an additional depth head to strengthen
the robustness of the proposed frameworks. Extensive experiments on the
NYU-Depth-v2, KITTI and SUN RGB-D datasets demonstrate that our method exceeds
in performance prior state-of-the-art monocular depth estimation and completion
competitors. The source code will be available at
https://github.com/ShuweiShao/NDDepth.Comment: Extension of previous work arXiv:2309.1059
The contribution of fMRI in the study of visual categorization and expertise
No description supplie
Semi-orthogonal subspaces for value mediate a tradeoff between binding and generalization
When choosing between options, we must associate their values with the action
needed to select them. We hypothesize that the brain solves this binding
problem through neural population subspaces. To test this hypothesis, we
examined neuronal responses in five reward-sensitive regions in macaques
performing a risky choice task with sequential offers. Surprisingly, in all
areas, the neural population encoded the values of offers presented on the left
and right in distinct subspaces. We show that the encoding we observe is
sufficient to bind the values of the offers to their respective positions in
space while preserving abstract value information, which may be important for
rapid learning and generalization to novel contexts. Moreover, after both
offers have been presented, all areas encode the value of the first and second
offers in orthogonal subspaces. In this case as well, the orthogonalization
provides binding. Our binding-by-subspace hypothesis makes two novel
predictions borne out by the data. First, behavioral errors should correlate
with putative spatial (but not temporal) misbinding in the neural
representation. Second, the specific representational geometry that we observe
across animals also indicates that behavioral errors should increase when
offers have low or high values, compared to when they have medium values, even
when controlling for value difference. Together, these results support the idea
that the brain makes use of semi-orthogonal subspaces to bind features
together.Comment: arXiv admin note: substantial text overlap with arXiv:2205.0676
A Featureless Approach to 3D Polyhedral Building Modeling from Aerial Images
This paper presents a model-based approach for reconstructing 3D polyhedral building models from aerial images. The proposed approach exploits some geometric and photometric properties resulting from the perspective projection of planar structures. Data are provided by calibrated aerial images. The novelty of the approach lies in its featurelessness and in its use of direct optimization based on image rawbrightness. The proposed framework avoids feature extraction and matching. The 3D polyhedral model is directly estimated by optimizing an objective function that combines an image-based dissimilarity measure and a gradient score over several aerial images. The optimization process is carried out by the Differential Evolution algorithm. The proposed approach is intended to provide more accurate 3D reconstruction than feature-based approaches. Fast 3D model rectification and updating can take advantage of the proposed method. Several results and evaluations of performance from real and synthetic images show the feasibility and robustness of the proposed approach
Multi-tier framework for the inferential measurement and data-driven modeling
A framework for the inferential measurement and data-driven modeling has been proposed and assessed in several real-world application domains. The architecture of the framework has been structured in multiple tiers to facilitate extensibility and the integration of new components. Each of the proposed four tiers has been assessed in an uncoupled way to verify their suitability. The first tier, dealing with exploratory data analysis, has been assessed with the characterization of the chemical space related to the biodegradation of organic chemicals. This analysis has established relationships between physicochemical variables and biodegradation rates that have been used for model development. At the preprocessing level, a novel method for feature selection based on dissimilarity measures between Self-Organizing maps (SOM) has been developed and assessed. The proposed method selected more features than others published in literature but leads to models with improved predictive power. Single and multiple data imputation techniques based on the SOM have also been used to recover missing data in a Waste Water Treatment Plant benchmark. A new dynamic method to adjust the centers and widths of in Radial basis Function networks has been proposed to predict water quality. The proposed method outperformed other neural networks. The proposed modeling components have also been assessed in the development of prediction and classification models for biodegradation rates in different media. The results obtained proved the suitability of this approach to develop data-driven models when the complex dynamics of the process prevents the formulation of mechanistic models. The use of rule generation algorithms and Bayesian dependency models has been preliminary screened to provide the framework with interpretation capabilities. Preliminary results obtained from the classification of Modes of Toxic Action (MOA) indicate that this could be a promising approach to use MOAs as proxy indicators of human health effects of chemicals.Finally, the complete framework has been applied to three different modeling scenarios. A virtual sensor system, capable of inferring product quality indices from primary process variables has been developed and assessed. The system was integrated with the control system in a real chemical plant outperforming multi-linear correlation models usually adopted by chemical manufacturers. A model to predict carcinogenicity from molecular structure for a set of aromatic compounds has been developed and tested. Results obtained after the application of the SOM-dissimilarity feature selection method yielded better results than models published in the literature. Finally, the framework has been used to facilitate a new approach for environmental modeling and risk management within geographical information systems (GIS). The SOM has been successfully used to characterize exposure scenarios and to provide estimations of missing data through geographic interpolation. The combination of SOM and Gaussian Mixture models facilitated the formulation of a new probabilistic risk assessment approach.Aquesta tesi proposa i avalua en diverses aplicacions reals, un marc general de treball per al desenvolupament de sistemes de mesurament inferencial i de modelat basats en dades. L'arquitectura d'aquest marc de treball s'organitza en diverses capes que faciliten la seva extensibilitat així com la integració de nous components. Cadascun dels quatre nivells en que s'estructura la proposta de marc de treball ha estat avaluat de forma independent per a verificar la seva funcionalitat. El primer que nivell s'ocupa de l'anàlisi exploratòria de dades ha esta avaluat a partir de la caracterització de l'espai químic corresponent a la biodegradació de certs compostos orgànics. Fruit d'aquest anàlisi s'han establert relacions entre diverses variables físico-químiques que han estat emprades posteriorment per al desenvolupament de models de biodegradació. A nivell del preprocés de les dades s'ha desenvolupat i avaluat una nova metodologia per a la selecció de variables basada en l'ús del Mapes Autoorganitzats (SOM). Tot i que el mètode proposat selecciona, en general, un major nombre de variables que altres mètodes proposats a la literatura, els models resultants mostren una millor capacitat predictiva. S'han avaluat també tot un conjunt de tècniques d'imputació de dades basades en el SOM amb un conjunt de dades estàndard corresponent als paràmetres d'operació d'una planta de tractament d'aigües residuals. Es proposa i avalua en un problema de predicció de qualitat en aigua un nou model dinàmic per a ajustar el centre i la dispersió en xarxes de funcions de base radial. El mètode proposat millora els resultats obtinguts amb altres arquitectures neuronals. Els components de modelat proposat s'han aplicat també al desenvolupament de models predictius i de classificació de les velocitats de biodegradació de compostos orgànics en diferents medis. Els resultats obtinguts demostren la viabilitat d'aquesta aproximació per a desenvolupar models basats en dades en aquells casos en els que la complexitat de dinàmica del procés impedeix formular models mecanicistes. S'ha dut a terme un estudi preliminar de l'ús de algorismes de generació de regles i de grafs de dependència bayesiana per a introduir una nova capa que faciliti la interpretació dels models. Els resultats preliminars obtinguts a partir de la classificació dels Modes d'acció Tòxica (MOA) apunten a que l'ús dels MOA com a indicadors intermediaris dels efectes dels compostos químics en la salut és una aproximació factible.Finalment, el marc de treball proposat s'ha aplicat en tres escenaris de modelat diferents. En primer lloc, s'ha desenvolupat i avaluat un sensor virtual capaç d'inferir índexs de qualitat a partir de variables primàries de procés. El sensor resultant ha estat implementat en una planta química real millorant els resultats de les correlacions multilineals emprades habitualment. S'ha desenvolupat i avaluat un model per a predir els efectes carcinògens d'un grup de compostos aromàtics a partir de la seva estructura molecular. Els resultats obtinguts desprès d'aplicar el mètode de selecció de variables basat en el SOM milloren els resultats prèviament publicats. Aquest marc de treball s'ha usat també per a proporcionar una nova aproximació al modelat ambiental i l'anàlisi de risc amb sistemes d'informació geogràfica (GIS). S'ha usat el SOM per a caracteritzar escenaris d'exposició i per a desenvolupar un nou mètode d'interpolació geogràfica. La combinació del SOM amb els models de mescla de gaussianes dona una nova formulació al problema de l'anàlisi de risc des d'un punt de vista probabilístic
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