waashk/dblp huggingface dataset:
face authentication test on the banca database. this paper details the results of a face authentication test (fat2004) [http://www.ee.surrey.ac.uk/banca/icpr2004] held in conjunction with the 17th international conference on pattern recognition. the contest was held on the publicly available banca database [http://www.ee.surrey.ac.uk/banca] according to a defined protocol [the banca database and evaluation protocol]. the competition also had a sequestered part in which institutions had to submit their algorithms for independent testing. 13 different verification algorithms from 10 institutions submitted results. also, a standard set of face recognition software packages from the internet [http://www.cs.colostate.edu/evalfacerec] were used to provide a baseline performance measure.
unsupervised decomposition of mixed pixels using the maximum entropy principle. due to the wide existence of mixed pixels, the derivation of constituent components (endmembers) and their proportions (abundances) at subpixel scales has become an important research topic. in this paper, we propose a novel unsupervised decomposition method based on the classical maximum entropy principle, termed umaxent. the algorithm integrates a global least square error-based endmember detection and a per-pixel maximum entropy learning to find the most possible proportions. we apply the proposed method to the subject of spectral unmixing. the experimental results obtained from both simulated and real hyperspectral data demonstrate the effectiveness of the umaxent method.
regression analysis and automorphic orbits in free groups of rank 2. the main goal of this paper is to show that pattern recognition techniques can be successfully used in abstract algebra. we introduce a pattern recognition system to recognize words of minimal length in their automorphic orbits in free groups of rank 2. this system is based on linear regression and does not use any particular results from group theory. the corresponding classifier is very fast and surprisingly accurate.
a hierarchical projection pursuit clustering algorithm. we define a cluster to be characterized by regions of high density separated by regions that are sparse. by observing the downward closure property of density, the search for interesting structure in a high dimensional space can be reduced to a search for structure in lower dimensional subspaces. we present a hierarchical projection pursuit clustering (hppc) algorithm that repeatedly bi-partitions the dataset based on the discovered properties of interesting 1-dimensional projections. we describe a projection search procedure and a projection pursuit index function based on cho, haralick and yi's improvement of the kittler and illingworth optimal threshold technique. the output of the algorithm is a decision tree whose nodes store a projection and threshold and whose leaves represent the clusters (classes). experiments with various real and synthetic datasets show the effectiveness of the approach.
local multiple orientation estimation: isotropic and recursive oriented network. in this paper we propose a new operator for texture orientation estimation. we focus on directional textures which can have more than a single orientation at the same point. our operator consists in a steerable network of parallel lines along which a homogeneity feature is computed in the spatial domain. the analysis of the network response along each direction allows us to set up the presence of single or multiple orientations. in order to reduce the computing cost, we propose a recursive implementation of our operator, thanks to the rotations of the image instead of the rotation of the network. our operator works on a small support, and thus provides a local estimation of the orientations. results obtained both with synthetic textures and natural images are accurate and show the selectivity and the isotropic behaviour of our operator.
image acquisition enhancement for active video surveillance. last years have seen a big effort of research community to solve the security problems by developing video surveillance systems. normally, developed systems have the characteristic to be executed on images acquired by autonomous cameras. in this paper we propose a new way to develop visual surveillance systems which would not be a set of passive modules but systems that actively decide both what to see and how to see it. in particular, our idea is to act on the regulation of the acquisition parameters as consequence of what the system needs to see. the regulation strategy is based on two parameters, focus and iris, and aims to identify an optimal sequence of steps to enhance the acquisition quality of the object of interest. to this end, a hierarchy of neural networks has been employed to select first which parameter must be regulated then to adjust it.
focusing on target's features while tracking. usually tracking objects by means of a moving camera means solving the problem of segmenting its motion from the background and maintain the gaze on it. usually key points, i.e. features like corners, are matched throughout consecutive frame to register frames and detecting objects or to compute a fixation point on the object of interest. none of the methods proposed so far investigates how, more then just matching features between frames, the tuning of their quality would increase the robustness of the methods. hence, the novelty proposed in this paper concerns the automatic tuning of the focus parameter to increase the tracking capabilities and strength the robustness of the entire system.
bayesian rendering with non-parametric multiscale prior model. this paper investigates the use of the bayesian inference for devising an example-based rendering procedure. as prior model of this bayesian inference, we exploit the multiscale non-parametric model recently proposed by wei et al. for texture synthesis. this model appears to be interesting to also capture some characteristics of a rendering style from an artistic illustration example. obtained results, with a prior model capturing the rendering style ofdrawing samples or trained with synthetic and real input textures, are presented. our results indicate that the proposed method allows to simulate automatic synthesis of various illustration style. more generally, the proposed scheme is able to re-render an input image in the style of an other image allowing, in this way, to create a very broad range of artistic and visual effects.
lossless data compression for image decomposition with recursive idp algorithm. specific algorithms for lossless compression of the data, obtained after processing of halftone and color images with the inverse difference pyramid decomposition (idp), are described in this paper. the basic steps of the idp algorithm (coding and decoding) are defined, and the corresponding diagram for modified huffman code generation is shown. the presented lossless run-length and modified huffman algorithms are used together with a lossy image coding method, and are aimed at preserving the image quality after the idp decomposition unchanged. some practical results and comparison with the standard jpeg2000 are given also.
two-stage classification system combining model-based and discriminative approaches. for the tasks of classification, two types of patterns can generate problems: ambiguous patterns and outliers. furthermore, it is possible to separate classification algorithms into two main categories. discriminative approaches try to find the better separation among all classes and minimize the first type of error. but, in general they cannot deal with outliers. besides, model-based approaches make the outlier detection possible but are not sufficiently discriminative. thus, we propose to combine a model-based approach with support vectors classifiers (svc) in a two-stage classification system. another advantage of this combination is to reduce the principal burden of svc: the processing time necessary to make a decision. finally, the experiments on handwriting digit recognition have shown that it is possible to maintain the accuracy of svcs, while decreasing complexity significantly.
exact view-dependent visual hulls. the visual hull is widely used to produce three dimensional models from multiple views, due to the reliability of the resulting surface. this paper presents a novel method for efficiently evaluating the exact view-dependent visual hull without using approximations. methods for selecting intersections and ordering them via the cross ratio are presented. results show the high quality of the surfaces produced using this method.
word completion with latent semantic analysis. current word completion tools rely mostly on statistical or syntactic knowledge. can using semantic knowledge improve the completion task? we propose a languageindependent word completion algorithm which uses latent semantic analysis (lsa) to model the semantic context of the word being typed. we find that a system using this algorithm alone achieves keystroke savings of 56% and a hit rate of 42%. this represents improvements of 6.9% and 17%, respectively, over existing approaches.
activity recognition based on multiple motion trajectories. we propose an method for activity recognition based on multiple motion trajectories. motion trajectotires generated from body parts (hand, feet, and joints) are used as features. we not only recognize each activity but also temporally locate the start and end point of its duration. input sequences are divided into separate temporal segments based on the number of detected trajectories. segments with same number of trajectories are temporally segmented using the hmm model for each movement (activity). the experimental results show that our approach can successfully locate each activity in continuous video sequences.
tensor voting accelerated by graphics processing units (gpu). this paper presents a new gpu-based tensor voting implementation which achieves significant performance improvement over the conventional cpu-based implementation. although the tensor voting framework has been used for many vision problems, it is computationally very intensive when the number of input tokens is very large. however, the fact that each token independently collects votes allows us to take advantage of the parallel structure of gpus. also, the good computing power of modern gpus contributes to the performance improvement as well. our experiments show that the processing time of gpu-based implementation can be, for example, about 30 times faster than the cpu-based implementation at the voting scale factor ó = 15 in 5d.
edge-preserving simultaneous joint motion-disparity estimation. we propose an energy-based joint motion and disparity estimation algorithm with an anisotropic diffusion operator to yield correct and dense displacement vectors. the model estimates the left and right motions simultaneously in order to increase accuracy. we use the euler-lagrange equation with variational methods and solve the equation with the finite difference method (fdm). then, the method computes the initial disparity in the current frame with joint estimation constraint, and regularizes this disparity by using our energy model. experimental results show that the proposed algorithm provides accurate motion-disparity maps, and preserve the discontinuities of the object boundaries well.
multi-layer mosaics in the presence of motion and depth effects. in this paper, we present a new segmentation-based 2d mosaic framework. most of current mosaic algorithms do not explicitly remove moving objects from images before registration, so that they often fail when the size of the moving objects is relatively large. to solve this problem, we first segment moving objects from the input images using the tensor voting framework, and then only the remaining backgrounds are processed for the background mosaic. the second mosaicking step is straightforward because the first motion segmentation step also produces very accurate dense matches. by providing comparative examples, we show that the quality of the background mosaics can be significantly improved by our framework.
model estimation for photometric changes of outdoor planar color surfaces caused by changes in illumination and viewpoint. in this paper we compare different ways of representing the global photometric changes in image intensities caused by changes in illumination and viewpoint, aiming at a balance between goodness-of-fit and low complexity. a series of model selection tests are performed for the case of outdoor imagery consisting of several views of several instances of billboards taken under different viewing angles and different illumination (natural light). possible candidates for a transformation model on (r,g,b) color space are investigated and different approaches for the model selection problem are considered.the results are used within ongoing research into computation of new invariant features for planar color patterns, as the model choice is an important issue to decide on when extracting invariants. these results can be of benefit to other areas of research into color pattern or object recognition, which motivated us to report here in detail about the model selection work.
robust tracking of soccer players based on data fusion. this paper presents a technique for integrating multiple visual features for tracking moving objects. our proposed method consists of observation (pattern-matching) units and prediction units, which form a ladder structure.the major feature of our proposed method is that each of the observation units with different pattern matching algorithms is executed step-by-step to innovate the state vector considering the reliability of the observation. the fusion of multiple observations makes the tracks robust to occlusion and to deformation.in this paper, experiments with soccer sequences are shown to validate the technique's robustness. its applications to broadcasting services are also briefly discussed.
a probabilistic approach to fast and robust template matching and its application to object categorization. this paper presents a new statistic, called probabilistic increment sign correlation (probabilistic isc), for evaluating similarity between images of objects which have intra-class variation such as individual differences of human faces. the new statistic evaluates similarity between an input image and object classes, whereas most conventional methods, such as normalized cross-correlation, calculate correlation between an input image and a template. the new statistic is defined as a log-likelihood based on probabilities of observing the increment signs. probabilistic isc provides two advantages over conventional correlationbased methods: 1) robustness against the intra-class variation because it gives larger weights to stable features which are commonly observed in reference images and 2) robustness against noise and change in illumination. it yields higher performance even if a small number of reference images are given, whereas other methods such as the subspace method and adaboost cannot maintain their accuracy. we show these advantages through several experiments of face detection and face orientation estimation.
artificial images for classifying diffuse lung opacities in thin-section computed tomography images. the classification of diffuse lung opacities in thin-section computed tomography(hrct) images is very fundamental for developing a computer-aided diagnosis(cad) system.however, in designing such a cad system, the number of the available samples is usually small.this leads to the difficulties of designing the cad system.one way to overcome this problem is to generate artificial images from available real images by image rotation and reversal.in this paper, we discuss the use of artificial images for designing the cad system.
environment recognition based on analysis of human actions for mobile robot. in this paper, we propose a novel method for recognizing environment based on relationship between human actions and objects for a mobile robot. most of previous works on environment recognition for robots focused on generating obstacle maps for path-planning. in addition, model-based object recognition techniques are also used for searching particular objects. it is, however, difficult in reality to prepare a lot of models in advance for recognizing various objects in unknown environments. on the other hand, human can often recognize objects not from their appearances but by watching other person taking actions on them. this is because the function and/or the usage of the objects are closely related with human actions. we have introduced conceptual models of human actions and objects for classifying objects by observing human activities in our previous work. in this paper, we apply this key idea to a mobile robot. we also demonstrate that the arrangement of objects can be recognized by analyzing human actions.
combining the gabor and histogram features for classifying diffuse lung opacities in thin-section computed tomography. the classification of diffuse lung opacities in thin-section computed tomography (hrct) images is an important step for developing a computer-aided diagnosis (cad) system. in designing the cad system for classifying diffuse lung opacities in hrct images, a gabor filter-based approach has been shown to be effective. in order to improve further the classification performance of the cad system, we explore the combination of the gabor and histogram features. the ex-perimental results show that combining the gabor and histogram features leads to clear improvement of the classification performance.
connected pattern segmentation and title grouping in newspaper images. this paper presents an algorithm that performs automated segmentation and classification of newspaper images. the algorithm discusses a technique for segmenting components that are connected to other components and presents another technique to correctly group titles and subtitles. the algorithm uses a bottom-up approach to initially segment the image, classify patterns and extract text lines. the classified patterns are then merged into complete regions. the algorithm is tested on a set of complex newspaper images taken from the first international newspaper segmentation contest, and the results are compared with the contest results.
a moe framework for biclustering of microarray data. biclustering or simultaneous clustering of both genes and conditions have generated considerable interest over the past few decades, particularly related to the analysis of high-dimensional gene expression data in information retrieval, knowledge discovery, and data mining. the objective is to find sub-matrices, i.e., maximal subgroups of genes and subgroups of conditions where the genes exhibit highly correlated activities over a range of conditions. since these two objectives are mutually conflicting, they become suitable candidates for multi-objective modeling. in this study, a novel multi-objective evolutionary biclustering framework is introduced by incorporating local search strategies. the experimental results on benchmark datasets demonstrate better performance as compared to existing algorithms available in literature.
feature selection and gene clustering from gene expression data. in this article we describe an algorithm for feature selection and gene clustering from high dimensional gene expression data. the method is based on measuring similarity between features/genes whereby redundancy therein is removed. this does not need any search and therefore is fast. a novel feature similarity measure, called maximum information compression index, is used. the feature selection algorithm also obtains gene clusters in a multiscale fashion. the superiority of the algorithm, in terms of speed and performance, is established on a real life molecular cancer classification dataset.
comparison of microarray-based predictive systems for early recurrence of cancer. in our previous study, we successfully developed a novel predictive system with microarray data in order to predict early intrahepatic recurrence of hepatocellular carcinoma after curative resection. in this paper, we compare our system with other systems in terms of the predictive performance. from experimental results, our system was superior to other systems. this success comes from our gene selection procedure that can cope with the sample variability.
an online handwritten music score recognition system. the objective of this study is to produce a system that would allow music symbols to be written by hand using a pen-based computer that would simulate the feeling of writing on sheets of paper and that would also accurately recognize the music symbols. to accomplish these objectives, the following methods are proposed: (1) two features, time-series data and an image of a handwritten stroke, are used to recognize strokes; and (2) the strokes are combined, as efficiently as possible, and outputted automatically as a music symbol. as a result, recognition rates of 97.60 and 98.80% were obtained in tests with strokes and music symbols, respectively.
automatic extraction of buildings utilizing geometric features of a scanned topographic map. we have been developing the feature oriented and progressive algorithm in order to is to make an object-oriented digital map database (dmd)from printed topographic maps by using raster-vector conversion and extracting various objects such as natural objects, man-made objects, and notations. this paper concentrates to discuss how to extract the buildings.
a learning process to the identification of feature points on chinese characters. the paper describes a new stroke extraction approach to identify the feature points of a character, using line-filtering and learning-based techniques. the line-filtering technique based on the convolution operations with a set of l-d gabor templates is efficient in extracting the stroke segments of the character and robust in noise tolerance. furthermore, unlike convectional feature-point detection techniques where decision rules and thresholds have to be specified, our learning-based technique for feature-point identification implicitly represents the rules and thresholds without further parameter adjustments. experimental results show that the learning-based technique is capable of generalizing the learning knowledge to identify feature points and can get an average identification rate of 95.27% for hand-printed test characters and 96.78% for machine-printed test characters.
a proposal of neural network architecture for non-linear function approximation. in this paper, a neural network architecture for non-linear function approximation is proposed. we point out problems in non-linear function approximation with traditional neural networks, that is, difficulty in analyzing internal representation, no reproducibility in function approximation due to the random scheme for weight initialization, and the insufficient generalization ability in learning without enough samples. based on these considerations, we suggest three main improvements. the first is the design of a sigmoidal function with localized derivative. the second is a deterministic scheme for weight initialization. the third is an updating rule for weight parameters. simulation results show beneficial characteristics of our proposed method; low approximation error at the beginning of iterative calculation, smooth convergence of error and its improvement for difficulty in analyzing internal representation.
constraint-based prototyping for understanding three orthographic views. we propose a prototyping approach to understanding three orthographic views based on constraint satisfaction and discuss practicability of the proposed prototyping system. our approach reconstructs 3d objects by solving the constraint satisfaction problem that is equivalently transformed from the input 2d data. we perform some experiments comparing our prototyping system with the handcoded system developed separately in which a set of restoration knowledge is implemented. after evaluating efficiency for several drawing examples, we point out two factors that cause inefficiency and propose ideas to cope with them.