waashk/dblp huggingface dataset:
fast and accurate facial pose estimation by aligning a 3d appearance model. this paper proposes a method to estimate facial pose, ranging from frontal to profile, from an image captured under various illumination conditions. our proposed method formulates pose estimation by minimization of the errorbetween the target image and an image reproduced by a 3dappearance model. taking a rough initial estimate, the pose is optimized so that the error is decreased by the steepest descent. the performances for static and realtime pose estimation are evaluated with test images captured under drastically varied illumination conditions. the experimental results show that our proposed method estimates facial poses including rotations indepth up to 60 degrees from the frontal with an average error of 2.3 degreeseven when the initial error is 10 degrees. our proposed method is fast enough to realize a realtime face tracking system which runs at 20 frames per second on a pentium 4 3.2 ghz pc.
an enhanced appearance model for ultrasound image segmentation. active appearance model (aam) [active appearance models] had been popular on object segmentation for medical images. however, its performance is not good on ultrasound (us) images. in this paper, we propose an enhanced appearance model which represents the texture by edge structure [on representing edge structure for model matching] and reduces the view-dependent feature of us images by a novel data normalization process. in our experiments on general us data, the proposed model shows considerable improvements compared to the original one [active appearance models].
a method of reducing speckle noise of sar images based on wavelets and wedgelet hmt models. in terms of the statistical characteristic of sar images, combining the multi-scale wavelets and wedgelet approximation with hmt models, a novel method of reducing speckle noise of sar images is presented in this paper. furthermore, analyze the mechanism and computation complexity of this technique. the validity and efficiency are shown by experiments.
detection and recognition of moving objects by using motion invariants. motion invariant features, which are called either absolute or relative invariant linear features, are useful for detection and recognition of moving objects from image streams. we conducted simple experiments using these invariant features. the first experiment tested our method for moving object recognition with a fixed camera using absolute invariant linear features. in the second experiment, another method was used to detect moving objects with a moving camera using both relative and absolute invariant linear features. the results of these experiments confirmed the effectiveness of the proposed methods.
a non-parametric hmm learning method for shape dynamics with application to human motion recognition. the shape dynamics, i.e., the spatial-temporal shape deformation of an object during its movement, provides much important information about the identity of the object, and even motions performed by the object. in this paper, we proposed a system recognizing object motions based on their shape dynamics. in the proposed system, we use kenall's definition of shape to represent the object contour extracted from each frame, and construct a tangent space with the full procrustes mean shape as the pole to approximate a linear space for the dataset, in which the euclidean distance metric can be used to approximate the full procrustes distance between shapes. the spatial-temporal shape deformation in motions is captured by hidden markov models. since in the traditional hmm framework the hidden states are typically coupled with the training data, which will bring many undesired problems to the learning procedure, we introduce a non-parametric hmm approach that uses continuous output hmms with arbitrary states (decoupled from training data) to learn the shape dynamics directly from large amounts of training data where a non-parametric kernel density estimation algorithm is applied to learn the observation probability distribution in order to compensate for the uncertainty introduced by those arbitrary hidden states. this optimizes the hmm training procedure. we then use the proposed system for view-dependent human motion recognition.
recovering structures and motions from mutual projection of cameras. in this paper, we propose a new method for recovering camera motions and structures of the scene accurately and reliably.for recovering motions and structures, it is very important to compute the epipolar geometry accurately. although some linear methods have been proposed for computing theepipolar geometry, they are still less accurate than non-linear methods. the non-linear methods, on the other hand, require a lot of computational power, and sometimes fall into local minima. in this paper, we show that by using the actual projection of cameras, the epipolar geometry can be computed much more reliably from less image correspondences by a linear method. we also show that by using the epipolar geometry derived from the mutual projection of cameras, the structures of the scene can be recovered much more accurately and reliably than the existing methods.
a probabilistic model for camera zoom detection. camera motion detection is essential for automated video analysis. we propose a new probabilistic model for detecting zoom-in/zoom-out operations. the model uses em to estimate the probability of a zoom versus a non-zoom operation from standard mpeg motion vectors.traditional methods usually set an empirical threshold after deriving parameters proportional to zoom, pan, rotate and tilt. in contrast, our probabilistic model has a solid probabilistic foundation and a clear, simple probability threshold. experiments show that this probabilisticmodel significantly out-performs a baseline parametric method for zoom detection in both precision and recall.
spoken document classification with svms using linguistic unit weighting and probabilistic couplers. the task addressed by this paper is spoken document classification (sdc) of german tv news with support vector machines (svms). it shows the benefits of weighting different linguistic units when combined into one feature vector. further experiments show that probabilistic svms (psvms) with recently introduced couplers perform well on a sdc task. new couplers for multi-category classification, both for psvms and non-psvms, will be discussed. they are easy to implement and show good and promising results. it turns out that using the distance instead of the decision value can be favorable. theoretical justification is given for our approaches, and some results are explained theoretically.
a novel adaptive image enhancement algorithm for face detection. image enhancement techniques are discussed in this paper as a necessary preprocessing step for face detection. first, a measure of the distribution of image information, termed the entropy error rate (eer), is presented on the basis of information theory. then, by integrating a histogram ridge analysis technique and an optimal intensity transform method that aims to minimize the eer of an enhanced image, a novel adaptive enhancement algorithm is proposed. in a baseline face detection test using the algorithm presented by viola et al. in [robust real-time object detection], comparison experiments are conducted with the yale b face dataset and our own movie face dataset. the results demonstrate that image enhancement preprocessing can significantly improve face detection accuracy, and that the adaptive enhancement algorithm performs much better than classical histogram-based enhancement techniques such as linear stretching and histogram equalization.
a hybrid resampling framework for facial shape alignment. the modelisation of human faces from single or multiple images can be solved by fitting an active appearance model (aam). however, fitting such models with unknown prior estimation is a challenging task since shape and appearance are closely linked. in this paper, we address the efficiency of the sampling technique in regard to the shape alignment accuracy. the hybrid method we propose is based on a non-uniform barycentric resampling of the face model. the selected subsampled locations are improving the representativity of the generic texture reference.
speech separation from background of music based on single-channel recording. this paper presents a speech separation algorithm, which is capable of extracting speech signals from music background when given only a single-channel recording. the main idea is to employ both the tool of spectrogram and the image processing methods to solve the problem of speech signals processing. edge detection algorithm and hough transform are used to detect the fundamental frequency and the corresponding harmonic components of the music. the experimental results show that the proposed algorithm can effectively remove background music from the input signals and output the speech signals needed.
dense wide-baseline disparities from conventional stereo for immersive videoconferencing. we propose an algorithm creating consistent, dense disparity maps from incomplete disparity data generated by a conventional stereo system used in a wide-baseline configuration. the reference application is ibr-oriented immersive videoconferencing, in which disparities are used by a view synthesis module to create instantaneous views of remote speakers consistent with the local speaker's viewpoint.we perform spline-based disparity interpolation within non-overlapping regions.regions are defined by discontinuity boundaries identified in the incomplete disparity map.we demonstrate very good results on significantly incomplete disparity data computed by a conventional correlation-based stereo algorithm on real wide-baseline stereo pair acquired by an immersive videoconferencing system.
neural network based modeling and color rendering for mixed reality. this paper describes a new approach to color image rendering for mixed reality applications. the approach has two steps. the first uses neural network based photometric stereo to obtain both a 3d shape model and color reflectance factors simultaneously. the second uses neural network based image rendering to generate realistic virtual images for arbitrary viewpoint and direction of light source. the proposed approach achieves high quality 3d modeling of real objects and subsequent virtual image rendering based on empirical measurements obtained in a simple experimental environment. the approach is empirical using only a standard digital camera and light sources. assumptions about surface reflectance are reasonably generic. experiments using real data are demonstrated.
a hybrid classifier for precise and robust eye detection. eye location is an important visual cue for face image processing such as alignment before face recognition, gaze tracking, expression analysis, etc. in this paper a novel eye detection algorithm is presented, which integrates the characteristics of single eye and eye-pair images to develop a hybrid classifier under the learning paradigm. the low dimensional features representing eye patterns yield by subspace projection are selected via a filter and a wrapper method for a simplified maximum likelihood and a svm classifier respectively. eye candidates determined by a cascade of the two classifiers are further verified with eye-pair template matching scores to reject false detections. the performance of this eye detector is assessed on several publicly available face databases and the experimental results demonstrate its robustness to the variations in head pose, facial expressions, partial occlusions and lighting conditions.
a novel vision based finger-writing character recognition system. a new vision based finger writing character recognition system (fwcrs) is proposed in this paper. the fwcrs allows people to write characters virtually just using his finger-tip (we call this "fingerwriting"). the trajectories of the finger-tip are tracked and reconstructed as a kind of inkless character pattern and finally recognized by a classifier. in this paper, a simple but effective background model is built for the fwcrs to segment human finger from cluttered background. a robust fingertip detection algorithm based on feature matching is presented. the fingerwriting character is finally recognized by a dtw classifier. experiments show that the fwcrs can recognize finger-writing uppercase & lowercase english characters with the accuracy of 95.6%, 98.5% respectively.
scanner artifact removal in simultaneous eeg-fmri for epileptic seizure prediction. simultaneous recording of electroencephalographic (eeg) and functional magnetic resonance imaging (fmri) has been a new modality in brain imaging very recently. blood oxygen level-dependent (bold) in fmri provides sufficient information about the hemodynamics of the brain during changes in the brain metabolism such as in the event of seizure. however, the major drawback of these joint recordings is the destructive effect of the fmri scanner artifact. without removal of such artifact the eeg signals cannot be processed. in this paper, an effective method for removal of the scanner artifact has been established by applying blind source extraction (bse) algorithm followed by the averaging-and-subtraction method. the results have shown that the scanner artifacts have been effectively mitigated.
parallel tracking of all soccer players by integrating detected positions in multiple view images. soccer, one of the popular sports around the world, is often broadcasted on tv, and various researches have done on soccer scene images such as strategy analysis, scene recovery, automatic indexing of soccer scenes, and automatic intelligent sports casting. as robust player tracking is fundamental to those researches, there is a demand for an automatic player tracking system using soccer imaging data. in this paper, we propose a method of tracking soccer players using multiple views. tracking is done by integrating the tracking data from all cameras, using the geometrical relationship between cameras called homography. integrating information from all cameras enables stable tracking on the scene, where the tracking by a single camera often fails in the case of occlusion.
learning in hidden annotation-based image retrieval. learning in an image retrieval scheme that uses hidden annotations is investigated. compared with low level visual features that are straightforward functions of the raw pixel values, hidden annotations are higher level hidden semantic attributes. in the proposed scheme, a small set of images is manually labeled with several hidden annotations. for each annotation, a support vector machine (svm) classifier is trained using the images labeled with it as positive examples and others as negative examples. based on the trained svms, the annotations are propagated to the unlabeled images in the database. to perform relevance feedback in the annotation space, a probabilistic re-weighting algorithm is proposed. experimental results on a general-purpose database of 10,000 images demonstrate the potential of hidden annotation-based image retrieval and the superiority of the proposed relevance feedback algorithm over two existing algorithms.
development of a guide dog system for the blind people with character recognition ability. this paper introduces a guide dog system with character recognition ability. the main purpose of this system is to assist blind people. a guide dog helps a visually impaired person to act corresponding to the surrounding environment. however, it cannot do complex tasks such as reading words. we constructed a system that can read characters to support them. usually, character recognition systems segment character from the general background by using some information. this time, our system focuses exclusively on reading a room number. therefore, we used a method of character searching by template matching.
a novel approach for lexical noise analysis and measurement in intelligent information retrieval. latent semantic indexing (lsi) is a technique used in information retrieval (ir) as an alternative to traditional keyword matching search techniques. lsi is a preferred technique as it can cope with problems and inaccuracies that arise due to synonymy and polysemy. in this paper a new philosophy for lsi analysis and evaluation is presented based on the use of image processing tools. the term document matrix (tdm) generated in the lsi process can now be visualized and treated as an image. once in this form, techniques from image processing can be applied. the new approach has been validated and evaluated using different key performance metrics used in image processing and ir.
relationship between identification metrics: expected confusion and area under a roc curve. the mathematical relationship between the expected-confusion metric and the area under a receiver operating characteristic (roc) curve is derived. given a limited database of subjects and an identification technique that generates a feature vector per subject, expected confusion is used to predict how well the feature vector will filter identity in a larger population. related is the area under a roc curve that can be used to determine the probability ofcorrectly discriminating between subjects given the feature vector. these two measures have different connotations, but we show mathematically and verify experimentally that a simple transformation can be applied to the expected confusion to find the probability of incorrectly discriminating between subjects, which is the complement of the area under a roc curve. furthermore, we show that as a function of the number of subjects, this transformed expected-confusion measure converges more quickly than direct calculation of the area under a roc curve.
an event-based execution model for efficient image processing on workstation clusters and the grid. the event model of the functional language cml (concurrent meta language) is used to capture concurrency, which normally remains unexploited within conventional parallel geometric harnesses running on (virtual) arrays of processors. this complexity is hidden from the application programmer, who merely supplies conventional geometric sequential code which is automatically executed in parallel. an example of a low-level image filtering operation is used, to show how execution efficiency can be maintained in spite of the communication delays and indeterminacies encountered in real networks.
informational classifier fusion. classifier combination has proven itself a powerful tool for achieving high recognition rates with otherwise moderately discriminating classifiers. while progress has been made during the last decade in terms of generating powerful classifier ensembles, the actual combination process is not understood yet. in this paper, i present an information-theoretical solution to classifier combination that integrates the information conveyed by each classifier. my proposed method transforms the likelihood values of a classifier in such a way that they equal the information conveyed, without affecting its individual performance. this implicitly postulates that the elementary sum-rule performs at least as good as any other, more complex combination scheme. i evaluated my method by combining on-line and off-line japanese character recognizers, computing a considerable improvement of more than 4.5% compared to the best single recognition rate.
correspondence-free associative learning. we study the problem of learning a non-parametric mapping between two continuous spaces without having access to input-output pairs for training, but rather to groups of input-output pairs, where the correspondence structure within each group is unknown and where outliers may be present. this problem is solved by transforming each space using the channel representation, and finding a linear mapping on the transformed domain. the asymptotical behavior of the method for a large number of training samples is found to be very related to the case of known correspondences. the results are evaluated on simulated data.
dense stereo matching using kernel maximum likelihood estimation. there has been much interest, recently, in the use of bayesian formulations for solving image correspondence problems. for the two-view stereo matching problem, typical bayesian formulations model the disparity prior as a pairwise markov random field (mrf). approximate inference algorithms for mrfs, such as graph cuts or belief propagation, treat the stereo matching problem as a labelling problem yielding discrete valued disparity estimates. in this paper, we propose a novel robust bayesian formulation based on the recently proposed kernel maximum likelihood (kml) estimation framework. the proposed formulation uses probability density kernels to infer the posterior probability distribution of the disparity values. we present an efficient iterative algorithm, which uses a variational approach to form a kml estimate from the inferred distribution. the proposed algorithm yields continuous-valued disparity estimates, and is provably convergent. the proposed approach is validated on standard stereo pairs, with known sub-pixel disparity ground-truth data.
fpga based real-time visual servoing. real-time image processing tasks not only require high computing power but also high data bandwidth. though current processors excel in computing power, memory throughput is still the bottleneck for stream-oriented applications such as low-level image processing tasks. the alternative of special-purpose systems lacks flexibility at a high design effort and long development time. this effort often becomes void by the rapid advance of mainstream computing technology. fpga technology promises flexibility and the necessary computing performance at affordable design costs. in this paper we describe our approach for a prototype image processing system for robot vision applications, based on fpga technology. we use a commercially available pci-board to implement a typical application based on the experimental servicing satellite (ess) scenario.
estimation of the bayesian network architecture for object tracking in video sequences. it was recently proposed the use of bayesian networks for object tracking. bayesian networks allow to model the interaction among detected trajectories, in order to obtain a reliable object identification in the presence of occlusions. however, the architecture of the bayesian network has been defined using simple heuristic rules which fail in many cases. this paper addresses the above problem and presents a new method to estimate the network architecture from the video sequences using supervised learning techniques. experimental results are presented showing that significant performance gains (increase of accuracy and decrease of complexity) are achieved by the proposed methods.
pores and ridges: fingerprint matching using level 3 features. fingerprint friction ridge details are generally described in a hierarchical order at three levels, namely, level 1 (pattern), level 2 (minutiae points) and level 3 (pores and ridge shape). although high resolution sensors (¡«1000dpi) have become commercially available and have made it possible to reliably extract level 3 features, most automated fingerprint identification systems (afis) employ only level 1 and level 2 features. as a result, increasing the scan resolution does not provide any matching performance improvement [1]. we develop a matcher that utilizes level 3 features, including pores and ridge contours, for 1000dpi fingerprint matching. level 3 features are automatically extracted using wavelet transform and gabor filters and are locally matched using the icp algorithm. our experiments on a median-sized database show that level 3 features carry significant discriminatory information. eer values are reduced (relatively ¡«20%) when level 3 features are employed in combination with level 1 and 2 features.