waashk/dblp huggingface dataset:
a supervised learning framework for generic object detection in images. in recent years kernel principal component analysis (kernel pca) has gained much attention because of its ability to capture nonlinear image features, which are particularly important for encoding image structure. boosting has been established as a powerful learning algorithm that can be used for feature selection. in this paper we present a novel framework for object class detection that combines the feature reduction and feature selection abilities of kernel pca and adaboost respectively. the classifier obtained in this way is able to handle change in object appearance, illumination conditions, and surrounding clutter. a nonlinear subspace is learned for positive and negative object classes using kernel pca. features are derived by projecting example images onto the learned subspaces. base learners are modeled using bayes classifier. adaboost is then employed to discover the features that are most relevant for the object detection task at hand. the proposed method has been successfully tested on wide range of object classes (cars, airplanes, pedestrians, motorcycles, etc) using standard data sets and has shown remarkable performance. using a small training set, a classifier learned in this way was able to generalize the intra-class variation while still maintaining high detection rate. in most object categories we achieved detection rates of above 95% with minimal false alarm rates. we demonstrate the effectiveness of our approach in terms of absolute performance parameters and comparative performance against current state of the art approaches.
reconstructing complex surfaces from multiple stereo views. we present a framework for 3d surface reconstruction that can be used to model fully 3 dimensional scenes from an arbitrary number of stereo views. taken from vastly different viewpoints. this is a key step toward producing 3d world descriptions of complex scenes using stereo and is a very challenging problem: real world scenes tend to contain many 3d objects, they do not usually conform to the 2-1/2d assumption made by traditional algorithms, and one cannot take it for granted that the computed 3d points can easily be clustered into separate groups. by combining a particle based representation, robust fitting, and optimization of an image based objective function, we have been able to reconstruct surfaces without any a priori knowledge of their topology and in spite of the noisiness of the stereo data. our current implementation goes through three steps-initializing a set of particles from the input 3d data, optimizing their location, and finally grouping them into global surfaces. using several complex scenes containing multiple objects, we demonstrate its competence and ability to merge information and thus to go beyond what can be done with conventional stereo alone.
spectral gradient: a material descriptor invariant to geometry and incident illumination. the light reflected from a surface depends on the scene geometry, the incident illumination and the surface material. a novel methodology is presented which extracts reflectivity information of the various materials in the scene independent of incident light and scene geometry. a scene is captured under different narrow-band color filters and the spectral derivatives of the scene are computed. the resulting spectral derivatives form a spectral gradient at each pixel. this spectral gradient is a material descriptor which is invariant to scene geometry and incident illumination for smooth diffuse surfaces. spectral gradients can discriminate among smooth dielectrics with different reflectance properties independent of viewing conditions.
voxel carving for specular surfaces. we present an novel algorithm that reconstructs voxels of a general 3d specular surface from multiple images of a calibrated camera. a calibrated scene (i.e. points whose 3d coordinates are known) is reflected by the unknown specular surface onto the image plane of the camera. for every viewpoint, surface normals are associated to the voxels traversed by each projection ray formed by the reflection of a scene point. a decision process then discards voxels whose associated surface normals are not consistent with one another. the output of the algorithm is a collection of voxels and surface normals in 3d space, whose quality and size depend on user-set thresholds. the method has been tested on synthetic and real images. visual and quantified experimental results are presented.
epipole and fundamental matrix estimation using virtual parallax. the paper addresses the problem of computing the fundamental matrix which describes a geometric relationship between a pair of stereo images: the epipolar geometry. we propose a novel method based on virtual parallax. instead of computing directly the 3/spl times/3 fundamental matrix, we compute a homography with one epipole position, and show that this is equivalent to computing the fundamental matrix. simple equations are derived by reducing the number of parameters to estimate. as a consequence, we obtain an accurate fundamental matrix of rank two with a stable linear computation. experiments with simulated and real images validate our method and clearly show the improvement over existing methods.
seeing behind the scene: analysis of photometric properties of occluding edges by the reversed projection blurring model. this paper analyzes photometric properties of occluding edges and proves that (1) we can observe surface edges on the farther object located close to the occluding edge even if they are occluded by the nearer object, (2) the image of an occluding edge coincides with that of a surface edge on the nearer object if the brightness of the farther object is uniform around the occluding edge. first, we propose a blurring model named the reversed projection blurring model to analyze photometric properties of blurring phenomena of an occluding edge. using this model, the theoretical proof of the two properties mentioned above is given. finally, experimental results in real world environments demonstrate the validity of our blurring model as well as the observability of the photometric properties of occluding edges.
from projective to euclidean space under any practical situation, a criticism of self-calibration. for many practical applications it is important to relax the self-calibration conditions to allow for changing internal camera parameters (e.g. zooming/focusing ¿). classical techniques failed for such conditions. we present the available constraints that allow us to right a projective calibration to a euclidean one. meanwhile, we found that the estimations of the internal parameters were rather inaccurate. we discuss theoretically this difficulty and above all the resulting effect on the 3d reconstruction. in fact, we show that the uncertainty on the focal length estimation leads to a euclidean calibration up to a quasi anisotropic homothety whereas the error on the principal point can often be interpreted as a translation. hopefully, the calibration we come up with, is quite acceptable for reconstruction of models.
visual navigation using a single camera. we assess the usefulness of monocular recursive motion estimation techniques for vehicle navigation in the absence of a model for the environment. for this purpose we extend a recently proposed recursive motion estimator, the essential filter, to handle scale estimation. we examine experimentally the accuracy with which the motion and position of the vehicle may be computed on an 8000 frame indoors sequence. the issues of sampling time frequency and number of necessary features in the environment are addressed systematically.
an axis-based representation for recognition. we present a new axis-based shape representation scheme along with a matching framework to address the problem of generic shape recognition. the main idea is to define the relative spatial arrangement of local symmetry axes and their metric properties in a shape centered coordinate frame. the resulting descriptions are invariant to scale, rotation, small changes in viewpoint and articulations. symmetry points are extracted from a surface whose level curves roughly mimic the motion by curvature. by increasing the amount of smoothing on the evolving curve, only those symmetry axes that correspond to the most prominent parts of a shape are extracted. the representation does not suffer from the common instability problems of the traditional connected skeletons. it captures the perceptual qualities of shapes well. therefore finding the similarities and the differences among shapes becomes easier. the matching process gives highly successful results on a diverse database of 2d shapes.
3d photography on your desk. a simple and inexpenssive approach for extracting the three-dimentional shape of objects is presented. it is based on 'weak structured lighting'; it differs from other conventional structured lighting approaches in that it requires very little hardware besides the camera: a desk-lamp, a pencil and a checkerboard. the camera faces the object, which is illuminated by the desk-lamp. the user moves a pencil in front of the light source casting a moving shadow on the object. the 3d shape of the object is extracted from the spatial and temporal location of the observed shadow. experimental results are presented on three different scenes demonstrating that the error in reconstructing the surface is less than 1%.
multi-view surface reconstruction using polarization. a new technique for surface reconstruction is developed that uses polarization information from two views. one common problem arising from many multiple view techniques is that of finding correspondences between pixels on each image. in the new method, these correspondences are found by exploiting the spontaneous polarization of light caused by reflection to recover surface normals. these normals are then used to recover surface height. the similarity between reconstructed surface regions determines whether or not a pair of points correspond to each other. the technique is thus able to overcome the convex/concave ambiguity found in many single view techniques. because the technique relies on smooth surface regions to detect correspondences, rather than feature detection, it is applicable to objects normally inaccessible to stereo vision. also due to this fact, it is possible to remove noise without causing oversmoothing problems.
layered representation of motion video using robust maximum-likelihood estimation of mixture models and mdl encoding. representing and modeling the motion and spatial support of multiple objects and surfaces from motion video sequences is an important intermediate step towards dynamic image understanding. one such representation, called layered representation, has recently been proposed. although a number of algorithms have been developed for computing these representations, there has not been a consolidated effort into developing a precise mathematical formulation of the problem. this paper presents one such formulation based on maximum likelihood estimation (mle) of mixture models and the minimum description length (mdl) encoding principle. the three major issues in layered motion representation are: (i) how many motion models adequately describe image motion, (ii) what are the motion model parameters, and (iii) what is the spatial support layer for each motion model.
a theory of catadioptric image formation. conventional video cameras have limited fields of view which make them restrictive for certain applications in computational vision. a catadioptric sensor uses a combination of lenses and mirrors placed in a carefully arranged configuration to capture a much wider field of view. when designing a catadioptric sensor, the shape of the mirror(s) should ideally be selected to ensure that the complete catadioptric system has a single effective viewpoint. in this paper, we derive the complete class of single-lens single-mirror catadioptric sensors which have a single viewpoint and an expression for the spatial resolution of a catadioptric sensor in terms of the resolution of the camera used to construct it. we also include a preliminary analysis of thedefocus blur caused by the use of a curved mirror.
computing geodesics and minimal surfaces via graph cuts. geodesic active contours and graph cuts are two standard image segmentation techniques. we introduce a new segmentation method combining some of their benefits. our main intuition is that any cut on a graph embedded in some continuous space can be interpreted as a contour (in 2d) or a surface (in 3d). we show how to build a grid graph and set its edge weights so that the cost of cuts is arbitrarily close to the length (area) of the corresponding contours(surfaces) for any anisotropic riemannian metric. there are two interesting consequences of this technical result. first, graph cut algorithms can be used to find globally minimum geodesic contours (minimal surfaces in 3d) under arbitrary riemannian metric for a given set of boundary conditions. second, we show how to minimize metrication artifacts in existing graph-cut based methods in vision. theoretically speaking, our work provides an interesting link between several branches of mathematics - differential geometry, integral geometry, and combinatorial optimization. the main technical problem is solved using cauchy-crofton formula from integral geometry.
fast approximate energy minimization via graph cuts. many tasks in computer vision involve assigning a label (such as disparity) to every pixel. a common constraint is that the labels should vary smoothly almost everywhere while preserving sharp discontinuities that may exist, e.g., at object boundaries. these tasks are naturally stated in terms of energy minimization. in this paper, we consider a wide class of energies with various smoothness constraints. global minimization of these energy functions is np-hard even in the simplest discontinuity-preserving case. therefore, our focus is on efficient approximation algorithms. we present two algorithms based on graph cuts that efficiently find a local minimum with respect to two types of large moves, namely expansion moves and swap moves. these moves can simultaneously change the labels of arbitrarily large sets of pixels. in contrast, many standard algorithms (including simulated annealing) use small moves where only one pixel changes its label at a time. our expansion algorithm finds a labeling within a known factor of the global minimum, while our swap algorithm handles more general energy functions. both of these algorithms allow important cases of discontinuity preserving energies. we experimentally demonstrate the effectiveness of our approach for image restoration, stereo and motion. on real data with ground truth, we achieve 98 percent accuracy.
resolution-appropriate shape representation. we present a new type of "snake" in which the dimensionality of the shapes is scaled appropriately for the resolution of the images in which the shapes are embedded. we define shapes as an ordered list of control points and compute the principal components of the shapes in a prior training set. our energy function is based upon the mahalanobis distance of a given shape from the mean shape and on the mahalanobis distance of the image attributes from image attribute values extracted from the training set. we show that the derivative of this energy function with respect to the modal weights is reduced as the image resolution is reduced, and that the derivative of the energy scales with the variance associated with each mode. we exploit this property to determine the subset of the modes which are relevant at a particular level of image resolution, thereby reducing the dimensionality of the shapes. we implement a coarse-to-fine search procedure in the image and shape domains simultaneously, and demonstrate this procedure on the identification of anatomic structures in computed tomography images.
what does motion reveal about transparency? the perception of transparent objects from images is knownto be a very hard problem in vision. given a single image,it is difficult to even detect the presence of transparent objectsin the scene. in this paper, we explore what can be saidabout transparent objects by a moving observer. we showhow features that are imaged through a transparent objectbehave differently from those that are rigidly attached to thescene. we present a novel model-based approach to recoverthe shapes and the poses of transparent objects from knownmotion. the objects can be complex in that they may be composedof multiple layers with different refractive indices. wehave conducted numerous simulations to verify the practicalfeasibility of our algorithm. we have applied it to real scenesthat include transparent objects and recovered the shapes ofthe objects with high accuracy.
surface classification using conformal structures. 3d surface classification is a fundamental problem incomputer vision and computational geometry. surfaces canbe classified by different transformation groups. traditionalclassification methods mainly use topological transformationgroups and euclidean transformation groups. this paperintroduces a novel method to classify surfaces by conformaltransformation groups. conformal equivalent classis refiner than topological equivalent class and coarser thanisometric equivalent class, making it suitable for practicalclassification purposes. for general surfaces, the gradientfields of conformal maps form a vector space, which hasa natural structure invariant under conformal transformations.we present an algorithm to compute this conformalstructure, which can be represented as matrices, and use itto classify surfaces. the result is intrinsic to the geometry,invariant to triangulation and insensitive to resolution. tothe best of our knowledge, this is the first paper to classifysurfaces with arbitrary topologies by global conformal invariants.the method introduced here can also be used forsurface matching problems.
shadow puppetry. the mapping between 3d body poses and 2d shadows is fundamentally many-to-many and defeats regression methods, even with windowed context. we show how to learn a function between paths in the two systems, resolving ambiguities by integrating information over the entire length of a sequence.the basis of this function is a configurable and dynamical manifold that summarizes the target system's behavior. this manifold can be modeled from data with a hidden markov model having special topological properties that we obtain via entropy minimization. inference is then a matter of solving for the geodesic on the manifold that best explains the evidence in the cue sequence. we give a closed-form maximum a posteriori solution for geodesics through the learned density space, thereby obtaining optimal paths over the dynamical manifold.these methods give a completely general way to perform inference over time-series; in vision they support analysis, recognition, classification and synthesis of behaviors in linear time. we demonstrate with a prototype that infers 3d from monocular monochromatic sequences (e.g., back-subtractions), without using any articulatory body model. the framework readily accommodates multiple cameras and other sources of evidence such as optical flow or feature tracking.
scale-space from nonlinear filters. decomposition by extrema is put into the context of linear vision systems and scale-space. one dimensional discrete m- and n-sieves neither introduce new edges as the scale increases nor create new extrema. they share this property with diffusion based filters. furthermore m- and n-sieve algorithms are extremely fast with order complexity n. used to decompose an image, the resulting granularity is appropriate for pattern recognition.
shape recovery of 3d data obtained from a moving range sensor by using image sequences. for a large object, scanning from the air is one of the most efficient methods of obtaining 3d data. in the case of large cultural heritage objects, there are some difficulties in scanning with respect to safety and efficiency. to remedy these problems, we have been developing a novel 3d measurement system, the floating laser range sensor (flrs), in which a range sensor is suspended beneath a balloon. the obtained data, however, have some distortions due to sensor-movements during the scanning process. in this paper, we propose a method to recover 3d range data obtained by a moving laser range sensor. this method is applicable not only to our flrs, but also to a general moving range sensor. using image sequences from a video camera mounted on the flrs enables us to estimate the motion of the flrs without any physical sensors such as gyros or gps. in the first stage, the initial values of camera motion parameters are estimated by full-perspective factorization. the next stage refines camera motion parameters using the relationships between camera images and range data distortion. finally, by using the refined parameters, the distorted range data are recovered. in addition, our method is applicable with an uncalibrated video camera and range sensor system. we applied this method to an actual scanning project, and the results showed the effectiveness of our method.
efficient visual event detection using volumetric features. this paper studies the use of volumetric features as an alternative to popular local descriptor approaches for event detection in video sequences. motivated by the recent success of similar ideas in object detection on static images, we generalize the notion of 2d box features to 3d spatio-temporal volumetric features. this general framework enables us to do real-time video analysis. we construct a real-time event detector for each action of interest by learning a cascade of filters based on volumetric features thatefficiently scans video sequences in space and time. this event detector recognizes actions that are traditionally problematic for interest point methods ¿ such as smooth motions where insufficient space-time interest points are available. our experiments demonstrate that the technique accurately detects actions on real-world sequences and is robust to changes in viewpoint, scale and action speed. we also adapt our technique to the related task of human action classification and confirm that it achieves performance comparable to a current interest point based human activity recognizer on a standard database of human activities.
quasiconvex optimization for robust geometric reconstruction. geometric reconstruction problems in computer vision are often solved by minimizing a cost function that combines the reprojection errors in the 2d images. in this paper, we show that, for various geometric reconstruction problems, their reprojection error functions share a common and quasiconvex formulation. based on the quasiconvexity, we present a novel quasiconvex optimization framework in which the geometric reconstruction problems are formulated as a small number of small-scale convex programs that are ready to solve. our final reconstruction algorithm is simple and has intuitive geometric interpretation. in contrast to existing local minimization approaches, our algorithm is deterministic and guarantees a predefined accuracy of the minimization result.the quasiconvexity also provides an intuitive method to handle directional uncertainties and outliers in measurements. when there are outliers in the measurements, our method provides a mechanism to locate the global minimum of a robust error function. for large scale problems and when computational resources are constrained, we provide an efficient approximation that gives a good upper bound (but not global minimum) on the reconstruction error. we demonstrate the effectiveness of our algorithm by experiments on both synthetic and real data.