waashk/dblp huggingface dataset:
towards image retrieval for eight percent of color-blind men. about 8% of men (but not women) are suffering from color blindness. the objective of this work was to investigate the problem of image retrieval based on color co-occurrence features when comparing normal vision and three kinds of color blindness (dichromasia): protanopia, deuteranopia, and tritanopia. original database comprises 12000 images that were also converted into three dichromatic versions using the vischeck simulation tool. results of 48000 queries were used to study influence of color blindness on retrieval results. principal component analysis, multidimensional scaling, hierarchical clustering, support vector machines, and statistical methods were employed for investigating feature space distortions associated with color blindness.
a new method for quantification of age-related brain changes. a new method is proposed for quantification of age-related brain changes. the method includes calculating 3d volumetric texture descriptors, extracting principal components, and assessing the significance of brain changes using multivariate analysis techniques. structural changes were evaluated using high resolution anatomical mri-t1 brain images of a group of 152 healthy subjects aged from 18 to 70 years (76 males and 76 females). the talairach parcellation system was applied to study normal brain aging on four scale levels: the whole cerebrum, the nine coronal sections, the twelve axial sections, and 108 box-shaped sections resulting from both subdivisions. statistical analysis has revealed significant brain deteriorations with age at different scale levels. most of the brain regions are affected with a slight predominance in the frontal lobes. we concluded that 3d texture analysis followed by statistical evaluation procedures is a robust technique for detecting age-related changes in the anatomical mr images of the human brain.
structural brain asymmetry as revealed by 3d texture analysis of anatomical mr images. we used 3d texture analysis approach to examine the structural brain asymmetry. the method is based on extended, multi-sort co-occurrence matrices that employ intensity, gradient and anisotropy image features in a uniform way. the asymmetry in normals and in patients with pathological findings was evaluated on a large sample of 310 mri-t1 images with the focus on the asymmetry differences associated with gender. the analysis revealed higher asymmetry in males comparing females (t=7.23, p
the classification gradient. we propose a method that uses bootstraping to classify the pixles in the two halves of a sliding window, assuming that if there is a real image boundary separating the two halves, the pixels in the two halves will be classified in two separate classes. the accuracy of the classification is used as a local "gradient". high values of this gradient allow us to detect weak statistical borders in 2d and 3d images.
initialization and system modeling in 3-d pose tracking. initialization and choice of adequate motion models are two important but seldom discussed problems in 3d modelbased pose (position and orientation) tracking. in this paper, we propose an automatic initialization approach suitable for textured objects. in addition, we define, study and experimentally evaluate three motion models commonly used in visual servoing and augmented reality.
iris identification using wavelet packets. in this paper, we present a new method for iris identification particularly convenient for visible light images. it relies on the use of packets of wavelets [texture classification by wavelet packet signatures] for the production of an iris code. experiments, conducted on a database of 700 iris images, acquired with visible light illumination, show an improvement of 2% of far and of around 11.5% of frr with the proposed method relatively to the classical wavelet method [high confidence recognition of persons by rapid video analysis of iris texture]. the contribution of colour information is also studied with such method.
robust detection of buildings in digital surface models. there is growing interest in the automatic interpretation of range data acquired by airborne laser scanners. huge volumes of data involved in land use analysis and building reconstruction applications necessitate a preliminary step to focus attention on interesting areas. in this paper, we present a technique to detect and discriminate buildings and vegetation in dense digital surface models (dsm) by combining curvature based features and edge information. our results demonstrate the effectiveness of differential geometric properties in the analysis of range data.
feature selection based on the training set manipulation. a novel filter feature selection technique is introduced. the method exploits the information conveyed by the evolution of the training samples weights similarly to the adaboost algorithm. features are selected on the basis of their individual merit using a simple error function. the weights dynamics and its effect on the error function are utilised to identify and remove redundant and irrelevant features. in experiments we show that the performance of commonly employed learning algorithms using features selected by the proposed method is the same or better than that obtained with features selected by the traditional state-of-theart techniques.
mixture of support vector machines for hmm based speech recognition. speech recognition is usually based on hidden markov models (hmms), which represent the temporal dynamics of speech very efficiently, and gaussian mixture models, which do non-optimally the classification of speech into single speech units (phonemes). in this paper we use parallel mixtures of support vector machines (svms) for classification by integrating this method in a hmm-based speech recognition system. svms are very appealing due to their association with statistical learning theory and have already shown good results in pattern recognition and in continuous speech recognition. they suffer however from the effort for training which scales at least quadratic with respect to the number of training vectors. the svm mixtures need only nearly linear training time making it easier to deal with the large amount of speech data. in our hybrid system we use the svm mixtures as acoustic models in a hmm-based decoder. we train and test the hybrid system on the darpa resource management (rm1) corpus, showing better performance than hmm-based decoder using gaussian mixtures.
singular point detection in fingerprints using quadrant change information. automatic localization of singular points in fingerprints is of critical importance in many algorithms. existing methods of detecting singular points often require tedious ad-hoc parameter tuning, particularly in the presence of degraded quality fingerprints. in this paper we present an approach towards singular point detection in fingerprints that operates on the quadrant change information and is largely insensitive to the degradation of fingerprint quality.
lossless compression methods for hyperspectral images. a novel efficient algorithm for lossless compression of hyperspectral images has been developed. the algorithm uses entropy function as a measure of interband similarity instead of correlation and mutual information functions that have been previously involved. the algorithm owes its performance to a newly invented adaptive prediction model. image band reordering and prediction have been done according to the model steps. edmond's algorithm has been proposed for finding optimal band ordering as an alternative to prim's algorithm. the results obtained are outstanding. moreover, three best lossless compression algorithms have been reviewed, tested using the same satellite data and compared to our method. we show that the proposed algorithm is capable of achieving compression ratios superior to that of the best-known lossless compression algorithms for hyperspectral images.
virtual view generation by linear processing of two differently focused images. in this sketch, we present a novel approach to image-based rendering (ibr) techniques for generating a virtual view image from different positions with arbitrary focus using two differently focused images captured from a fixed position. in the conventional approach [potmesil and chakravarty 1981], focus blur have been produced by applying a realistic camera model to given 3d objects. we propose here a much simpler and more effective method only using space-invariant filters to render both parallax and focusing effects on objects in a scene, according to the virtual camera's position and focus depth respectively. the proposed method does not need any segmentation and 3d modeling.
training of classifiers using virtual samples only. this paper describes the training of classifiers entirely based on virtual images, rendered by a ray-tracing software. two classifers, a support vector machine and a polynomial classifier, are trained solely with virtual samples and used for classification of real samples.the objects to be distinguished are holes vs. garbage (non-holes) out of a set of hole candidates in images of flanges.we analysed the effect of different classifier parameters and manipulation of the virtual samples.error rates of 1.6% on real test samples are achieved.
transitions of the pre-symmetry set. the symmetry set (ss) and its subset the medial axis (ma), can be used to describe a shape.the representation of the ss in parameter space is called the pre-symmetry set.changes in the shape are directly related to so-called transition (topological changes) of the ss.they have also effect on the pre-ss.since the pre-ss can be used to represent the shape efficiently, it is important to study the transitions of the pre-ss and their impact.we present these transitions, as well as the presence of the ma in the pre-ss.
matching 2d shapes using their symmetry sets. we introduce a shape descriptor that is based on the symmetry set. this set represents pairwise symmetric points and consists of several branches. the begin and end points of the branches relate to extrema of the curvature along the shape. consequently, extrema of the curvature are pairwise connected via a symmetry set branch with a certain finite length. the novel shape descriptor is given by a string representing these extrema, together with the pair wise connections and a length measure. next, an algorithm is given to match strings. this algorithm is based on a modified shortest path algorithm, taking into account the allowed changes of the symmetry set. examples show the usability of the presented theory, applied to different types of shapes, including noise and occlusions.
video clip recognition using joint audio-visual processing model. the automatic recognition of video clips is an important capability with applications in broadcast monitoring for content theft and adherence to advertisement campaign. in this paper, we present an approach for video clip recognition based on hmm and gmm for modeling video and audio streams respectively. the approach is used to model tv commercials. the recognition results of using single stream and joint models are compared. the error rate of 0.01% is achieved when the joint audio-visual processing model is employed.
visual servoing in presence of non-rigid motion. most robotic vision algorithms have been proposed by envisaging robots operating in industrial environments, where the world is assumed to be static and rigid. these algorithms cannot be used in environments where the assumption of a rigid world does not hold. in this paper, we study the problem of visual servoing in presence of nonrigid objects and analyze the design of servoing strategies needed to perform optimally even in unconventional environments. we also propose a servoing algorithm that is robust to non-rigidity. the algorithm extracts invariant features of the non-rigid object and uses these features in the servoing process. we validate the technique with experiments and demonstrate the applicability.
combining fingerprint, palmprint and hand-shape for user authentication. this paper investigates a new approach for personal authentication by combining unique biometric features which can be acquired from hand images alone. the proposed method attempts to improve the performance of fingerprint-based verification system by integrating palmprint and hand-shape features. the matching scores for the fingerprint images are computed using the number of matched minutiae on the overlapping areas while those for palmprint and hand-shape images are based on distance of feature vectors. these matching scores are combined using simple fusion rule which does not require any training. our experimental results on the database of 100 users achieve promising results and therefore confirm the usefulness of proposed method.
multiscale fourier descriptor for shape-based image retrieval. the shapes occurring in the images are important in the content-based image retrieval. in this paper we introduce a new fourier-based descriptor for the characterization of the shapes for retrieval purposes. this descriptor combines the benefits of the wavelet transform and fourier transform. this way the fourier descriptors can be presented in multiple scales, which improves the shape retrieval accuracy of the commonly used fourier-descriptors. the multiscale fourier descriptor is formed by applying the complex wavelet transform to the boundary function of an object extracted from an image. after that, the fourier transform is applied to the wavelet coefficients in multiple scales. this way the multiscale shape representation can be expressed in a rotation invariant form. the retrieval efficiency of this multiscale fourier descriptor is compared to an ordinary fourier descriptor and css-shape representation.
embodied proactive human interface "pico-2". we are conducting research on "embodied proactive human interface". the aim of this research is to develop a new human-friendly active interface based on two key technologies, an estimation mechanism of human intention for supporting natural communication named "proactive interface", and a tangible device using robot technology. this paper introduces the humanoid-type two-legged robot named "pico-2", which was developed as a tangible telecommunication device for the proactive human interface. in order to achieve the embodied telecommunication with pico-2, we propose new tracking technique of human gestures using a monocular video camera mounted on pico-2, and natural gesture reproduction by pico-2 which absorbs the difference of body structure between the user and the robot.
a neural network classifier for occluded images. this paper proposes a neural network classifier which can automatically detect the occluded regions in the given image and replace that regions with the estimated values. an auto-associative memory is used to detect outliers such as pixels in the occluded regions. certainties of each pixels are estimated by comparing the input pixels with the outputs of the auto-associative memory. the input values to the associative memory are replaced with the new values which are defined depending on the certainties. by repeating this process, we can get an image in which the pixel values of the occluded regions are replaced with the estimates. the proposed classifier is designed by integrating this associative memory with a simple classifier.
a robust audio searching method for cellular-phone-based music information retrieval. we propose a search method for detecting a query audio signal fragment in long audio recordings. the query signal is assumed to be captured by a portable terminal, such as a cellular phone, in the real world. a major problem in this kind of search is that the features of the query sound may include distortions due to terminal characteristics or environment noise. the method proposed here comprises local time-frequency-region normalization and robust sub-space spanning. the former is used to make features invariant to additive noise and frequency characteristics, and the latter to choose frequency bands that minimize the effect offeature distortions. experiments using cellular phones in the real world show the proposed method is effective.
kanji recognition in scene images without detection of text fields - robust against variation of viewpoint, contrast, and background texture. with the goal of indexing scene images, we propose a novel recognition method for kanji characters captured in scene images. our method scans multi-resolution images and classifies clipped regions with recognition dictionaries generated by learning a large amount of partial patterns of characters with large geometric transformation. the problem of scanning time, which tends to be impractically long, is solved by using multi-compression coarse-to-fine scanning, and by detecting peak points after coarse searching. despite the wrong results generated in the background, our method well supports image retrieval since it uses the regular spacing of characters. experimental results show that this recognition method recognized characters at the rate of 82%. precision was 84% and recall was 64% for image retrieval.
planar shape recognition across multiple views. multiview studies in computer vision have concentrated on the constraints satisfied by individual primitives such as points and lines. not much attention has been paid to the properties of a collection of primitives in multiple views, which could be studied in the spatial domain or in an appropriate transform domain. we derive an algebraic constraint for planar shape recognition across multiple views based on the rank of a matrix of fourier domain descriptor coefficients of the shape in different views. we also show how correspondence between points on the boundary can be computed for matching shapes using the phase of a measure for recognition.
structure relaxation method for self-organizing neural networks. self-organizing neural networks achieve more predictable and accurate results then the classic ones with the static architecture.neurons and connections of such neural networks are dynamically built during the learning process.self-organizing neural networks based on the group method of data handling (gmdh) have proven to be one of the most efficient approaches to solving the problems of pattern recognition with the statistical learning data.in this article we propose a new method for searching deeper interrelations of the inputs and the output of the system under the study of such a neural network.the method allows eliminating links to the inputs that are no longer useful at the later steps of the neural network construction, thus allowing to simplify the neural network structure and increase prediction accuracy.hence the method is called the structure relaxation method.for complex problems the method helps to find deeper system inputs interrelations, increase the prediction accuracy, and, at the same time, decrease the number of the inputs being used.the proposed relaxation method was tested on the real world problems; the results are also presented herein.
hangul tree classifier for type clustering using horizontal and vertical strokes. hangul is clustered into six different types in general. type clustering has an effect of coarse classification in syllable matching and becomes the pre-processing stage for the segmentation in the grapheme matching process. in this paper, we define a set of grapheme region of a preconsonants, vowel and post-consonant, that can absorb the change of the character's shape and the noise. a method for extracting the main stroke of the horizontal vowel and vertical vowel that appear in the region is proposed in this paper.
optimum block size detection for image quality measure. this paper deals with the quality of the image itself as well as the algorithm used for evaluating the quality of the fingerprints to construct an effective algorithm for evaluating the quality of the fingerprints. the quality of the fingerprint is acquired by taking the regional quality of each fingerprint image. the amount of fingerprint varies according to the size of each block which makes the result unsteady. we concentrated on finding the right size for the block used to acquire the fingerprint image. also, the quality distribution included in the fingerprint image was acquired when the optimal block size was adopted.
fingerprint matching method using minutiae clustering and warping. solving non-linear distortion problems in fingerprint matching is important and still remains as a challenging topic. we have developed a new fingerprint matching method to deal with non-linear distortion problems efficiently by clustering locally matched minutiae and warping the fingerprint surface using minutiae clusters. specifically, local invariant structures encoding the neighborhood information of each minutia are utilized in clustering the matched minutiae and then the fingerprint surface is warped to describe the deformation pattern properly. finally, to make an additional increase in performance, the overlapped region of two fingerprints is considered in the score computation stage. experimental results show that the proposed algorithm is performed best compared with other ones.
local variance driven self-organization for unsupervised clustering. we propose a new, novel unsupervised clustering technique based on traditional kohonen self organization, competitive hebbian learning (chl), and the hebbian based maximum eigenfilter (hme). this method fits into the family of dynamic selfgenerating, self-organizing map (som) algorithms. the approach uses a vigilance based, global parsing strategy as a guide for the hierarchical partitioning of an underlying data distribution into a set of dominant prototypes: each consisting of a dual memory element for the online estimation of both position and maximal local variance. a co-operative scheme exploits the interplay between global vigilance and maximal local variance such that an informed choice may be made regarding insertion sites for new nodes into the map. the network is related to self-organizing tree maps (sotm), growing neural gas (gng) and their variants. a framework is presented and performance demonstrated against gng.
scheduling of image processing using anytime algorithm for real-time system. this paper proposes an adaptive scheduling method using anytime algorithm under such a condition that the processing time is restricted or is not enough. by using typical image processing procedure as an example, this paper discusses how to modify conventional image processing algorithms to anytime algorithmic image processing, modeling of timeprecision function and an adaptive scheduling method to realize the maximum performance in the restricted time.
contact lens extraction by using thermo-vision. the surface of the eye is always covered with water, and if he/she opens his/her eyes, the temperature gradually falls because of the evaporation of water. the transition of the temperature differs whether he/she wears contact lenses or not, and furthermore, there exists some differences between soft and hard lenses. basing on these properties, this paper proposes a system to extract and distinguish contact lenses by using a thermo-vision camera. some experimental results show about 72% success and this means the possibility to apply this basic method to the preprocessing of biometrics.
resolution independent deformable model. in this paper we propose a parametric deformable model that automatically adapts its topology and that recovers accurately image components with a complexity independent from the resolution of the input image. the main idea is to equip the image space with a metric that expands interesting features in the image depending on their geometry.