waashk/dblp huggingface dataset:
epipolar geometry from two correspondences. a novel algorithm for robust ransac-like estimation of epipolar geometry (of uncalibrated camera pair) from two correspondences of local affine frames (lafs) is presented. each laf is constructed from three points independently detected on a maximally stable extremal region. the algorithm assumes that a sufficiently accurate approximation of the fundamental matrix is obtained from two laf correspondences by the 6-point algorithm of stewe´nius et al. the so-far-the-best hypotheses are further processed by so-called local optimization to estimate the epipolar geometry. special attention is paid to planar sample degeneracy, since the probability of drawing two coplanar laf correspondences is not negligible. combining the 6-point solver, local optimization, and the degeneracy test enables ransac to draw samples of only two lafs to generate hypotheses and thus to reduce the number of samples drawn. we experimentally show that using the 6-point algorithm (approximating the real camera by camera with unit aspect ratio, zero skew, principal point in the center of image, and a common unknown focal length) generates hypotheses that are sufficient for eg estimation in lo-ransac framework.
bayesian object-level change detection in grayscale imagery. we present a change detection algorithm formulated in a bayesian framework that uses the output of an object detector to reason about change at a higher level than comparing pixels. the object detector mitigates pixel-level noise, and presents objects to the change detection framework. this in turn ties the objects across images and determines change. the bayesian framework allows us to easily add domain knowledge into the change detection process to improve detection. we show that our approach can successfully detect changes across grayscale images with significantly greater variance in imaging conditions (such as viewpoint, resolution, and illumination) than those handled by traditional methods.
recognition of airborne fungi spores in digital microscopic images. we propose and evaluate a method for the recognition of airborne fungi spores. we use a model-based object recognition method to identify spores in a digital microscopic image. we do not use the gray values of the model, but use the object edges instead. the similarity measure measures the average angle between the vectors of the template and the object. model generation is done semi-automatically by manually tracing the object, automatic shape alignment, similarity calculation, clustering and prototype calculation.
3d surface inspection using coupled hmms. this paper proposes coupled hidden markov models (chmm) for analysis of steel surfaces containing three-dimensional flaws. due to scale on the surface, the reflection property across the intact surface changes and intensity imaging fails. hence, the light sectioning method is used to acquire the surface range data. the steel block is vibrating on the conveyor during data acquisition which complicates the task. after depth map recovery and feature extraction, segments of the surface are classified by means of chmms. we present classification results of the chmm and compare them to the naïve bayes classifier. the chmm outperforms the naïve bayes approach.
2d and 3d vegetation resource parameters assessment using marked point processes. high resolution aerial and satellite images of forests have a key role to play in natural resource management. as they enable to study forests at the scale of trees, it is now possible to get a more accurate evaluation of the forest resources, from which can be deduced information on biodiversity and ecological sustainability. in that prospect, automatic algorithms are needed to give a further exploitation of the data and to assist human operators. in this paper, we present a stochastic geometry approach to extract 2d and 3d parameters of the trees, by modelling the stands as some realizations of a marked point process of ellipses or ellipsoids, whose points are the positions of the trees and marks their geometric features. this approach gives also the number of stems, their position, and their size. it is an energy minimization problem, where the energy embeds a regularization term (prior density), which introduces some interactions between the objects, and a data term, which links the objects to the features to be extracted. results are shown on aerial images provided by the french national forest inventory (ifn).
visual learning and recognition of a probabilistic spatio-temporal model of cyclic human locomotion. we present a novel representation of cyclic human locomotion based on a set of spatio-temporal curves of tracked points on the surface of a person. we start by extracting a set of continuous, phase aligned spatio-temporal curves from trajectories of random points tracked over several cycles of locomotion in a monocular video sequence. we analyze a pca representation of a set of cyclic curves, pointing out properties of the representation which can be used for spatio-temporal alignment in tracking and recognition tasks. we model the curve distribution density by a mixture of gaussians using expectation-maximization algorithm. for recognition, we use maximum a posteriori estimate combined with linear data adaptation. we tested the algorithms on cmu mobo database with favourable results for the recognition of people "by walking" from monocular video sequences captured from the side view.
p-aflc: a parallel scalable fuzzy clustering algorithm. clustering is the unsupervised classification of data items into homogeneous groups called clusters. clustering algorithms are computationally intensive, particularly when they are used to analyze large amounts of data and this is the case in many pattern recognition, image analysis applications. a possible approach to reduce the processing time is based on the implementation of clustering algorithms on scalable parallel computers. this paper describes the design and implementation of p-aflc, a parallel version of the adaptive fuzzy leader clustering system based upon the competitive learning model for determining optimal classes in large data sets. the system architecture, its implementation, and experimental performance results are reported, together with theoretical performance evaluation.
vehicle type recognition with match refinement. we describe a system for automatic recognition of verhicle type (make and model) from frontal views, aimed at secure access, surveillance and traffic monitoring applications. the system extracts gradient features from reference patches in images of car fronts and performs recognition in two stages.in the first stage gradient based feature vectors are used to produce a ranked list of possible candidate classes.the result is then refined by using a novel match refinement algorithm that maximizes the discrimination between the subset of most likely classes by optimising for objectpose and adaptively normalising feature vectors.we test the system on over 1000 images containing 77 difference vehicle classes, and demonstrate that such a system can provide reliable verification (eer
automatic hip bone segmentation using non-rigid registration. this paper presents a method for automatic segmentation of bone from volumetric computed tomography (ct) data. due to osteoporosis, which degenerates the bone density and hence decreases the intensity of the bone in the ct dataset, it is not possible to use conventional thresholding techniques to handle the segmentation. furthermore we want to use prior knowledge about shapes and relations of the bones in the area of interest to be able to e.g. separate adjoining bones from each other. the method we suggest is the morphon algorithm [4]. this is a non-rigid registration technique where an 2d or 3d image is iteratively deformed to match the corresponding structure in a target image. the method uses difference in local quadrature phase and certainty measures to estimate the deformations.
human action segmentation via controlled use of missing data in hmms. segmentation of individual actions from a stream of human motion is an open problem in computer vision. this paper approaches the problem of segmenting higher-level activities into their component sub-actions using hidden markov models modified to handle missing data in the observation vector. by controlling the use of missing data, action labels can be inferred from the observation vector during inferencing, thus performing segmentation and classification simultaneously. the approach is able to segment both prominent and subtle actions, even when subtle actions are grouped together. the advantage of this method over sliding windows and viterbi state sequence interrogation is that segmentation is performed as a trainable task, and the temporal relationship between actions is encoded in the model and used as evidence for action labelling.
observation-switching linear dynamic systems for tracking humans through unexpected partial occlusions by scene objects. this paper focuses on the problem of tracking people through occlusions by scene objects. rather than relying on models of the scene to predict when occlusions will occur as other researchers have done, this paper proposes a linear dynamic system that switches between two alternatives of the position measurement in order to handle occlusions as they occur. the filter automatically switches between a foot-based measure of position (assuming z=0) to a head-based position measure (given the person's height) when an occlusion of the person's lower body occurs. no knowledge of the scene or its occluding objects is used. unlike similar research [2, 14], the approach does not assume a fixed height for people and so is able to track humans through occlusions even when they change height during the occlusion. the approach is evaluated on three furnished scenes containing tables, chairs, desks and partitions. occlusions range from occlusions of legs, occlusions whilst being seated and near-total occlusions where only the person's head is visible. results show that the approach provides a significant reduction in false-positive tracks in a multi-camera environment, and more than halves the number of lost tracks in single monocular camera views.
fundamental matrix and slightly overlapping views. this paper proposes a method to compute the fundamental matrix from slightly overlapping views. slight overlaps are preferable to substantial overlaps in large multi-camera surveillance applications, because otherwise the number of cameras and thus the overall application costs would gravely increase. instead of using point correspondences alone, we additionally use the infinite homography. the infinite homography can be computed from line segments when we have a manhattan world scene and assume cameras with square pixels and the same alignment of the scene in both views. all assumptions are not too restrictive and occur frequently. the infinite homography and one of the epipoles determine the fundamental matrix. the paper demonstrates a simple solution to compute the epipoles from at least two point correspondences without requiring points uniformly distributed within the images. experiments on synthetic images clearly show the advantages of the proposed method compared to the classical eight point algorithm. images of an office scene show the idea on real world images.
metric tree partitioning and taylor approximation for fast support vector classification. this paper presents a method to speed up support vec- tor classification, especially important when data is high- dimensional. unlike previous approaches which focus on less support vectors, we partition the data space into lo- cal regions, and perform approximation by linear functions. the experimental results on 31 datasets show that the per- formance degrades marginally, while the speedup is signif- icant, up to three orders of magnitude.
the gait identification challenge problem: data sets and baseline algorithm. recognition of people through gait analysis is an important research topic, with potential applications in video surveillance, tracking, and monitoring. recognizing the importance of evaluating and comparing possible competing solutions to this problem, we previously introduced the humanid challenge problem consisting of a set of experiments of increasing difficulty, a baseline algorithm, and a large set of video sequences (about 300 gb of data related to 452 sequences from 74 subjects) acquired to investigate important dimensions of this problem, such as variations due to viewpoint, footwear, and walking surface. in this paper, we present a detailed investigation of the baseline algorithm, quantify the dependence of the various co-variates on gait-based identification, and update the previous baseline performance with optimized ones. we establish that the performance of the baseline algorithm is robust with respect to its various parameters. the overall identification performance is also stable with respect to the quality of the silhouettes. we find that the approximately lower 20% of the silhouette accounts for most of the recognition achieved. viewpoint has barely statistically significant effect on identification rates, whereas footwear and surface-type does have significant effects with the effect due to surface-type being approximately 5 times that ofshoe-type. the data set, the source code for the baseline algorithm, and unix scripts to reproduce the basic results reported here are available to the research community at marathon.csee.usf.edu/gaitbaseline/
adaptive region growing impulse noise estimator for color images. in this paper, a novel region growing impulse noise estimator for color images is proposed. the aim of this estimator is to distinguish noisy pixels from uncorrupted pixels and subsequently measure the noise proportion efficiently. we use a region growing technique to segment the images into clusters of pixels and propose an adaptive decision scheme to measure the noise proportion. performance analyses show the proposed scheme outperforms some of the state-of-theart techniques.
incremental statistical geo-temporal structuring of a personal camera phone image collection. this paper makes a proposal for automatically organizing the personal image collection that would be collected from a mobile phone equipped with a digital camera. doing so, it attempts to address emerging needs from this rapidly developping device family. having sketched user needs, we underline the interest of temporal and spatial meta-data in the chosen context. collection organization is then formulated as an unsupervised classification problem, in both space and time. a criterion and an estimation procedure are proposed, based on the statistical integrated completed likelihood criterion, providing effective solutions to model complexity determination, non-gaussianity of clusters, and incrementality. for further summarization, a technique for fusing the temporal and geolocation-based partitions is finally put forward, to ease browsing of the image collection along a single dimension.
morphological recognition of the spatial patterns of olive trees. a pair of algorithms to segment olive groves and recognize its individual trees in high spatial resolution remotely sensed images is presented. the developed algorithms are applied with success by exploiting the typical spatial patterns presented by this cover and are mainly based on mathematical morphology operators.
learned probabilistic image motion models for event detection in videos. we present new probabilistic motion models of interest for the detection of relevant dynamic contents (or events) in videos. we separately handle the dominant image motion assumed to be due to the camera motion and the residual image motion related to scene motion. these two motion components are then represented by different probabilistic models which are further recombined for the event detection task. the motion models associated to pre-identified classes of meaningful events are learned from a training set of video samples. the event detection scheme proceeds in two steps which exploit different kinds of information and allow us to progressively select the video segments of interest using maximum likelihood (ml) criteria. the efficiency of the proposed approach is demonstrated on sports videos.
feature extraction for improved profile hmm based biological sequence analysis. state-of-the-art systems for biological sequence analysis employ statistical modeling techniques, most notably so-called profile hmms. however, all approaches still rely on a purely symbolic sequence representation, which severely limits their capabilities in describing weak similarities between remotely homologue members of sequence families. therefore, we propose a multi-channel signal-like sequence representation based on a combination of several numerically encoded biochemical properties of the individual residues. from this representation features are extracted capturing relevant local sequence properties by applying wavelet and principal component analysis. evaluation results on a challenging task of sequence family classification prove that profile hmms trained on the feature-based sequence representation significantly outperform discrete models.
automatic detection of song changes in music mixes using stochastic models. the annotation of song changes in music mixes created by djs or radio stations for direct access in digital recordings is, usually, a very tedious work. in order to support this process we developed an automatic song change detection method which can be used for arbitrary music mixes. stochastic models are applied to music data aiming at their segmentation with respect to automatically obtained abstract generic acoustic units. the local analysis of these stochastic music models provides hypotheses for song changes. results of an experimental evaluation processing music mix data demonstrate the effectiveness of our method for supporting the annotation with respect to song changes.
comparison of eigenface-based feature vectors under different impairments. we study the performance of a new eigenface-based method for face recognition. specifically, we perform dct preprocessing followed by the pca-lda combination. we compare the new method to existing ones (pca, pca-lda, dct-pca) under impairments like changes in brightness, direction-of-illumination, hairstyle, clothing, expression, head orientation, and added noise.
optimal cascade construction for detection using 3d models. we describe a method for optimal construction of a detection cascade comprising 3d models of increasing levelof- detail (lod). an lod 3d model hierarchy of the target object is first generated. by analyzing detection performance of each individual model in the lod hierarchy, an optimization framework that allows trade-off between speed and accuracy is formulated. the formulation allows models to be explicitly selected for inclusion in the final detection cascade while achieving optimal running time with respect to a target detection performance.
a new method in locating and segmenting palmprint into region-of-interest. various techniques in analyzing palmprint have been proposed but to the best of our knowledge, none has been studied on the selection and division of the region-of-interest (roi). previous methods were always applied only to a fixed size square region chosen as the central part of the palm, which were then divided into square blocks for extraction of local features. in this paper, we proposed a new method in locating and segmenting the roi for palmprint analysis, where the selected region varies with the size of the palm. instead of square blocks, the region is divided into sectors of elliptical half-rings, which are less affected by misalignment due to rotational error. more importantly, our arrangement of the feature vectors ensures that only features extracted from the same spatial region of two aligned palms will be compared with each other. encouraging results obtained favor the use of this method in the future development of palmprint analysis techniques.
on performance evaluation of face detection and localization algorithms. when comparing different methods for face detection or localization, one realizes that just simply comparing the reported results is misleading as, even if the results are reported on the same dataset, different authors have different views of what a correct detection/localization means. this paper addresses exactly this problem, proposing an objective measure for the goodness of a detection/localization for the case of frontal faces. the usage of the proposed technique insures a fair and unbiased way of reporting the results, making the experiment repeatable, measurable, and comparable by anybody else.
scale-adaptive orientation estimation. this paper focuses on directional textures. it provides a new framework for the design of convolution masks dedicated to orientation estimation and an adaptive algorithm which chooses the best mask size for each pixel.the design of the adaptive algorithm is based on the combination of two complementary operators: a gradient based operator which is adapted to sloped regions and a valleyness detector which fits the crests and valleys. each operator is optimized in terms of bias reduction. the scale adaptive implementation of the operator is carried out in two steps. firstly, characteristic points are detected. their relative positions provide us with an estimation of the scale. secondly, the size of the convolution masks is chosen according to the estimated scale.experiments on synthetic and natural textures are provided and show the efficiency and the relevance of our approach.
tangent vector kernels for invariant image classification with svms. this paper presents an application of the general sample-to-object approach to the problem of invariant image classification. the approach results in defining new svm kernels based on tangent vectors that take into account prior information on known invariances. real data of face images are used for experiments. the presented approach integrates virtual sample and tangent distance methods. we observe a significant increase in performance with respect to standard approaches. the experiments also illustrate (as expected) that prior knowledge becomes more important as the amount of training data decreases.
graph-based transformation manifolds for invariant pattern recognition with kernel methods. we present here an approach for applying the technique of modeling data transformation manifolds for invariant learning with kernel methods. the approach is based on building a kernel function on the graph modeling the invariant manifold. it provides a way for taking into account nearly arbitrary transformations of the input samples. the approach is verified experimentally on the task of optical character recognition, providing state-of-the-art performance on harder problem settings.
improving shape from focus using defocus information. shape from focus (sff) method determines the degree of focus in a sequence of observations to estimate the shape of a 3-d object. existing sff algorithms use an ad hoc interpolation strategy to account for the error due to the finite step-size by which the translational table is moved while capturing the images. we propose an improved sff method that uses relative defocus blur derived from actual image data to arrive at the final estimates of the shape of the object. a space-variant image restoration scheme is also proposed to obtain a focused image of the 3-d object. the shape estimates as well as the quality of the restored image using the proposed method are superior to that of traditional sff.