waashk/dblp huggingface dataset:
improved 3d head reconstruction system based on combining shape-from-silhouette with two-stage stereo algorithm. in this paper we present a quality improvement algorithm for the system, which models a human head. we have already proposed and applied the hybrid algorithm combining shape-from-silhouette with active stereo to our product of 3d head reconstruction system. our system is characterized by the ability of the reconstruction of the whole shape and texture of the human head, even the black hair parts, while other existing systems cannot do it. feature-based stereo algorithm adopted into our current system is the fast and robust approach. however, the depth data may be sparse and the accuracy depends on how accurate the edge detection is. on the other hand, area-based stereo algorithms generally provide dense depth data and can apply subpixel estimation, but it is comparatively time-consuming and may be inaccurate influenced by the difference of how to reflect the object in each image. to overcome these problems for improving our system practicably, we propose a novel two-stage stereo algorithm. in our algorithm, first, we adopt feature-based approach to get the depth data robustly. next, an interpolation of the depth data is performed to predict the depth data at the unestimated pixels on the edge. finally applying area-based approach and subpixel estimation refine the depth data. in this paper, we describe our hybrid modeling algorithm and two-stage stereo algorithm with some experimental results.
a method for crack detection on a concrete structure. recently, interest in automatic crack detection on concrete structure images for non-destructive inspection has been increasing. in general, there are various noises such as irregularly illuminated conditions, shading, blemishes and divots in the concrete images. these lead to difficulties for automatic crack detection. this paper presents two pre-processings in order to remove such noises for crack detection. first, slight variations like irregularly illuminated conditions and shading are removed from concrete images by the subtraction pre-processing with the smoothed image. secondly, a line filter based on the hessian matrix is used to emphasize line structures associated with cracks. finally, thresholding processing is used to separate cracks from background. the performance of the proposed method is evaluated by roc analysis with 50 real images. the experimental results show that the proposed method is effective for detecting cracks on noisy concrete images.
using multiple graphics cards as a general purpose parallel computer : applications to computer vision. pattern recognition and computer vision tasks are computationally intensive, repetitive, and often exceed the capabilities of the cpu, leaving little time for higher level tasks. we present a novel computer architecture which uses multiple, commodity computer graphics devices to perform pattern recognition and computer vision tasks many times faster than the cpu. this is a parallel computing architecture that is quickly and easily constructed from readily available hardware. it is based on parallel processing done on multiple graphics processing units (gpus). an eigenspace image recognition approach is implemented on this parallel graphics architecture. this paper discusses methods of mapping computer vision algorithms to run efficiently on multiple graphics devices to maximally utilize the underlying graphics hardware. the additional memory and memory bandwidth provided by the graphics hardware provided for significant speedup of the eigenspace approach. we show that graphics devices parallelize well and provide significant speedup over a cpu implementation, providing an immediately constructible low cost architecture well suited for pattern recognition and computer vision.
spine posture estimation method from human images using 3d spine model - computation of the rough approximation of the physical forces working on vertebral bodies. this paper describes a method for estimating a human spine posture from human images using a human spine model that is possible to compute the rough approximation of the physical forces working on vertebral bodies. the spine posture estimation model is composed of the vertebral bodies, each of which is modeled as a rigid body, and the intervertebral discs that are modeled as springs. our method uses the positions of the neck and waist in addition to the positions of the head, torso, and arms estimated from the actual human images. the spine model is deformed so as to locate the top and the bottom vertebrae of the spine model to the estimated neck and waist positions. according to the experiments based on one real mr image dataset of one subject person, our methods estimated the positions of the vertebrae within positional shifts of about 6.3 mm and the rotational variation of about 3.1 degrees. we also confirmed the methods calculated the reasonable estimation of the physical forces working on the vertebral body.
object localization/segmentation using generic shape priors. generally object segmentation is an ill-posed problem. approaches that use only plain image information will often fail. to overcome these limitations, prior knowledge (like information of the object contour) can be added to the segmentation process. in this paper, we present a novel generic shape model. we use the expertise from the field of object class recognition, namely a boundary-fragment- model (bfm) as prior knowledge for our level set segmentation approach. commonly, shape models need synthetically generated or pre-segmented training sets that are usually trained on one specific object or a small group of objects. with our new approach we are able to train shape models for whole categories, which makes the segmentation method much more flexible. additionally we overcome the difficulty of the correct initialization and reduce the segmentation effort. experimental results demonstrate the excellent performance of our method on different types of objects (categories).
object recognition using segmentation for feature detection. a new method is presented to learn object categories from unlabeled and unsegmented images for generic object recognition. we assume that each object can be characterized by a set of typical regions, and use a new segmentation method - "similarity-measure segmentation" - to split the images into regions of interest. this approach may also deliver segments, which are split into several disconnected parts, which turns out to be a powerful description of local similarities. several textural features are calculated for each region, which are used to learn object categories with boosting. we demonstrate the flexibility and power of our method by excellent results on various datasets. in comparison, our recognition results are significantly higher than results published in related work.
differentiating between many similar features using relational information in space and scale. we present an approach for differentiating between large numbers of similar feature points. the approach employs a learning strategy which utilizes mutual information to yield relational information or structure between feature points. it learns an ordered list of jumps in space and scale which is used for differentiation. to test the viability and potential of the approach, two datasets containing faces and objects were used.
missing microarray data estimation based on projection onto convex sets method. dna microarrays have gained widespread uses in biological studies. missing values in a microarray experiment must be estimated before further analysis. in this paper, we propose a projection onto convex sets based algorithm to incorporate all a priori knowledge about missing values into the estimation process. two convex sets applicable to all microarray datasets are constructed based on singular value decomposition (svd). in addition, in the two most popular missing value estimation methods knnimpute and svdimpute, there is a trade-off whether to use a specific group of genes for the missing value estimation or to use all genes. our algorithm can provide an optimal combination of these two strategies. experiments show our algorithm can achieve a reduction of 16% to 20% error than the knnimpute and svdimpute methods.
detecting periodically expressed genes based on time-frequency analysis and l-curve method. in microarray experiments, gene expression profiles are often affected by biological properties, such as synchronization loss, and show some non-stationarity. worse still, the microarray data usually suffers from missing values. the conventional spectrum-based methods, when used to identify a subset of genes that are periodically expressed, are degraded by these factors. in this paper, we use the wigner-ville distribution analysis and l-curve method for detection of periodically expressed genes. we provide a graphical exploratory device for assessment of the presence of periodically expressed genes. then, we identify the subset of genes actually involved in the cell cycle using the l-curve method. the experiments on several widely used datasets show that our algorithm can effectively reduce the effect of non-stationarity and missing values problems.
microarray missing data imputation based on a set theoretic framework and biological constraints. gene expressions measured using microarrays usually suffer from the missing value problem. existing missing value imputation algorithms have some limitations. for example, some algorithms have good performance only when strong local correlation exists in data while some provide the best estimate when data is dominated by a global structure. in addition, these algorithms do not take into account many biological constraints in the imputation procedure. in this paper, we propose a set theoretic framework for missing data imputation. we design our algorithm by taking into consideration the biological characteristic of the data and exploit the local correlation and the global correlation structure adaptively. experiments show that our algorithm can achieve a significant reduction of error compared with existing methods.
single camera stereo using planar parallel plate. a system of using a planar parallel plate to achieve single camera stereo has been proposed by nishimoto and shirai[a feature-based stereo model using small disparities]. their work was based onan assumption that the optical axis of the camera was equally displaced by a tilted planar plate with two fixed tilt angles. such assumption is invalid in general. in this paper, we propose a general framework to more accurately model such a single camera system using a planar parallel plate and to deal with arbitrary orientation of the plate. our model has no limitation on the fov of the camera. the proposed framework includes: a mathematical formulation to calculate the displacement of scene points; a plate calibration method to determine the plate intrinsic parameters as well as its extrinsic pose, and a generalized correspondence method which is capable of dealing with arbitrary number of images captured from different plate poses; experimental result is included to validate the proposed framework.
dining activity analysis using a hidden markov model. we describe an algorithm for dining activity analysis in a nursing home. based on several features, including motion vectors and distance between moving regions in the subspace of an individual person, a hidden markov model is proposed to characterize different stages in dining activities with certain temporal order. using hmm model, we are able to identify the start (and ending) of individual dining events with high accuracy and low false positive rate. this approach could be successful in assisting caregivers in assessments of resident's activity levels over time.
an ensemble classifier learning approach to roc optimization. an ensemble learning framework is proposed to optimize the receiver operating characteristic (roc) curve corresponding to a given classifier. the proposed ensemble maximal figure-ofmerit (e-mfom) learning framework meets four key requirements desirable for roc optimization, namely: (1) each classifier in the ensemble can be learned with any specified performance metric for any given classifier design; (2) such a classifier is discriminative in nature and attempts to optimize a particular operating point on the roc curve of the classifier; (3) an ensemble approximation to the overall behavior of the roc curve can be established by sampling a set of operating points; and (4) ensemble decision rules can be formulated by grouping these sampled classifiers with a uniform scoring function. we evaluate the proposed framework using 3 testing databases, the reuters and two uci sets. our experimental results clearly show that e-mfom learning outperforms the state-of-the-art algorithms using wilcoxon-mann-whitney rank statistics.
indexing with musical events and its application to content-based music identification. in this paper a musical event based indexing approach is proposed and its application to content-based music identification is studied. the events, which function as term words used in text retrieval or basic speech units in speech recognition, are inferred using an unsupervised learning algorithm. its differences with the existing methods are in that the learned low-level musicology knowledge and model selection technique are exploited to extract musical events. our experimental analyses on a task of music identification demonstrate that the proposed indexing method is efficient, compact and robust. using a collection of 20-second query segments on the evaluation set, the equal error rate reaches 1.57%. for applications that demand fewer false alarms, we could operate the system at a reduced false acceptance rate of 0.57% while increasing the false rejection rate to 4.58%.
boosting in random subspaces for face recognition. boosting is an excellent machine learning algorithm. in this paper, we propose a novel boosting method boosting in random subspaces. instead of boosting in original feature space, whose dimensionality is usually very high, multiple feature subspaces with lower dimensionality are randomly generated, and boosting is carried out in each random subspace. then the trained classifiers are further combined with simple fusion method. compared with boosting in original feature space, there are two advantages. the first is that the computation complexity of training is reduced, which is obvious. the second is that fusion further improves accuracy, which is verified by our extensive experiments on feret database.
face recognition using most discriminative local and global features. numerous studies in psychophysics and neurophysiological literatures have shown that both local and global features are important for representing and recognizing face. in this paper, a face recognition method, using local and global multi-resolution discriminative information, is proposed. first, face is represented by multi-scale and multiorientation gabor features. then adaboost is employed to learn local feature classifier, and lda (linear discriminant analysis) is used to extract global discriminative information. finally, their recognition results are fused. we evaluate both score and rank based combination schemes on feret and xm2vts face databases. experimental results demonstrate that almost all combination methods improve recognition rates and the best fusion method achieves 99% rank-1 recognition rate on feret fb probe set.
identification of embedded mathematical expressions in scanned documents. efficient extraction of mathematical expressions is considered as an important pre-processing step to apply existing ocr systems to convert scientific papers into their electronic format. in this correspondence, a technique for extracting embedded (or in-line) expressions has been presented. the proposed method for expression extraction initially invokes an existing ocr to recognize the input document. several features including word n-grams (a statistical analysis of a corpus of scientific documents reveals that the word level n-gram profile for sentences containing embedded expressions is quite different from that of the sentences without any expression) are computed on sentence level to spot sentences containing expressions. expression zones are pin pointed by exploiting ocr inability to handle expressions and by using some common typographical aspects followed in typing mathematical expressions. experimental results on a considerable size of dataset show high efficiency of the proposed technique.
lossless compression of textual images: a study on indic script documents. this paper presents a method for lossless compression of indian language textual images. the study is an extension of the previously developed pattern matching and substitution (pm&s)-based method for lossy compression of similar images. here an efficient method for residue coding is proposed and its performance is compared with ccitt gr-iv and jbig. a set of 20 text images for two most popular indic scripts, namely devanagari (hindi) and bengali, is used in the experiment. it is noted that the best results is achieved by pm&s-based approach followed by lzw-based residue coding. this combined scheme gives lossless compression ratio1 of about 37.9.
a multiple-classifier system for recognition of printed mathematical symbols. this paper deals with recognition of printed mathematical symbols. a group of classifiers arranged hierarchically is used to achieve robust recognition of the large number of symbols appearing in expressions. the classifier used at the top level employs stroke-based classification technique to recognize some of the frequently occurring symbols. the second level uses three classifiers to recognize the rest of the expression symbols. different combination techniques have been attempted to integrate the second level classifiers to achieve high recognition accuracy. experiment shows that the proposed approach is quite robust for recognition of a large number of symbols appearing in various expressions.
improvement of ocr accuracy by similar character pair discrimination: an approach based on artificial immune system. artificial immune system (ais) based classification approach is relatively new in the field of pattern recognition (pr). this paper explores this paradigm in the context of a frequently occurring pr problem, namely discrimination of similar shaped character pairs. the problem has been studied in the context of improving the recognition accuracy of ocr (optical character recognition) systems that often make mistakes to properly classify the confusion pairs. a set of binary classifiers is designed following immune principles to achieve pair-wise discrimination. the performance of the proposed approach has been investigated in detail and compared with classification schemes like nearest neighbor and support vector machines (svm)-based approach.
online handwritten indian script recognition: a human motor function based framework. this paper presents the online handwriting recognition for indian scripts. the primary concern of the approach is the modeling of human motor functionality while writing characters. this is achieved by looking at the whole pen trajectory where the time evaluation of the pen coordinates plays a crucial role. a low complexity classifier has been designed and the proposed similarity measure appears to be quite robust against wide variations in writing styles.initially, the approach has been applied for online recognition of handwritten characters in devnagari and bangla, the two major indian scripts. a test on a dataset of considerable size shows promising recognition rates namely, 97.29% for devnagari and 96.34% for bangla.
summarization of jbig2 compressed indian language textual images. this paper presents a method for automatic summarization of jbig2 coded textual images without optical character recognition (ocr). compressed images are partially (less than 10% of the uncompressed image size) decompressed and text lines and words are marked. a few features are computed at each sentence level. based on the feature values sentences are then marked as a summary sentence or not. the system finally generates a set of sentences as summary. in addition, sentences are ranked within the summary. experiment considers indian language text images. test results show a sentence selection efficiency of about 56% when judged against summarization generated by human. a nonparametric (distribution-free) rank statistic shows a correlation coefficient of 0.28 as a measure of the (minimum) strength of the associations between sentence ranking by machine and human.
object-based image retrieval using active nets. in this work, extraction of relevant objects from images and their matching for retrieval is proposed. objects are represented by using a two dimensional deformable structure referred to as active net, capable to adapt to relevant image regions according to chromatic and edge information. in particular, this representation allows a joint description of color, shape and structural information of extracted objects. a similarity measure between active nets is also defined and validated in a set of retrieval experiments on the eth-80 objects database.
performance of the kullback-leibler information gain for predicting image fidelity. this paper presents a new method for characterizing information of a compressed image relative to the original one. we show how the kullback-leibler information gain is based on three basic postulates which are natural for image processing and thus desirable. as an example of the proposed measure, we analyze the effects of lossy compression on the identification of breast cancer microcalcifications. we also show the comparative results of the kullback-leibler information gain and various quantitative measures for predicting image fidelity in the sense of diagnostic usefulness.
improving texture pattern recognition by integration of multiple texture feature extraction methods. this paper proposes a pixel-based texture classifier that integrates multiple texture feature extraction methods in order to identify the regions of an input image that belong to a given set of texture patterns. experimental results with textured images of outdoor scenes show that the proposed technique yields lower classification errors than widely recognized texture classifiers based on specific families of texture methods.
estimation of distance to planar surfaces and type of material with infrared sensors. this paper proposes a new technique for computing the distance to an unknown planar surface and, at the same time, estimating the material of the surface through the use of low-cost infrared sensors. previous approaches to this problem require more costly ultrasound systems as a complement or, alternatively, an exhaustive training process that records the sensor response corresponding to known materials measured at different predefined distances. experimental results with an off-the-shelf infrared sensor mounted on a mobile robot are presented.
fusion of global and local information for object detection. this paper presents a framework for fusing together global and local information in images to form a powerful object detection system. we begin by describing two detection algorithms. the first algorithm uses independent component analysis (ica) to derive an image representation that captures global information in the input data. the second algorithm uses a part-based representation that relies on local properties of the data. the strengths of the two detection algorithms are then combined to form a more powerful detector. the approach is evaluated on a database of real-world images containing side views of cars. the combineddetector gives distinctly superior performance than each of the individual detectors, achieving a high detection accuracy of 94% on this difficult test set.