waashk/dblp huggingface dataset:
"firefly capturing method": motion capturing by monocular camera with large spherical aberration of lens and hough-transform-based image processing. we demonstrate a new motion capturing method that uses the monocular camera with large spherical aberration of lens to measure 3d positions of point light sources attached on an object in real time without any sequential lighting. point light sources are transformed into circle patterns by the large spherical aberration of lens mounted in the camera. the diameter and center position of circle pattern give the distance and direction to the light source, resulting in measuring its 3d position. circle patterns are extracted by video image processing based on hough transform even if they are overlapped each other. we tracked the circle patterns by predicting their next positions by kalman filter that includes the acceleration of movement. by combining these processing techniques we succeeded in demonstrating the motion capturing of several leds in real time, which is shown in 3d graphics.
proposal of recordable pointer: pointed position measurement by projecting interference concentric circle pattern with a pointing device. we propose a new pointing device that can measure pointed positions by processing the interference concentric circles projected with a pointing device. the pointing device has a donut-shaped lens that is designed so as both to make the laser source be two hypothetical sources for forming optical interference and to project the concentric circle pattern widely. two image sensors set on the projected side capture small parts of the concentric circles, and its center coordinate that is a pointed position of the pointing device is calculated from two normal lines to the arcs of the circles. in practice, we succeeded in measuring the pointed position accurately by real-time processing of the widely projected concentric circle patterns. we demonstrated mouse cursor operation on a large screen with the pointing device and also used it as real-object-based user interface to show the related information of real objects by pointing them.
improving appearance-based object recognition in cluttered backgrounds. appearance-based object recognition systems are currently the most successful approach for dealing with 3d recognition of arbitrary objects in the presence of clutter and occlusion. however, no current system seems directly scalable to human performance levels in this domain. in this report we describe a series of experiments on a previously described object recognition system that try to see which, if any, design axes of such systems hold the greatest potential for improving performance. we look at the potential effect of different design modifications and we conclude that the greatest leverage lies at the level of intermediate feature construction.
image analysis for core geological descriptions : strata and granulometry detection. digital signal processing is commonly used in geophysics. methods combining core sample physical property measurements with image analysis are beginning to emerge. in this paper, we describe a digital processing method applied directly to core sample images. our approach uses a number of time-frequency and scale-frequency transformations as well as morphological operators that reach a resolution equivalent to and even better than the geologist's eye. the resources presented below are free from operator-related subjectivity and provide efficient, reliable and repeatable sequential descriptions of geological borehole samples that include both layer detection and grain analysis. our process's efficiency is demonstrated through analyses carried out on a number of core samples taken from cretaceous to upper eocene volcano-sedimentary series on new caledonia's west coast.
blind audio watermark decoding using independent component analysis. this paper proposes an audio watermark extraction technique which adopts independent component analysis (ica) for blind watermark decoding. unlike the existing work, our method allows to combine data synchronization and watermark decoding into one optimization procedure, thus robust to transmission over different channels. watermark encoder is designed as a nonlinear data embedding machine which is compatible to mpeg layer1 model 1. it is shown that the proposed ica based watermark decoding scheme allows decoding watermark info accurately even though the watermark to signal ratio is less than -20 db. the method is robust to stereo-to-mono conversions and performs very well when the channel noise level is high.
a new affine invariant curve normalization technique using independent component analysis. a new affine invariant curve normalization method using independent component analysis (ica) is presented. first, principal component analysis (pca) is used for translation, scale and shear normalization. ica and the third order moments are then employed for rotation and reflection normalization. it is shown that all affine transformed versions of an object have a unique or canonical representation. experiments are conducted to asses the robustness of our approach. proposed normalization technique can be used as a pre-processing for object modelling and recognition.
efficient tracking in 6-dof based on the image-constancy assumption in 3-d. in this contribution maximum likelihood (ml) based approaches are presented which track an a-priori known surface and texture in monocular video streams. in contrast to established tracking algorithms based on homographies the surface is not modeled as planar or piecewise planar but as a collection of 3-d surface points and surface normals. thus, any free-form surface can be modeled. this paper introduces a novel description of the image jacobian in terms of a reference jacobian based on the image-constancy (ic) assumption in 3-d. tracking with this computationally efficient description is compared to the standard ml approach with respect to the region and speed of convergence.
probabilistic object tracking using multiple features. we present a generic tracker which can handle a variety of different objects. for this purpose, groups of low-level features like interest points, edges, homogeneous and textured regions, are combined on a flexible and opportunistic basis. they sufficiently characterize an object and allow robust tracking as they are complementary sources of information which describe both the shape and the appearance of an object. these low-level features are integrated into a particle filter framework as this has proven very successful for non-linear and non-gaussian estimation problems. in this paper we concentrate on rigid objects under affine transformations. results on real-world scenes demonstrate the performance of the proposed tracker.
differentiation of alphabets in handwritten texts. our aim is to differentiate between parts of handwritten text written using different alphabets. we achieve our goal thanks to a fractal analysis of handwriting style. for each alphabet, a set of characteristics is extracted. advantage is taken from the autosimilarity properties that are present in the handwriting. in order to do that, some invariant patterns characterizing the writing are statistically extracted. during the training step these invariant patterns appear while performing a fractal compression process, then they are organized in a reference base that can be associated with the alphabet. the alphabet identification is performed during a pattern matching process using the different reference bases successively. the results of this analyze are estimated through a correlation coefficient between the initial image of the text and a synthetic reconstruction of the text based on the references.
distance between 2d-scenes based on oriented matroid theory. in this paper a novel method for representing and comparing views of objects is presented. the topological properties of the regions of the views of objects are used to define a structure, called set of cocircuits, based on the oriented matroid theory. it is a formalism for qualitative spatial representation and reasoning and encodes information about relative position of the disjoint regions of the view and give local and global topological information about their spatial distribution. this topological technique is applied to recognising indoor scenes for the localization of a mobile robot.
vision-based robot positioning by an exact distance between hi. most vision-based robot positioning techniques rely on analytical formulations of the relationship between the robot pose and the projected image coordinates of several geometric features of the observed scene. this usually requires that several simple features such as points, lines or circles be visible in the image and be properly extracted. in this paper, we present a method to compare images (scenes that the robot has learned) based on a fast and exact distance between histograms. in contrast to the methods described before, our method is faster and with less storage space do to the images do not need to be segmented and only a lossless description of the histograms are stored in the data base.
orientation-improved minutiae for fingerprint matching. for minutiae-based fingerprint matching, alignment-based point pattern matching algorithm has been proposed to reduce the computational cost with a suitable criterion on ridge similarity. however, a large percentage of spurious pairs have rather similar ridges as well, which slows down the matching process and limits the matching accuracy. in this paper, orientation-based ridge patterns are utilized to improve the minutiae feature, which remove more spuriously matched pairs. in addition, to reduce errors caused by broken bifurcation, we propose a new scheme that compares two minutiae regardless their types. experiments conducted on a large fingerprint database (nist-4) show that the proposed method produces a much improved performance.
minutiae-based fingerprint matching using subset combination. in this paper, we propose an effective fingerprint matching algorithm based on ridge count matching and minutiae subset combination. in the algorithm, the orientation-based ridge patterns are first utilized to remove the spuriously matched minutiae pairs. then the reliable ridge counts between every two minutiae are estimated to improve the minutiae relationship, and finally the matched minutiae subsets corresponding to different alignments are selectively combined to reduce the influence caused by distortions in fingerprints. experimental results on nist-4 show that our method achieves a much better matching performance.
pixel-accurate representation and evaluation of page segmentation in document images. this paper presents a new representation and evaluation procedure of page segmentation algorithms and analyzes six widely-used layout analysis algorithms using the procedure. the method permits a detailed analysis of the behavior of page segmentation algorithms in terms of over- and undersegmentation at different layout levels, as well as determination of the geometric accuracy of the segmentation. the representation of document layouts relies on labeling each pixel according to its function in the overall segmentation, permitting pixel-accurate representation of layout information of arbitrary layouts and allowing background pixels to be classified as "don't care". our representations can be encoded easily in standard color image formats like png, permitting easy interchange of segmentation results and ground truth.
natural image correction by iterative projections to eigenspace constructed in normalized image space. image correction is discussed for realizing both effective object recognition and realistic image-based rendering. three image normalizations are compared in relation with the linear subspaces and eigenspaces, and we conclude that the normalization by l1-norm, which normalizes the total sum of intensities, is the best for our purposes. based on noise analysis in the normalized image space (nis), an image correction algorithm is constructed, which is accomplished by iterative projections along with corrections of an image to an eigenspace in nis. experimental results show that the proposed method works well for natural images which include various kinds of noise shadows, reflections and occlusions. the proposed method provides a feasible solution to the object recognition based on the illumination cone [2]. the technique can also be extended to face detection of unknown person and registration/recognition using eigenfaces.
review the strength of gabor features for face recognition from the angle of its robustness to mis-alignment. gabor feature has been widely recognized as better representation for face recognition in terms of rank-1 recognition rate. in this paper, we review the strength of gabor feature for face recognition from the new angle of its robustness to mis-alignment using a novel quantificational evaluation method combining both the alignment precision and the recognition accuracy. our experiments show that, compared with the gray-level intensity, gabor feature is much more robust to image variation caused by the imprecision of facial feature localization, which further support the feasibility of gabor representation.
face recognition robust to head pose from one sample image. most face recognition systems only work well under quite constrained environments. in particular, the illumination conditions, facial expressions and head pose must be tightly controlled for good recognition performance. in 2004, we proposed a new face recognition algorithm, adaptive principal component analysis (apca) [4], which performs well against both lighting variation and expression change. but like other eigenface-derived face recognition algorithms, apca only performs well with frontal face images. the work presented in this paper is an extension of our previous work to also accommodate variations in head pose. following the approach of cootes et al, we develop a face model and a rotation model which can be used to interpret facial features and synthesize realistic frontal face images when given a single novel face image. we use a viola-jones based face detector to detect the face in real-time and thus solve the initialization problem for our active appearance model search. experiments show that our approach can achieve good recognition rates on face images across a wide range of head poses. indeed recognition rates are improved by up to a factor of 5 compared to standard pca.
a riemannian weighted filter for edge-sensitive image smoothing. this paper describes a new method for image smoothing. we view the image features as residing on a differential manifold, and we work with a representation based on the exponential map for this manifold (i.e. the map from the manifold to a plane that preserves geodesic distances). on the exponential map we characterise the features using a riemannian weighted mean. we show how both gradient descent and newton's method can be used to find the mean. based on this weighted mean, we develop an edge-preserving filter that combines gaussian and median filters of gray-scale images. we demonstrate our algorithm both on direction fields from shape-from-shading and tensor-valued images.
ensemble of piecewise fda based on spatial histograms of local (gabor) binary patterns for face recognition. spatial histogram* of local binary pattern (lbp) and local gabor binary pattern (lgbp) has been successfully applied to face recognition and achieved state-of-the-art performance. both lbp and lgbp utilize traditional histogram matching method such as histogram intersection for face classification. in this paper, we propose a statistical extension for l(g)bp similarity computation by introducing fisher discriminant analysis (fda) of the l(g)bp spatial histogram "features". more than a simple application of fda, we have constructed ensemble of piecewise fda (epfda) classifiers, each of which is designed using one segment of the entire spatial histogram features. we show that this extension not only greatly reduces the feature dimension but also brings very impressive performance improvement. especially, we have made a large step to recognizing all the faces in the standard feret face database.
robust appearance-based tracking of moving object from moving platform. we present a robust algorithm for tracking moving objects from a moving platform. robustness is achieved by incorporating temporal differencing and shape detection in an appearance-based object tracking algorithm. in addition, the incorporation of these two methods also improves accuracy and computational efficiency of detection. some experimental results using airborne-video tracking are given to illustrate the effectiveness of this method.
multinational license plate recognition system: segmentation and classification. image-based car license plate recognition (clpr) systems provide an inexpensive automatic solution for remote vehicle identification. localization stage of the clpr yields a gray-scale plate clip with printed characters. this paper describes the method of plate clip segmentation into isolated characters, feature extraction and classification. the method is independent on character size, thickness, illumination and is capable of handling plates from various countries. the method uses extensively the gray-scale information and is robust to breaks in character connectivity. it is tolerant to character deformations, such as shear and skew. promising results have been obtained on israeli and bulgarian plates.
an iterative algorithm for segmentation of isolated handwritten words in gurmukhi script. segmentation of handwritten text in gurmukhi script is an uphill task primarily because of the structural features of the script and varied writing styles. the presence of a horizontal line connecting characters of a word (i.e. head line), half characters and overlapping of some vowel between middle and lower zone of a word make the task even more difficult. handwritten text is also prone to the problem of overlapped, connected and merged characters with in a word. structural features are helpful in segmentation of machine printed text but these are of little help for segmentation of handwritten words. the proposed technique segments the words in an iterative manner by focusing on presence of headline, aspect ratio of characters and vertical and horizontal projection profiles. the proposed approach of segmentation can be used for handwritten text of indian language scripts like devnagri, bangla etc. having structural feature similar to gurmukhi script.
manifold pursuit: a new approach to appearance based recognition. manifold pursuit (mp) extends principal component analysis to be invariant to a desired group of image-plane transformations of an ensemble of un-aligned images.we derive a simple technique for projecting a misaligned target image onto the linear subspace defined by the superpositions of a collection of model images. we show that it is possible to generate a fixed projection matrix which would separate the projected image into the aligned projected target and a residual image which accounts for the misalignment. an iterative procedure is then introduced for eliminating the residual image and leaving the correctaligned projected target image.taken together, we demonstrate a simple and effective technique for obtaining invariance to image-plane transformations within a linear dimensionality reduction approach.
gabor wavelets and kernel direct discriminant analysis for face recognition. a novel gabor-kernel face recognition method is proposed in this paper. this involves convolving a face image with a series of gabor wavelets at different scales, locations, and orientations and extracting features from resulting gabor filtered images. kernel discriminant analysis (kdda) is then applied to the feature vectors for dimension reduction as well as class separability enhancement. a database of 600 frontal-view face images from the feret face database is used to test the method. experimental results demonstrate the advantage of kdda over other kernel methods such as kernel principal component analysis (kpca) and general discriminant analysis (gda). significant improvements are also observed when features are extracted from gabor filtered images instead of the original images. a 94% accuracy has been observed for the novel gabor + kdda method on the feret database using a simple classifier, which could be further improved by employing a more complex classifier and distance measurer.
finding text in natural scenes by figure-ground segmentation. much past research on finding text in natural scenes uses bottom-up grouping processes to detect candidate text features as a first processing step. while such grouping procedures are a fast and efficient way of extracting the parts of an image that are most likely to contain text, they still suffer from large amounts of false positives that must be pruned out before they can be read by ocr. we argue that a natural framework for pruning out false positive text features is figure-ground segmentation. this process is implemented using a graphical model (i.e. mrf) in which each candidate text feature is represented by a node. since each node has only two possible states (figure and ground), and since the connectivity of the graphical model is sparse, we can perform rapid inference on the graph using belief propagation. we show promising results on a variety of urban and indoor scene images containing signs, demonstrating the feasibility of the approach.
video completion for perspective camera under constrained motion. this paper presents a novel technique to fill in missing background and moving foreground of a video captured by a static or moving camera. different from previous efforts which are typically based on processing in the 3d data volume, we slice the volume along the motion manifold of the moving object, and therefore reduce the search space from 3d to 2d, while still preserve the spatial and temporal coherence. in addition to the computational efficiency, based on geometric video analysis, the proposed approach is also able to handle real videos under perspective distortion, as well as common camera motions, such as panning, tilting, and zooming. the experimental results demonstrate that our algorithm performs comparably to 3d search based methods, and however extends the current state-of-the-art repairing techniques to videos with projective effects, as well as illumination changes.
an undecimated wavelet transform based denoising, ppca based pulse modeling and detection-classification of pd signals. authors address the problem of recognition and retrieval of relatively weak industrial signal such as partial discharges (pd) buried in excessive noise. the major bottleneck being the recognition and suppression of stochastic pulsive interference (pi) which has similar frequency characteristics as pd pulse. also, the occurrence of pi is random like pd pulses. in this paper we provide techniques to de-noise, detect, estimate and classify the pd signal in a statistical perspective. to avoid aliasing due to interference of high frequency noise, pd signals are generally digitized in much higher sampling rates (in terms of tens of mhz), than actually required. a multi-resolution analysis based technique is incorporated to discard the huge amount of redundant data in acquired signal. a scale dependent mmse based estimator is implemented in undecimated wavelet transform (udwt) domain to enhance the noisy signal, due to its inherent advantages offered in the analysis of pd signal. the probability density function of the enhanced signal is derived using probabilistic principal component analysis (ppca) in which pd/pi pulses are modeled as mean of the distribution. the parameters of the pulses are estimated using maximum aposteriroi probability (map) based technique. a statistical test known as generalized log likelihood ratio test (glrt) was incorporated to ensure the existence of the pulse. the decision as to whether a pulse is a noise or a desired signal has been made based on a weighted-nearest neighbor methodology.