waashk/dblp huggingface dataset:
representing and recognizing complete set of geons using extended superquadrics. in this paper, we take the advantages of extended superquadrics to represent and recognize the entire set of 36 geons. extended superquadrics are novel volumetric shape models that include superquadrics as a special case. an extended superquadric model can be deformed in any direction because it extends the exponents of the superquadric model from constants to functions of the latitude and longitude angles in the spherical coordinate system. thirteen features derived from the extended superquadric parameters are recovered in order to distinguish between all the 36 geon classes. classification error rates are estimated for the nearest neighbor classifier and the back-propagation neural network. both simulated data (at different noise levels) and real geon models are tested in our experiments. the results are very encouraging and has significant benefits for an object recognition system.
estimation of the size and location of multiple area light sources. most illuminant estimation algorithms worked on point light sources or directional light sources. little attempt has been made, however, to estimate area light sources. in this paper, we present a novel scheme that estimates the size and location of multiple area light sources using a set of stereo images of a sphere with a shiny surface. the parameters of the area light source are estimated by a novel algorithm which minimizes the matching error between the corresponding specular patches. experiments on real images show that our method is accurate and robust in estimating the parameters of the area light source.
the pattern classification based on the nearest feature midpoints. in this paper, we propose a novel method, called the nearest feature midpoint (nfm), for pattern classification. any two feature points of the same class are generalized by the feature midpoint (fm) between them. the representational capacity of available prototypes is thus expanded. the classification is based on the nearest distance from the query feature point to each fm. a theoretical proof is provided in this paper to show that for the n-dimensional gaussian distribution, the classification based on the nfm distance metric will achieve the least error probability as compared to those based on any other points on the feature lines. furthermore, a theoretical investigation indicates that under some assumption the nfl is approximately equivalent to the nfm when the dimension of the feature space is high. the empirical evaluation on a simulated data set concurs with all the theoretical investigations.
unusual event detection via multi-camera video mining. this paper describes a framework for detecting unusual events in surveillance videos. most surveillance systems consist of multiple video streams, but traditional event detection systems treat individual video streams independently or combine them in the feature extraction level through geometric reconstruction. our framework combines multiple video streams in the inference level, with a coupled hidden markov model (chmm).we use two-stage training to bootstrap a set of usual events, and train a chmm over the set. by thresholding the likelihood of a test segment being generated by the model, we build a unusual event detector. we evaluate the performance of our detector through qualitative and quantitative experiments on two sets of real world videos.
rectification with intersecting optical axes for stereoscopic visualization. there exist various methods for stereoscopic viewing of images, most requiring some special glasses for controlling what goes to the left and the right eyes of the viewer. recent technology developments have resulted in displays that enable 3d viewing without glasses. however, these displays demand a true stereo pair as the input, which greatly limits their practical use, as true stereoscopic media are scarce. in our recent work [6], we developed a systematic approach to automatic rectification of two images of the same scene captured by cameras at general positions, so that the results can be viewed on a 3d display. however, the approach cannot work well for large camera displacement (i.e., very wide baseline). in this paper, we propose a new rectification scheme to address this wise baseline rectification problem, with the basic idea of using a special stereo setup with intersecting optical axes,. in a sense, the idea mimics human vision when viewing objects close to the eyes. experiments with a 3d display demonstrate the feasibility and effectiveness of the proposed approach.
flag guided integration of multiple registered range images. integration of multiple registered range images finds applications in numerous areas. while most existing integration algorithms detect corresponding points between neighbouring views based on interpoint distances, we define in this paper corresponding area for each point and then carry out geometrical, rigidity, and orientation tests for the same purpose, not only reflecting the scanning nature of sampling resolution, but also the geometry of object surface. to more accurately fuse corresponding points, while most existing algorithms consider the confidence of points, we consider an accumulative fusion scheme of confidences, reflecting the number of times of observation. the fused points are finally triangulated using the improved delaunay triangulation method, guaranteeing a watertight surface. a comparative study based on real images shows that the proposed algorithm is accurate and efficient and is able to compensate both the accumulative registration errors and imaging noise.
symmetric pixel-group based stereo matching for occlusion handling. in this paper, we propose a symmetric pixel-group stereo model for handling occlusion in a segment-based style. firstly, both images are segmented based on color, disparity, and the segments of the other image sequencely. then the uniqueness constraint is embodied in pixel-group level. finally, a symmetric belief propagation (bp) optimization framework is used to find correspondence and occlusions simultaneously. results obtained for benchmark indicate that the proposed method is able to compete with the state-of-the-art algorithms.
face verification using gaborwavelets and adaboost. this paper presents a new face verification algorithm based on gabor wavelets and adaboost. in the algorithm, faces are represented by gabor wavelet features generated by gabor wavelet transform. gabor wavelets with 5 scales and 8 orientations are chosen to form a family of gabor wavelets. by convolving face images with these 40 gabor wavelets, the original images are transformed into magnitude response images of gabor wavelet features. the adaboost algorithm selects a small set of significant features from the pool of the gabor wavelet features. each feature is the basis for a weak classifier which is trained with face images taken from the xm2vts database. the feature with the lowest classification error is selected in each iteration of the adaboost operation. we also address issues regarding computational costs in feature selection with adaboost. a support vector machine (svm) is trained with examples of 20 features, and the results have shown a low false positive rate and a low classification error rate in face verification.
tracking periodic motion using bayesian estimation. this paper presents a bayesian approach to achieve efficient and accurate motion tracking in monocular image sequences. we first extract a deterministic motion model with six degrees of freedom in an on-line learning phase. this is followed by predicting the image points in successive frames, and achieving correspondence in the context of monte carlo estimation. meanwhile, the motion parameters of the camera are simultaneously estimated. the experimental results show that the stable and accurate ego-motion parameters can be obtained.
face recognition by combining several algorithms. there are many algorithms for human face recognition proposed in recent years. here, we will study how to combine these algorithms to perform better. a fusion framework combining the existing recognition algorithms based on support vector machine is developed. the experimental results demonstrate its effectiveness.
compound stochastic models for fingerprint individuality. the question of fingerprint individuality can be posed as follows: given a query fingerprint, what is the probability that the observed number of minutiae matches with a template fingerprint is purely due to chance? an assessment of this probability can be made by estimating the variability inherent in fingerprint minutiae. we develop a compound stochastic model that is able to capture three main sources of minutiae variability in actual fingerprint databases. the compound stochastic models are used to synthesize realizations of minutiae matches from which numerical estimates of fingerprint individuality can be derived. experiments on the fvc2002db1 and ibm hursley databases show that the probability of obtaining a 12 minutiae match purely due to chance is 1.6×10-5 when the number of minutiae in the query and template fingerprints are both 46.
comparing decision boundary curvature. classifier decision boundary is important for classification. by studying properties of decision boundary, we can predict the classifier's performance. an algorithm of comparing classifiers' decision boundary curvature is introduced in this paper. geometrical methods are used to carry out the comparison. this comparison can be used to measure difference between classifiers which is useful for designing multiple classifier systems. the effectiveness of this comparison is confirmed by experiments.
3d face pose tracking from an uncalibrated monocular camera. we propose a new near-real time technique for 3d face pose tracking from a monocular image sequence obtained from an uncalibrated camera. the basic idea behind our approach is that instead of treating 2d face detection and 3d face pose estimation separately, we perform simultaneous 2d face detection and 3d face pose tracking. specifically, 3d face pose at a time instant is constrained by the face dynamicsusing kalman filtering and by the face appearance in the image. the use of kalman filtering limits possible 3d face poses to a small range while the best matching betweenthe actual face image and the projected face image allows to pinpoint the exact 3d face pose.face matching is formulated as an optimization problem so that the exact face location and 3d face pose can be estimated e ciently. another major feature of our approach lies in the use of active ir illumination, which allows to robustly detect eyes. the detected eyes can in turn constrain the face in the imageand regularize the 3d face pose, therefore the tracking drift issue can be avoided and the processing can speedup. finally, the face model is dynamically updated to account for variations in face appearances caused by face pose, face expression, illumination and the combination of them.
robust pose invariant facial feature detection and tracking in real-time. in this paper, a robust technique is proposed to detect and track a set of twenty-eight prominent facial features under various facial expressions and face orientations in real-time. specifically, after the face image is captured from the camera, a trained face mesh is first employed to estimate a rough position for each facial feature based on the located eye positions. subsequently, an accurate position is obtained for each facial feature by searching around its roughly estimated position. once the facial features are located, by using the appearance information of each facial feature together with the geometry information among the facial features, a shape-constrained correction-based tracking mechanism is activated to track them in the subsequent image frames. finally, the performance of the proposed technique is demonstrated through building a real-time facial feature tracking system that can detect and track a set of twenty-eight facial features automatically as soon as a person is sitting in front of the camera.
nonlinear eye gaze mapping function estimation via support vector regression. we propose a novel method for tracking eye gaze that allows natural head movement. most existing remote eye gaze trackers cannot work under natural head movement due to the difficulty of building a gaze mapping function that can incorporate head motion information. therefore, the user is required to hold his/her head unnaturally still, possibly with the use of chin-rest. in addition, before each usage of the tracking system, a cumbersome calibration procedure must be performed to obtain a gaze mapping function. our proposed method significantly improves the conventional pupil center corneal refection (pccr) technique to permit natural head movement and to minimize calibration. support vector regression (svr) is used to construct a highly nonlinear generalized gaze mapping function that accounts for head movement. as the head moves naturally in front of the camera, the associated gaze mapping function with each new head position will be obtained automatically by the learned generalized gaze mapping function. once learned, the generalized gaze mapping function can be used by other users via a simple personal adaptation without retraining. experiments for multiple users show that eye gaze can be estimated accurately under natural head movement via the proposed technique.
key-based melody segmentation for popular songs. in music theory, a key specifies the tonal structure of a music piece. popular songs are mostly composed using particular music keys. it is common that the key changes at some point of the piece for an emotional uplifting effect. the change is usually in pitch of the tonic rather than in key style. automatically segmenting melody based on the key changes can facilitate content-based music retrieval. little work has been done for this problem. in this paper, we present a novel approach for detecting multiple keys and locating the key boundaries in the melody of popular songs in midi format. a tonality analysis of the melody using diatonic scale model first extracts overlapping segments of the melody that each conforms to the tonal structure of a single key. a modality (key style) analysis then determines the center mode of the melody based on the modes of the extracted melody segments. the segments of unrelated modes are then eliminated. and finally the keys and the key boundaries are determined by grouping remaining segments according to the pitch of the tonic. the proposed method is not confined to the two key styles, major and minor, as the previous techniques. thus this method is effective for detecting key changes and insensitive to the presence of accidental notes. experimental results on 50 randomly selected midi songs have demonstrated the performance of the proposed method.
car detection based on multi-cues integration. in this paper we present a novel fast multi-cues based car detection technique in still outdoor images. on the bottom level, two novel area templates based on edge cue and interest points cue are first designed, which can rapidly reject most of the non-car sub-windows at the cost of missing few of the car sub-windows. on the top level, both global structure cue and local texture cue are considered. to character the global structure property the odd gabor moments are introduced and trained by svms. the multi channels even gabor based local texture property extracted from corner area is modeled as a gaussian distribution. the final experiment results show that the integration of global structure property and local texture property is more powerful in discrimination between car and non-car objects and a high detection accurate 93% is obtained.
a landweber algorithm for 3d confocal microscopy restoration. a new landweber algorithm for 3d microscopy deconvolution is introduced in this paper. the algorithm is formulated from the fredholm equation of the first kind. artificial 3d images are used to test this algorithm and the restored results are compared with a nonlinear iterative de-convolution algorithm (ida). the experimental results show that the landweber algorithm can effectively suppress background noise and remove asymmetric point spread function (psf) degradation. finally, a typical real 3d confocal image is restored by the landweber algorithm and the results are compared with ida.
a robust regularised restoration algorithm based on topkis-veinott optimisation method. we present a robust iterative algorithm for restoration of blurred and noisy images. the restoration model is first regularised and then solved iteratively. an optimal regularisation parameter is estimated using the topkis-veinott gradient method. this restoration algorithm is compared with other competitive restoration algorithms in the literature. experimental results are presented to demonstrate the comparative important features of our algorithm, namely, robustness in the presence of noise and high quality restoration results.
scalable representative instance selection and ranking. finding a small set of representative instances for large datasets can bring various benefits to data mining practitioners so they can (1) build a learner superior to the one constructed from the whole massive data; and (2) avoid working on the whole original dataset all the time. we propose in this paper a scalable representative instance selection and ranking (sristar pronounced 3star) mechanism, which carries two unique features: (1) it provides a representative instance ranking list, so that users can always select instances from the top to the bottom, based on the number of examples they prefer; and (2) it investigates the behaviors of the underlying examples for instance selection, and the selection procedure tries to optimize the expected future error. given a dataset, we first cluster instances into small data cells, each of which consists of instances with similar behaviors. then we progressively evaluate data cells and their combinations, and order them into a list such that the learners built from the top cells are more accurate.
gmm-based classification method for continuous prediction in brain-computer interface. brain-computer interface (bci) requires effective classification algorithms for electroencephalogram (eeg) signal processing. to train a classifier for continuous prediction, trials in training dataset are first divided into segments. the difficulty here is how to combine the predictions across time to make the final decision of a whole trial as early and as accurately as possible. in this paper, we propose a novel statistical approach based on gaussian mixture models (gmm) to classify the eeg trials by combining the predictions of segments according to the discriminative powers at individual time intervals during a trial. we evaluate the proposed method on two datasets of bci competition 2003 and 2005. the experimental results have shown that the performance of the proposed method is among the best.
document image binarization based on stroke enhancement. this paper proposes a novel document image binarization approach based on stroke neighborhood enhancement. first, foreground pixels are initially labeled. then, the strokes are enhanced based on their neighborhood information which include gradient information and foreground-background pixel distances of foreground pixels and their neighboring background pixels. at last, the enhanced image is finally binarized. this approach can provide good result for those document images suffering from lighting variance, low resolution and blurring.
action recognition in broadcast tennis video. motion analysis in broadcast sports video is a challenging problem especially for player action recognition due to the low resolution of players in the frames. this paper presents a novel approach to recognize the basic player actions in broadcast tennis video where the player is about 30 pixels tall. two research challenges, motion representation and action recognition, are addressed. a new motion descriptor, which is a group of histograms based on optical flow, is proposed for motion representation. the optical flow here is treated as spatial pattern of noisy measurement instead of precise pixel displacement. to recognize the action performed by the player, support vector machine is employed to train the classifier where the concatenation of histograms is formed as the input features. the experimental results demonstrate that our method is promising.
a new textual/non-textual classifier for document skew correction. a robust approach is proposed for document skew detection. we use fourier analysis and svm to classify textual areas from non-textual areas of documents. we also propose a robust method to determine the skew angle from textual areas. our approach achieves good performance on documents with large area of non-textual contents.
a digital watermarking algorithm and implementation based on improved svd. an improved digital watermarking algorithm based on block-svd is put forward, which has better robustness. it can deal with the rectangle matrices directly and can extract better-quality watermarks. it takes little time to embed and extract the watermark in large images and this method can avoid some disadvantages such as the distortion caused by the computing error when extracting the watermark in the diagonal direction. robustness testing results and time comparing analysis between svd and block-svd are presented in this paper. compared with traditional cox method, this algorithm has better robustness, faster speed and is more practical.
optimizing the integration of a statistical language model in hmm based o.ine handwritten text recognition. although handwritten text recognition has been studied for some years, only few authors have used statistical language models to increase the performance of their recognizers. in those few cases where a language model has been used, its integration has not been systematically optimized. in this paper we investigate the optimization ofthe integration of statistical language models into hmm based recognition systems for offline handwritten text. based on experiments with the iam database we show that the recognition performance of a general offline handwritten text recognizer can be substantially improved.
phoneme segmentation of speech. in most approaches to speech recognition, the speech signals are segmented using constant-time segmentation, for example into 25 ms blocks. constant segmentation risks losing information about the phonemes. different sounds may be merged into single blocks and individual phonemes lost completely. a more satisfactory approach is to attempt to segment the phoneme boundaries from the speech signals and use these boundaries to define blocks. the discrete wavelet transform (dwt) is interesting in the analysis of speech since it is easy to extract parameters which take into account the properties of the human hearing system. the analysis of the power in different frequency bands offers potential for distinguishing the start and end of phonemes. for many boundaries, there is no discernible drop in overall power, and at some frequencies, the power is broadly constant over the lifetime of the phoneme. however, many phonemes exhibit rapid changes in particular subbands which can be used to detect their start and endpoints. in this paper we apply the dwt to speech signals and analyse the resulting power spectrum and its derivatives to locate candidates for the boundaries of phonemes in continuous speech. we compare the results with hand segmentation and constant segmentation over a number of words. the method proves effective for finding most phoneme boundaries.