waashk/dblp huggingface dataset:
background learning for robust face recognition. in this paper, we propose a robust face recognition technique based on the principle of eigenfaces. the traditional eigenface recognition (efr) method works quite well when the input test patterns are cropped faces. however, when confronted with recognizing faces embedded in arbitrary backgrounds, the efr method fails to discriminate effectively between faces and background patterns, giving rise to many false alarms. in order to improve robustness in the presence of background, we argue in favor of learning the distribution of background patterns. a background space is constructed from the background patterns and this space together with the face space is used for recognizing faces. the proposed method outperforms the traditional efr technique and gives very good results even on complicatedscenes.
spatial texture analysis: a comparative study. in this paper we compare some of the traditional, and some fairly new techniques of texture analysis on the meastex and vistex benchmarks to illustrate their relative abilities. the methods considered include autocorrelation (acf), co-occurrence matrices (cm), edge frequency (ef), law's masks (lm), run length (rl), binary stack method (bsm), texture operators (to ), and texture spectrum (ts). in addition, we illustrate the advantage of using feature selection on a combined set that improves the overall recognition performance.
a comparison of image enhancement techniques for explosive detection. the enhancement of images plays a crucial role in the detection of illicit objects from airport luggage. on the basis of 11 statistical measures of image viewability, we compare both new and old methods of image enhancement for aviation security application. our research shows that some of the newer methods of enhancement consistently outperform most others that are regularly used at airports on measures of viewability, which supports our argument that airport security consoles must be updated with our suggested enhancement techniques.
feature selection for face recognition based on data partitioning. feature selection is an important consideration in several applications where one needs to choose a smaller subset of features from a complete set of raw measurements such that the improved subset generates as good or better classification performance compared to original data. in this paper, we describe a novel feature selection approach that is based on the estimation of classification complexity though data partitioning. this approach allows us to select the n best features from a given set in order of their ability to separate data from different classes. in this paper, we perform our experiments on the orlface database thatconsists of 400 images. the results show that the proposed approach outperforms the probability distance approach and is a viable method for implementing more advanced search methods of feature selection.
synchronization and calibration of camera networks from silhouettes. we propose an automatic approach to synchronize a network of uncalibrated and unsynchronized video cameras, and recover the complete calibration of all these cameras. in this paper, we extend recent work on computing the epipolar geometry from dynamic silhouettes, to deal with unsynchronized sequences and find the temporal offset between them. this is used to compute the fundamental matrices and the temporal offsets between many view-pairs in the network. knowing the time-shifts between enough view-pairs allows us to robustly synchronize the whole network. the calibration of all the cameras is recovered from these fundamental matrices. the dynamic shape of the object can then be recovered using a visual-hull algorithm. our method is especially useful for multi-camera shape-from-silhouette systems, as visual hulls can now be reconstructed without the need for a specific calibration session.
fast color image quantization using squared euclidean distance of adjacent color points along the highest color variance axis. a new color image quantization algorithm that uses the squared euclidean distance of adjacent color points along the highest color variance axis is proposed. this algorithm is a hierarchically divisive colormap design technique. colors are sorted along the axis with the highest variance of color distribution. the squared euclidean distances between any adjacent colors' along the axis are then used to find the cutting plane that divides a color cell into two subcells with approximately equal quantization errors respect to their centroids. the proposed algorithm is effective and yields a short execution time.
recognizing expressions in a new database containing played and natural expressions. we describe a new expression database which contains video sequences of both played and natural expressions and an expression classification system based on warped optical flow fields and texture features. we analyze the system's generalization performance when confronted with subjects that were not present in the training set and its recognition performance when tested on natural expressions. we evaluate several techniques for combining the classifier outputs computed on single images to perform classification of a temporal sequence of expression images.
evaluation of nine similarity measures used in rigid registration. because image registration is carried out by optimising a criterion function or similarity measure, the behaviour of a similarity measure greatly influences the final result of registration. in this paper we describe a protocol to quantitatively evaluate the behaviour of similarity measures. registered mr/ct images of the spine and mr/mr brain images were used to evaluate nine similarity measures. besides, the effects of binning and noise on the similarity measures were studied. the normalised and non-normalised mutual information were the two measures that performed the best.
defuzzification of discrete objects by optimizing area and perimeter similarity. we present a defuzzification method which produces a crisp digital object starting from a fuzzy digital one, while keeping selected properties of them as similar as possible. our main focus is on defuzzification based on the invariance of perimeter and area measures while taking into account with the membership values. we perform a similarity optimization procedure using on a region growing approach to obtain a crisp object with the desired properties.
ranklets: orientation selective non-parametric features applied to face detection. we introduce a family of multiscale, orientation-selective, non-parametric features ("ranklets") modelled on haar wavelets. we clarify their relation to the wilcoxon rank-sum test and the rank transfor and provide an efficient scheme for computation based on the mann-whitney statistics. finally, we show that ranklets outperform other rank features, haar wavelets, snow and linear svms (based on independently published results) in face detection experiments over the 24'045 test images in the mit-cbcl database.
estimating cast shadows using sfs and class-based surface completion. this paper describes a method for cast shadow removal from obliquely illuminated images of faces. the method draws on a statistical model of surface normal directions. the model is fitted to shadowed facial images using robust statistics and constraints provided by shape-from-shading. regions associated with poor fit residuals are associated with shadow regions. we illustrate the method on the yale b database where it gives both good shadow map estimates and fills-in the facial surface in the shadow regions.
face recognition using angular lda and svm ensembles. one successful approach to feature extraction in face recognition problems is that of linear discriminant analysis (lda). we examine an extension of this technique, called angular lda, in which a non-linear transformation is applied after the lda representation has been determined. we present experimental evidence, using the xm2vts face database, that an ensemble of svm classifiers operating in the angular lda space is capable of making more accurate face verification and identification decisions than the same classifiers operating in the standard lda space. we also compare experimentally the relative effectiveness of a number of techniques for ensemble design, ensemble decoding metric and svm calibration algorithm.
measurement function design for visual tracking applications. extracting human postural information from video sequences has proved a difficult research question. the most successful approaches to date have been based on particle filtering, whereby the underlying probability distribution is approximated by a set of particles. the shape of the underlying observational probability distribution plays a significant role in determining the success, both accuracy and efficiency, of any visual tracker. in this paper we compare approaches used by other authors and present a cost path approach which is commonly used in image segmentation problems, however is currently not widely used in tracking applications.
skin reflectance modelling for face recognition. we present a parameter-free method for estimating the brdf of a subject's skin from a single image. we show how the technique can be used for photometric correction as a preprocessing step for face analysis tasks, and show its application to graphics by re-rendering faces with different skin reflectance models.
on the fast modification of the vector median filter. a new filtering approach designed to eliminate impulsive noise in color images, while preserving fine image details is presented in this paper. the comparison shows that the newfilter outperforms the vmf, as well as other standard procedures used in color image processing for the elimination of impulsive noise.
watershed lines suppression by waterfall marker improvement and line-neighbourhood analysis. in this paper we present a new self-sufficient image segmentation method based on watershed-lines neighbourhood. this work-study uses two morphological concepts, watershed and waterfall, for the development of a line-based catalogue concerning neighbourhood analysis. innovation in waterfall hierarchical evolution is achieved with a marker-constraint implementation along the process, improving detail suppression. watershed lines are than separated into multiple semi-lines for a further line suppression by semi-lines neighbourhood analysis. in many cases, unless a feature has a high thinness, different from image background (i.e., a thin road, in which case can be defined by a single watershed line), its representation is a set of watershed-connected lines that makes ambiguous feature-line detection for recognition of image structures. applying watershed to the morphological gradient image, feature contouring is better characterized or waterfall application.
inter-stage feature propagation in cascade building with adaboost. a modification of the cascaded detector with the ada-boost trained stage classifiers is proposed and brought to bear on the face detection problem. the cascaded detector is a sequential classifier with the ability of early rejection of easy samples. each decision in the sequence is made by a separately trained classifier, a stage classifier. in proposed modification the features from one stage of training are propagated to the next stage classifier. the proposed intra-stage feature propagation is shown to be greedily optimal, does not increase computational complexity of the stage classifier and leads to shorter stage classifiers and accordingly to faster detectors. a cascaded face detector is built with the intra-stage feature propagation and is compared with the viola and jones approach. the same detection and false positive rates are achieved with a detector that is 25% faster and consists of only two thirds of the weak classifiers needed for a cascade trained by the viola and jones approach. the latter property facilitates hardware implementation, the former opens scope for the increase in the search space, e.g. the range of scales at which faces are sought.
a comparative analysis of face recognition performance with visible and thermal infrared imagery. we present a comprehensive performance analysis of multiple appearance-based face recognition methodologies, on visible and thermal infrared imagery. we compare algorithms within and between modalities in terms of recognition performance, false alarm rates and requirements to achieve specified performance levels. the effect of illumination conditions on recognition performance is emphasized, as it underlines the relative advantage of radiometrically calibrated thermal imagery for face recognition.
thermal face recognition over time. we present a comparative study of face recognition performance with visible and thermal infrared imagery, emphasizing the influence of time-lapse between enrollment and testing images. most previous research in this area, with few exceptions, focused on results obtained when enrollment and testing images were acquired in the same session. we show that the performance difference between visible and thermal recognition in a time-lapse scenario is smaller than previously believed, and in fact is not statistically significant on existing data sets.
fuzzy direction field method for fringe and tree-like patterns analysis. the problem of geometrical structure detection and measurements for fringe patterns and tree-like objects images is considered. we introduce the notion of a local-structure function that generalizes the concepts of direction and spatial frequency fields and describes local geometrical properties of the images under study. it can be considered as a fuzzy set of directions and spatial frequencies in every point of image. on basis of fuzzy sets operations we develop a number of general-purpose image analyzing algorithms that have parametrical tuning on image type. we have shown experimentally that this approach allows us to analyze efficiently various technical and medical diagnostic images of specified classes, such as interference patterns, objects registered in structural light, blood vessels images, and so on.
geodesic curves for analysis of continuous implicit shapes. a method is proposed for performing shape analysis of m-surfaces, e.g. planar curves and surfaces, with a geometric interpretation. the analysis uses an implicit surface representation and connects the popular level set approach with shape analysis. the representation is continuous and completely landmark-free. shapes are represented as points on an infinite-dimensional manifold and the distance between two surfaces is given by the length of a path on this manifold. the analysis is valid in any dimension and examples of applications such as interpolation and clustering are given.
estimating surface shape and extending known structure using specular reflections . in this paper a method for shape estimation and structure extension of a surface using information from specularities is proposed. the structure of the scene is obtained from an image sequence using standard structure and motion techniques. if there are specularities in the scene, there may be parts where the scene geometry can not be determined. the method proposed in this paper extends the known structure over these regions. methods for determining the light source position are introduced and together with the reflections, this gives constraints on the surface normals. these constraints are complemented by a smoothness condition in order to estimate the surface shape unambiguously. the viability of the proposed method is demonstrated in experiments with real data from image sequences.
initialization techniques for segmentation with the chan-vese model. this paper introduces an effective initialization approach for segmentation using the chan-vese model. the initial curve is found by searching among the extremals of the fidelity term, as a form of intelligent thresholding where the regularity of the threshold level is incorporated. the method has a nice connection to the curvature of the optimal initial partition boundary. the method is tested on several examples and gives considerable increase in performance.
dynamic foveation model for video compression. there are some techniques of video compression based on the foveation image coding and processing algorithms. one of the most frequently asked questions is: "how do we know the foveation points?" in this paper a computational model of automatic localization of the foveation point is proposed. till now, except for a few cases, the research related to foveation points determination has mainly concerned with the analysis of static images. the proposed model, on the contrary, simulates the gaze shifting of a human observer when the scene in development is dynamic. through the selective attention the most relevant part of an image is selected, the so called "focus of attention" (foa).
edge color distribution transform: an efficient tool for objectdetection in images. object detection in images is a fundamental task in many image analysis applications. existing methods for low-level object detection always perform the color-similarity analyses in the 2d image space. however, the crowded edges of different objects make the detection complex and error-prone. this paper proposes to detect objects in a new edge color distribution space (ecds) rather than in the image space. in the 3d ecds, the edges of different objects are segregated and the spatial relation of a same object is kept as well, which make the object detection easier and less error-prone. since uniform-color objects and textured objects have different distribution characteristics in ecds, this paper gives a 3d edge-tracking algorithm for the former and a cuboid-growing algorithm for the latter. the detection results are correct and noise-free, so they are suitable for the high-level object detection. the experimental results on a synthetic image and a real-life image are included.
a new approach for line recognition in large-size images using hough transform. the application of hough transform (ht) has been limited to small-size images for a long time. for large-size images, the peak detection and the line verification become much more time-consuming. many ht-based line detection methods are not able to detect line width. thispaper proposes a new approach for detecting line segments using ht, which makes ht applicable to large-size images, especially for those applications whose line width is critical. our approach applies a boundary recorder to eliminate redundant analyses, and employs an image-analysis-based line-verification method to overcome the difficulty of using a threshold to distinguish short lines from noise. it avoids the overlapping lines by removing the pixels of detected line segments, which is more robust than only clearing the n × n neighborhood. this approach could be easily extended to improved ht methods that perform the global accumulation. the experimental result shows that this approach is very time-efficient for large-size images.
graphics recognition from binary images: one step or two steps. recognizing graphic objects from binary images is an important task in many real-life applications. generally, there are two ways to do the graphics recognition: one-step methods and two-step methods. the former recognizes graphic objects from binary images directly,while the latter consists of vectorization and postprocessing. neither of them is perfect enough to handle all difficulties. this paper first reviews popular graphics recognition methods to understand their advantages and disadvantages. next, the performance comparison between two classes of methods is made in two important aspects: the time efficiency and the graphics quality, and the experimental results of time-efficiency comparison of 7 popular methods are also reported. finally, we propose a new hybrid graphics-recognition paradigm to integrate the advantages of both one-step methods and two-step methods and minimize their disadvantages. the proposed paradigm is capable of recognizing straight lines, arcs, circles and curves efficiently, and is helpful for extracting text images in text-graphics touching cases.
simultaneous gesture segmentation and recognition based on forward spotting accumulative hmms. in this paper, we propose a forward spotting scheme that executes gesture segmentation and recognition simultaneously by detecting start point. by using competitive differential observation probability, sliding window and accumulative hmms, we apply the proposed method to recognize the upper-body gestures for controlling the curtains and lights in a smart home environment.
combined face-body tracking in indoor environment. background subtraction is commonly used for tracking objects in outdoor environment. but it doesn't work that well indoors, because of the problems caused by illumination change, shadows, occlusion and targets' changing appearances. in contrast, color tracking is relatively resistant to these problems, but suffers from the need of initialization. to complete the specific human tracking task in indoor environment, this paper utilizes the specific human face-body structure, and tracks face and body simultaneously and cooperatively. the advantage of this approach is that it can keep tracking in some bad situations, when one of the parts is missing, which makes it more robust than single-part tracking. experimental tracking results on a meeting room video data are given.
unsupervised band selection for multispectral images using information theory. in this paper, the implication of the relations of information in the case of multispectral images is analyzed. higher-order mutual information can adopt positive or negative values depending of the correlation among ensembles. therefore, the existence of negative values reflects higher-order correlations in the conditional informations. on the other hand, the extraction of optimal subsets of spectral images is proposed as a maximization of the conditional entropies at same time that the dependent information among images is minimized.
attribute relevance in multiclass data sets using the naive bayes rule. feature selection using the naive bayes rule is presented for the case of multiclass data sets. in this paper, the em algorithm is applied to each class projected over the features in order to obtain an estimation of the class probability density function. a matrix of weights per class and feature is then obtained, where it collects the level of relevance of each feature for the different classes. we show different ways to extract this information and compare the behavior of the ranking of relevance obtained applying the naive bayes and k-nn classifiers.
bayesian networks classifiers applied to documents. this paper discusses the use of the bayesian network model for a classification problem related to the document image understanding field. our application is focused on logical labeling in documents, which consists in assigning logical labels to text blocks. the objective is to map a set of logical tags, composing the document logical structure, to the physical text components. we build a bayesian network model that allows this mapping using supervised learning, and without imposing a priori constraints on the document structure. the learning strategy is based partly on genetic programming tools. a prototype has been implemented, andtested on tables of contents found in periodicals and magazines.
evaluating the range flow motion constraint. the instantaneous three-dimensional velocity field of a moving surface can be computed from a sequence of dense range data sets. here we discuss the computation of the underlying motion constraint equation. this involves the evaluation of derivatives of the depth coordinate with respect to the other world coordinates. as these are not evenly sampled the sampling has to be taken into account explicitly. we quantitatively compare four methods to compute derivatives based on the validity of the resulting constraint equation.