waashk/dblp huggingface dataset:
skin color detection in low bit-rate 3-d multiwavelet-based videos. there is a growing thrust of studies on exploring applications of multiwavelets in image compression, denoising and recognition. for compression, multiple scaling functions and multiple wavelets provide more flexible approximation of the original signal at high compression ratio than a comparable single scaling function and single wavelet, leading to potential improvement in detection of some features even in low bit-rate videos. this paper presents a study on detection of human skin colors in low bit-rate videos as a performance evaluation of low bit-rate videos encoded using spatio-temporal 3-d multiwavelets.
fingerprint representation using localized texture features. fingerprint representations can be broadly divided into three categories: image level, texture features and minu- tiae features. both image based and texture based repre- sentations require accurate alignment before comparison. this presents a problem since accurate registration of fin- gerprints is challenging. on the other hand, minutiae based matchers are invariant to changes in orientation and posi- tion, but completely ignore the rich visual content in the image. in this paper, we present a localized texture based representation scheme that relies solely on visual content for identification and at the same time does not require ab- solute alignment. we outline techniques to efficiently com- pute these features and also propose an algorithm to per- form identification based on these features. our experimen- tal evaluations over database of several sizes show that the proposed features are both accurate and scalable.
an embedded real-time vision system for 24-hour indoor/outdoor car-counting applications. we describe an embedded vision system, which integrates a web-cam quality cmos imaging chip with a risc processor, to perform real-time car-counting functions in the indoor and outdoor environment. the challenge of this application, especially for the outdoor environment, is to develop vision algorithms for day and night, and during the light-transition periods (i.e., dawn and dusk). the vision system also needs to accommodate a tremendous range of illumination change (from sunny summer to snowy winter). finally we report briefly the result of an outdoor system we deployed in germany since june 2003. the entire system consists of a network of 13 embedded vision systems covering a parking facility over 1 square kilometer. the vision-network has been in daily use for the employee parking guidance.
matching interest points using affine invariant concentric circles. we present a new method to perform reliable matching between different images. this method finds complete region correspondences between concentric circles and the corresponding projected ellipses centered on interest points. it matches interest points exploiting all the available luminance information in the regions under affine transformation. experiments have been conducted on many different data sets to compare our approach to two sift-based local descriptors. the results show the new method is more effective in natural scenes without distinctive texture patterns. it also offers increased robustness to partial visibility, object rotation in depth, and viewpoint angle change.
a time warping based approach for video copy detection. the proliferation of digital video urges the need of video copy detection for content and rights management. an efjcient video copy detection technique should be able to deal with spatiotemporal variations (e.g., changes in brightness or frame rates), and lower down the computation cost. m i l e most studies put more emphases on spatial variations, less effort is made for temporal variations and computation cost. to address the above issues, we propose a time warping based approach for video copy detection. a time warping matching algorithm is used to deal with video temporal variations. to reduce matching times, a fast pltering method to generate key parries and select candidate clips from video is presented. our experiments demonstrate promising results of the proposed approach.
a script matching algorithm for oriental characters on pdas. recently growth of pen-computers and personal digital assistants has made electronic ink a first-class object. one of the most important problems under this model is to search ink data at previously stored pen-strokes. in this paper we proposed and implemented a matching algorithm of write-dependant ink search for oriental characters, especially mixed korean with chinese characters. our algorithm is very simple guaranteeing the performance of the matching time for the mobile computer which has limitations of hardware. various experiments showed matching rate over 98% for only the korean scripts and 94% for the data mixed korean with chinese scripts.
extending dynamic range of two color images under different exposures. we present a method of extending the dynamic range of an picture with two different exposure images. since pictures under different exposure times show different scene dynamic ranges, if we make use of the visible information of each different exposure image, we can recover a high dynamic range image which contains what cannot be visualized by an auto-exposure time. in this method, we did not follow the ordinary fusion methods which were based on multi-layered or radiance map based approach of multiple images. instead, we used the filling method of undesirable regions of an auto-exposure picture, preserving its overall image quality. our filling method uses the gradient information of the visible regions and recovers the saturated regions by the energy minimization approach. the results show that our fusion method is simple and practical to make a hdr image, if we have two proper exposure images.
a novel virus infection clustering for flower images identification. computer-aided flower identification is a very useful tool for plant species identification aspect. in this paper, a study was made on a development of content based image retrieval system to characterize flower images efficiently. in this system, a novel virus infection clustering is proposed to cluster the image database to improve the searching efficiency. experimental results show that the developed system can yield promising results for flower image retrieval.
optimal global mosaic generation from retinal images. we present a method to construct a mosaic from multiple color and fluorescein retinal images. a set of images taken from different views at different times is difficult to register sequentially due to variations in color and intensity across images. we propose a method to register images globally in order to minimize the registration error and to find optimal registration pairs. the reference frame that gives the minimum registration error is found by the floyd- warshall's all-pairs shortest path algorithm, and all other images are registered to this reference frame using an affine transformation model. we present experimental results to validate the proposed method.
a noise robust front-end for speech recognition using hough transform and cumulative distribution mapping. this paper describes a novel and noise robust frontend that employs the use of hough transform for simultaneous frequency and temporal masking, together with cumulative distribution mapping of cepstral coefficients, for noisy speech recognition. recognition experiments on the aurora ii connected digits database have revealed that the proposed frontend achieves an average digit recognition accuracy of 83.67%. compared with the recognition results obtained by using the etsi standard mel-cepstral front-end, this accuracy represents a relative error rate reduction of around 58%.
face alignment using segmentation and a combined aam in a ptz camera. in this paper, we propose a novel framework for face alignment based on the active appearance model (aam) in surveillance systems with pan-tilt-zoom (ptz) cameras. the aam converges poorly in face images which are affected by illumination factors, cluttered backgrounds and status of the camera. to search for robust face model parameters, we propose a robust aam fitting method based on segmenting faces and combining person-specific and generic models to achieve accurate face alignment. we segment faces using histogram back-projection and a skin color histogram, which is updated using a skin mask extracted by the aam. for robust face recognition, we combined generic and person-specific models with a slight reduction in processing time. the extracted aam parameters are as accurate as those when using the person-specific model and can be used as features for face recognition. empirical experiments show that our proposed method extracts very accurate face parameters and is not sensitive to initial shapes.
feature extraction for bank note classification using wavelet transform. in this paper, we investigate an approach to feature extraction for bank note classification by exploiting the potential of wavelet transform. in the proposed method, high spatial frequency coefficients taken from the wavelet domain are examined to extract features. we first perform edge detection on bill images to facilitate the wavelet feature extraction. the construction of feature vectors is then conducted by thresholding and counting of wavelet coefficients. the proposed feature extraction method can be applied to classifying any kind of bank note. however, in this paper we examine korean won bills of 1000, 5000 and 10000 won types. experimental results with a set of 10,800 bill images show that the proposed feature extraction method provides a correct classification rate of 99% even by using the euclidean minimum distance matching as classifier.
a model-based approach for rigid object recognition. most object recognition systems require large databases of real images for classifier training. to collect real images for this purpose is a difficult and expensive process. this paper introduces a unified framework based on the creation and use of synthetic images for training various classifiers to achieve recognition of real-world objects. a 3d model of the object (i.e. trolley in this case) is constructed from a minimum of two photographs. the constructed 3d model is used to automatically generate the relevant synthetic images that are subsequently used to train the adaboost and support vector machine-based recognition systems. experimental results obtained are very encouraging suggesting that synthetically generated images generated by our approach can augment the real training samples used in current recognition systems.
the generalized condensed nearest neighbor rule as a data reduction method. in this paper, we propose a new data reduction algorithm that iteratively selects some samples and ignores others that can be absorbed, or represented, by those selected. this algorithm differs from the condensed nearest neighbor (cnn) rule in its employment of a strong absorption criterion, in contrast to the weak criterion employed by cnn; hence, it is called the generalized cnn (gcnn) algorithm. the new criterion allows gcnn to incorporate cnn as a special case, and can achieve consistency, or asymptotic bayes-risk efficiency, under certain conditions. gcnn, moreover, can yield significantly better accuracy than other instance- based data reduction methods. we demonstrate the last claim through experiments on five datasets, some of which contain a very large number of samples.
iris recognition with multi-scale edge-type matching. in this paper, we propose a novel descriptor which characterizes an iris pattern with multi-scale step/ridge edgetype (et) maps. the et maps are determined with the derivative of gaussian (dog) and the laplacian of gaussian (log) filters. there are two major advantages of our approach. first, both the feature extraction and the pattern classification are simple and efficient. the iris pattern classification is accomplished by et matching. the matching of each et flag can be regarded as a weak classifier and the final decision is based on the vote of each weak classifier. second, the number of free filter parameters is only three, and hence they can be easily determined. furthermore, we propose a method for designing the parameters of the filters with the genetic algorithm. the experimental results show that our approach can achieve a recognition rate of 99.98% which is comparable to that of the gabor filter approach.
boosting and structure learning in dynamic bayesian networks for audio-visual speaker detection. bayesian networks are an attractive modeling tool for human sensing, as they combine an intuitive graphical representation with efficient algorithms for inference and learning. earlier work has demonstrated that boosted parameter learning could be used to improve the performance of bayesian network classifiers for complex multi-modal inference problems such as speaker detection. in speaker detection, the goal is to use video and audio cues to infer when a person is speaking to a user interface. in this paper we introduce a new boosted structure learning algorithm based on adaboost. given labeled data, our algorithm modifiesboth the network structure and parameters so as to improve classification accuracy. we compare its performance to both standard structure learning and boosted parameterlearning on a fixed structure. we present results for speaker detection and for the uci "chess" dataset.
note on feature selection for polyp detection in ct colonography. in this paper we describe a computer aided detection (cad) algorithm for robust detection of polyps in computed tomography (ct) colonography. the devised algorithm identifies suspicious polyp candidate surfaces using the surface normal intersection, hough transform, 3d histogram analysis, region growing and a convexity test. from these detected surfaces we extract statistical and morphological features in order to evaluate if the surface in question is a polyp or fold. in order to devise the optimal classification scheme the performance of two different classifiers are evaluated when the algorithm is applied to synthetic and real patient data. the experimental results indicate that the overall polyp detection performance shows sensitivity higher than 92% for polyps larger than 5mm with an average of 4.7 to 6.0 false positives per dataset.
epipolar geometry estimation via ransac benefits from the oriented epipolar constraint. the efficiency of epipolar geometry estimation by ransac is improved by exploiting the oriented epipolar constraint. performance evaluation shows that the enhancement brings up to a two-fold speed-up. the orientation test is simple to implement, is universally applicable and takes negligible fraction of time compared with epipolar geometry computation.
physics-based extraction of intrinsic images from a single image. a technique for extracting intrinsic images, including the reflectance and illumination images, from a single color image is presented. the technique first convolves the input image with a prescribed set of derivative filters. the pixels of filtered images are then classified into reflectance-related or illumination-related based on a set of chromatic characteristics of pixels calculated from the input image. chromatic characteristics of pixels are defined by a photometric reflectance model based on the kubelka-munk color theory. from the classification results of the filtered images, the intrinsic images of the input image can be computed. real images have been utilized in our experiments. the results have indicated that the proposed technique can effectively extract the intrinsic images from a single image.
improvement in range segmentation parameters tuning. a great effort has been done during last years to improve range image segmentation results. the efficacy of the algorithms is affected by the parameters tuning.in this work two well-known search techniques have been applied to this task: genetic algorithms and simulated annealing. these techniques are adopted in cascade: the former to obtain a rough seed point set and the latter to have a more precise refinement of suitable solutions.we addressed our efforts towards the range segmenter proposed by the university of bern, that seems to be the best in term of versatility, being able to segment planar and curved surfaces, and in term of speed and quality of the performed segmentations.
ontodoc: an ontology-based query system for digital libraries. the decreasing cost and the increasing availability of new technologies is enabling people to create their own digital libraries. one of the main topic in personal digital libraries is allowing people to select interesting information among all the different digital formats available today (pdf, html, tiff, etc.). moreover the increasing availability of these on-line libraries, as well as the advent of the so called semantic web [weaving the web], is raising the demand for converting paper documents into digital, possibly semantically annotated, documents. these motivations drove us to design a new system which could enable the user to interact and query documents independently from the digital formats in which they are represented. in order to achieve this independence from the format we consider all the digital documents contained in a digital library as images. our system tries to automatically detect the layout of the digital documents and recognize the geometric regions of interest. all the extracted information is then encoded with respect to a reference ontology, so that the user can query his digital library by typing free text or browsing the ontology.
contour features for colposcopic image classification by artificial neural networks. this article presents colposcopic image classification based on contour parameters used in a comparison study of different artificial neural networks and the k-nearest neighbors reference method. in this study, significant image data bases are used (283 samples) from which a set of original parameters is extracted to characterize the attribute of contour. more precisely, wequantify the notion of sharp contours vs blurred contours in computing spatial parameters based on the number of small regions near boundaries of objects and frequency parameters based on power spectrum of lines cutting these boundaries. experimental results show thefeasibility of this study and the efficiency of the set of parameters since 95.8% of contour image set has been correctly classified.
feature fusion for image texture segmentation. a design-based method to fuse gabor filter and grey level co-occurrence probability (glcp) features for improved texture recognition is presented. feature space separability and unsupervised image segmentation are used for testing. the fused features are robust with respect to the curse of dimensionality and additive noise. feature reduction methods are typically detrimental to the segmentation performance. overall, the fused features are a definite improvement over non-fused features and are advocated in texture analysis applications.
texture segmentation comparison using grey level co-occurrence probabilities and markov random fields. the discrimination ability of texture features derived from gaussian markov random fields (gmrfs) and grey level co-occurrence probabilities (glcps) are compared and contrasted. more specifically, the role of window size in feature consistency and separability as well as the role of multiple textures within a window are investigated. glcps are demonstrated to have improved discrimination ability relative to mrfs with decreasing window size, an important concept when performing image segmentation. on the other hand, glcps are more sensitive to texture boundary confusion than gmrfs.
visually significant dynamics for watershed segmentation. the watershed transform is a powerful tool for segmentation once we can deal with oversegmentation. to solve the oversegmentation problem, hierarchical approaches are considered in order to retain the most significant regions of the image at different scales. the dynamics of the regional minima have been used to build this hierarchy. in this paper we present a new measure for computing the dynamics of the minima based on human perception of shapes. the described technique solves the major drawbacks of the hierarchical segmentations based on contrast dynamics or volume dynamics.
handwritten syriac character recognition using order structure invariance. this paper demonstrates how order structure invariance may be used for matching handwritten characters in which non-rigid deformation is tolerated and a training set is not required. we make two contributions. first we show how how to define and use order structure invariants together with local object features. second, we show how recognition by alignment can be accomplished on a sparse set of points by matching order structure invariants. we evaluate the method on the problem of recognising handwritten syriac characters obtained from historical documents. experiments indicate that the method tolerates the variations found in real handwriting while having high discrimination power. the method is potentially applicable to cursive scripts similar to syriac such as aramaic and arabic.
a systematic design procedure for scalable near-circular laplacian of gaussian operators. in low-level image processing tasks, the circularity of an operator has been shown to be an important factor affecting its accuracy as circular differential edge operators are effective in minimising the angular error in the estimation of image gradient direction. we present a general approach to the computation of scalable near-circular low-level laplacian image processing operators that is based on the finite element method. we use gaussian basis functions, together with a virtual finite element mesh, to illustrate the design of operators that are scalable to near-circular neighbourhoods through the use of an explicit scale parameter. the general design technique may be applied to a range of operators. here we illustrate the approach by discussing the implementation of a laplacian operator, and we evaluate our approach by presenting comparative results with the laplacian of gaussian operators.
relaxation labeling processes for protein secondary structure prediction. the prediction of protein secondary structure is a classical problem in bioinformatics, and in the past few years several machine learning techniques have been proposed to attack it. from an abstract pattern recognition viewpoint, the problem can be formulated as a (continuous) consistent labeling problem, whereby one has to assign symbolic labels to a set of objects by taking into account potential constraints between nearby objects. motivated by this observation, in this paper we propose a new approach to the problem based on (optimally trained) relaxation labeling algorithms, a well-known class of iterative procedures that aim at reducing labeling ambiguities and achieving global consistency through a parallel exploitation of local information. preliminary experiments performed on standard benchmark data confirm the effectiveness of the approach as compared to standard state-of-the-art machine learning predictors.
vessel segmentation in 2d-projection images using a supervised linear hysteresis classifier. 2d projection imaging is a widely used procedure for vessel visualization. for the subsequent analysis of the vasculature, precise measurements of e.g. vessel area, vessel length or the number of vessel segments are needed. to achieve these goals vessel enhancement and segmentation are required. while there are already many vasculature specific vessel segmentation algorithms, we describe in this contribution a more general supervised segmentation method which includes a feature extraction step followed by feature selection and segmentation based on the hysteresis classification paradigm. the method was tested on retina photographies. the rates of false positives and correct classifications were comparable with dedicated methods on similar data sets while it needed less time for both training and providing a segmentation result.