waashk/dblp huggingface dataset:
a probabilistic framework for specular shape-from-shading. in this paper we address the problem of separating lambertian and specular reflection components in order to improve the quality of surface normal information recoverable using shape-from-shading (sfs). the framework for our study is provided by the iterated conditional modes algorithm. we develop a maximum a posteriori probability (map) estimation method for estimating the mixing proportions for the two reflectances, and also, for recovering local surface normals. the map estimation scheme has two model ingredients. firstly, there are separate conditional measurement densities which describe the distributions of surface normals for the two reflectance components. the second ingredient is a smoothness prior which models the distribution of surface normals over local image regions. we experiment with the method on real-world data. ground truth data is provided by imagery obtained with crossed polaroid filters. this reveals not only that the method accurately estimates the proportion of specular reflection, but that it also results in good surface normal reconstruction in the proximity of specular highlights.
reflectance from surfaces with layers of variable roughness. a new model for the scattering of light from layered surfaces with boundaries of variable roughness is introduced. the model contains a surface scattering component together with a subsurface scattering component. the former component corresponds to the roughness on the upper surface boundary and is modeled using the modified beckmann model. the latter component accounts for both refraction due to fresnel transmission through the layer and rough scattering at the lower layer boundary. by allowing independent roughness parameters for each surface boundary we can achieve excellent fits of the model to the measured brdf data. we experiment with brdf data from skin surface samples (human volunteers) and show that the new model outperforms alternative variants of the beckmann model and the lafortune et al. reflectance model.
robust appearance-based human action recognition. an automatic human action representation and recognition technique is proposed in this paper. appearance-change problem due to human wearing dresses and body shapes is also investigated in this study for automatic human action recognition. a tuned eigenspace technique is proposed for automatic human posture and/or motion recognition that successfully overcome the preceding problems. we employ image pre-processing by gaussian and sobel edge filter, called the first stage tuning, for reducing a dress effect, and a mean eigenspace produced by taking a mean of the similar postures, called the second stage tuning, for avoiding the preceding problems. an eigenspace called a tuned eigenspace is obtained from the mentioned processes and it is used for further recognition of unfamiliar postures and actions. the proposed method is compared with a related technique and the robustness of this approach is presented.
recognizing human behavior using universal eigenspace. an efficient application of eigenspace technique to recognize human behaviors is described. the present paper investigates two types of recognition, i.e., recognition of an unknown human posture and a particular behavior by identifying human postures among several behaviors. a number of different posture sets from some selected behaviors create universal eigenspace and different sets of unknown postures are recognized from it. in contrast to the classical method, the paper proposes to employ some image processing of input images for better performance of the eigenspace technique instead of using just original images for human postures recognition. a new approach of producing eigenspace is described and the robustness of the method is effectively proved in the experiment.
object classification with multi-scale autoconvolution. this paper assesses the recently proposed affine invariant image transform called multi-scale autoconvolution (msa) in some practical object classification problems. a classification framework based on msa and support vector machines is introduced. as shown by the comparison with another affine invariant technique, it appears that this new technique provides a good basis for problems where the disturbances in classified objects can be approximated with spatial affine transformation. the paper also introduces a new property clarifying the parameter selection in the multi-scale autoconvolution.
convexity recognition using multi-scale autoconvolution. this paper introduces a novel measure for object convexity using the recently introduced multi-scale autoconvolution transform. the proposed measure is computationally efficient and recognizes even small errors in a convex domain. we also consider its implementation and give a complete matlab algorithm for computing this measure for digital images. finally, we give examples to verify its applicability and accuracy. the examples also consider convexity as a measure for complexity.
generalized affine moment invariants for object recogntion. this paper introduces a new way of extracting affine invariant features from image functions. the presented approach is based on combining affine moment invariants (ami) with multiscale invariants, in particular multiscale autoconvolution (msa) and spatial multiscale affine invariants (sma). our approach includes all of these invariants as special cases, but also makes it possible to construct new ones. according to the performed experiments the introduced features provide discriminating information for affine invariant object classification, clearly outperforming standard ami, msa, and sma.
auto-calibration of multi-projector display walls. by treating projectors as pin-hole cameras, we show it is possible to calibrate the projectors of a casually-aligned, multi-projector display wall using the principles of planar auto-calibration. we also use a pose estimation technique for planar scenes to reconstruct the relative pose of a calibration camera, the projectors and the plane they project on. together with assumptions about the pose of the camera, we use the reconstruction to automatically compute the projector-display homographies needed to render properly scaled and oriented imagery on the display wall. the main contribution of this paper is thus to provide a fully automated approach to calibrate a multi-projector display wall without the need for fiducials or interaction.
using boosting to improve oil spill detection in sar images. marine surveillance system which uses synthetic aperture radar (sar) images to oil spill detection must minimize false alarms in order to improve its reliability. this paper presents an application that uses boosting method to minimize misclassification and yields better generalization. different feature sets were applied to neural network classifiers and its performance compared do boosting methods. the experiments reached substantial improvement in the classification accuracy to discriminate oil spots from the look-alike ones.
efficient alignment of fingerprint images. fingerprint matching is a common technique for biometric authentication. solid state sensors allow that fingerprint recognition is used in small sized embedded systems. the size of these sensors makes it necessary to store several impressions of the same fing er. in order to reduce memory requirements and matching time all these images can be fused into one larger image. we present a ransac based method to determine a rigid transformation which aligns two fingerprint images using solely minutiae coordinates and minutiae angles. the reliability of the method is demonstrated with experimental results.
object detection using background context. detection of objects is in general a computationally demanding task. to simplify the problem it is of interest to utilize contextual information and perform a staggered recognition where context is recognized as a precursor to object detection. in this paper an approach to object detection, using context is presented. the presented methodology is evaluated in the context of a tabletop scenario.
finding gait in space and time. we describe an approach to characterize the signatures generated by walking humans in spatio-temporal domain. to describe the computational model for this periodic pattern, we take the mathematical theory of geometry group theory, which is widely used in crystallographic structure research. both empirical and theoretical analysis prove that spatio-temporal helical patterns generated by legs belong to the frieze groups because they can be characterized by a repetitive motif along the direction of walking. the theory is applied to an automatic detection-and-tracking system capable of counting heads and handling occlusion by recognizing such patterns. experimental results for videos acquired from both static and moving ground sensors are presented. our algorithm demonstrates robustness to nonrigid human deformation as well as background clutter.
joint correspondence and background modeling based on tree dynamic programming. foreground segmentation with moving camera is a challenging task due to the presence of parallax effect, registration error, scene variations in out door, and etc. currently, background modeling techniques either assumes correspondence among pixels in concurrent frames or do not model it explicitly. the contribution by this paper is in two folds. first, we achieve a new background model by introducing correspondence into it. second, we pose foreground segmentation and correspondence estimation as a labeling problem. spatial context is enforced in shape of tree structure and global optimal label at each node is computed using dynamic programming. finally, based on the optimal correspondence, background model is updated. resultantly, parallax effect and registration error are reduced significantly. primary experiments proved our algorithm to be robust in performance.
a heterogeneous feature-based image alignment method. in this paper, we propose a robust heterogeneous feature based image alignment method that utilizes points, lines and regions in a unified framework. the image motion is decomposed into progressively complex components, i.e., translation, similarity, affine, and projective motion models, and alignment is obtained with deliberatively selected suitable feature types and associated descriptors. large convergence range is obtained by gradually constraining the search range of features in each stage. notably, point and line features are jointly used and formulated in a ransac (random sample consensus) framework for robust estimation of a homography between low textured images. further improvement is obtained with region based direct method. experiments demonstrate superior alignment results of our approach to both gradient-based direct method and tradition point feature based alignment method.
cancelable biometrics: a case study in fingerprints. biometrics offers usability advantages over traditional token and password based authentication schemes, but raises privacy and security concerns. when compromised, credit cards and passwords can be revoked or replaced while biometrics are permanently associated with a user and cannot be replaced. cancelable biometrics attempt to solve this by constructing revocable biometric templates. we present several constructs for cancelable templates us- ing feature domain transformations and empirically exam- ine their efficacy. we also present a method for accurate registration which is a key step in building cancelable trans- forms. the overall approach has been tested using large databases and our results demonstrate that without losing much accuracy, we can build a large number of cancelable transforms for fingerprints.
temporal color correlograms for video retrieval. this paper presents a novel method to retrieve segmented video shots based on thier color content. the temporal color correlogram captures the spatio-temporal relationship of colors in a video shot using co-occurrence statistics. the temporal color correlogram extends the hsv color correlogram that has been found to be very effective in content-based image retrieval. temporal color correlograms compute the autocorrelation of quantized hsv color values from a set of frame samples taken from a video shot. in this paper, the efficiency of the temporal color correlogram and hsv color correlograms are evaluated against other retrieval systems participating the trec video track evaluation and against color histograms used commonly in content-based retrieval. we used queries and relevance judgments on the 11 hours of segmented mpeg-1 video provided to track participants. tests are executed using our content-based multimedia retrieval systems that was specifically developed for multimedia information retrieval applications.
a simple linear method to obtain height ordering of scene points. we present a novel algorithm to extract height ordering information for points relative to the ground plane. the ordering of scene points is obtained by exploiting the idea of linearly invariant representation of space points, image points, and optical center of the camera, from which we derive a measure that is monotonic with respect to the height of points relative to the ground plane. we present results of the algorithm on data obtained from a mobile robot.
new operators of genetic algorithms for traveling salesman problem. this paper describes an application of genetic algorithm to the traveling salesman problem. new knowledge based multiple inversion operator and a neighborhood swapping operator are proposed. experimental results on different benchmark data sets have been found to provide superior results as compared to some other existing methods.
head pose estimation by nonlinear manifold learning. in this paper we propose an isomap-based nonlinear alternative to the linear subspace method for manifold representation of view-varying faces. being interested in user-independent head pose estimation, we extend the isomap model [a global geometric framework for nonlinear dimensionality reduction] to beable to map (high-dimensional) input data points which are not in the training data set into the dimensionality-reduced space found by the model. from this representation, a pose parameter map relating the input face samples to view angles is learnt. the proposed method is evaluated on a large database of multi-view face images in comparison to two other recently proposed subspace methods.
a new predictive full-search block motion estimation. this paper describes a novel and fast approach to full search block matching (fsbm) employing prediction of search region, based on the sampled statistics of sum-of-absolute difference (sad) distributions. the motion vector is predicted to belong to either of two regions, representing long-motion and short-motion, with limited amount of overlap between the two. the proposed prediction is statistically validated with more than 98% successes for different video sequences. experimental results reveal that the proposed predicted fsbm (pfsbm) algorithm saves up to 89% of computations, as compared to fsbm and the estimation accuracy is very close to that of fsbm. its performance has been compared with three-step search (tss) and fsbm for different standard video sequences.
a feasibility study of on-board data compression for infrared cameras of space observatories. in this paper, the feasibility of on-board data reduction/compression concept described in[on-board data compression: noise and complexity related aspects] is evaluated for infrared images taken from space observatories. the method described in [on-board data compression: noise and complexity related aspects], which was initially designed and developed for the pacs (photodetector array camera and spectrometer) instrument [http://www.mpe.mpg.de/projects.html], makes use of on-board integration to achieve higher compression ratio (cr) for applications with modest telemetry rate. the evaluation of the reduction concept takes into account the visual performance (distortion) and the compression ratio. the distortion is assessed by calculating the reconstruction error using 4 metrics, namely, root mean square error (rmse), signal-to-noise-ratio (snr), peak-signal-to-noise-ratio (psnr) and potential information loss (pil). a quantitative evaluation of the on-board compression concept is performed on data from the infrared camera isocam (infrared space observatory camera). we conclude with a short summary.
invariant features for 3d-data based on group integration using directional information and spherical harmonic expansion. due to the increasing amount of 3d data for various applications there is a growing need for classification and search in such databases. as the representation of 3d objects is not canonical and objects often occur at different spatial position and in different rotational poses, the question arises how to compare and classify the objects. one way is to use invariant features. group integration is a constructive approach to generate invariant features. several variants of group integration features are already proposed. in this paper we present two main extensions, we include local directional information and use the spherical harmonic expansion to compute more descriptive features. we apply our methods to 3d-volume data (pollen grains) and 3d-surface data (princeton shape benchmark)
3d and infrared face reconstruction from rgb data using canonical correlation analysis. in this paper, we apply a multiple regression method based on canonical correlation analysis (cca) to face data modelling. cca is a factor analysis method which exploits the correlation between two high dimensional signals. we first use cca to perform 3d face reconstruction and in a separate application we predict near-infrared (nir) face texture. in both cases, the input data are color (rgb) face images. experiments show, that due to the correlation between input and output signal, only a small number of canonical factors are needed to describe the functional relation of rgb images to the respective output (nir images and 3d depth maps) with reasonable accuracy.
segmentation and classification of meeting events using multiple classifier fusion and dynamic programming. in this paper the segmentation of a meeting into meeting events is investigated as well as the recognition of the detected segments. first the classification of a meeting event is examined. five different classifiers are combined through multiple classifier fusion. then a way for finding the optimal segment boundaries is presented. with a dynamic programming approach quite encouraging results can be obtained. the results show further that by classifier fusion a more stable result can be achieved than using only one single classifier.
a gauss-markov model for hyperspectral texture analysis of urban areas. in this paper we tackle the problem of texture segmentation using a joint spectral and spatial analysis of pixel distribution. hyperspectral images are considered and using a markovian model we develop a vectorial approach for this image type. a classification algorithm using this model has been implemented for extracting and classifying urban areas. results obtained from aviris images are shown.
video sequence matching with spatio-temporal constraints. the main aim of this paper is to introduce a novel spatio-temporal model of video matching. this model uses information on image object spatial relationship in frames as well as transition between frames to generate a vector representing that frame. we discuss how these vectors can be generated and matched for video retrieval.
extraction of consistent subsets of descriptors using choquet integral. this paper presents a novel approach to automatically extract a subset of shape descriptors dedicated to an application under consideration. basic descriptors having low time processing and allowing to keep nice geometric properties were implemented. then a model based on choquet integral and shapley values, is proposed to select suitable descriptors. experimental studies using real databases attest of the robustness of our approach.
color correction for the virtual recomposition of fragmented frescos. the paper describes the solution to the color correction problem used in the digital system we have designed for the reconstruction of the s. matthew fresco, painted by cimabue for the upper church of s. francis in assisi. the characteristics of the problem make difficult to evaluate correspondences between colors and require specific corrections to be applied to different parts of the fresco. the obtained improvement in terms of color similarity and retrieval results is shown.
virtual recomposition of frescos: separating fragments from the background. the paper addresses the segmentation of fragments from the background using a clustering approach in the color space. the problem is involved in the on-going development of a digital system for the virtual aided recomposition of fragmented frescos, an innovative approach currently being proved on the s. matthew's fresco, made by cimabue for the upper church of s. francis in assisi and broken, during the earthquake in 1997, into more than 140.000 pieces. the developed technique has been used to build the fragments' database. the segmentation process is based on the fast global k-means algorithm. experiments show that separation of foreground and background on a so large number of images is much more manageable and effective using prototypes built by the clustering algorithm.
sparse, variable-representation active contour models. active contours are a widely used class of models that locate object boundaries in an image by minimizing an energy function which depends on "internal" terms such as the length and curvature of the contour, and "external" terms which are functions of the image values on and near the contour. if we use the inverse rate of change of the image value as the external term, the energy will be low when the contour coincides with a strong, short, smooth boundary in the image. it is well known that this basic active contour model has difficulties in detecting object boundaries that are initially far from the contour; in locating boundary shape details; and in avoiding local minima due to image noise. the first two difficulties can be overcome by varying the energy function during the minimization process, and we show in this paper that the third difficulty can be overcome by modifying the contour representation during the process.
geometric approach for simultaneous projective reconstruction of points, lines, planes, quadrics, plane conics and degenerate quadrics. in this paper we present an algorithm for the simultaneous projective reconstruction of points, lines, planes, quadrics, plane conics and degenerate quadrics using bundle adjustment. in contrast, most existing work on projective reconstruction focuses mainly on one type of primitive. furthermore, for the reconstruction of quadrics (both full-rank and degenerate) and plane conics, a novel algorithm for the rectification of the outlines projected in n views is presented.finally, we show that reaching the global minimum during bundle adjustment is not always necessary or desirable since this state might break the topology of the quadrics being reconstructed.
an evidence combining approach to shape-from-shading. in this paper we describe a statistical framework for shape-from-shading. first, we commence by making least squares estimates of local hessian matrices using samples of surface normals. these estimated hessians are used to perform parallel transport of the surface normals across the surface. by transporting neighbouring surface normals in this way we are able collect samples of votes for the surface normal direction at each location. we select between these alternatives on the basis of compliance with the image irradiance equation.