waashk/dblp huggingface dataset:
a new perceptive system for the recognition of cursive handwriting. we present a new system for the recognition of cursive handwriting that is based on a perceptive model and neural networks. at the high level, our system takes into account several psychological effects such as the word superiority effect. at the low level, it utilizes a global feature extraction method which models how some features might be preattentively detected by the human visual system. it presents a very good tolerance to noise and stroke disconnections and captures most of the information contained in the singular part of the cursive word. at the pre-recognition stage, external letters are better recognized than middle letters. thus, because it uses a recognition process that is based on an interactive activation mechanism, recognition is performed from the outside to the inside of the word. we have obtained encouraging results.
semantic understanding of continued and recursive human activities. this paper presents a methodology for semantic understanding of complex and continued human activities. a context-free grammar (cfg) based representation scheme developed earlier is extended to construct a description for continued and recursive human activities. new system recognizes recursively described high-level interactions: fighting and greeting. the system understands activities by detecting the time intervals that satisfy their semantic descriptions.
template adaptation based fingerprint verification. this paper proposes a minutiae-based template adaptation algorithm which can be applied after the fingerprint authentication process. the algorithm updates a template by using a query fingerprint, which is successfully verified by the fingerprint matcher as a high quality genuine input. this algorithm generates an updated minutiae set by using not only the minutiae but also local fingerprint quality information and utilizes a successive bayesian estimation to evaluate the credibility of minutiae and their types. the proposed algorithm updates fingerprint minutiae information in the template as well as appends new minutiae from the query fingerprint. preliminary experiments show an average 32.7% eer reduction and an even higher matching accuracy improvement at low false accept rates.
shape decomposition approach for ultrasound color doppler image segmentation. we present a novel technique for segmenting blood vessels in ultrasound color doppler images based on shape decomposition. ultrasound color doppler images frequently suffer from a vessel linking artifact called 'color bleeding'. the proposed technique decomposes a complex binary object representing either two or more vessels artificially linked together or a main vessel with its branches. the resulting simple parts represent distinct vessels that can be used in further object recognition and quantification applications.
optimally regularised kernel fisher discriminant analysis. mika et al. [fisher discriminant analysis with kernels] introduce a non-linear formulation of fisher's linear discriminant, based the now familiar "kernel trick", demonstrating state-of-the-art performance on a wide range of real-world benchmark datasets.in this paper, we show that the usual regularisation parameter can be adjusted so as to minimise the leave-one-out cross-validation error with a computational complexity of only o(l^2) operations, where l is the number of training patterns, rather than the o(l^4) operations required for a naïeve implementation of the leave-one-out procedure.this procedure is then used to form a component of an efficient heirarchical model selection strategy where the regularisation parameter is optimised within the inner loop while the kernel parameter are optimised in the outer loop.
combining shape from silhouette and shape from structured light for volume estimation of archaeological vessels. an algorithm for the automatic construction of a 3d model of archaeological vessels using two different 3d algorithms is presented. in archeology the determination of the exact volume of arbitrary vessels is of importance since this provides information about the manufacturer and the usag of the vessel. to acquire the 3d shape of objects with handles is complicated, since occlusions of the object's surface are introduced by the handle and can only be resolved by taking multiple views. therefore, the 3d reconstruction is based on a sequence of images of the object taken from different viewpoints with two different algorithms; shape from silhouette and shape from structured light. the out-put of both algorithms are then used to construct a single 3d model. results of the algorithm developed are presented for both synthetic and real input images.
a new clustering method for improving plasticity and stability in handwritten character recognition systems. this paper presents a new online clustering algorithm in order to improve plasticity and stability in handwritten character recognition systems. our clustering algorithm is able to automatically determine the optimal number of clusters in the input data. an incremental learning technique similar to adaptive resonance theory (art) is used to determine the best cluster for new data. our technique also allows the previously learned clusters to be merged whenever the newly arrived data points push their centers close together. we also developed new features and similarity measures in order to describe and compare the shapes of handwritten digits to be used in our clustering algorithm. results of our algorithm on clustering the shapes of the handwritten numerals from the cenparmi isolated digit database are shown. our method can incrementally learn new handwriting styles of digits, without forgetting the previous ones, therefore it can improve plasticity and stability.
joint spatial and temporal structure learning for task based control. we present an example of a joint spatial and temporal task learning algorithm that results in a generative model that has applications for on-line visual control. we review work on learning transformed mixture of gaussians (due to frey and jojic) and variable length markov models (vlmms due to ron, singer and tishby). we show how a temporal model, learned through an extension of vlmms to deal with multinomially distributed input symbol vectors, can be used as an improvement on maximum likelihood (ml) for prior parameter estimation for the expectation maximisation (em) process.
fast feature extraction approach for multi-dimension feature space problems. recently, we proposed a fast feature extraction approach denoted fsom utilizes self organizing map (som). fsom [1] overcomes the slowness of traditional som search algorithm. we investigated the superiority of the new approach using two lip reading data sets which require a limited feature space as the experiments in [1] showed. in this paper, we continue fsom investigation but using an rgb face recognition database across different poses and different lighting conditions. we believe that such data sets require multi-dimensional feature space to extract the information included in the original data in an effective way especially if you have a big number of classes. again, we show here how is fsom reduces the feature extraction time of traditional som drastically while preserving same som's qualities.
a texture based matching approach for automated assembly of puzzles. the puzzle assembly problem has many application areas such as restoration and reconstruction of archeological findings, repairing of broken objects, solving jigsaw type puzzles, molecular docking problem, etc. the puzzle pieces usually include not only geometrical shape information but also visual information such as texture, color, and continuity of lines. this paper presents a new approach to the puzzle assembly problem that is based on using textural features and geometrical constraints. the texture of a band outside the border of pieces is predicted by inpainting and texture synthesis methods. feature values are derived from these original and predicted images of pieces. an affinity measure of corresponding pieces is defined and alignment of the puzzle pieces is carried out using an fft based image registration technique. the optimization of total affinity gives the best assembly of puzzle. experimental results are presented on real and artificial data sets.
cbir using perception based texture and colour measures. we have designed and implemented an experimental cbir system that uses a texture co-occurence matrix. fuzzy index of major colours are also used as colour feature to improve performance. a new measure is suggested to find out the relevance of the retrieved images and to evaluate the cbir system. accordingly, the performance study of the proposed system is carried out and compared with a similar system. the study has established the effectiveness of the features.
coarse-to-fine support vector classifiers for face detection. we describe a new hierarchical face detection algorithm which allows fast background rejection in major parts of images and fine processing in area containing faces. this coarse-to-fine classification strategy is based on learning support vector classifiers (svms) with increasing evaluation complexity (resp. decreasing invariance and false alarm rates) top-down in the hierarchy. the complexity, in terms of the number of support vectors, of each detector inthe hierarchy is reduced by clustering. we introduce the bias variation technique which allows each simplified svm function to satisfy the conservation hypothesis as a criterion to get a consistent classifier in terms of detection rate, false alarms and background rejection efficiency . face detection is performed using a depth-first search and cancel strategy which, for a given "face pattern", finds a root-leaf path with a sequence of positive answers.
a robust semi-supervised em-based clustering algorithm with a reject option. in this paper, we address the problem of semi-supervision in the framework of parametric clustering by using labeled and unlabeled data together. clustering algorithms can take advantage from few labeled instances in order to tune parameters, improve convergence and overcome local extrema due to bad initialization. we extend a robust parametric clustering algorithm able to manage outlier rejection to the semi-supervision approach. this is achievedby modifying the expectation-maximization algorithm. the proposed method shows good performance with respect to data structure discovering, even facing to outliers.
automatic recognition of blooming flowers. this paper describes an automatic method for recognizing a blooming flower based on a photograph taken with a digital camera in natural scene. the problem of identifying an object against the background is known to be difficult. in this paper, we employ a photograph where the object (a blooming flower) is focused but the background is defocused. for extracting a flower region, we propose a new method that extracts a boundary by selecting a route with minimizing a sum of the local cost divided by the route length. experiments were conducted for 600 pictures (20 pictures each for 30 species). a successful boundary extraction rate of 97% and a flower recognition rate of 90% were obtained.
automatic segmentation of liver region through blood vessels on multi-phase ct. recently four phases ct has been widely used for diagnosing liver diseases, especially cancer. this paper describes an automatic method for segmenting the liver region from third phase abdominal ct. first, blood vessels in the liver are extracted with a threshold. then morphological dilation enables us to define an approximate liver region, which is found to be very useful for removing abutting organs. the final liver region is extracted with a threshold. experiments were conducted for eight ct data. the extracted regions agree well with those detected manually.
human and object detection in smoke-filled space using millimeter-wave radar based measurement system. in recent years, crisis management's response to terrorist attacks and natural disasters, as well as accelerating rescue operations have become an important issue. in rescue operations of fire disaster, one of the biggest problems is that the firefighter's view is obstructed by dense smoke. we considered the most important task for firefighters is to understand the inside situation of the dense smoke space. therefore, we aim to develop a system visualizing the situation of the space. first, we scanned target space by using millimeter-wave radar combined with a gyro sensor. then, due to detect humans and objects, we construct a 3d map from signal-reflection datasets using 3d image processing techniques. in this paper, we introduce our system and report the results of the measurement experiment in the real smoke space situation.
genetic translator: how to apply query learning to practical ocr. we propose a novel learning method combining query learning and a "genetic translator" we developed.query learning is a useful technique for high-accuracy, high-speed learning. however, it has not been applied for practical optical character readers (ocrs), since human beings cannot recognize queries in the feature space used in practical ocr devices. we previously proposed a character image reconstruction method using the genetic algorithm. here, this method is applied as a "translator" from feature space for query learning of character recognition. the results of an experiment with hand-printed numeral recognition demonstrate the potential of the proposed method.
a real-life test of face recognition system for dialogue interface robot in ubiquitous environments. this paper discusses a face recognition system for a dialogue interface robot that really works in ubiquitous environments and reports an experimental result of real-life test in a ubiquitous environment. while a central module of the face recognition system is composed of the decomposed eigenface method, the system also includes a special face detection module and the face registration module. since face recognition should work on images captured by a camera equipped on the interface robot, all the methods are tuned for the interface robot. the face detection and recognition modules accomplish robust face detection and recognition when one of the registered users is talking to the robot. some interesting results are reported with careful analysis of a sufficient real-life experiment.
image-recognition technologies towards advanced automated teller machines. several image-recognition techniques that can provide the important functions required by advanced-intelligent automated teller machines (ai-atms) are described. these functions are (1) basic cash handling such as withdrawal and deposit of money, (2) handling remittance forms and cheques, (3) delivering information as an "information kiosk," and (4) security maintenance such as identification of users and surveillance of the atm environment. the image-recognition techniques for realising these ai-atm functions are (1) banknote image processing, (2) form processing and character recognition, (3) intention recognition, and (4) biometric techniques.
incremental mixtures of factor analysers. a mixture of factor analyzer is a semiparametric density estimator that performs clustering and dimensionality reduction in each cluster (component) simultaneously. it performs nonlinear dimensionality reduction by modeling the density as a mixture of local linear models. the approach can be used for classification by modeling each class-conditional density using a mixture model and the complete data is then a mixture of mixtures. we propose an incremental mixture of factor analysis algorithm where the number of components (local models) in the mixture and the number of factors in each component (local dimensionality) are determined adaptively. our results on different pattern classification tasks prove the utility of our approach and indicate that our algorithms find a good trade-off between model complexity and accuracy.
object segmentation and feature estimation using shadows. we introduce an automatic underwater video-based system for estimating features such as length and 3-d velocity of fish from a single-view camera system. artificial light provides a high contrast object shadow, which is exploited both in the segmentation process, and to achieve 3-d inference about the fish. the proposed video segmentation method is demonstrated successfully on video-recordings of greenland halibut.
a new method to detect arcs and segments from curvature profiles. in this paper a new method of arc detection based on arithmetic discrete lines is presented. key points are extracted from such a profile and used for the reconstruction. the used method is fast and easy to implement. experimental studies on several series of test images show the stability and the robustness of the proposed method.
learning classes for video interpretation with a robust parallel clustering method. we propose an original learning approach for image classification problems. recognizing semantic events in video requires to preliminary learn the different classes of events. this first stage is crucial since it conditions the further classification results. in video content analysis, the task is especially difficult due to the high intra-class variability and to noisy measurements. we then represent each class by the centers of several sub-classes (or clusters) thanks to a robust partitional clustering algorithm which can be applied in parallel to a (non-predefined) number of classes. our clustering technique overcome three main limitations of standard k-means methods: sensitivity to initialization, choice of the number of clusters and influence of outliers. moreover, it can process the training data in an incremental way. experimental results on sports videos are reported.
prioritized region of interest coding in jpeg2000. a method is proposed to encode multiple regions of interest in the jpeg2000 image-coding framework. the algorithm is based on the rearrangement of packets in the code-stream to place the regions of interest before the background coefficients. in order to improve the quality of the reconstructed image, partial background information is included with the regions of interest. the proposed technique is fully compatible with the current jpeg2000 standard and allows transmission of different regions of interest with different priorities. experimental results demonstrating the validity of the proposed approach are presented and compared with existing region of interest coding techniques.
a theory of the quasi-static world. we present the theory behind a novel unsupervised method for discovering quasi-static objects, objects that are stationary during some interval of observation, within image sequences acquired by any number of uncalibrated cameras. for each pixel we generate a signature that encodes the pixel's temporal structure. using the set of temporal signatures gathered across views, we hypothesize a global schedule of events and a small set of objects whose arrivals and departures explain the events. the paper specifies observability conditions under which the global schedule can be established and presents the qsl algorithm that generates the maximally-informative mapping of pixels' observations onto the objects they stem from. our framework ignores distracting motion, correctly deals with complicated occlusions, and naturally groups observations across cameras. the sets of 2d masks we recover are suitable for unsupervised training and initialization of object recognition and tracking systems.
on authorship attribution via markov chains and sequence kernels. we investigate the use of recently proposed character and word sequence kernels for the task of authorship attribution and compare their performance with two probabilistic approaches based on markov chains of characters and words. several configurations of the sequence kernels are studied using a relatively large dataset, where each author covered several topics. utilising moffat smoothing, the two probabilistic approaches obtain similar performance, which in turn is comparable to that of character sequence kernels and is better than that of word sequence kernels. the results further suggest that when using a realistic setup that takes into account the case of texts which are not written by any hypothesised authors, about 5000 reference words are required to obtain good discrimination performance.
motion analysis using frame differences with spatial gradient measures. the paper considers making inferences about the underlying true 2-d motion when only evaluations of a local block-based cost function, the mean of absolute or squared differences, for a set of motion candidates are available. considering bounds for these criteria, it is shown that simple local image gradient measures provide useful information for interpreting the criterion values. based on analysis, a thresholding scheme for the criteria is proposed. using a gaussian approximation for the thresholding result, estimates of local motions and related uncertainties can be obtained.
shape similarity image retrieval by hypothesis and test. image retrieval by shape similarity systems usually either focus their attention on images with isolated objects (uniform backgrounds and no occluding objects) or perform time consuming exhaustive initializations to localize the portion of the image possibly containing the searched shape. we propose a content based image retrieval (cbir) method merging classic alignment techniques for an efficient shape localization with an innovative verification strategy able to deal with an inexact matching between the searched and the found shape.
class-specific subspace-based two-dimensional principal component analysis for face recognition. in this paper, we proposed a class-specific subspacebased two-dimensional principal component analysis (2dpca) for face recognition. in 2dpca, 2d face image matrices do not need to be previously transformed into a vector. in this way, the spatial information can be preserved. moreover, 2dpca can achieve higher performance than pca both in face recognition and face representation task. however, both pca and 2dpca are unsupervised techniques, no information of class labels are considered. therefore, the directions that maximize the scatter of the data might not be as adequate to discriminate between classes. in recognition task, a projection is required to emphasize the discrimination between classes. the face-specific subspace (fss) was proposed in concept of class-specific subspace. each subspaces learned from the training images which correspond to only one class, thus the number of these subspaces is equal to the number of classes. since the information of class labels are considered in fss, so the discriminant power can be improved. we apply 2dpca to class-specific concept in our framework which consists of two methods: the first one, we apply fss to 2dpca method and the second one, we use the bilateral-projection-based 2dpca (b2dpca) instead of 2dpca. the b2dpca does not only allows further reducing of the dimension of feature matrix of 2dpca-based but also improving the classi fi cation accuracy. experimental results on yale face database showed an improvement of our proposed techniques over the conventional 2dpca.
texture analysis using level-crossing statistics. we present a novel statistical texture descriptor employing level-crossing statistics. images are first mapped into 1d signals using space-filling curves, such as peano or hilbert curves, and texture features are extracted via signal-dependent sampling. texture parameters are based on the level-crossing statistics of the 1d signal, i.e. crossing rate, crossing slope and sojourn time. despite the simplicity of texture features used, our approach offers state-of-the art performance in the texture classification and texture segmentation tasks, outperforming other tested algorithms.