waashk/dblp huggingface dataset:
finding symmetry plane of 3d face shape. the symmetry plane detection from 3d face shape is much helpful for pose estimation and feature extraction for a wide range of applications, e.g. 3d face recognition. this paper proposes a robust symmetry plane detection method for 3d face models, which is an extension of egi-based approach. the experiments using two data sets, usf humanid 3d face database and 3dfed database covering significant variation in facial expression, demonstrate the effectiveness.
on automated tongue image segmentation in chinese medicine. automated tongue image segmentation in chinese medicine is difficult due to two special factors: (1) there are a lot of pathological details on the surface of tongue, which have a large influence on edge extraction; (2) the shapes of tongue bodies captured from various diseases or persons are quite different, so they are impossible to be properly described by a predefined deformable template. to address these problems, in this paper, we propose an original technique based on the combination of a bi-elliptical deformable template and an active contour model, namely bi-elliptical deformable contour (bedc). applying our approach to clinical tongue images, the experimental results indicate that it is superior over both traditional dt (deformable templates) and acm (active contour model or snakes) with respect to stability and veracity.
structure in errors: a case study in fingerprint verification. measuring the accuracy of biometrics systems is important. accuracy estimates depend very much on the quality of the test data that are used. including poor quality data will degrade the accuracy estimates. what are the good quality data and what are the poor quality data is not revealed by simple accuracy estimates. we propose a novel methodology to analyze how the overall accuracy estimate of a system relates to the specific quality of biometrics samples. using a large collection of fingerprint samples, we present an analysis of system accuracy, which suggests that a signifi cant part of the error is due to few fingers.
mandarin emotional speech recognition based on svm and nn. the exploration of how we as human beings react to the world and interact with it and each other remains one of the greatest scientific challenges. the ability to recognize emotional states of a person perhaps the most important for successful inter-personal social interaction. automatic emotional speech recognition system can be characterized by the used features, the investigated emotional categories, the methods to collect speech utterances, the languages, and the type of classifier used in the experiments. in this paper, we used svm and nn classifiers and feature selection algorithm to classify five emotions from mandarin emotional speech and compared their experimental results. the overall experimental results reveal that the svm classifier (84.2%) outperforms than nn classifier (80.8%) and detects anger perfectly, but confuses happiness with sadness, boredom and neutral. the nn classifier achieves better performance in recognizing sadness and neutral and differentiates happiness and boredom perfectly.
quasi-invariants for human action representation and recognition. although human action ecognition has been the subject of much esearch in the past, the issue of viewpoint invariance has eceived scarce attention. in this paper, we present an approach to detect human action with a high tolerance to viewpoint change. canonical body poses are modeled in a view invariant manner to enable detection from a general viewpoint. while there exist no invariants for 3d to 2d projection, there exists a wealth of techniques in 2d invariance that can be used to advantage in 3d to 2d projection. we employ 2d invariants to recognize canonical poses of the human body leading to an effective way to represent and recognize human action which we evaluate theoretically and experimentally on 2d projections of publicly available human motion capture data.
learning prototypes and distances (lpd). a prototype reduction technique based on nearest neighbor error minimization. a prototype reduction algorithm is proposed which simultaneous train both a reduced set of prototypes and a suitable local metric for these prototypes. starting with an initial selection of a small number of prototypes, it iteratively adjusts both the position (features) of these prototypes and the corresponding local-metric weights. the resulting prototypes/metric combination minimizes a suitable estimation of the classification error probability. good performance of this algorithm is assessed through experiments with a number of benchmark data sets and through a real two-class classification task which consists of detecting human faces in unrestricted-background pictures.
ancient initial letters indexing. discrimination of images is necessary in many tasks, either understanding or indexing for example. here we are concerned by indexing. more precisely we are working about initial letters extracted from early renaissance printed documents as an application. different observation levels can be considered according to the applications, either details can be observed or more globally what could be called the style. here we are concerned with a global view of image. then we are going to present a new method to index ornamental letters in ancient books. we show how the zipf law, originally used in mono-dimensional domains can be adapted to the image domain. we use it as a model to characterize the distribution of patterns occurring in these special images that are initial letters. based on this model some new features are extracted and we show their efficiency for image indexing and retrieval.
event semantics in two-person interactions. this paper presents a method to represent two-person interactions at a semantic level with a natural language description. a human interaction is composed of two single-person actions, which in turn are made up of torso and arm/leg motions. we adopt the 'everb argument structure' in linguistics to represent human action in terms of triplets. various two-person interactions are represented at a detailed level using multiple triplets aligned along a time line according to the spatial/temporal constraints of the interactions. our method provides a user-friendly natural-language description of various human interactions, and properly describes positive, neutral, and negative interactions occurring between two persons.
efficient measurement of eye blinking under various illumination conditions for drowsiness detection systems. in this paper, we propose an efficient way of measuring the level of eye blinking under various illumination conditions (such as day and night) for drowsiness detection systems which use a single camera. determining the level of drowsiness by using eye blinking, it is an important way of detection eye positions and measuring eyelid movements. for robust eye detection under various illumination conditions, we propose a simple illumination compensation algorithm and a novel way of measuring of eyelid movements. in order to estimate the performance of the proposed methods, we collected video data during real driving situations under various illumination conditions, such as during the day and during the night. experimental results demonstrate an average eye detection rate of over 98% and an accurate measurement of eye blinking when using the proposed drowsiness detection system.
vise: visual search engine using multiple networked cameras. we propose a visual search engine (vise) as a semi-automatic component in a surveillance system using networked cameras. the vise aims to assist the monitoring operation of huge amounts of captured video streams, which tracks and finds people in the video based on their primitive features with the interaction of a human operator. we address the issues of object detection and tracking, shadow suppression and color-based recognition for the proposed system. the experimental results on a set of video data with ten subjects showed that vise retrieves correct candidates with 83% recall at 83% precision.
support vector clustering combined with spectral graph partitioning. in this paper, we propose a new support vector clustering (svc) strategy by combining (svc) with spectral graph partitioning (sgp). svc has two main steps: support vector computation and cluster labeling using adjacency matrix. spectral graph partitioning (sgp) method is applied to the adjacency matrix to determine the cluster labels. it is feasible to combine multiple adjacency matrices computed using different parameters. a novel multi-resolution combination method is proposed for cluster labeling using the sgp for the purpose of boosting the clustering performance.
enhancing low-resolution facial images using error back-projection for human identification at a distance. this paper proposes a new method of enhancing the resolution of low-resolution facial images using an error back-projection method based on a top-down learning. a face is represented by a linear combination of prototypes of shape and texture. with the shape and texture information about the pixels in a given low-resolution facial image, we can estimate optimal coefficients for a linear combination of prototypes of shape and those of texture by solving least square minimization. then high-resolution facial image can be obtained by using the optimal coefficients for linear combination of the high-resolution prototypes. in addition, an error back-projection procedure is applied to improve the accuracy of resolution enhancement.
automatic microarray image segmentation based on watershed transformation. microarrays are miniature arrays of gene fragments attached to glass chips. microarrays allow the detection of subtle differences in genome sequences so that they can be used to detect and classify genetic diseases very accurately. microarray experiments generate large amounts of data, because they allow thousands of genes to be processed in a single experiment. to obtain meaningful information from the massive microarray experimental results, it is needed to develop a fully automatic subgrid and spot segmentation algorithm which can measure the expression levels of each gene and the relative ratios of the genes in different situations without additional information or user intervention. in this paper, we used watershed transformation to get basic features of microarray images. then, a graph model was used for subgrid gridding and spot segmentation based on the watershed transformation results. to verify the efficiency of our algorithm, we compared its performance with that of two previous methods: profile and mknn(modified k nearest neighbor) algorithm. the result demonstrated the accuracy and robustness of the proposed algorithm in subgrid and spot segmentation.
superimposing 3d virtual objects using markerless tracking. this paper presents a novel methods to estimate the coordinates of a 3d object using the four vertices of a quadrangle and to track the markerless feature points. these methods are basic and important problems in augmented reality. however, many tracking solutions are dependent on fiducial markers in video or known coordinate systems which are required to superimpose virtual objects on frames. in this paper, we begin with the fact that the rectangular objects in 3d real world are projected the perspective quadrangle onto image plane through camera. we can estimate 3d object coordinates from 4 vertices of quadrangle through transformations of image coordinates. also, the markerless tracking method of 4 vertices is presented and the camera motion parameters between successive frames are calculated using epipolar geometry.
shape decomposition and skeleton extraction of character patterns. this paper proposes an approach to extract skeletons from the character patterns. it first decomposes the pattern into a set of near-convex parts and then extracts skeletons from the parts. in shape decomposition stage, the convex hull information is used to identify the splitting paths. for the skeleton extraction, an operation that ties the adjacent strokes by a knot is developed. our control procedure processes a variety of different situations of the adjacentstrokes in a systematic way.
a two-stage approach for segmentation and recognition of handwritten digit strings collected from mail pieces. in this paper, we present an approach to interpret handwritten digit strings by employing a two-stage segmentation and recognition scheme. the first stage processing is to deal with possible touching between digits. an input string is represented with an abstractive means, called primitive. candidate segmentation points are sought from the representation. to locate a boundary between digits, a recognition engine, which is rough but fast, is applied to a combined segment. the second stage recognition process, which is more accurate but slow, is applied to the combined segment depending on the recognition confidence of the first stage process. features for the segmentation and recognition are defined and described. to evaluate the effectiveness of the approach, experiments have been performed on a set of digit string images collected from real mail pieces, and promising preliminary results have been observed.
regularized patch motion estimation. this paper presents a new formulation of the problem of motion estimation which attempts to give solutions to classical problems in the field, such as detection of motion discontinuities and insufficiency of the optical flow constraint in areas with low intensity variation. an initial intensity segmentation phase partitions each frame into patches so that areas with low intensity variation are guaranteed to belong to the same patch. a parametric model is assumedto describe the motion of each patch. regularization in the motion parameter space provides the additional constraints for patches where the intensity variation is insufficient to constrain the estimation of the motion parameters and smooths the corresponding motion field. in orderto preserve motion discontinuities we use robust functions as a regularization mean. experimental results show that the proposed method deals successfully with motions large in magnitude, motion discontinuities and produces accurate piecewise smooth motion fields.
pose estimation for central catadioptric systems: an analytical approach. the number of applications for central catadioptric systems, often called central panoramic systems, are increasing. among these are surveillance systems, commercial systems for web navigation and robot localization and navigation. this works includes itself in the last case, and presents an analytical method for pose estimation using any central catadioptric system. any known four points in the world, forming a planar rectangle, may be used for pose computation. the computed pose includes the rotation and the translation from the camera coordinate system to the world coordinate system.
non-linear local harmonic filters for edge-preserving image denoising. we propose a new class of non-linear image recovery operators based on local smoothing applied both in the spatial domain (horizontal smoothing) and in the gray-level domain (vertical smoothing). the commonly used horizontal smoothing gives the way of reducing the noise level (noise filtering), whereas the vertical smoothing yields the edge preservation property. the method is indexed by a real parameter yielding a generalized class of local harmonic filters. in particular the classical min/max filters as well as all linear locally weighted filters can be obtained as special cases.
fast atomic decomposition by the inhibition method. a new algorithm is introduced which is related to matching pursuit but allows updating more than one coding coefficient per iteration: the updated coefficients correspond to mutually orthogonal elements of the dictionary. coding experiments on natural images show that the new method achieves the same trade-off as matching pursuit between number of coding coefficients and reconstruction error, but significantly faster convergence.
novel error concealment method with adaptive prediction to the abrupt and gradual scene changes. in this paper, the impact of the scene change on the conventional error concealment method is addressed and a novel error concealment method is proposed to improve the insufficiency of conventional temporal error concealment algorithm due to the occurrence of scene change. combining with the low complexity scene change detection algorithm using macroblock type information, the corrupt blocks resulting from bit errors are concealed either temporally or spatially depending on whether or not an abrupt scene change is found. in the case of gradual scene change, a novel error concealment method of interpolation and extrapolation is proposed to utilize the linear property of gradual scene change sequence, and effectively reduce the concealment error in comparison with the conventional algorithm. great improvement about 3 to 5 db psnr is obtained with only little overhead memory and computation.
a method for ir point target detection based on spatial-temporal bilateral filter. the problem of detecting and locating point targets in cluttered and noised ir (infrared) images is a difficult and important task in ir applications. in this paper, we proposed a new method for detecting dim moving point target in ir image sequence. the significance of this work is that the ideal of bilateral filtering is adopted, which leads to a simple detection. in order to make use of the temporal information of ir sequence, we extended the conventional 2d bilateral filter to the 3d spatial-temporal bilateral filter, which can remove noise efficiently and enhance the contrast between targets and background. the filter can finish the preprocessing job within three frames, which is important for real-time application for fast moving target detection. the limited qualitative results in the experiments demonstrate that the new method can produce an accurate result.
hiding multiple data in color images by histogram modification. in this paper, we present a new histogram-based data-hiding algorithm that secret data is embedded in the least significant bit of the histogram value. to change the pixel value, it will alter the histogram to accomplish data-hiding work. in the proposed algorithm, it is able to perform data hiding on the one-dimension histogram, two-dimension histogram map and three-dimension histogram cube. besides, the multiple secret data hiding in various combinations of histogram spaces are successfully demonstrated in our experimental results. in addition, the natural and limited color images are tested in our experiments.
prototype selection for finding efficient representations of dissimilarity data. the nearest neighbor (n n) ru le is a simple and intuitive method for solving classification problems. originally, it uses distances to the complete training set. it performs well, however, it is sensitive to noisy objects, due to its operation on local neighborhoods only . a more global approach is possible by mapping the distance data on to a pseudo-euclidean space, such that the distances are preserved as well as possible. then, a classifier built in such a space can out perform the nn rule. however, again all objects from th e training set are used for a projection of new data.this paper addresses the issue of reducing the training set while possibly preserving the original structure of the mapped data. some criteria are introduced and evaluated against two problems, polygon recognition and digit recognition. our experiments show that the representation mismatch criterion is beneficial for the applications considered. moreover, the linear classifier built in the pseudo-euclidean space, determined by 20% -25% of the training objects, outperforms the nn rule based on all of them.
dissimilarity-based classification for vectorial representations. general dissimilarity-based learning approaches have been proposed for dissimilarity data sets [11, 10]. they arise in problems in which direct comparisons of objects are made, e.g. by computing pairwise distances between images, spectra, graphs or strings. in this paper, we study under which circumstances such dissimilarity-based techniques can be used for deriving classifiers in feature vector spaces. we will show that such classifiers perform comparably or better than the nearest neighbor rule based either on the entire or condensed training set. moreover, they can be beneficial for highlyoverlapping classes and for non-normally distributed data sets, with categorical, mixed or otherwise difficult features.
adaptive kernel metric nearest neighbor classification. nearest neighbor classification assumes locally constant class conditional probabilities. this assumption becomes invalid in high dimensions due to the curse-of-dimensionality. severe bias can be introduced under these conditions when using the nearest neighbor rule. we propose an adaptive nearest neighbor classification method to try to minimize bias. we use quasiconformal transformed kernels to compute neighborhoods over which the class probabilities tend to be more homogeneous. as a result, better classification performance can be expected. the efficacy of our method is validated and compared against other competing techniques using a variety of data sets.
a new efficient svm-based image registration method. a frequently felt difficulty with image registration is the lack of guiding rules to choose a model for unknown geometric distortion. previous work has concentrated on the use of certain model of mapping function to deal with arbitrarily structured data. the performance of such technique may deteriorate if the model is not well. we consider a general case where a set of models is trained in advance, instead of using one model to register images directly. this technique can find an optimal model for particular deformation. moreover, central to our approach is that it constitutes a practical implementation of the structural risk minimization principle (srm) that aims at minimizing a bound on the generalization error of a model, rather than minimizing the mean square error over control points.
images similarity detection based on directional gradient angular histogram. in this paper, an images similarity measure based on directional gradient angular histogram (dgah) is proposed. analysis and experimental results reveal that the proposed technique not only acquires a superior detection performance, but also the invariance to illumination changes and the robustness to translation, scale and rotation of the image object. in addition, the proposed technique is easily integrated within a wavelet-based image coder since the angular histogram features can be constructed directly on the wavelet coefficients decomposed using anti-symmetrical bi-orthogonal wavelets (asbw).
a computational model of social signalin. i have proposed that unconscious voice, face, hand, and body gestures form a motion texture that convey social signals, and that these signals are an important determinant of human behavior [1]. in this paper i will describe the theoretical and computational framework that i have developed for measuring social signaling, and survey the results obtained using this computational model for the perception of social displays.