waashk/dblp huggingface dataset:
learning wormholes for sparsely labelled clustering. distance functions are an important component in many learning applications. however, the correct function is context dependent, therefore it is advantageous to learn a distance function using available training data. many existing distance functions is the requirement for data to exist in a space of constant dimensionality and not possible to be directly used on symbolic data. to address these problems, this paper introduces an alternative learnable distance function, based on multi-kernel distance bases or wormholes that connects spaces belonging to similar examples that were originally far away close together. this work only assumes the availability of a set data in the form of relative comparisons, avoiding the need for having labelled or quantitative information. to learn the distance function, two algorithms were proposed: 1) building a set of basic wormhole bases using a boosting-inspired algorithm. 2) merging different distance bases together for better generalisation. the learning algorithms were then shown to successfully extract suitable distance functions in various clustering problems, ranging from synthetic 2d data to symbolic representations of unlabelled images.
scale invariants of three-dimensional legendre moments. three-dimensional digital images are gaining more attention in pattern recognition field. mostly literatures, however, only focus on theoretical framework of twodimensional moment invariants, that are only implemented on two-dimensional images. consequently, it reduces the invariance flexibility to support three-dimensional objects. in this paper, we introduce three-dimensional scale invariants of legendre moments. they are algebraically derived directly from legendre polynomials. simulated experiments using three-dimensional binary images are carried out to verify the validity of proposed invariance.
deciphering layered meaning in gestures. signs produced by gestures (such as in american sign language) can have a basic meaning coupled with additional meanings that are layered over the basic meaning of the sign. these layered meanings are conveyed by temporal and spatial modification of the basic form of thegesture movement. the work reported in this paper seeks to recognize temporal and spatial modifiers of hand movement and integrates them with the recognition of the basic meaning of the sign. to this end, a bayesian network framework is explored with a simulated vocabularyof 4 basic signs which give rise to 14 different combinations of basic meanings and layered meanings. recognition accuracies of upto 88.2% were obtained.
shooting the lecture scene using computer-controlled cameras based on situation understanding and evaluation of video images. in this paper, we propose a computer-controlled camera work that shoots object scenes to model the professional cameramen's work and selects the best image among plural video images as a switcher. we apply this system to a shooting of a lecture scene. in the first, our system estimates a teacher's action based on features of a teacher and a blackboard. in the next, each camera is directed to a shooting area based on the teacher's action, automatically. in the last, this system selects the best image among plural images under the evaluation rule. moreover, we have tried experiments of shooting lecture scene and have confirmed the effectiveness of our approach.
space-time analysis of spherical projection image. in this paper, a novel analysis of space-time volume of spherical projection image is presented. so far, space-time analyses have been extensively conducted for various purposes, i.e. 3-d reconstruction, estimation of camera motion and novel view synthesis and most of them consider only a planer projection and a single camera. in contrast, we conducted analysis on spherical projection for multiple cameras. since spherical projection does not change its appearance in relation to rotation around the origin of the sphere, extrinsic camera parameters and synchronous parameters of multiple video cameras can be simultaneously estimated by registering multiple space-time volumes of spherical projection, which can be easily achieved by block-matching technique. by using the parameters, multiple video images can be successfully integrated into single omni-directional images without distortions.
moving object detection using a cross correlation between a short accumulated histogram and a long accumulated histogram. this paper presents a method for detecting moving objects effectively in the weather whose visibility is bad, such as in a snowfall or in a dense fog. in such weather, the visibility changes rapidly in a short time and the intensity of each pixel changes hard every frame. in order to overcome these problems, the proposed method divides an input image into grid regions and in each region, calculates a cross correlation between two histograms whose accumulated number of frames are different. a short accumulated histogram, generated from accumulating a few number of frames, changes quickly whenever moving objects go into the region. on the other hand, a long accumulated histogram, generated from accumulating the more number of frames, changes slowly. therefore, moving objects are detected by measuring a variation on a cross correlation between a short accumulated histogram and a long accumulated histogram. experimental results obtained with heavy snow images have shown the effectiveness of the proposed method.
large scene reconstruction with local details recovery. we propose a method to recover the global structure with local details around a point. to handle a large scale of motion i.e. 360 degree around the point, we use an optimization-based algorithmto estimate the structure from the panorama around the fixed camera point. the global structure estimated can thus be used to initialize a structure from motion algorithm to recover the local details through simple camera motion such as panning. synthetic as well as real data are used to test the validity of the algorithm. our method can be used in applications such as authoring of virtual environments from real scene.
2d silhouette and 3d skeletal models for human detection and tracking. in this paper we propose a statistical model for detection and tracking of human silhouette and the corresponding 3d skeletal structure in gait sequences. we follow a point distribution model (pdm) approach using a principal component analysis (pca). the problem of non-lineal pca is partially resolved by applying a different pdm depending of pose estimation; frontal, lateral and diagonal, estimated by fisher's linear discriminant. additionally, the fitting is carried out by selecting the closest allowable shape from the training set by means of a nearest neighbor classifier. to improve the performance of the model we develop a human gait analysis to take into account temporal dynamic to track the human body. the incorporation of temporal constraints on the model increase reliability and robustness.
monocular lie algebra approach for 3d motion estimation. this work presents a monocular method to estimate 3d motion of an object in the visual space. our method is based on the theory of system identification, it identifies the optimal estimated value of the parameters that defines a 3d motion. the lie algebra approach assures the estimation of the parameters of the shortest orbit (geodesic) of the involved group action. experiments validate the effectiveness of the method.
omnidirectional vision tracking with particle filter. visual tracking is one of the most important topics of computer vision. challenging situations occurs when the time between frames is long enough to show significant spatial jumps of the target being tracked or when the target has unexpected motions. in this paper we offer an alternative to these situations. we use background subtraction to find the foreground regions (regions that move between frames). using the foreground regions and a modified version of the well-known particle filter we are able to track targets in an omnidirectional sequence with a low frame rate (one or two frames per second). our approach can tackle unexpected discontinuities and changes in the direction of the motion.
gaussian noise elimination in colour images by vector-connected filters. this paper deals with the use of vector-connected filters for eliminating gaussian noise in colour images. this class of morphological filters suppresses noise but preserves the contours of the objects. we impose a total order between pixels for morphological processing. once the hsi space has been adapted, we employ it in the lexicographical order. as such, all of the morphological operations are vectorial. after having defined the vectorial geodesic operators, they are then employed to eliminate gaussian noise.
comparison of colour spaces for optic disc localisation in retinal images. the location of the optic disc is of critical importance in retinal image analysis. in this work we improve on an approach introduced in [3] which localises an optic disc region through greylevel morphology followed by snake fitting. we propose and implement both the automaticinitialisation of the snake and the application of morphology in colour space. we examine various methods of performing the morphology step (to remove the interference of blood vessels) and compare them against each other. we demonstrate that our proposed simple lab colour morphology method is particularly suitable for the characteristics of our optic disc images. results indicate 90.32% average accuracy in localising the optic disc boundary.
human tracking by particle filtering using full 3d model of both target and environment. this work presents a new approach based on particle filtering to directly estimate the 3d positions of humans. our system can predict occlusions due to other movements because we track humans in a 3d space, not on a 2d image plane. in addition, we introduce a 3d environmental model as the background model for tracking. this makes it easier to handle occlusions due to fixed objects in the environment. the 3d environmental model is automatically constructed by our original method from video sequences. experiments show that our system is stable under occlusions due to the movements of both other subjects and fixed objects.
talking faces - technologies and applications. this paper gives an overview of facial animation techniques. while facial animation is currently used in the entertainment and advertisement industry, it will become part of dialog systems in commercial applications. animation techniques based on 3d models and image-based rendering are presented and evaluated by the following characteristics: automatism, realism and flexibility. while animation techniques based on 3d models provide for high flexibility and automatism, they often lack realism. image-based techniques achieve photo realism, but lack in automatism and flexibility.
a particle filter for tracking densely populated objects based on explicit multiview occlusion analysis. a novel particle filter is presented for tracking densely populated objects moving on a two-dimensional plane; it is based on a probabilistic framework of explicit multiview occlusion analysis. the spatial structure of 2-d occlusion process between objects is modeled as a hidden process controlled by a markov probability structure. the tracking problem is then formulated as a recursive bayesian framework for solving the simultaneous estimation problem of two interactive processes; hypothesis generation/testing of the occlusion structure and the computation of posterior probability distribution of object states such as position and pose. for efficient implementation of the formulated framework, we develop a novel particle filter in which each particle can support multiple posterior distributions of object states on different occlusion hypotheses. experiments using synthetic and real data confirm the robustness of the proposed method even in the face of severe occlusion.
multiclass pattern classification using neural networks. multiclass neural learning involves finding appropriate neural network architecture, encoding schemes, learning algorithms, etc. in this paper, we discuss major approaches used in neural networks for classifying multiple classes. the discussion is focused d on these architectures using either a system of multiple neural networks or a single neural network. we will discuss various learning algorithms, one-again-all, one-against-one, and p-against-q. we will also discuss training procedures associated with each approach, implementation and time complexity. these methods are evaluated though their performances on the nist handwritten digit database.
self-supervised writer adaptation using perceptive concepts: application to on-line text recognition. we recently designed a hand-printed text recognizer. the system is based on three set of experts respectively used to segment, classify and validate the text (with a french lexicon : 200k words). we present in this communication writer adaptation methods. the first is supervised by the user. the others are self-supervised strategies which compare classification hypothesis with lexical hypothesis and modify consequently classifier parameters. the last method increases the system accuracy and the classification speed. experiments are presented on a large database of 90 texts (5400 words) written by 54 different writers and good recognition rates (82%) have been obtained.
developing assistant tools for geometric camera calibration: assessing the quality of input images. this paper proposes two indicators for predicting the quality of camera model parameters from a set of input images. the first indicator is based on the acutance. it can quickly indicate static or motion blur during image capture and correlates well with the 3d reconstruction error when using stereo cameras. the second indicator provides the overall distribution of control points in a set of input images. although the importance of covering the entire field of view is verified, the spatial distribution of control points need not be uniform. the analysis is supported by experiments with mono and stereo cameras calibrated using both a low cost planar and a high quality 3d target.
markovian random fields energy minimization algorithms. this paper introduces two frameworks that are modified versions of iterated conditional modes (icm) algorithm. these methods provide an efficient minimization of markovian random fields (mrf) energy function with substantial reduction in computing time. an application to a multi-resolution motion detection scheme is then presented and the results of the two frameworks are discussed and compared to those obtained by icm.
fingerprint matching with rotation-descriptor texture features. a novel texture correlation matching method for fingerprint verification using fourier-mellin descriptor and phase-only correlation function is proposed in this paper. fourier-mellin descriptor correlation is used to align the template and query fingerprint images and a matching score is obtained. matching takes about 1 second in celeron 2.0 ghz processor, and the experimental results show that eer is 3.8%; fusion with minutia matching gets a better result.
exploring human eye behaviour using a model of visual attention. it is natural in a visual search to look at any object that is similar to the target so that it can be recognised and a decision made to end the search. eye tracking technology offers an intimate and immediate way of interpreting users' behaviours to guide a computer search through large image databases. this paper describes experiments carried out to explore the relationship between gaze behaviour and a visual attention model that identifies regions of interest in image data. results show that there is a difference in behaviour on images that do and do not contain a clear region of interest.
tracking hands and objects for an intelligent video production system. we propose a novel method for detecting hands and hand-held objects in desktop manipulation situations. in order to achieve robust tracking under few constraints, we use multiple image sensors, that is, a rgb camera, a stereo camera, and an ir camera. by using these sensors, our system realized robust tracking without the prior knowledge of an object even if there are moving people or objects in the background. we experimentally verified the performance of object tracking by each of the three sensors and evaluated the effectiveness of their integration.
a trainable similarity measure for image classification. in object recognition problems a two-stage system is usually adopted composed of a fast and simple detector and a more complex classifier. this paper studies a design of the second stage classifier based on the recently proposed trainable similarity measure which is specifically designed for supervised classification of images. common global measures such as correlation suffer from uninformative pixels and occlusions. the proposed measure is based on local matches in a set of regions within an image which increases its robustness. the configuration of local regions is derived specifically for each prototype by a training procedure. the paper compares the classifiers built using the trainable similarity to the state-of-the-art adaboost classifiers on a real-world pedestrian recognition problem. the paper illustrates that for a given range of sample sizes the trainable similarity represents a better solution for secondstage classification than the adaboost algorithm which requires significantly larger training sets.
multi-class extensions of the gldb feature extraction algorithm for spectral data. the generalized local discriminant bases (gldb) algorithm proposed by kumar, ghosh and crawford in [best-bases feature extraction algorithm for classification of hyperspectral data], is a effective feature extraction method for spectral data.it identifies groups of adjacent spectral wavelengths and for each group finds a fisher projection maximizing the separability between classes. the authors defined gldb as a two-class feature extractor and proposed a bayesian pairwise classifier (bpc) building all pairwise extractors and classifiers followed by a classifier combining scheme. with a growing number of classes the bpc classifier quickly becomes computationally prohibitive solution. in this paper, we propose two alternative multi-class extensions of gldb algorithm, and study their respective performances and execution complexities on two real-world datasets. we show how to preserve high classification performance while mitigating the computational requirements of the gldb-based spectral classifiers.
a new optimised de bruijn coding strategy for structured light patterns. coded structured light is an optical technique based on active stereovision that obtains the shape of objects. one-shot techniques are based on projecting a unique light pattern with an lcd projector so that grabbing an image with a camera, a large number of correspondences can be obtained. then, a 3d reconstruction of the illuminated object can be recovered by means of triangulation. the most used strategy to encode one-shot patterns is based on de bruijn sequences. in this paper a new way to design patterns using this type of sequences is presented. the new coding strategy minimises the number of required colours and maximises both the resolution and the accuracy.
recognition of english multi-oriented characters. there are some printed artistic documents where text lines may be curved in shape. as a result, characters of a single line may be multi-oriented. to handle such artistic documents, in this paper, we present a scheme towards the recognition of multi-oriented and multi-sized english characters. the features used here are invariant to character orientation and computed based on the angular information of the border points of the characters. we used modified quadratic discriminant function (mqdf) for recognition. we tested our proposed scheme on a dataset of 18232 characters and obtained 98.34% accuracy from the system.
fast object localization using multi-scale image relevance function. an object detection method using a model-based visual attention mechanism is proposed in application to visual inspection problems and medical diagnostic imaging. the proposed method is based on a multi-scale operator called image relevance function - a non-linear multi-scale filter bank that has local maxima at the centers of object locations. model-based design of this operator takes into account intensity, shape and texture features of the objects to be detected. this approach offers several advantages, including fast and accurate object localization, simple extraction of shape features, adaptive segmentation of object regions.
multi-scale model-based skeletonization of object shapes using self-organizing maps. in this paper, a new skeletonization algorithm suitable for the skeletonization of sparse shape is described. it is based on self-organizing maps (som) -a class of neural networks with unsupervised learning. the so-called structured som with local shape attributes such as scale and connectivity of vertices are used to determine the object shape in the form of piecewise linear skeletons. the location of each vertex of piecewise linear generating lines on the image plane corresponds to the position of a particular som unit. this method makes it possible to extract the object skeletons and to reconstruct the planar shape of sparse objects based on the topological constraints of generating lines and estimation of scales.
perceptual knowledge extraction using bayesian networks of salient image objects. a novel approach to perceptual knowledge extraction from images based on the concept of salient image objects is proposed. salient image object - a concise description of a image fragment within a circular region - is a vector of salient image features, which describes the fragment invariantly to geometrical transformations and some intensity changes. bayesian network of salient image objects - a kind of generative image modeling - is used as a model for the knowledge representation, which includes semantic entities (e.g., real-world objects) and provides probabilistic relations between image features and semantic entities. the proposed technique of multi-scale image relevance function permits a fast and ordered extraction of salient image objects.
bottom-up hierarchical image segmentation using region competition and the mumford-shah functional. this paper generalizes the methods in a previous paper [10] in two ways. first, a more comprehensive analysis of the initialization problem of the chan-vese models is given. second, the image segmentation method proposed in [10] is improved by applying bimodal curve evolution with region competition. the improved method maintains the advantages of the previous method. it is efficient, stable in the presence of strong noise and able to handle complicated images. it outperforms the previous method for images with weak edges. experimental results in this paper demonstrate these improvements.
novel seed selection for multiple objects detection and tracking. this paper proposes a unified approach for initializing, detecting and tracking of multiple moving objects. object initialization is achieved through novel seed selection which is adaptively activated, depending on the quality of tracking, to select the best possible frames along the temporal direction for object detection. em algorithm is then employed to robustly segment and detect multiple objects in a selected frame. each detected object is represented by an appearance-based model and mean shift tracking procedure is adopted to rapidly and effectively track the target objects.