waashk/dblp huggingface dataset:
object recognition with informative features and linear classification. in this paper we show that efficient object recognition canbe obtained by combining informative features with linear classification. the results demonstrate the superiority of informative class-specific features, as compared with generic type features such as wavelets, for the task of object recognition. we show that information rich features can reach optimal performance with simple linear separation rules, while generic feature based classifiers require more complex classification schemes. this is significant because efficient and optimal methods have been developed for spaces that allow linear separation. to compare different strategies for feature extraction, we trained and compared classifiers working in feature spaces of the same low dimensionality, using two feature types (image fragments vs. wavelets) and two classification rules (linear hyperplane and a bayesian network). the results show that by maximizing the individual information of the features, it is possible to obtain efficient classification by a simple linear separating rule, aswell as more efficient learning.
motion analysis with a camera with unknown, and possibly varying intrinsic parameters. in the present paper we address the problem of computing structure and motion, given a set point correspondences in a monocular image sequence, considering small motions when the camera is not calibrated. we first set the equations defining the calibration, rigid motion and scene structure. we then review the motion equation, the structure from equation and the depth evolution equation, including the particular case of planar structures, considering a discrete displacement between two frames. a step further, we develop the first order expansion of these equations and analyse the observability of the related infinitesimal quantities. it is shown that we obtain a complete correspondence between these equations and the equation derived in the discrete case. however, in the case of infinitesimal displacements, the projection of the translation (focus of expansion or epipole) is clearly separated from the rotational component of the motion. this is an important advantage of the present approach. using this last property, we propose a mechanism of image stabilization in which the rotational disparity is iteratively canceled. this allows a better estimation of the focus of expansion, and simplifies different aspects of the analysis of the equations: structure from motion equation, analysis of ambiguity, geometrical interpretation of the motion equation.
invariant-based recognition of complex curved 3d objects from image contours. to recognize three-dimensional objects bounded by smooth curved surfaces from monocular image contours, viewpoint-dependent image features must be related to object geometry. contour bitangents and inflections along with associated parallel tangents points are the projection of surface points that lie on the occluding contour for a five-parameter family of scaled orthographic projection viewpoints. an invariant representation can be computed from these image features and seen for modeling and recognizing objects. modeling is achieved by moving an object in front of a camera to obtain a curve of possible invariants. the relative camera-object motion is not required, and 3d models are not utilized. at recognition time, invariants computed from a single image are used to index the model database. using the matched features, independent qualitative and quantitative verification procedures eliminate potential false matches. examples from an implementation are presented.
robust real-time face detection. this paper describes a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates. there are three key contributions. the first is the introduction of a new image representation called the &ldquo;integral image&rdquo; which allows the features used by our detector to be computed very quickly. the second is a simple and efficient classifier which is built using the adaboost learning algorithm (freund and schapire, 1995) to select a small number of critical visual features from a very large set of potential features. the third contribution is a method for combining classifiers in a &ldquo;cascade&rdquo; which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions. a set of experiments in the domain of face detection is presented. the system yields face detection performance comparable to the best previous systems (sung and poggio, 1998; rowley et al., 1998; schneiderman and kanade, 2000; roth et al., 2000). implemented on a conventional desktop, face detection proceeds at 15 frames per second.
detecting pedestrians using patterns of motion and appearance. this paper describes a pedestrian detection system that integratesimage intensity information with motion information.we use a detection style algorithm that scans a detectorover two consecutive frames of a video sequence. thedetector is trained (using adaboost) to take advantage ofboth motion and appearance information to detect a walkingperson. past approaches have built detectors based onmotion information or detectors based on appearance information,but ours is the first to combine both sources ofinformation in a single detector. the implementation describedruns at about 4 frames/second, detects pedestriansat very small scales (as small as 20x15 pixels), and has avery low false positive rate.our approach builds on the detection work of viola andjones. novel contributions of this paper include: i) developmentof a representation of image motion which is extremelyefficient, and ii) implementation of a state of theart pedestrian detection system which operates on low resolutionimages under difficult conditions (such as rain andsnow).
alignment by maximization of mutual information . a new information-theoretic approach is presented for finding the pose of an object in an image. the technique does not require information about the surface properties of the object, besides its shape, and is robust with respect to variations of illumination. in our derivation, few assumptions are made about the nature of the imaging process. as a result, the algorithms are quite general and can foreseeably be used in a wide variety of imaging situations. experiments are presented that demonstrate the approach in registering magnetic resonance images, aligning a complex 3d object model to real scenes including clutter and occlusion, tracking a human head in a video sequence and aligning a view-based 2d object model to real images. the method is based on a formulation of the mutual information between the model and the image. as applied in this paper, the technique is intensity-based, rather than feature-based. it works well in domains where edge or gradient-magnitude based methods have difficulty, yet it is more robust then traditional correlation. additionally, it has an efficient implementation that is based on stochastic approximation.
using frontier points to recover shape, reflectance and illumunation. we describe a method to recover the surface reflectance and the 3-d shape of a non-lambertian object as well as illumination, from a collection of images. it is based on the so-called frontier points, which are extracted from the outlines of an object. frontier points provide 3-d locations on the object surface where the surface normal is known. this information is exploited to infer the surface reflectance of the object and the light distribution of the scene both under varying illumination and fixed vantage point, and under varying vantage point and fixed illumination. we also show how to apply frontier points for shape recovery in photometric stereo. the effectiveness of frontier points for recovering reflectance, illumination and shape is confirmed by a number of experiments on both real and synthetic data.
asl recognition based on a coupling between hmms and 3d motion analysis. we present a framework for recognizing isolated and continuous american sign language (asl) sentences from three-dimensional data. the data are obtained by using physics-based three-dimensional tracking methods and then presented as input to hidden markov models (hmms) for recognition. to improve recognition performance, we model context-dependent hmms and present a novel method of coupling three-dimensional computer vision methods and hmms by temporally segmenting the data stream with vision methods. we then use the geometric properties of the segments to constrain the hmm framework for recognition. we show in experiments with a 53 sign vocabulary that three-dimensional features outperform two-dimensional features in recognition performance. furthermore, we demonstrate that context-dependent modeling and the coupling of vision methods and hmms improve the accuracy of continuous asl recognition.
shape from shading with interreflections under proximal light source: 3d shape reconstruction of unfolded book surface from a scanner image. we address the problem to recover the 3d shape of an unfolded book surface from the shading information in a scanner image. from a technical point of view, this shape from shading problem in real world environments is characterized by (1) proximal light source, (2) interreflections, (3) moving light source, (4) specular reflection, and (5) nonuniform albedo distribution. taking all these factors into account, we first formulate the problem based on an iterative nonlinear optimization scheme. then we introduce piecewise polynomial models of the 3d shape. image restoration experiments for a real book surface demonstrated that geometric and photometric distortions are almost completely removed by the proposed method.
recognition with local features: the kernel recipe. recent developments in computer vision have shown that local features can provide efficient representations suitable for robust object recognition. support vector machines have been established as powerful learning algorithms with good generalization capabilities. in this paper, we combine these two approaches and propose a general kernel method for recognition with local features. we show that the proposed kernel satisfies the mercer condition and that it is suitable for many established local feature frameworks. large-scale recognition results are presented on three different databases, which demonstrate that svms with the proposed kernel perform better than standard matching techniques on local features. in addition, experiments on noisy and occluded images show that local feature representations significantly outperform global approaches.
facial expression decomposition. in this paper, we propose a novel approach for facialexpression decomposition - higher-order singular valuedecomposition (hosvd), a natural generalization ofmatrix svd. we learn the expression subspace and personsubspace from a corpus of images showing seven basicfacial expressions, rather than resort to expert-coded facialexpression parameters as in [3]. we propose a simultaneousface and facial expression recognition algorithm,which can classify the given image into one of the sevenbasic facial expression categories, and then other facialexpressions of the new person can be synthesized using thelearned expression subspace model. the contributions ofthis work lie mainly in two aspects. first, we propose a newhosvd based approach to model the mapping betweenpersons and expressions, used for facial expression synthesisfor a new person. second, we realize simultaneous faceand facial expression recognition as a result of facialexpression decomposition. experimental results are presentedthat illustrate the capability of the person subspaceand expression subspace in both synthesis and recognitiontasks. as a quantitative measure of the quality of synthesis,we propose using gradient minimum square error (gmse)which measures the gradient difference between the originaland synthesized images.
region correspondence by inexact attributed planar graph matching. an efficient graph matching approach is proposed for finding region correspondence between two images of the same scene but taken from different viewpoints. regions and their relations in an image are represented with region adjacency graph (rag), which is a kind of attributed planar graph. the problem to find an optimal region correspondence, which matches the regions in two images with maximal similarity in region features and region relations, is formulated into the problem to find the optimal inexact matching between two rags. the properties specific to planar graph and that of the region adjacency relations are utilized to invent an efficient algorithm to solve the problem. experimental results on various kinds of images show the effectiveness of the method.
learning models for predicting recognition performance. this paper addresses one of the fundamental problems encountered in performance prediction for object recognition. in particular we address the problems related to estimation of small gallery size that can give good error estimates and their confidences on large probe sets and populations. we use a generalized two-dimensional prediction model that integrates a hypergeometric probability distribution model with a binomial model explicitly and considers the distortion problem in large populations. we incorporate learning in the prediction process in order to find the optimal small gallery size and to improve its performance. the chernoff and chebychev inequalities are used as a guide to obtain the small gallery size. during the prediction we use the expectation-maximum (em) algorithm to learn the match score and the non-match score distributions (the number of components, their weights, means and covariances) that are represented as gaussian mixtures. by learning we find the optimal size of small gallery and at the same time provide the upper bound and the lower bound for the prediction on large populations. results are shown using real-world databases.
perceptual scale space and its applications. in this paper, we study a perceptual scale space by constructing a so-called sketch pyramid which augments the gaussian and laplacian pyramid representations in traditional image scale space theory. each level of this sketch pyramid is a generic attributed graph ¿ called the primal sketch which is inferred from the corresponding image at the same level of the gaussian pyramid. when images are viewed at increasing resolutions, more details are revealed. this corresponds to perceptual transitions which are represented by topological changes in the sketch graph in terms of a graph grammar. we compute the sketch or perceptual pyramid by bayesian inference upwards-downwards the pyramid using markov chain monte carlo reversible jumps. we show two example applications of this perceptual scale space: (1) motion tracking of objects over scales, and (2) adaptive image displays which can efficiently show a large high-resolution image in a small screen (of a pda for example) through a selective tour of its image pyramid. other potential applications include super-resolution and multi-resolution object recognition.
an iterative optimization approach for unified image segmentation and matting. separating a foreground object from the background in a static image involves determining both full and partial pixel coverages, also known as extracting a matte. previous approaches require the input image to be pre-segmented into three regions: foreground, background and unknown, which is called a trimap. partial opacity values are then computed only for pixels inside the unknown region. this pre-segmentation based approach fails for images with large portions of semi-transparent foreground where the trimap is difficult to create even manually. in this paper we combine the segmentation and matting problem together and propose a unified optimization approach based on belief propagation. we iteratively estimate the opacity value for every pixel in the image, based on a small sample of foreground and background pixels marked by the user. experimental results show that compared with previous approaches, our method is more efficient to extract high quality mattes for foregrounds with significant semi-transparent regions.
mutual information-based 3d surface matching with applications to face recognition and brain mapping. face recognition and many medical imaging applications require the computation of dense correspondence vector fields that match one surface with another. in brain imaging, surface-based registration is useful for tracking brain change, and for creating statistical shape models of anatomy. based on surface correspondences, metrics can also be designed to measure differences in facial geometry and expressions. to avoid the need for a large set of manually-defined landmarks to constrain these surface correspondences, we developed an algorithm to automate the matching of surface features. it extends the mutual information method to automatically match general 3d surfaces (including surfaces with a branching topology). we use diffeomorphic flows to optimally align the riemann surface structures of two surfaces. first, we use holomorphic1-forms to induce consistent conformal grids on both surfaces. high genus surfaces are mapped to a set of rectangles in the euclidean plane, and closed genus-zero surfaces are mapped to the sphere. next, we compute stable geometric features (mean curvature and conformal factor) and pull them back as scalar fields onto the 2d parameter domains. mutual information is used as a cost functional to drive a fluid flow in the parameter domain that optimally aligns these surface features. a diffeomorphic surface-to-surface mapping is then recovered that matches surfaces in 3d. lastly, we present a spectral method that ensures that the grids induced on the target surface remain conformal when pulled through the correspondence field. using the chain rule, we express the gradient of the mutual information between surfaces in the conformal basis of the source surface. this finite-dimensional linear space generates all conformal reparameterizations of the surface. illustrative experiments apply the method to face recognition and to the registration of brain structures, such as the hippocampus in 3d mri scans, a key step in understanding brain shape alterations in alzheimer¿s disease and schizophrenia.
a novel approach for texture shape recovery. in vision and graphics, there is a sustained interest incapturing accurate 3d shape with various scanning devices.however, the resulting geometric representation isonly part of the story. surface texture of real objects isalso an important component of the representation and fine-scalesurface geometry such as surface markings, roughness,and imprints, are essential in highly realistic renderingand accurate prediction. we present a novel approachfor measuring the fine-scale surface shape of specular surfacesusing a curved mirror to view multiple angles in asingle image. a distinguishing aspect of our method is thatit is designed for specular surfaces, unlike many methods(e.g. laser scanning) which cannot handle highly specularobjects. also, the spatial resolution is very high so that itcan resolve very small surface details that are beyond theresolution of standard devices. furthermore, our approachincorporates the simultaneous use of a bidirectional texturemeasurement method, so that spatially varying bidirectionalreflectance is measured at the same time as surfaceshape.
retrieval with knowledge-driven kernel design: an approach to improving svm-based cbir with relevance feedback. the performance of svm-based image retrieval is often constrained by the scarcity of training samples. the total number of image samples labelled by users in a retrieval session is very limited, and these small number of labelled samples cannot effectively represent the true distributions of positive and negative image classes, especially for the negative image class. this paper proposes a novel approach to deal with this problem. instead of treating it as a problem, the mere existence of the small number of labelled images and their desired distribution in the kernel space is considered as prior knowledge from image retrieval to aid the design of the kernel used by svms. this is achieved by maximizing a criterion, such as one based on scatter matrices, through gradient-based search methods, incurring very little computational overhead to real-time retrieval process. experimental results on two benchmark image databases demonstrate the improved retrieval performance by the dynamically designed kernel and hence the effectiveness of the proposed approach for svm based image retrieval.
surface parameterization using riemann surface structure. we propose a general method that parameterizes general surfaces with complex (possible branching) topology using riemann surface structure. rather than evolve the surface geometry to a plane or sphere, we instead use the fact that all orientable surfaces are riemann surfaces and admit conformal structures, which induce special curvilinear coordinate systems on the surfaces. we can then automatically partition the surface using a critical graph that connects zero points in the global conformal structure on the surface. the trajectories of iso-parametric curves canonically partition a surface into patches. each of these patches is either a topological disk or a cylinder and can be conformally mapped to a parallelogram by integrating a holomorphic 1-form defined on the surface. the resulting surface subdivision and the parameterizations of the components are intrinsic and stable. for surfaces with similar topology and geometry, we show that the parameterization results are consistent and the subdivided surfaces can be matched to each other using constrained harmonic maps. the surface similarity can be measured by direct computation of distance between each pair of corresponding points on two surfaces. to illustrate the technique, we computed conformal structures for anatomical surfaces in mri scans of the brain and human face surfaces. we found that the resulting parameterizations were consistent across subjects, even for branching structures such as the ventricles, which are otherwise difficult to parameterize. our method provides a surface-based framework for statistical comparison of surfaces and for generating grids on surfaces for pde-based signal processing.
high resolution tracking of non-rigid 3d motion of densely sampled data using harmonic maps. we present a novel fully automatic method for high resolution, non-rigid dense 3d point tracking. high quality dense point clouds of non-rigid geometry moving at video speeds are acquired using a phase-shifting structured light ranging technique. to use such data for the temporal study of subtle motions such as those seen in facial expressions, an efficient non-rigid 3d motion tracking algorithm is needed to establish inter-frame correspondences. the novelty of this paper is the development of an algorithmic framework for 3d tracking that unifies tracking of intensity and geometric features, using harmonic maps with added feature correspondence constraints. while the previous uses of harmonic maps provided only global alignment, the proposed introduction of interior feature constraints guarantees that non-rigid deformations will be accurately tracked as well. the harmonic map between two topological disks is a diffeomorphism with minimal stretching energy and bounded angle distortion. the map is stable, insensitive to resolution changes and is robust to noise. due to the strong implicit and explicit smoothness constraints imposed by the algorithm and the high-resolution data, the resultingregistration/deformation field is smooth, continuous and gives dense one-to-one inter-frame correspondences. our method is validated through a series of experiments demonstrating its accuracy and efficiency.