waashk/dblp huggingface dataset:
image representation and retrieval using support vector machine and fuzzy c-means clustering based semantical spaces. this paper presents a learning based framework for content-based image retrieval to bridge the gap between low-level image features and high-level semantic information presented in the images on semantically organized collections. both supervised (probabilistic multi-class support vector machine) and unsupervised (fuzzy c-means clustering) learning based techniques are investigated to associate global mpeg-7 based color and edge features with their high-level semantical and/or visual categories. it represents images in a successive semantic level of information abstraction based on confidence or membership scores obtained from the learning algorithms. a fusion-based similarity matching function is employed on these new image representations to rank and retrieve most similar images compared to a query image. experimental results on a generic image database with manually assigned semantic categories and on a medical image database with different modalities and examined body parts demonstrate the effectiveness of the proposed approach compared to the commonly used euclidean distance measure on mpeg-7 based descriptors.
modular neural networks for seismic tomography. we propose in this paper a modular approach for the problem of traveltime inversion or seismic tomography. this problem consists in the inference of the velocity of wave propagation in the subsurface after an explosion has been produced at the surface, relying on such waves' traveltimes. these traveltimes are recorded by several receivers on the surface. in the present work, we consider data synthetically generated, thanks to the use of a particular "earth-model". an earth-model is a multilayered media in which each layer is homogeneous, that is, the seismic wave's propagation velocity in each layer is constant, and each layer's thickness is different. we compare, on these synthetic data, a multilayer perceptron (mlp) to a modular neural architecture. we show that the modular approach is better suited for theinversion problem stated, and study the experimental conditions in which the potential of this approach is optimally exploited.
approximate fingerprint matching using kd-tree. fast and robust fingerprint matching is a challenging task today in fingerprint-based biometric systems. a fingerprint matching algorithm compares two given fingerprints and returns either a degree of similarity or a binary decision. minutiae-based fingerprint matching is the most well-known and widely used method [handbook of fingerprint recognition]. this paper reveals a new technique of fingerprint matching, using an efficient data structure, combining the minutiae representation with the individual usefulness of each minutia, to make the matching more powerful. experimental results exhibit the strength of this method.
anti-personnel mine detection and classification using gpr image. the automated anti-personnel mine (apm) detection and classification is currently a broad issue. the detection success depends on the feature selection that we obtain from the sensors. ground penetrating radar (gpr) is one of the established sensors for detecting buried apm. in this paper, we introduce a method which improves the accuracy of detecting apm by using gpr imaging. this method adopts a segmentation technique for feature extraction and neural network as a pattern classifier. a seeded region growing algorithm is applied as region based segmentation for pattern construction following the median filtering and threshold of the original gpr image. a feed forward neural network (ffnn) with backpropagation training is employed for classifying the patterns. the ffnn takes the patterns (apm signature) that are constructed from each salient region and generate the classification. this method significantly improves accuracy in the detection and classification of apm.
dtm generation from lidar data using skewness balancing. light detection and ranging (lidar) data for terrain and land surveying has contributed to many environmental, engineering and civil applications. however, the analysis of digital surface models (dsms) from complex lidar data is still challenging. commonly, the first task to investigate lidar data point clouds is to separate ground and object points as a preparatory step for further object classification. in this paper, the authors present a novel unsupervised segmentation algorithm - skewness balancing - to separate object and ground points efficiently from high resolution lidar point clouds by exploiting statistical moments. the results presented in this paper have shown its robustness and its potential for commercial applications.
object localization using input/output recursive neural networks. localizing objects in images is a dificult task and represents the first step to the solution of the object recognition problem. this paper presents a novel approach to the localization problem based on recursive neural networks (rnns). in particulal; a recursive learning paradigm is proposed to process directed acyclic graphs with labeled edges, and to realize mappings between graphs which are isomorph, i.e. that share the same topology of the links. the rnn model, that assumes a graph-based representation of images, uses a state transition function that depends on the edge labels and is independent from both the number and the order of the children of each node. moreover, the presence of targets attached to the internal nodes guarantees a fast learning, particularly sensitive to the local features of the graph. some preliminary experiments, carried out on artijcial images created using the coil collection, are reported, showing very promising results.
foveated online 3d visualization. spatially varying sensing (foveation) was first used as a means for image compression in our past research [1]. in this report we extend previous work by us to address the advantages of foveation in improving the performance of interactive 3d visualization over bandwidth limitedchannels, such as the internet. we develop an algorithm that is implemented in java3d. the method combines foveating jpeg texture files with level-of-detail representation in java3d. applications of the work include model based coding following the synthetic-natural hybrid (snhc) paradigm in mpeg-4 to electronic commerce with 3d visualization of merchandise. experimental results are presented to validate our approach.
distributed retrieval of wavelet images using bandwidth monitoring. limitations on the bandwidth to a server makes the retrieval of a large-size high-resolution image, even compressed, very time-consuming. to speed up this process we replace the retrieval process from a single server with the retrieval from multiple servers, i.e., simultaneously retrieving different part of an image from multiple server sites. in this paper, we present a block-based distributed retrieval strategy for images compressed by the embedded zerotree of wavelet coefficients (ezw) algorithm. the experimental results validate that block-based distributed retrieval is an efficient strategy to speed up downloading.
multi-user natural interaction system based on real-time hand tracking and gesture recognition. we present a computer vision based system that enables multiple people to interact naturally with a large display table using their own bare-hand gestures. the display presents and supports a particular multimedia application that can be used at the same time even by remote users. finally we describe two different applications designed for didactic and entertainment scenarios.
a new lda-based method for face recognition. linear discriminant analysis (lda) is a feature extraction technique for classification. in this paper, we propose a new lda-based method that can overcome the drawback existed in the traditional lda methods. it redefines the between-class scatter by adding a weight function according to the between-class distance, which helps to separate the classes as much as possible. at the same time, it projects the between-class scatter into the null space of the within-class scatter that contains the most discriminant information. hence, the transformationmatrix composed with the eigenvectors corresponding to the largest eigenvalues of the transferred between-class scatter can maximize the fisher criteria. experimental results show our method achieves better performance in comparison with the traditional lda methods.
fast, illumination insensitive face detection based on multilinear techniques and curvature features. this paper brings together two recent developments in image analysis. we consider a new mathematical framework that provides illumination invariant descriptors for face detection. towards fast learning and processing, we understand images and the corresponding feature maps as multilinear entities and apply higher order classifiers for image analysis and object detection. experimental results underline that this approach indeed provides quick training, fast runtime and robust performance across a variety of illumination conditions.
recognizing faces with expressions: within-class space and between-class space. in this paper, we propose a novel technique for expression invariant face recognition, which is different from eigenfaces method from two aspects: the first is that instead of applying principal component analysis (pca) on the pixel domain to obtain eigenfaces, we train eigen-motion by applying pca on motion vectors getting from the training face images with expression variations; the second is to consider the reconstructed errors of a test image in two spaces: the between-class eigenmotion sub-space and the within-class eigenmotion subspace, whichare used as the classification rule, in contrast to the traditional methods such as euclidean distance or mahalanobis distance in one subs pace. experimental results show that this method performs better than eigenfaces method in the presence offacial expression variations.
benefits of separable, multilinear discriminant classification. this paper presents an empirical investigation of the merits of tensor-based discriminant classification for visual object detection. first, we briefly discuss 2d separable discriminant analysis for grey value image analysis. then, we contrast this tensorial approach with classical linear discriminant analysis. our findings on a standard data set for object detection in natural environments show that, for the task of image analysis, tensor-based discriminant classifiers perform very robust. they learn and run faster and also generalize better than conventional techniques based on vectorial representations of the data.
lightsphere: fast lighting compensation for matching a 2d image to a 3d model. we describe a fast object recognition method that identifies 2d color image queries among a set of 3d models. it is fast enough for searching a very large database. the main application is face recognition, for which we report very good accuracy over a wide range of pose and lighting conditions. we make weaker assumptions about both lighting and reflectance than are usual. we avoid finding eigenvectors or solving systems of equations. instead, we use the query to estimate a specialization of the brdf to the fixed lighting and pose of the query. in a single image pass, we compute a lookup table for re-rendering, which represents expectation values for the action of the light via the brdf. this yields a similarity measure of the consistency between model and query under the regularity assumptions. we report recognition results on a data set of 42 3d face models and 1764 query images, comprising 7 poses and 6 lighting conditions. the recognition accuracy is indistinguishable from much slower methods, methods which make stronger assumptions about the brdf and lighting.
a fast binary-image comparison method with local-dissimilarity quantification. image similarity measure is widely used in image processing. for binary images that are not composed of a single shape, a local comparison is interesting but the features are usely poor (color) or difficult to extract (texture, forms). we present a new binary image comparison method that uses a windowed hausdorff distance in a pixel-adaptive way. it enables to quantify the local dissimilarities and to give their spatial distribution which greatly improve the dissimilarity information. combined with a support vector machine classifier, this method is successfully tested on an medieval-impression database.
applying compiler techniques to diagram recognition. compiler techniques are effective and efficient in processing textual programming languages. these techniques can be adapted to recognition and processing of two-dimensional languages (diagrams). already, grammars and parsers have been used in a variety of diagram-recognition and diagram-processing tasks. here we explore the use of two other compiler techniques in pattern recognition systems. the first is compiler-style use of trees and tree transformation. the second is a multi-pass control structure, with a clear separation between layout, lexical, syntactic, and semantic analysis. our proposal is illustrated on a case study involving recognition of hand-drawn mathematics notation.
finding highly frequented paths in video sequences. we propose a novel algorithm to find highly frequented paths of motion trajectories obtained from video sequences. this is achieved by representing the motion trajectories in the scene as sequences of prototypes obtained by a combined vector quantization and growing neural gas algorithm. in contrast to existing methods, the proposed algorithm can be applied to data sets containing motion trajectories of varying length. the algorithm does not assume an a priori fixed number of prototypes. we demonstrate results on surveillance video sequences of cars driving on a highway and pedestrians walking in a major railway station.
using attention for video segmentation. in this paper we propose an original approach to partitioning of a video into shots based on a foveated representation of the video.the proposed scheme aims at detecting both abrupt and gradual transitions between shots using a single technique, rather than a set of dedicated methods. results on videos of various content types are reported and validate the proposed approach.
bayesian pot-assembly from fragments as problems in perceptual-grouping and geometric-learning. a heretofore unsolved problem of great archaeological importance is the automatic assembly of pots made on a wheel from the hundreds (or thousands) of sherds found at an excavation site. an approach is presented to the automatic estimation of mathematical models of such pots from 3d measurements of sherds. a bayesian approach is formulated beginning with a description of the complete set of geometric parameters that determine the distribution of the sherd measurement data. matching of fragments and aligning them geometrically into configurations is based on matching break-curves (curves on a pot surface separating fragments), estimated axis and profile curve pairs for individual fragments and configurations of fragments, and a number of features of groups of break-curves. pot assembly is a bottom-up maximum likelihood performance-based search. experiments are illustrated on pots which were broken for the purpose, and on sherds from an archaeological dig located in petra, jordan. the performance measure can also be an aposteriori probability, and many other types of information can be included, e.g., pot wall thickness, surface color, patterns on the surface, etc. this can also be viewed as the problem of learning a geometric object from an unorganized set of free-form fragments of the object and of clutter, or as a problem of perceptual grouping.
inference of moving forms via belief propagation. in this paper we address the issue of how form and motion can be integrated in order to provide suitable information to attentively track multiple moving objects. such integration is designed in a bayesian framework, and a belief propagation technique is exploited to perform coherent form/motion labelling of regions of the observed scene.
3d object reconstruction from non-parallel cross-sections. we propose a 3d object reconstruction algorithm from non-parallel object cross-sections. the algorithm is based on two-dimensional morphological morphing, affine transform and cubic spline interpolation. the algorithm is developed for processing 3d freehand ultrasound data.
iris individuality: a partial iris model. biometrics-based personal authentication systems are becoming popular with increased demand on security. a biometrics is expected to have significant amount of discriminatory information in representing uniqueness of a person. this discriminatory information about the biometric is loosely defined as the individuality. even though some individuality studies have been carried out for fingerprints, face and handwriting, formal analysis of iris individuality has not been carried out. in this paper, we propose a partial model for iris individuality and show its usefulness in predicting the empirical performance results.
an evaluation of error confidence interval estimation methods. reporting the accuracy performance of pattern recognition systems (e.g., biometrics id system) is a controversial issue and perhaps an issue that is not well understood [an introduction to evaluating biometric systems, confidence interval and test size estimation for biometric data]. this work focuses on the research issues related to the oft used confidence interval metric for performance evaluation. using a biometric (fingerprint) authentication system, we estimate the false reject rates and false accept rates of the system using a real fingerprint dataset. we also estimate confidence intervals of these error rates using a number of parametric (e.g., see [confidence interval and test size estimation for biometric data]) and non-parametric (e.g., bootstrapping [1, 3, 6]) methods. we attempt to assess the accuracy of the confidence intervals based on estimate and verify strategy applied to repetitive random train/test splits of the dataset. our experiments objectively verify the hypothesis that the traditional bootstrap and parametric estimate methods are not very effective in estimating the confidence intervals and magnitude of interdependence among data may be one of the reasons for their ineffective estimates. further, wedemonstrate that the resampling the subsets of the data samples (inspired from moving block bootstrap [moving blocks jackknife and bootstrap capture weak dependence]) may be one way of replicating interdependence among the data; the bootstrapping methods using such subset resampling may indeed improve the accuracy of the estimates. irrespective of the method of estimation, the results show that the (1 - ¿) 100% confidence intervals empirically estimated from the training set capture significantly smaller than(1 - ¿) fraction of the estimates obtained from the test set.
patterns of co-linear equidistant letter sequences and verses. it has been shown ([4], [3]) that equidistant letter sequence (els) pairs in the book of genesis (g) form more compact geometric patterns on the surface of a cylinder than is expected at random. this phenomenon has been demonstrated in g for specific lists of biographical data. we extend these results and show that: 1. the compactness phenomenon holds for triplets of elements where two elements are words, taken from a lexicon derived from all the words in the pentateuch, which form co-linear elss, and the third element is a verse in the text that contains two words with the same meanings as the first two elements respectively. 2. this phenomenon manifests itself in the entire hebrew pentateuch.
on the equivalence of local-mode finding, robust estimation and mean-shift analysis as used in early vision tasks. in this paper we show the equivalence of three techniques used in image processing: local-mode finding, robust-estimation and mean-shift analysis. the computational common element in all these image operators is the spatial-tonal normalized convolution, an image operator that generalizes the bilateral filter.
fuzzy border distance transforms and their use in 2d skeletonization. segmentation is always a difficult task in image analysis. in this paper, we propose a solution to computing distance transforms in images with fuzzy object borders. the difference from a standard distance transform is in the initialisation, which takes the fuzziness of the border into account. as an example of its usefulness, the new fuzzy border distance transform is used in skeletonization.
object and scene classification: what does a supervised approach provide us?. given a set of images of scenes containing different object categories (e.g. grass, roads) our objective is to discover these objects in each image, and to use this object occurrences to perform a scene classification (e.g. beach scene, mountain scene). we achieve this by using a supervised learning algorithm able to learn with few images to facilitate the user task. we use a probabilistic model to recognise the objects and further we classify the scene based on their object occurrences. experimental results are shown and evaluated to prove the validity of our proposal. object recognition performance is compared to the approaches of he et al. [3] and mart´ý et al. [6] using their own datasets. furthermore an unsupervised method is implemented in order to evaluate the advantages and disadvantages of our supervised classification approach versus an unsupervised one.
introduction to the concept of structural hmm: application to mining customers' preferences in automotive design. we have introduced in this paper the concept of structural hidden markov models (shmm's). this new paradigm adds the syntactical (or structural) component to the traditional hmm's. shmm's introduce relationships between the visible observations of a sequence. these observations are related because they are viewed as evidences of a same conclusion in a rule of inference. we have applied this novel concept to predict customer's preferences for automotive designs. shmmhas outperformed both the k-nearest neighbors and the neural network classifiers with an additional 12% increase in accuracy.
protein fold recognition using a structural hidden markov model. protein fold recognition has been the focus of computational biologists for many years. in order to map a protein primary structure to its correct 3d fold, we introduce in this paper a machine learning paradigm that we entitled "structural hidden markov model" (shmm). we show how the concept of shmm can efficiently use the protein secondary structure during the fold recognition task. experimental results showed that the shmmoutperforms the svm with a 6% improvement in the average accuracy. however, because in this application the two classifiers are not correlated, therefore their combination based on the highest rank criterion boosted the shmm average accuracy with 10%.
object recognition from 3d blurred images. this paper presents a novel approach for 3d object recognition and classification. the originality of this approach is the combination of statistical reasoning, feature learning and expertise knowledge from the application. it is based on 2d processing of images alternating with 3d validation of class characteristics for recognition. classification is based on iterative refinement of hypotheses. an application is presented for pollen grain observed with lightmicroscopy for allergy prevention.