waashk/dblp huggingface dataset:
visual pattern recognition in the years ahead. conventional classification algorithms have already reached a plateau at the trade-off imposed by the bias due to the structure of the classifier and the variance due to the limited size of the training set. the latter may be alleviated by exploiting known constraints, including class and style priors, language models, statistical correlations between spatially proximate patterns, statistical dependence due to isogeny (common source) of patterns, and even information-theoretic properties of the representations that have evolved for symbolic patterns intended for communication. another development that may lead to new applications of pattern recognition is more effective human intervention. the interplay of human and machine abilities requires models that are both human and computer accessible.
activity summarisation and fall detection in a supportive home environment. automatic semantic summarisation of human activity and detection of unusual inactivity are useful goals for a vision system operating in a supportive home environment. learned models of spatial context are used in conjunction with a tracker to achieve these goals. the tracker uses a coarse ellipse model and a particle filter to cope with cluttered scenes with multiple sources of illumination. summarisation in terms of semantic regions is demonstrated using acted scenes through automatic recovery of the instructions given to the actor. the use of 'unusual inactivity' detection as a cue for fall detection is also demonstrated.
simple and efficient colorization in ycbcr color space. we have already proposed a colorization method in rgb color space, where the colorization problem is formulated as the maximum a posteriori (map) estimation of a color image given a monochrome image. markov random field (mrf) is used for modeling a color image which is utilized as a prior for the map estimation. in this paper, a colorization method in ycbcr space is presented, which is derived by the same formulation as in rgb color space. the presented method in ycbcr space is much simpler than that in rgb space and requires much less computation time: about one fourth of computation time in rgb space. as for quality of estimated color image, both methods in ycbcr and rgb space produce color images with comparable psnr values.
a formalization of on-line handwritten japanese text recognition free from line direction constraint. this paper presents a formalization of an on-line writing-box free, line-direction free handwritten japanese text recognition and its effect. by normalizing character orientation, even text of arbitrary character orientation can be recognized. the method evaluates the likelihood composed of character segmentation, character recognition, character pattern structure and context. the likelihood of character pattern structure considers the plausible height, width and gaps within a character pattern that appear in chinese characters composed of multiple radicals (subpatterns). we show how the newly modeled factors in the likelihood affect the overall recognition rate.
speaker verification using a novel set of dynamic features. dynamic cepstral features such as delta and deltadelta cepstra have been shown to play an essential role in capturing the transitional characteristics of the speech signal. in this paper, a set of new dynamic features for speaker verification system are introduced. these new features, known as delta cepstral energy (dce) and delta-delta cepstral energy (ddce), can compactly represent the information in the delta and delta-delta cepstra. furthermore, it is shown theoretically that dce carries the same information as the delta cepstrum using an entropy criterion. experimental speaker verification results on the timit database support the theoretical result, showing a significant improvement in terms of equal error rate compared with conventional feature extraction methods using delta and delta-delta cepstra.
morphology analysis of physiological signals using hidden markov models. we describe a clustering algorithm based on continuous hidden markov models (hmm) to automatically classify both electrocardiogram (ecg) and intracranial pressure (icp) beats based on their morphology. the algorithm detects, classifies and labels each beat based on morphology. in order to avoid the numerical problems with classical expectation-maximization (em) algorithm we apply a novel method of simulated annealing (sim) for hmm optimization. we show that better results are achieved using simulated annealing approach.
sharpening of ct images by cubic interpolation using b-spline. computed tomography (ct) is a method by which an original image is reconstructed based on the data of projected images collected from various directions, and is widely used in the fields of medicine and industry. however, the edge of the image reconstructed by ct is unsharp, therefore, microflaws are often overlooked. in this paper, we demonstrated that one of the causes of the decreased sharpness of the reconstructed image is associated with the linear interpolation during the back-projection process, and in our method, the linear interpolation is replaced by cubic interpolation using the b-spline. in addition, by calculating the control points of b-spline by fourier transform, the process required for the calculation of the control points appears to be eliminated. in the experiment, the reduction of unsharpness to 1/2 that of the conventional method and the reduction of processing time to a level equivalent to that of the conventional method have been achieved.
embedding motion in model-based stochastic tracking. particle filtering (pf) is now established as one of the most popular methods for visual tracking. within this framework, two assumptions are generally made. the first is that the data are temporally independent given the sequence of object states, and the second one is the use of the transition prior as proposal distribution. in this paper, we argue that the first assumption does not strictly hold and that the second can be improved. we propose to handle both modeling issues using motion. explicit motion measurements are used to drive the sampling process towards the new interesting regions of the image, while implicit motion measurements are introduced in the likelihood evaluation to model the data correlation term. the proposed model allows to handle abrupt motion changes and to filter out visual distractors when tracking objects with generic models based on shape representations. experimental results compared against the condensation algorithm have demonstrated superior tracking performance.
improving human activity detection by combining multi-dimensional motion descriptors with boosting. a new, combined human activity detection method is proposed. our method is based on efros et al.'s motion descriptors[2] and ke et al.'s event detectors[3]. since both methods use optical flow, it is easy to combine them. however, the computational cost of the training increases considerably because of the increased number of weak classifiers. we reduce this computational cost by extend ke et al.'s weak classifiers to incorporate multi-dimensional features. the proposed method is applied to off-air tennis video data, and its performance is evaluated by comparison with the original two methods. experimental results show that the performance of the proposed method is a good compromise in terms of detection rate and of computation time of testing and training.
robust 3d head tracking using camera pose estimation. in this paper we present a robust method to recover 3d position and orientation (pose) of a moving head using a single stationary camera. head pose is recovered via a camera pose estimation formulation. 3d feature points (artificial or natural occurring) are acquired from the head prior to tracking and used as a model. pose is estimated by solving a robust version of "perspective n point" problem. the proposed algorithm can handle self occlusions, outliers and recover from tracking failures. results were validated by simultaneous tracking using our system and an accurate magnetic field 3d measuring device. our contribution is a system that is not restricted to track only human heads, and is accurate enough to be used as a measuring device. to demonstrate the applicability of our method, three types of heads (human, barn owl, chameleon) were tracked in a series of biological experiments.
pattern recognition and understanding for visual information media. pattern recognition and understanding, pru, is a fundamental technology to realize intelligent visual information media, which can select, summarize, and augment the visual information in the real world according to the need of each individual. a three years research project, "pattern recognition and understanding for visual information media (vis-pru)", sponsored by the mext (ministry of education, culture, sports, science and technology, japan), has finished this march. the purpose of this project is to restructure the pru technology, which has been developed for making automatic machines to replace humans, into a key technology in the multimedia fields where the information media are expected to support the human activity and to augment the human ability. the critical difference between the two cases is that a human consumes the output of pru in multimedia systems, while a machineconsumes it in automated systems for substituting humans. in this talk, the achievements of the research project will be introduced and the pru in real time, human centered systems will be discussed.
faxed form identification using histogram of the hough-space. ordering and charging goods have been increasingly treated with faxed forms. although the faxocr system for specified forms is used practically, the performance to unspecified forms is not enough, because of the effect of noise on the faxed forms during the facsimile transmission. the final target of this study is to construct a practical faxocr system for unspecified forms. as the first stage, an identification method for unspecified faxed forms is proposed in this paper. in our approach, character separation and position adjustment are performed in the hough-space as pre-processing. then the form identification is carried out by using vote histogram in the hough-space. the performance of the proposed technique is verified experimentally by using actual faxed forms.
automatic tracking of local myocardial motion by correlation weighted velocity method. in this paper, we propose a new method for automatically tracking the motion of local region in left ventricular myocardium by means of ultrasonic pulsed doppler signal. this method consists of a velocity detection procedure based on correlation weighted mean instantaneous velocity and a motion tracking procedure employing a myocardial elastic model. most of ultrasonic pulsed doppler signals observed in clinical diagnosis contain considerable amount of speckle noise, which causes detection error of velocity. the detection error is accumulated in the motion tracking procedure and yields obviously incorrect motion trajectory. the procedure of correlation weighted mean velocity is aimed to reduce the velocity detection error, and the myocardial elastic model is used to avoid the accumulation of the error to keep track of the motion of the myocardium in reasonable accuracy. the result of evaluation test shows that this method is able to improve the accuracy of tracking approximately 40 % relative to a conventional cls approach.
outex- new framework for empirical evaluation of texture analysis algorithms. this paper presents the current status of a new initiative aimed at developing a versatile framework and image database for empirical evaluation of texture analysis algorithms. the proposed outex framework contains a large collection of surface textures captured under different conditions, which facilitates construction of a wide range of texture analysis problems. the problems are encapsulated into test suites, for which baseline results obtained with algorithms from literature are provided. the rich functionality of the framework is demonstrated with examples in texture classification, segmentation and retrieval. the framework has a web site for public dissemination of the database and comparative results obtained by research groups world wide.
temporally evaluated optical flow: study on accuracy. in order for visual feedback systems to have quick response, an image sequence captured at a much higher frame rate than the video rate has recently been employed. however, when optical flow, which is often used for vision-based control, is computed by the conventional methods using such an image sequence, the result contains significant error because there is almost no variance between any two successive image frames. to deal with this problem, we propose temporally evaluated optical flow, which we compute by measuring the time required for each pixel to move a fixed distance using temporally consistent shifts of each pixel. experimental results show that the proposed method improves accuracy and is superior to the conventional methods in terms of robustness.
a method for fine registration of multiple view range images considering the measurement error properties. this paper presents a new method for fine registration of two range images from different viewpoints that have been roughly registered. our method deals with the properties of the measurement error of the range image data. the error distribution is different for each point in the image and is usually dependent on both the viewing direction and the distance to the object surface. we find the best transformation of two range images to align each measured point and reconstruct 3d total object shape by taking such properties of the measurement error into account. the position of each measured point is corrected according to the variance and the extent of the distribution of its measurement error. the best transformation is selected by the evaluation of the effectiveness of this correction of every measured point. the experiments showed that our method produced better results than the conventional icp method.
robust estimation of camera translation between two images using a camera with a 3d orientation sensor. it is still a difficult problem to establish correspondences of feature points and to estimate view relations for multiple images of a static scene, if the images have large disparities. in this paper we explore the possibility of applying a cheap and general-purpose 3d orientation sensor to improve the robustness of matching such two images. we attach a 3d orientation sensor to a camera and use the system to acquire the images. the camera orientation is obtained from the sensor. assuming known intrinsic parameters of the camera, we are to estimate only the camera translation between the two views. owing to the small number of parameters needed to be estimated, it becomes possible to apply a voting method. we show that the method by voting is more robust than the methods based on random sampling, especially for difficult pair of images to make corre-spondences. in addition, using the known camera orientation, the images can be rectified so that it is as if they were taken by parallel cameras, before the candidate matches are searched for. this helps finding as many correct matches as possible for pairs of images that include rotation around the camera axis. experimental results for synthetic images as well as real images are shown.
range image registration preserving local structures of object surfaces. we propose a registration method for range images that preserves local structures of object surfaces. the method introduces shape patterns and a skewness of correspondences, both of which are extracted from the local surface nearby a point of interest in each image. the shape patterns are used to eliminate false corresponding pairs of surfaces, while the skewness is used to estimate the transformation that relates the coordinates between different range images. these two features enable us to estimate the transformation that preserves local structures of object surfaces.
robust text detection from binarized document images. many document images are rich in color and have complex background. to detect text from them, a standard approach utilizes both color and binary information. this often leads to time-consuming processing and requires a lot of parameters to be tuned. in contrast, we propose a new method for text detection using a binary image alone. the main virtues of our method include detection of both normal and inverted text and robustness to various font types, styles and sizes and small skew angles, combined with a moderate number of free parameters.
honeybees as an intelligent based approach for 3d reconstruction. this work is about the communication system used by honeybees with the idea of designing a new intelligent approach for 3d reconstruction. a new framework is proposed to allow the communication between 3d points in order to achieve an improved quasi-dense reconstruction. this method could be used reliably in further visual computing tasks because the obtained reconstruction emerges as a by product of an algorithmic intelligent process.
improving rbf-dda performance on optical character recognition through parameter selection. the dynamic decay adjustment (dda) algorithm is a fast constructive algorithm for training rbf neural networks. in previous works it has been shown that for some datasets the generalization performance of rbf-dda depends only weakly on the algorithm parameters ¿+ and ¿-. however, we have observed experimentally that for some problems performance is considerably dependent on the value of ¿-. in this work we propose a method for selecting the value of ¿- for performance optimization. the proposed method has been evaluated on three optical recognition datasets from the uci repository. the results show that the proposed method considerably improves the performance of rbf-dda with default parameters on these tasks. the results are compared to mlp and k-nn results obtained in previous works. it is shown that the method proposed in this paper outperforms mlps and obtains results comparable to k-nn on these tasks.
feature selection using multi-objective genetic algorithms for handwritten digit recognition. this paper discusses the use of genetic algorithm for feature selection for handwriting recognition. its novelty lies in the use of a multi-objective genetic algorithms where sensitivity analysis and neural network are employed to allow the use of a representative database to evaluate fitness and the use of a validation database to identify the subsets of selected features that provide a good generalization. comprehensive experiments on the nist database confirm the effectiveness of the proposed strategy.
a new approach to the classification of mammographic masses and normal breast tissue. a new approach to mammographic mass detection is presented in this paper. although different algorithms have been proposed for such a task, most of them are application dependent. in contrast, our approach makes use of a kindred topic in computer vision adapted to our particular problem. in this sense, we translate the eigenfaces approach for face detection/classification problems to a mass detection. two different databases were used to show the robustness of the approach. the first one consisted on a set of 160 regions of interest (rois) extracted from the mias database, being 40 of them with confirmed masses and the rest normal tissue. the second set of rois was extracted from the ddsm database, and contained 196 rois containing masses and 392 with normal, but suspicious regions. initial results demonstrate the feasibility of using such approach with performances comparable to other algorithms, with the advantage of being a more general, simple and cost-effective approach.
pose clustering guided by short interpretation trees. it is common in object recognition algorithms based on viewpoint consistency to find object poses that align many of the object features with features extracted from a search image. algorithms usually treat these features as having no information other than location. however, in many applications, the features are much more distinctive than this. this distinctiveness can be used to improve recognition with respect to both the search time and the reliability of the recognition. we modify an efficient clustering method for detecting objects using geometry to incorporate short trees that help prune many of the possible matches between object features and image features prior to the more expensive clustering step. the methodology is applied to a problem of computing a spacecraft position with respect to a celestial body by recognizing the configuration of craters visible on the surface.
optimal estimation of perspective camera pose. in this paper we propose a practical and efficient method for finding the globally optimal solution to the problem of camera pose estimation for calibrated cameras. while traditional methods may get trapped in local minima, due to the non-convexity of the problem, we have developed an approach that guarantees global optimality. the scheme is based on ideas from global optimization theory, in particular, convex under-estimators in combination with branch and bound. we provide a provably optimal algorithm and demonstrate good performance on both synthetic and real data.
affine invariant information embedment for accurate camera-based character recognition. recognizing characters in a scene image taken by a digital camera has been studied for decades. however, it is still a challenging problem to achieve high accuracy. in this paper, we propose a method of embedding information in a character pattern so that the class of the character can be identified. the information should be robust against geometric distortions since an image taken by a digital camera is usually geometrically distorted. in the proposed method, a character pattern is designed in two colors so that the information is embedded as the area ratio of regions of two colors. since the area ratio is affine invariant, it is expected that the area ratio is correctly extracted even if a character image is affine-transformed. we generate character patterns with the embedded information and discuss the effectiveness of the proposed method.
precise estimation of high-dimensional distribution and its application to face recognition. in statistical pattern recognition, it is important to estimate true distribution of patterns precisely to obtain high recognition accuracy. normal mixtures are sometimes used for representing distributions. however, precise estimation of the parameters of normal mixtures requires a great number of sample patterns, especially for high dimensional vectors. for some pattern recognition problems, such as face recognition, very high dimensional feature vectors are necessary and there are always not enough training samples compared with the dimensionality. we present a method to estimate the distributions based on normal mixtures with small number of samples. the proposed algorithm is applied to face recognition problem which requires high dimensional feature vectors. experimental results show the effectiveness of the proposed algorithm.