waashk/dblp huggingface dataset:
closely coupled object detection and segmentation. we propose a closely coupled object detection and segmentation algorithm for enhancing both processes in a cooperative and iterative manner. figure-ground segmentation reduces the effect of background clutter on template matching; the matched template provides shape constraints on segmentation. more precisely, we estimate the probability of each pixel belonging to the foreground by a weighted sum of the estimates based on shape and color alone. the weight on the shape-based estimate is related to the probability that a familiar object is present and is updated dynamically so that we enforce shape constraints only where the object is present. experiments on detecting people in images of cluttered scenes demonstrate that the proposed algorithm improves both segmentation and detection. more accurate object boundaries are extracted; higher object detection rates and lower false alarm rates are achieved than performing the two processes separately or sequentially.
robust point matching for two-dimensional nonrigid shapes. recently, nonrigid shape matching has received more and more attention. for nonrigid shapes, most neighboring points cannot mow independently under deformation due to physical constraints. furthermore, the rough structure of a shape should be preserved under deformation, otherwise even people cannot match shapes reliably. therefore, though the absolute distance between two points may change sign@cantl>;, the neighborhood of a point is well preserved in general. based on this observation, we formulate point matching as a graph matching problem. each point is a node in the graph, and two nodes are connected by an edge if their euclidean distance is less than a threshold. the optimal match between two graphs is the one that maximizes the number of matched edges. the shape context distance is used to initialize the graph matching, followed by relaxation labeling for rejnement. nonrigid deformation is overcome by bringing one shape closer 10 the other in each iteration using deformation parameters estimated from the current point correspondence. experiments demonstrate the effectiveness of our approach: it outperforms the shape context and tps-rpm algorithms under nonrigid deformation and noise on a public data set.
shape and model from specular motion. this work investigates visual characteristics of specular surfaces during rotation, and gives approaches to qualitatively identify and quantitatively recover shapes of these surfaces. continuous images are taken when an object rotates. we look at specularly reflected patterns on the surfaces and their motion in the epis parallel to the rotation plane, from which estimation of each surface point and construction of the object model are carried out. we find very simple and direct methods to fulfill this objective; linear equations for multiple lights illumination, and a 1st-order differential equation for single light illumination. the motion of specular reflection has nice global characteristics in epi. the surface types range from very shiny metal surfaces to surfaces with only week specular reflectance. we give both simulation and experiments on real objects.
acquiring 3d object models from specular motion using circular lights illumination. this work recovers 3d graphics models of objects with specular surfaces. an object is routed and continuous images of it are taken. circular lights that generate cones of rays are used to illuminate the rotating object. when the lights are properly set, each point on the object can be highlighted during the rotation. the shape for each rotational plane is measured independently using its corresponding epipolar palne image. a 3d graphics model is subsequently reconstructed by combining shapes at different rotational planes. computing a shape is simple and requires only the motion of the highlight on each rotation plane. results not obtained before are given in the 3d shape recovery experiments on real objects.
object tracking using deformable templates. we propose a novel method for object tracking using prototype-based deformable template models. to track an object in an image sequence, we use a criterion which combines two terms: the deviation of the object shape from its shape in the previous frame, and the fidelity ofthe detected shape to the input image. shape and gradient information are used to track the object. we have also used the consistency between corresponding object regions throughout the sequence to help in tracking the object of interest. inter-frame motion is also used to track the boundary of moving objects. we have applied the algorithm to a number of image sequences from different sources. the inherent structure in the deformable template, together with region, motion, and image gradient cues, make the algorithm relatively insensitive to the adverse effects of weak image features and moderate partial occlusion.
segmenting foreground objects from a dynamic textured background via a robust kalman filter. the algorithm presented in this paper aims to segment the foreground objects in video (e.g., people) given time-varying, textured backgrounds. examples of time-varying backgrounds include waves on water, clouds moving, trees waving in the wind, automobile traffic, moving crowds, escalators, etc. we have developed a novel foreground-background segmentation algorithm that explicitly accounts for the non-stationary nature and clutter-like appearance of many dynamic textures. the dynamic texture is modeled byan autoregressive moving average model (arma). a robust kalman filter algorithm iteratively estimates the intrinsic appearance of the dynamic texture, as well as the regions of the foreground objects. preliminary experiments with this method have demonstrated promising results.
conditional feature sensitivity: a unifying view on active recognition and feature selection. the objective of active recognition is to iteratively collectthe next "best" measurements (e.g., camera angles orviewpoints), to maximally reduce ambiguities in recognition.however, existing work largely overlooked featureinteraction issues. feature selection, on the other hand,focuses on the selection of a subset of measurements for agiven classification task, but is not context sensitive (i.e.,the decision does not depend on the current input). thispaper proposes a unified perspective through conditionalfeature sensitivity analysis, taking into account both currentcontext and feature interactions. based on differentrepresentations of the contextual uncertainties, we presentthree treatment models and exploit their joint power fordealing with complex feature interactions. synthetic examplesare used to systematically test the validity of theproposed models. a practical application in medical domainis illustrated using an echocardiography databasewith more than 2000 video segments with both subjective(from experts) and objective validations.
robust estimation of albedo for illumination-invariant matching and shape recovery. we present a nonstationary stochastic filtering framework for the task of albedo estimation from a single image. there are several approaches in the literature for albedo estimation, but few include the errors in estimates of surface normals and light source direction to improve the albedo estimate. the proposed approach effectively utilizes the error statistics of surface normals and illumination direction for robust estimation of albedo, for images illuminated by single and multiple light sources. the albedo estimate obtained is subsequently used to generate albedo-free normalized images for recovering the shape of an object. traditional shape-from-shading (sfs) approaches often assume constant/piecewise constant albedo and known light source direction to recover the underlying shape. using the estimated albedo, the general problem of estimating the shape of an object with varying albedo map and unknown illumination source is reduced to one that can be handled by traditional sfs approaches. experimental results are provided to show the effectiveness of the approach and its application to illumination-invariant matching and shape recovery. the estimated albedo maps are compared with the ground truth. the maps are used as illumination-invariant signatures for the task of face recognition across illumination variations. the recognition results obtained compare well with the current state-of-the-art approaches. impressive shape recovery results are obtained using images downloaded from the web with little control over imaging conditions. the recovered shapes are also used to synthesize novel views under novel illumination conditions.
rectified surface mosaics. we approach mosaicing as a camera tracking problem within a known parameterized surface. from a video of a camera moving within a surface, we compute a mosaic representing the texture of that surface, flattened onto a planar image. our approach works by defining a warp between images as a function of surface geometry and camera pose. globally optimizing this warp to maximize alignment across all frames determines the camera trajectory, and the corresponding flattened mosaic image. in contrast to previous mosaicing methods which assume planar or distant scenes, or controlled camera motion, our approach enables mosaicing in cases where the camera moves unpredictably through proximal surfaces, such as in medical endoscopy applications.
efficient generic calibration method for general cameras with single centre of projection. generic camera calibration is a non-parametric calibration technique that is applicable to any type of vision sensor. however, the standard generic calibration method was developed such that both central and non-central cameras can be calibrated within the same framework. consequently, existing parametric calibration techniques cannot be applied for the common case of cameras with a single centre of projection (e.g. pinhole, fisheye, hyperboloidal catadioptric). this paper proposes improvements to the standard generic calibration method for central cameras that reduce its complexity, and improve its accuracy and robustness. improvements are achieved by taking advantage of the geometric constraints resulting from a single centre of projection in order to enable the application of established pinhole calibration techniques. input data for the algorithm is acquired using active grids, the performance of which is characterised. a novel linear estimation stage is proposed that enables a well established pinhole calibration technique to be used to estimate the camera centre and initial grid poses. the proposed solution is shown to be more accurate than the linear estimation stage of the standard method. a linear alternative to the existing polynomial method for estimating the pose of additional grids used in the calibration is demonstrated and evaluated. distortion correction experiments are conducted with real data for both an omnidirectional camera and a fisheye camera using the standard and proposed methods. motion reconstruction experiments are also undertaken for the omnidirectional camera. results show the accuracy and robustness of the proposed method to be improved over those of the standard method.
population shape regression from random design data. regression analysis is a powerful tool for the study of changes in a dependent variable as a function of an independent regressor variable, and in particular it is applicable to the study of anatomical growth and shape change. when the underlying process can be modeled by parameters in a euclidean space, classical regression techniques (hardle, applied nonparametric regression, 1990; wand and jones, kernel smoothing, 1995) are applicable and have been studied extensively. however, recent work suggests that attempts to describe anatomical shapes using flat euclidean spaces undermines our ability to represent natural biological variability (fletcher et al., ieee trans. med. imaging 23(8), 995---1005, 2004; grenander and miller, q. appl. math. 56(4), 617---694, 1998).in this paper we develop a method for regression analysis of general, manifold-valued data. specifically, we extend nadaraya-watson kernel regression by recasting the regression problem in terms of fréchet expectation. although this method is quite general, our driving problem is the study anatomical shape change as a function of age from random design image data.we demonstrate our method by analyzing shape change in the brain from a random design dataset of mr images of 97 healthy adults ranging in age from 20 to 79 years. to study the small scale changes in anatomy, we use the infinite dimensional manifold of diffeomorphic transformations, with an associated metric. we regress a representative anatomical shape, as a function of age, from this population.
limits of learning-based superresolution algorithms. learning-based superresolution (sr) is a popular sr technique that uses application dependent priors to infer the missing details in low resolution images (lris). however, their performance still deteriorates quickly when the magnification factor is only moderately large. this leads us to an important problem: "do limits of learning-based sr algorithms exist?" this paper is the first attempt to shed some light on this problem when the sr algorithms are designed for general natural images. we first define an expected risk for the sr algorithms that is based on the root mean squared error between the superresolved images and the ground truth images. then utilizing the statistics of general natural images, we derive a closed form estimate of the lower bound of the expected risk. the lower bound only involves the covariance matrix and the mean vector of the high resolution images (hris) and hence can be computed by sampling real images. we also investigate the sufficient number of samples to guarantee an accurate estimate of the lower bound. by computing the curve of the lower bound w.r.t. the magnification factor, we could estimate the limits of learning-based sr algorithms, at which the lower bound of the expected risk exceeds a relatively large threshold. we perform experiments to validate our theory. and based on our observations we conjecture that the limits may be independent of the size of either the lris or the hris.
estimation of the epipole using optical flow at antipodal points. we present algorithms for estimating the epipole or direction of translation of a moving camera. we use constraints arising from two points that are antipodal on the image sphere in order to decouple rotation from translation. one pair of antipodal points constrains the epipole to lie on a plane, and two such pairs will correspondingly give two planes. the intersection of these two planes is an estimate of the epipole. this means we require image motion measurements at two pairs of antipodal points to obtain an estimate. two classes of algorithms are possible and we present two simple yet extremely robust algorithms representative of each class. these are shown to have comparable accuracy with the state of the art when tested in simulation under noise and with real image sequences.
hierarchical ensemble of global and local classifiers for face recognition. in the literature of psychophysics and neurophysiology, many studies have shown that both global and local features are crucial for face representation and recognition. this paper proposes a novel face recognition method which exploits both global and local discriminative features. in this method, global features are extracted from the whole face images by keeping the low-frequency coefficients of fourier transform, which we believe encodes the holistic facial information, such as facial contour. for local feature extraction, gabor wavelets are exploited considering their biological relevance. after that, fisher's linear discriminant (fld) is separately applied to the global fourier features and each local patch of gabor features. thus, multiple fld classifiers are obtained, each embodying different facial evidences for face recognition. finally, all these classifiers are combined to form a hierarchical ensemble classifier. we evaluate the proposed method using two large-scale face databases: feret and frgc version 2.0. experiments show that the results of our method are impressively better than the best known results with the same evaluation protocol.
unsupervised clustering of symbol strings and context recognition. the representation of information based on symbolstrings has been applied to the recognition of context. aframework for approaching the context recognition problemhas been described and interpreted in terms of symbolstring recognition. the symbol string clustering map(scm) is introduced as an efficient algorithm for the unsupervisedclustering and recognition of symbol string data.the scm can be implemented in an on line manner usinga computationally simple similarity measure based ona weighted average. it is shown how measured sensor datacan be processed by the scm algorithm to learn, representand distinguish different user contexts without any user input.
a thorough experimental study of datasets for frequent itemsets. the discovery of frequent patterns is a famous problem in data mining. while plenty of algorithms have been proposed during the last decade, only a few contributions have tried to understand the influence of datasets on the algorithms behavior. being able to explain why certain algorithms are likely to perform very well or very poorly on some datasets is still an open question. in this setting, we describe a thorough experimental study of datasets with respect to frequent itemsets. we study the distribution of frequent itemsets with respect to itemsets size together with the distribution of three concise representations: frequent closed, frequent free and frequent essential itemsets. for each of them, we also study the distribution of their positive and negative borders whenever possible. from this analysis, we exhibit a new characterization of datasets and some invariants allowing to better predict the behavior of well known algorithms. the main perspective of this work is to devise adaptive algorithms with respect to dataset characteristics.
mining online users? access records for web business intelligence. this paper discusses about how business intelligence on awebsite could be obtained from users' access recordsinstead of web logs of "hits". users' access records arecaptured by implementing an access-control (ac)architectural model on the website. this model requiresusers to register their profiles in an exchange of apassword; and thereafter they have to login before gainingaccess to certain resources on the website. the links tothe resources on the website have been modified such thata record of information about the access would berecorded in the database when clicked. this way, data-miningcan be performed on a relatively clean set ofaccess records about the users. hence, a good deal ofbusiness intelligence about the users' behaviors,preferences and about the popularities of the resources(products) on the website can be gained. in this paper, wealso discussed how the business intelligence acquired, inturn, can be used to provide e-crm for the users.
a parameterless method for efficiently discovering clusters of arbitrary shape in large datasets. clustering is the problem of grouping data based on similarityand consists of maximizing the intra-group similaritywhile minimizing the inter-group similarity. the problem ofclustering data sets is also known as unsupervised classification,since no class labels are given. however, all exist-ingclustering algorithms require some parameters to steerthe clustering process, such as the famous k for the numberof expected clusters, which constitutes a supervision ofa sort. we present in this paper a new, efficient, fast andscalable clustering algorithm that clusters over a range ofresolutions and finds a potential optimum clustering withoutrequiring any parameter input. our experiments showthat our algorithm outperforms most existing clustering algorithmsin quality and speed for large data sets.
links between kleinberg's hubs and authorities, correspondence analysis, and markov chains. in this work, we show that kleinberg's hubs and authoritiesmodel is closely related to both correspondence analysis,a well-known multivariate statistical technique, and aparticular markov chain model of navigation through theweb. the only difference between correspondence analysisand kleinberg's method is the use of the average value ofthe hubs (authorities) scores for computing the authorities(hubs) scores, instead of the sum for kleinberg's method.we also show that correspondence analysis and our markovmodel are related to salsa, a variant of kleinberg's model.
test-cost sensitive naive bayes classification. inductive learning techniques such as the naive bayes and decision tree algorithms have been extended in the past to handle different types of costs mainly by distinguishing different costs of classification errors. however, it is an equally important issue to consider how to handle the test costs associated with querying the missing values in a test case. when the value of an attribute is missing in a test case, it may or may not be worthwhile to take the effort to obtain its missing value, depending on how much the value will result in a potential gain in the classification accuracy. in this paper, we show how to obtain a test-cost sensitive naive bayes classifier (csnb) by including a test strategy which determines how unknown attributes are selected to perform test on in order to minimize the sum of the mis-classification costs and test costs. we propose and evaluate several potential test strategies including one that allows several tests to be done at once. we empirically evaluate the csnb method, and show that it compares favorably with its decision tree counterpart.
empirical comparison of various reinforcement learning strategies for sequential targeted marketing. we empirically evaluate the performance of various re-inforcementlearning methods in applications to sequentialtargeted marketing. in particular, we propose and evaluatea progression of reinforcement learning methods, rangingfrom the "direct" or "batch" methods to "indirect" or"simulation based" methods, and those that we call "semi-direct"methods that fall between them. we conduct a num-berof controlled experiments to evaluate the performanceof these competing methods. our results indicate that whilethe indirect methods can perform better in a situation inwhich nearly perfect modeling is possible, under the morerealistic situations in which the system's modeling parametershave restricted attention, the indirect methods' performancetend to degrade. we also show that semi-directmethods are effective in reducing the amount of computationnecessary to attain a given level of performance, andoften result in more profitable policies.
modeling multiple time series for anomaly detection. our goal is to generate comprehensible and accurate models from multiple time series for anomaly detection. the models need to produce anomaly scores in an online manner for real-life monitoring tasks. we introduce three algorithms that work in a constructed feature space and evaluate them with a real data set from the nasa shuttle program. our offline and online evaluations indicate that our algorithms can be more accurate than two existing algorithms.