Haut PDF Randomness and Geometric Features in Computer Vision
Randomness and Geometric Features in Computer Vision
... Unite´ de recherche INRIA Lorraine, Technopoˆle de Nancy-Brabois, Campus scientifique, 615 rue du Jardin Botanique, BP 101, 54600 VILLERS LE` S NANCY Unite´ de recherche INRIA Rennes, Ir[r] ...
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Graph-based variational optimization and applications in computer vision
... foreground and background seeds via a maximum flow ...Boykov and Jolly [ 32 ], and this work has been subsequently extended by several groups to employ different features [ 25 ] or user ...
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Curvilinear structure modeling and its applications in computer vision
... curvilinear features from background ...image features are insufficient to re- construct underlying curvilinear ...define geometric constraints in a local configuration and globally ...
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Applications of Photogrammetric and Computer Vision Techniques in Shake Table Testing
... non-contact and extract three-dimensional information from the geometry and the texture of the visible surfaces in a ...surfaces. In the case of systems that operate with ambient light (stereo ...
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3-D computer vision in experimental mechanics
... used in experimental ...photoelasticity, geometric moire´, moire´ interferometry, holographic inter- ferometry, speckle interferometry (ESPI), the grid method and digital image correlation (DIC) ...
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Boosting 3D-Geometric Features for Efficient Face Recognition and Gender Classification
... 3D-Geometric Features for Efficient Face Recognition and Gender Classification Lahoucine Ballihi, Boulbaba Ben Amor, Mohamed Daoudi, Anuj Srivastava, and Driss Aboutajdine Abstract—We utilize ...
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Bio-Inspired Computer Vision: Towards a Synergistic Approach of Artificial and Biological Vision
... hierarchies. In particular, the dynamics of neural processing is much more complex than the hierarchical feedforward abstrac- tion and very important connectivity patterns such as lateral and ...
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Atoms of recognition in human and computer vision
... 0.11, in- dicating that much of the drop from full to no recognition occurs for a small change at the MIRC level (the MIRC itself or one level above, where the gradient also was found to be ...visual ...
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Computer vision on tap
... follows in the footsteps of Scratch ...programming and sharing platform designed to bring programming to under-served youth populations, and rethink the concept to fit the needs of the budding ...
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Sparse models for Computer Vision
... Efficiency and sparseness in biological representations of natural images The central nervous system is a dynamical, adaptive organ which constantly evolves to provide optimal decisions 1 for interacting ...
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Mineral grains recognition using computer vision and machine learning
... Maitre). and time consuming. Two approaches are typically used to identify and characterize minerals grains in sediments or milled rocks: visual sorting with optical microscopy and automated ...
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Computer Vision Tools for Rodent Monitoring
... In the first phase, a sliding window technique based on three features is used to track the rodent and determine its coarse position in the frame.. The second phase uses the edge map and[r] ...
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Modeling of Facial Wrinkles for Applications in Computer Vision
... (MPP). In their proposed model wrinkles were considered as stochastic spatial ar- rangements of sequences of line segments, and detected in an image by proper place- ment of line ...probable ...
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From Chaos to Randomness via Geometric Undersampling
... numbers. In both cases, with these numerical values, the collapsing effect disappears and the invariant measure of any component is the Lebesgue measure [11] as we show ...below. In the case of ...
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Challenges in the Certification of Computer Vision-Based Systems
... involved in a criterion depending on some data (e.g., image features) and whose functional form derives from a statistical modeling of the various components (sensor noise, prior distribution on ...
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Deep learning of representations and its application to computer vision
... S3C features help to regularize a classifier, we proceed to use them to improve performance on the CIFAR-100 dataset, which has ten times as many classes and ten times fewer labeled examples per ...Coates ...
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Understanding deep features with computer-generated imagery
... of features generated by convolutional neural networks (CNNs) with respect to scene factors that occur in natural ...color, and scene lighting ...CNN and responses for different layers are ...
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Comparison between Optical and Computer Vision Estimates of Visibility in Daytime Fog
... Imaging, Computer Vision 1 Introduction Fog is a quite common meteorological ...happens in certain wind, temperature, and humidity conditions when vapour condenses into microscopic water ...
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Handling Geometric Features in Nanoscale Characterization of Charge Injection and Transport in thin Dielectric Films
... 1µm) and do not influence the current collection. V. C ONCLUSION In this work, the influence of tip-plane configuration, involved in AFM configuration measurements, on the electric field in ...
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Continuous in situ measurement of quenching distortions using computer vision
... However, when a risk of distortions or cracks during quenching is detected for a given industrial component, numerical simulations are not systematically performed to quantify it. To the authors’ knowledge, the main ...
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