Haut PDF Randomness and Geometric Features in Computer Vision

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

Graph-based variational optimization and applications in computer vision

Graph cuts: The labeling produced by the graph cuts (GC) algorithm is determined by finding the minimum cut between the foreground and background seeds via a maximum flow computation. The original work on GC for interactive image segmentation was produced by Boykov and Jolly [ 32 ], and this work has been subsequently extended by several groups to employ different features [ 25 ] or user interfaces [ 151 , 188 ]. Although GC is relatively new, the use of minimal surfaces in segmentation has been a common theme in computer vision for a long time [ 26 , 99 , 162 ] and other boundary-based user interfaces have been previously employed [ 61 , 92 , 112 , 161 ]. Two concerns in the literature about the original GC algorithm are metrication error (“blockiness”) and the shrinking bias. Metrication error was addressed in subsequent work on GC by including additional edges [ 34 ], by using continuous max flows [ 10 ] or total variation [ 210 ]. These methods for addressing metrication error successfully overcome the problem, but may incur greater memory and computation time costs than the application of maximum flow on a 4-connected lattice. The shrinking bias can cause overly small object segments because GC minimizes boundary length. Although some techniques have been proposed for addressing the shrinking bias [ 10 , 34 , 214 ], these techniques all require additional parameters or computation.
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Curvilinear structure modeling and its applications in computer vision

Curvilinear structure modeling and its applications in computer vision

convolution filters to decompose curvilinear features from background textures. The main idea behind such filter design is to create line shape templates to extract locally oriented gradient information. Frangi et al. analyzes the eigenvalues of the Hessian ma- trix for a given image to obtain principle directions of the local structure [ Frangi 98 ]. Optimally Oriented Flux (OOF) measures the amount of outgoing gradient flux to find the curvilinear structures [ Law 08 ]. Morphological operator collects pixels according to the structural similarity on the elongated path [ Talbot 07 ]. However, due to the lack of shape interpretation, such methods based on local image features are insufficient to re- construct underlying curvilinear structure. On the other hand, graphical models such as [ González 10 , Türetken 13b ] define geometric constraints in a local configuration and globally minimize their cost function. More precisely, the graph-based algorithms initialize some points highly corresponding to the latent curvilinear structure, and then define a path which connects these points with geometric priors to provide plausible shapes. Geometric properties of line network are involved as constraint terms when an energy optimization problem is formulated. Stochastic models [ Lacoste 05 , Jeong 15a ] reconstruct curvilinear structures by sampling multiple line segments to maximize a posterior probability of given image data. Similarly to the graph-based representation, geometric priors are considered to define the connectivity and curvature of line seg- ments. Recently, machine learning algorithms have been proposed to detect curvilinear structure. Becker et al. [ Becker 13 ] applied a boosting algorithm to obtain an optimal set of convolution filter banks. Sironi et al. [ Sironi 14 ] developed a regression model to estimate the scale (width) of the curvilinear structures and to localize the centerlines. Although the contour grouping algorithms [ Tu 06 , Arbeáez 11 ] examine image fea- tures corresponding to curves and lines, the goal is quite different from the curvilinear structure reconstruction techniques. The contour grouping algorithms seek closed con- tour lines to divide an image into meaningful regions. Therefore, the cost function ex- ploits global texture cues in that the contours are associated with salient edges around object boundaries. On the other hand, we look for multiple curvilinear structures, which are not necessarily closed, are latent in the homogeneous texture. Compared with the contour, the curvilinear structures are estimated by subtle local image fea- tures. Internal similarity of the structure and an accurate design of shape prior are essential to solve our problem.
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Applications of Photogrammetric and Computer Vision Techniques in Shake Table Testing

Applications of Photogrammetric and Computer Vision Techniques in Shake Table Testing

Optical systems Optical systems are inherently non-contact and extract three-dimensional information from the geometry and the texture of the visible surfaces in a scene. Structured light (laser-based) can compute three- dimensional coordinates on most surfaces. In the case of systems that operate with ambient light (stereo or photogrammetry-based systems), the surfaces that are measured must contain unambiguous features. Obviously, external lighting can be projected on surfaces in order to ease the processing tasks. Finally, these systems can acquire a large number of three-dimensional points in a single image at high data rates. With recent technological advances made in electronics, photonics, computer vision and computer graphics, it is now possible to construct reliable, high-resolution and accurate three-dimensional optical measurement systems. The cost of high-resolution imaging sensors and high-speed processing workstations has decreased by an order of magnitude in the last 5 years. Furthermore, the convergence of photogrammetry and computer vision/graphics is helping system integrators provide users with cost effective solutions for three-dimensional motion capture.
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3-D computer vision in experimental mechanics

3-D computer vision in experimental mechanics

Keywords: Stereovision; Stereo-correlation; 3-D digital image correlation (3D-DIC); Shape measurement; Displacement/strain measurement; Experimental mechanics 1. Introduction Full-field optical techniques for displacement or strain measurements are now widely used in experimental mechanics. The main techniques are photoelasticity, geometric moire´, moire´ interferometry, holographic inter- ferometry, speckle interferometry (ESPI), the grid method and digital image correlation (DIC) [1–9] . It should be noted that some of these techniques can only measure in- plane displacements/strains on planar specimens and some of them can give both in-plane and out-of-plane displace- ment/strain fields on any kind of specimen (planar or not). Due to its (apparent) simplicity and versatility, the DIC method is probably one of the most commonly used methods, and many applications can be found in the literature 1 [10–45] . When it is used with a single camera (classical DIC), the DIC method can only give in-plane displacement/strain fields on planar objects. By using two cameras (stereovision), the 3-D displacement field and the
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Boosting 3D-Geometric Features for Efficient Face Recognition and Gender Classification

Boosting 3D-Geometric Features for Efficient Face Recognition and Gender Classification

Boosting 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 ideas from two growing but disparate ideas in computer vision – shape analysis using tools from dif- ferential geometry and feature selection using machine learning – to select and highlight salient geometrical facial features that contribute most in 3D face recognition and gender classification. Firstly, a large set of geometries curve features are extracted using level sets (circular curves) and streamlines (radial curves) of the Euclidean distance functions of the facial surface; together they approximate facial surfaces with arbitrarily high accuracy. Then, we use the well-known Adaboost algorithm for feature selection from this large set and derive a composite classifier that achieves high performance with a minimal set of features. This greatly reduced set, consisting of some level curves on the nose and some radial curves in the forehead and cheeks regions, provides a very compact signature of a 3D face and a fast classification algorithm for face recognition and gender selection. It is also efficient in terms of data storage and transmission costs. Experimental results, carried out using the FRGCv2 dataset, yield a rank-1 face recognition rate of 98% and a gender classification rate of 86%.
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Bio-Inspired Computer Vision: Towards a Synergistic Approach of Artificial and Biological Vision

Bio-Inspired Computer Vision: Towards a Synergistic Approach of Artificial and Biological Vision

cognition or concepts representation needs to reconsider this deep 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 recurrent interactions must be taken into account to overcome several pitfalls in understanding and modelling biological vision. In this section, we highlight some of these key novel features that should greatly influence com- putational models of visual processing. We also believe that identifying some of these problems could help in reunifying natural and artificial vision and addressing more challenging questions as needed for building adaptive and versatile artificial systems which are deeply bio-inspired. Vision processing starts at the retina and the lateral geniculate nucleus (LGN) levels. Although this may sound obvious, the role played by these two structures seems largely underestimated. Indeed, most current models take images as inputs rather than their retina- LGN transforms. Thus, by ignoring what is being processed at these levels, one could easily miss some key properties to understand what makes the efficiency of biological visual systems. At the retina level, the incoming light is transformed into electrical signals. This transformation was originally described by using the linear systems approach to model the spatio-temporal filtering of retinal images [86]. More recent research has changed this view and several cortex- like computations have been identified in the retina of different vertebrates (see [110, 156] for reviews, and more details in Sec. 4.1). The fact that retinal and cortical levels share similar computational principles, albeit working at different spatial and temporal scales is an important point to consider when designing models of biological vision. Such a change in perspective would have important consequences. For example, rather than considering how cortical circuits
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Atoms of recognition in human and computer vision

Atoms of recognition in human and computer vision

its five descendants. The average gradient was 0.57 ± 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 high). The examples in Fig. 4 illustrate how small changes at the MIRC level can have a dramatic effect on recognition rates. These changes disrupt visual features to which the recognition system is sensi- tive (6–9); these features are present in the MIRCs but not in the sub-MIRCs. Crucially, the role of these features is revealed uniquely at the MIRC level, because information is more re- dundant in the full-object image, and a similar loss of features will have a small effect. By comparing recognition rates of models at the MIRC and sub-MIRC levels, we were able to test computationally whether current models of human and computer vision extract and use similar visual features and to test the ability of recognition models to recognize minimal images at a human level. The models in our testing included HMAX (10), a high-performing biological model of the primate ventral stream, along with four state-of-the-art computer vision models: (i) the Deformable Part Model (DPM) (11); (ii) support vector machines (SVM) applied to histograms of gradients (HOG) representations (12); (iii) extended Bag-of-Words (BOW) (13, 14); and (iv) deep convolutional networks (Methods) (15). All are among the top-performing schemes in standard evaluations (16).
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Computer vision on tap

Computer vision on tap

1.4. Related Work 1.4.1 Online Programming Platforms Our system follows in the footsteps of Scratch [6]. We take the basic concept of Scratch, an online programming and sharing platform designed to bring programming to under-served youth populations, and rethink the concept to fit the needs of the budding computer vision hobbyist. Scratch requires users to download and install a local appli- cation. However, our system allows content authoring in the browser. Scratch uses Java as its publishing language, mak- ing it difficult to deploy camera-based applications since standard client installations do not include camera access features. In contrast, our system uses Flash, which includes camera access capabilities in the standard client installation. In addition to adding camera access, our system also allows programs to interact with services other than our own, al- lowing third party integration.
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Sparse models for Computer Vision

Sparse models for Computer Vision

14.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 14.1 Motivation 14.1.1 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 with the environment. The early visual pathways provides with a powerful system for probing and modeling these mechanisms. For instance, the primary visual cortex of primates (V1) is absolutely central for most visual tasks. There, it is observed that some neurons from the input layer of V1 present a selectivity for localized, edge-like features —as represented by their “receptive fields” (Hubel and Wiesel, 1968). Crucially, there is experimental evidence for sparse firing in the neocortex (Barth and Poulet, 2012; Willmore et al., 2011) and in particular in V1. A representation is sparse when each input signal is associated with a relatively small sub-set of simultaneously activated neurons within a whole population. For instance, orientation selectivity of simple cells is sharper than the selectivity that would be predicted by linear filtering. Such a procedure produces a rough “sketch” of the image on the surface of V1 that is believed to serve as a “blackboard” for higher-level cortical areas (Marr, 1983). However, it is still largely unknown how neural computations act in V1 to represent the image. More specifically, what is the role of sparseness —as a generic neural signature— in the global function of neural computations?
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Mineral grains recognition using computer vision and machine learning

Mineral grains recognition using computer vision and machine learning

∗ Corresponding author. E-mail address: julien.maitre1@uqac.ca (J. 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 Scanning Electron Mi- croscopy (SEM) ( Gottlieb et al. , 2000 ; Sutherland and Gottlieb , 1991 ). Techniques such as chemical analysis and X-ray diffraction of sands or milled rocks will not provide a real mineral count. In the case of optical microscopy a highly qualified mineralogist will identify each individual mineral grain in a Petri dish at a typical rate of 60 grains per minute. It is a tedious work that needs lot of attention where any minute distraction can ruin a day’s work. Also, it provides grain percentage instead of area percentage ( Nie and Peng , 2014 ). The main drawbacks of the optical approach are the fatigue of highly qualified personnel leading to misidentification of minerals due to their lack of distinctive features and their small size. Alternatively, the SEM produces images of a mineral grains sample by scanning the surface with a focused beam of high-energy electrons to generate a variety of signals. Those signals are produced by electron-sample interaction and provide information such as the grain surface characteristics by secondary electrons (SE), its atomic density by backscattered electrons (BSE) and/or the chemical composition (from characteristic peaks in the X-ray spectrum). Mineral
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Computer Vision Tools for Rodent Monitoring

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

Modeling of Facial Wrinkles for Applications in Computer Vision

Localization using Stochastic Modeling Batool & Chellappa [1, 2] were the first to propose a generative stochastic model for wrinkles using Marked Point Processes (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 segments. Under Bayesian framework, a prior probability model dic- tated more probable geometric properties and spatial interactions of line segments. A data likelihood term, based on intensity gradients caused by wrinkles and high- lighted by Laplacian of Gaussian (LoG) filter responses, indicated more probable locations for the line segments. Wrinkles were localized by sampling MPP posterior probability using the Reversible Jump Markov Chain Monte Carlo (RJMCMC) al- gorithm. They proposed two MPP models in their work, [1] and [2], where the latter MPP model produced better localization results by introducing different movements in RJMCMC algorithm and data likelihood term. They also presented an evaluation setup to quantitatively measure the performance of the proposed model in terms of detection and false alarm rates in [2]. They demonstrated localization results on a variety of images obtained from the Internet. Figures 17 and 18 show examples of wrinkle localization from the two MPP models in [1] and [2] respectively.
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From Chaos to Randomness via Geometric Undersampling

From Chaos to Randomness via Geometric Undersampling

ε = − for double precision 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 computation using floating points, starting form most initial condition, it is possible to find a Mega-Periodic orbit (i.e. with period equal to 1,320,752). When computations are done with double precision number it is not possible to find any periodic orbit, up to n = × 5 10 11 iterations. In [11] the computations have been performed on a Dell computer with a Pentium IV microprocessor using a Borland C compiler computing with ordinary (IEEE-754) double precision numbers.
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Challenges in the Certification of Computer Vision-Based Systems

Challenges in the Certification of Computer Vision-Based Systems

Formal approaches VO is a particular instance of statistical estimation, where a quantity of interest, the state of the system, is 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 variables) and their relationships. Optimization of this criterion leads to the optimal estimate of the state given the data, with the (implicit) relationship between data and estimated state being referred to as the estimator. Modeling efforts allow the properties of the estimator to be theoretically characterized. Some properties concern the discrepancy between the estimated state and the true one, such as bias (e.g., systematic error) and variance (statistical dispersion). Bias and variance are usually associated with the performance of the estimation. They are, themselves, characterized by another level of properties, called structural properties. Efficiency refers to the optimality of bias and vari- ance for the problem at hand; i.e., that no other estimator can achieve lower values. Consistency expresses the fact that they correctly charac- terize the performance; that is to say, that the true state indeed lies within the interval of values defined by bias and variance. It clearly pertains to the safety of vision-based navigation: with a consistent estimator it is, for instance, possible to guarantee that the plane remains within some known bounds around the requested trajectory. Unfortunately, consis- tency is very difficult to assess for vision-based odometry or SLAM estimators. This is due to the non-linearity of the relationship between image data and state parameters. Also, as already mentioned, vision is prone to outliers, which are not accounted for in the problem modeling and lead to inconsistency. Hence, consistency is not a definitive answer to VO/SLAM safety issues, yet the vast literature on the subject includes relevant works; for instance, regarding consistency check techniques, in indoor environments. In addition, contrasts should be stable
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Deep learning of representations and its application to computer vision

Deep learning of representations and its application to computer vision

S3C is a useful feature extractor that performs comparably to the best approaches when large amounts of labeled data are available. 5.7.2 CIFAR-100 Having verified that 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 class. We compare S3C to two other feature extraction methods: OMP-1 with thresholding, which Coates and Ng (2011) found to be the best feature extractor on CIFAR-10, and sparse coding, which is known to perform well when less labeled data is available. We evaluated only a single set of hyperparameters for S3C. For sparse coding and OMP-1 we searched over the same set of hyperparameters as Coates and Ng (2011) did: {0.5, 0.75, 1.0, 1.25, 1.25} for the sparse coding penalty and {0.1, 0.25, 0.5, 1.0} for the thresholding value. In order to use a comparable amount of computational resources in all cases, we used at most 1600 hidden units and a 3 ⇥ 3 pooling grid for all three methods. For S3C, this was the only feature encoding we evaluated. For SC (sparse coding) and OMP-1, which double their number of features via sign splitting, we also evaluated 2 ⇥2 pooling with 1600 latent variables and 3⇥3 pooling with 800 latent variables to be sure the models do not su↵er from overfitting caused by the larger feature set. These results are summarized in Fig. 5.9.
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Understanding deep features with computer-generated imagery

Understanding deep features with computer-generated imagery

Abstract We introduce an approach for analyzing the variation of features generated by convolutional neural networks (CNNs) with respect to scene factors that occur in natural images. Such factors may include object style, 3D viewpoint, color, and scene lighting configuration. Our approach analyzes CNN feature responses corresponding to different scene fac- tors by controlling for them via rendering using a large database of 3D CAD models. The rendered images are pre- sented to a trained CNN and responses for different layers are studied with respect to the input scene factors. We per- form a decomposition of the responses based on knowledge of the input scene factors and analyze the resulting compo- nents. In particular, we quantify their relative importance in the CNN responses and visualize them using principal component analysis. We show qualitative and quantitative results of our study on three CNNs trained on large image datasets: AlexNet [ 18 ], Places [ 40 ], and Oxford VGG [ 8 ]. We observe important differences across the networks and CNN layers for different scene factors and object categories. Finally, we demonstrate that our analysis based on computer- generated imagery translates to the network representation of natural images.
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Comparison between Optical and Computer Vision Estimates of Visibility in Daytime Fog

Comparison between Optical and Computer Vision Estimates of Visibility in Daytime Fog

Keywords: Fog, Meteorological Optical Range, Contrast, Visibility, Imaging, Computer Vision 1 Introduction Fog is a quite common meteorological phenomenon. It happens in certain wind, temperature, and humidity conditions when vapour condenses into microscopic water droplets around airborne particles, causing the optical density of the atmosphere to rise dramatically. The net result is that visibility drops to levels where traffic becomes hazardous, with disrupting effects on ground (and other modes of) transport. Presently, the main solution to prevent such disruptions is to give warning to the drivers ahead of a foggy area, so that they can adapt their behaviour (Al-Ghamdi, 2007).
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Handling Geometric Features in Nanoscale Characterization of Charge Injection and Transport in thin Dielectric Films

Handling Geometric Features in Nanoscale Characterization of Charge Injection and Transport in thin Dielectric Films

enough (arithmetic roughness equal to 1.2nm over 1µm 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 thin dielectric layer is studied. Experimental and FEM results demonstrate that concerning the charge injection mechanism the radial electric field influences the charge lateral spreading whereas the axial electric field governs the amount of injected charges. Moreover, the nanostructured nature of the dielectric layer influences mainly the injection process. Concerning the C-AFM measurements, the macroscopic laws failed to interpret experimental results and new model needs to be developed to reproduce the real configuration (electric field heterogeneity distribution and influence of injected charge). Indeed, taking into account the electric field at the contact point is not enough to reproduce the real conditions and its distribution in the
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Continuous in situ measurement of quenching distortions using computer vision

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 reason is that the models involved require a very large amount of data, such as the phase transformation curves, mechanical properties of each constituent, heat exchange coefficients, etc. Moreover, the consequences of the use of any simplified set of data on the numerical results are not clearly understood. For exam ple, it is known that, during the cooling part of the quenching process, distortions occur at high temperature (at the begin ning of cooling), while residual stresses essentially develop at low temperature (at the end of cooling). Nevertheless, the influence of high and low temperature parameters on residual distortions and stresses, respectively, has not yet been quan tified. It turns out that, before any numerical simulation of a quenching process, a long time period is often necessary to
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