waashk/dblp huggingface dataset:
robustness and specificity in object detection. in this paper we discuss the role of robustness to geometric conformity versus specificity in machine learning. the key observation made here is that object variation due to appearance and due to geometric deformation are often, for good reasons, intermixed in typical object detection applications. in the paper we consider a whole range of differently specific object detectors. it is shown that such detectors vary in their robustness to geometric deformation and also their specificity. such detectors can then be used in a cascade, where coarse detectors operate on a less-specific and more robust scale. this makes it possible to use coarse sampling of the space of geometric transformations. further on more-specific and less robust detectors are used. this requires as input the detections at a coarser scale combined with an optimization search step. in the paper it is also discussed how such detectors can automatically be obtained from a coarsely defined database of ground truth.
tracking soccer players using the graph representation. in this work, we consider the problem of tracking soccer players during a game by using multiple cameras. the main goal consists in finding the position of the players on the pitch at each instance of time. the occlusion is treated by splitting segmented blobs and the tracking is performed using a graph representation, where nodes correspond to the blobs obtained by image segmentation and edges represent the distance between the blobs.
bijective image registration using thin-plate splines.. image registration is the process of geometrically aligning two or more images. in this paper we describe a method for registering pairs of images based on thin-plate spline mappings. the proposed algorithm minimizes the difference in gray-level intensity over bijective deformations. by using quadratic sufficient constraints for bijectivity and a least squares formulation this optimization problem can be addressed using quadratic programming and a modified gauss-newton method. this approach also results in a very computationally efficient algorithm. example results from the algorithm on three different types of images are also presented.
non-linear reflectance model for bidirectional texture function synthesis. a rough texture modelling involves a huge image data-set - the bidirectional texture function (btf). this 6-dimensional function depends on planar texture coordinates as well as on view and illumination angles. we propose a new non-linear reflectance model, based on a lafortune reflectance model improvement, which restores all btf database images independently for each view position and herewith significantly reduces stored btf data size. the extension consists in introducing several spectral parameters for each btf image which are linearly estimated in the second estimation step according to the original data. the model parameters are computed for every surface reflectance field contained in the original bft data. this technique allows btf data compression by the ratio 1:15 while the synthesised images are almost indiscernible from the originals. the method is universal, and easily implementable in a graphical hardware for purpose of real-time btf rendering.
maximizing validity in 2d motion analysis. classifying and analyzing human motion from a video is relatively common in many areas. since the motion is carried out in 3d space, the 2d projection provided by a video is somewhat limiting. the question we are investigating in this article is how much information is actually lost when going from 3d to 2d and how this information loss depends on factors, such as viewpoint and tracking errors that inevitably will occur if the 2d sequences are analysed automatically.
fast synthesis of dynamic colour textures. textural appearance of many real word materials is not static but shows progress in time. if such a progress is spatially and temporally homogeneous these materials can be represented by means of dynamic texture (dt). dt modelling is a challenging problem which can add new quality into computer graphics applications. we propose a novel hybrid method for colour dts modelling. the method is based on eigen-analysis of dt images and subsequent preprocessing and modelling of temporal interpolation eigencoefficients using a causal auto-regressive model. the proposed method shows good performance for most of the tested dts, which depends mainly on the properties of the original sequence. moreover, this method compresses significantly the original data and enables extremely fast synthesis of artificial sequence, which can be easily performed by means of contemporary graphics hardware.
prescient paper: multimedia document creation with document image matching. a system is described for creating paper documents that show images of presentation slides and bar codes that point to a multimedia recording of a presentation that has not yet occurred. an image-matching algorithm applied after a presentation determines when each slide was displayed. these time-stamps map the bar codes onto commands that control a multimedia player. we describe the system infrastructure that allows us to prepare such prescient documents and the document image-matching algorithm that enables the mapping of bar codes onto the times when slides were displayed. this provides a multimedia annotation tool that requires no electronic device at capture time.
ecoc-one: a novel coding and decoding strategy. error correcting output codes (ecoc) represent a classification technique that allows a successful extension of binary classifiers to address the multiclass problem. in this paper, we propose a novel technique called ecoc-one to improve an initial ecoc configuration by including new dichotomies guided by the confusion matrix over exclusive training subsets. in this way, the initial coding represented by an optimal decision tree is extended adding binary classifiers forming a network. since not all dichotomies have the same relevance, a weighted methodology is included. moreover, to decode we introduce a new distance to attenuate the error accumulated by zeros in the ecocone matrix. we compare our strategy to other well-known ecoc coding strategies on the uci data set achieving very promising results.
error-less colour correctio. colour correction is the problem of mapping device dependent rgbs to standard cie xyzs. traditionally it is solved for by an error minimising one-to-one linear transform. however this problem is ill-posed. there exist multiple reflectances, known as metamers, which induce the same rgb but different xyzs (and vice versa). in this paper we propose that this ill-posedness might be viewed positively. indeed, that it leads to an error-less transform for colour correction. we propose that a mapping is error-less if it takes an rgb to an xyz such that there exists a real reflectance spectrum which integrates to this rgb-xyz pair. we show how we can solve for a mapping which satisfies this error-less criterion. as in previous studies, we seek a linear transform that is error-less. we show that we can solve for such a transform by quadratic programming. experiments demonstrate 3 important results. first, that a linear least squares transform is not error-less. specifically, saturated rgb-xyz pairs do not correspond to a plausible reflectance. second, there exists a linear transform that is error-less. finally, that the best error-less transform performs almost as well as least-squares, but substantially better for saturated colours. it is possible to map rgb to xyz with zero error.
forest extension of error correcting output codes and boosted landmarks. in this paper, we introduce a robust novel approach for detecting objects category in cluttered scenes by generating boosted contextual descriptors of landmarks. in particular, our method avoids the need of image segmentation, being at the same time invariant to scale, global illumination, occlusions and to small affine transformations. once detected the object category, we address the problem of multiclass recognition where a battery of classifiers is trained able to capture the shared properties between the object descriptors across classes. a natural way to address the multiclass problem is using the error correcting output codes technique. we extend the ecoc technique proposing a methodology to construct a forest of decision trees that are included in the ecoc framework. we present very promising results on standard databases: uci database and caltech database as well as in a real image problem.
perceptual grouping for contour extraction. this paper describes an algorithm that efficiently groups line segments into perceptually salient contours in complex images. a measure of affinity between pairs of lines is used to guide group formation and limit the branching factor of the contour search procedure. the extracted contours are ranked, and presented as a contour hierarchy. our algorithm is able to extract salient contours in the presence of texture, clutter, and repetitive or ambiguous image structure. we show experimental results on a complex line-set.
radon space and adaboost for pose estimation. in this paper, we present a new approach to camera pose estimation from single shot images in known environment. such a method comprises two stages, a learning step and an inference stage where given a new image we recover the exact camera position. lines that are recovered in the radon space consist of our feature space. such features are associated with [adaboost] learners that capture the wide image feature spectrum of a given 3d line. such a framework is used through inference for pose estimation. given a new image, we extract features which are consistent with the ones learnt, and then we associate such features with a number of lines in the 3d plane that are pruned through the use of geometric constraints. once correspondence between lines has been established, pose estimation is done in a straightforward fashion. encouraging experimental results based on a real case demonstrate the potentials of our method.
scene recovery from many randomly distributed single pixel cameras. this paper examines two scene recovery problems arising when interpreting data from massive numbers of randomly distributed single pixel cameras. assuming that the camera positions and orientations are approximately known, the paper shows that both distant and nearby scenes can be reconstructed, and analyzes how recovery performance varies with sensor parameters.
cross validation and segment support for stereo belief propagati. typically, algorithms for generating stereo disparity maps have been developed to minimise the energy equation of a single image. this paper proposes a method for implementing cross validation in a belief propagation optimisation. when tested using the middlebury online stereo evaluation, the cross validation improves upon the results of standard belief propagation. furthermore, it has been shown that regions of homogeneous colour within the images can be used for enforcing the so-called "segment constraint". developing from this, segment support is introduced to boost belief between pixels of the same image region and improve propagation into textureless regions.
an empirical model for saturation and capacity in classifier spaces. when assessing reported classification results based on selection of members from a database (e.g. a face database), one would like to know what is an achievable classification rate, given the noise level, dimensionality of the feature set and number of classes in the database. as best we can tell, no general results exist for this question, although many classification rates appear in different papers. this paper presents an empirical formula for map classification that links the number of discriminable classes to the error rate, dimensionality of the feature data and the feature noise level.
automated visual identification of characters in situation comedies. the objectives of the work described in this paper are simply stated: given examples of a particular person and an unlabelled video, we wish to find every instance of that person in the video and in others. this is an extremely difficult problem because of the many sources of variation in the person's appearance. we present a two stage approach. a 3-d ellipsoid approximation of the person's head is used to train a set of generative parts-based 'constellation' models which propose candidate detections in an image. the detected parts are then used to align the model, and the detections verified by global appearance. novel aspects of the approach include the minimal supervision required and the generalization across a wide range of pose. we demonstrate results of detecting three characters in a tv situation comedy.
fast exhaustive robust matching. this work presents a fast exhaustive robust matching technique for aligning translated images. the key idea is to express a robust matching surface function in terms of correlation operations. speed is obtained from computing correlations in the frequency domain. different sized images and arbitrary shapes may be matched. the method outputs a matching surface representing the quality of match at each translatory position. experimental results of a comparison with the standard method of phase correlation are shown.
improving cut detection in mpeg videos by gop-oriented frame difference normalization. the detection of abrupt shot changes ("cuts") in videos is a basic step in video content analysis. many cut detection algorithms based on histogram differences have been proposed in the literature, and it has been shown for the mpeg (moving picture experts group) domain that using small sub-images, namely approximated dc-frames, is sufficient to achieve very good detection results. in this paper, the characteristics of histogram based difference measurements of mpeg (dc-)frames are analyzed and an effective technique is presented to enhance the performance of cut detection algorithms, called "gop-oriented frame difference normalization" (gop: group of pictures). experimental results for the mpeg-7 video test set will be presented to demonstrate the benefits of our proposal. furthermore, the proposed method is not limited to a particular algorithm but it is applicable to an entire class of cut detection algorithms.
fast face detection with precise pose estimation. we present a fast algorithm for face detection and precise pose estimation. our detection scheme is based on a tree-structured hierarchy of face vs.background classifiers combined with a lazy evaluation strategy which concentrates computation on ambiguous areas of the image. the hierarchy corresponds to successive partitions of the pose parameter space, and thereby provides, at no additional cost, a fine estimate of the 2d pose of detected faces.
estimation of arbitrary camera motion in mpeg videos. several algorithms have been proposed to solve the problem of camera motion estimation in digital videos. however, the distinction between translation along the x-axis (y-axis) and rotation around the y-axis (x-axis) has only rarely been considered, and no approach of this kind is known to us for the mpeg domain. in this paper, we present such an algorithm for camera motion estimation in mpeg videos. for performance reasons it is reasonable to extract motion vectors directly from the compressed stream. however, since motion vectors are optimal with respect to compression, they often do not model real motion adequately and can thus be considered as "outliers" with respect to camera motion estimation. consequently, an outlier removal algorithm is incorporated into our approach to solve this problem. furthermore, we have investigated the minimum number of motion vectors required to obtain satisfactory results. comprehensive experiments with 32 video clips demonstrate the performance of the proposed approach.
using texture-based symbolic features for medical image representation. at present time the internet has become a major source of information and a powerful didactic tool. furthermore, the development of digital equipment, allows to acquire and store large quantities of medical data, including images. in the context of the cismef on-line health-catalogue, our work is centered on the automatic categorization of medical images according to their visual content, for further indexation and retrieval tasks. the aim of the present study is to assess the performance of a new image symbolic descriptor for medical modality, anatomic region and view angle image categorization. this descriptor is issued from the unsupervised partition of statistical and texture image subblock representations. a medical image database of 10322 images from 33 classes was ground-truthed by a domain expert. despite the complexity and variability of medical images, the compact symbolic representation approach proposed in this paper achieves high recognition rates. thus, using knn classifiers, we obtain an average precision of 83% and a top performance of 91.19%.
text detection from natural scene images: towards a system for visually impaired persons. we propose a system that reads the text encountered in natural scenes with the aim to provide assistance to the visually impaired persons. this paper describes the system design and evaluates several character extraction methods. automatic text recognition from natural images receives a growing attention because of potential applications in image retrieval, robotics and intelligent transport system. camera-based document analysis becomes a real possibility with the increasing resolution and availability of digital cameras. however, in the case of a blind person, finding the text region is the first important problem that must be addressed, because it cannot be assumed that the acquired image contains only characters. at first, our system tries to find in the image areas with small characters. then it zooms into the found areas to retake higher resolution images necessary for character recognition. in the present paper, we propose four character-extraction methods based on connected components. we tested the effectiveness of our methods on the icdar 2003 robust reading competition data. the performance of the different methods depends on character size. in the data, bigger characters are more prevalent and the most effective extraction method proves to be the sequence: sobel edge detection, otsu binarization, connected component extraction and rule-based connected component filtering.
a smale-like decomposition for discrete scalar fields. in this paper, we address the problem of representing the structure of the topology of a d-dimensional scalar field as a basis for constructing a multiresolution representation of the structure of such a field. to this aim, we define a discrete decomposition of a triangulated d-dimensional domain, on whose vertices the values of the field are given. we extend a smale decomposition, defined by thom and smale for differentiable functions, to the discrete case, to what we call a smale-like decomposition. we introduce the notion of discrete gradient vector field, which indicates the growth of the scalar field and matches with our decomposition. we sketch an algorithm for building a smale-like decomposition and a graph-based representation of this decomposition. we present results for the case of two-dimensional fields.
invariants to convolution with circularly symmetric psf. we introduce a new class of moment-based features invariant to rotation and to convolution with an unknown point-spread function having circular symmetry. unlike the invariants published earlier, they comprise both even and odd order moments, which increases their discrimination power and robustness.
automatic fish age estimation from otolith images using statistical learning. in this paper, we investigate the use of statistical learning techniques for fish age estimation from otolith images. the core of this study lies in the definition of relevant image-related features. we rely on the characterization of a 1d signal summing up the image content within a predefined area of interest. fish age estimation is then viewed as a multi-class classification issue using neural networks and svms. a procedure based on demodulation and remodulation of fish growth patterns is used to improve the generalization properties of the trained classifiers. we also investigate the combination of additional biological and shape features to the image-related ones. the performances are evaluated for a database of several hundred of plaice otoliths.
content-based image retrieval using fourier descriptors on a logo database. a system that enables the pictorial specification of queries in an image database is described. the queries are comprised of rectangle, polygon, ellipse, and b-spline shapes. the queries specify which shapes should appear in the target image as well as spatial constraints on thedistance between them and their relative position. the retrieval process makes use of an abstraction of the contour of the shape which is invariant against translation, scale, rotation, and starting point that is based on the use of fourier descriptors. these abstractions are used in a system to locate logos in an image database. the utility of this approach is illustrated using some sample queries.
face recognition from video using active appearance model segmentation. face recognition from video can be improved if good face segmentation of the subject under test is achieved. many video based face recognition rely on simple background modeling and coarse alignment strategies for segmentation. this work presents a face recognition from video framework based on using active appearance models (aam) to achieve accurate face segmentation and consistent shape free representation across a video sequence. the segmentation provided by the aam can be effectively normalized (morphed) to a mean shape. the resulting subimage can then be delivered to conventional face recognition from video algorithms for robust classification. we present preliminary results on a dataset of 17 individuals and outline the problems encountered in this approach.
hierarchical object indexing and sequential learning. this work is about scene interpretation in the sense of detecting and localizing instances from multiple object classes. we concentrate on object indexing: generate an over-complete interpretation - a list with extra detections but none missed. pruning such an index to a final interpretation involves a global, often intensive, contextual analysis. we propose a tree-structured hierarchy as a framework for indexing; each node represents a subset of interpretations. this unifies object representation, scene parsing, and sequential learning (modifying the hierarchy as new samples, poses and classes are encountered). then we specialize to learning - designing and refining a binary classifier at each node of the hierarchy dedicated to the corresponding subset of interpretations. the whole procedure is illustrated by experiments in reading license plates.
edge model based segmentation. segmentation is an important operation in image analysis. it is employed to extract interested objects from an image under test. much research work has been performed and the optimal graph theoretic approach to data clustering is one of the promising methods. however, when the image size is large, the graph size will be very large. as a result the graph becomes complex and its processing is computation demanding. in this paper, we propose to simplify the problem by pre-segmenting the image under test using an edge model before applying the optimal graph theoretic approach to data clustering. the experimental results show that the proposed method can efficiently segments an image with satisfactory results.