nils-herrmann/clean_scirepeval huggingface dataset:
Likelihood Ratio-Based Biometric Score Fusion

Multibiometric systems fuse information from different sources to compensate for the limitations in performance of individual matchers. We propose a framework for the optimal combination of match scores that is based on the likelihood ratio test. The distributions of genuine and impostor match scores are modeled as finite Gaussian mixture model. The proposed fusion approach is general in its ability to handle 1) discrete values in biometric match score distributions, 2) arbitrary scales and distributions of match scores, 3) correlation between the scores of multiple matchers, and 4) sample quality of multiple biometric sources. Experiments on three multibiometric databases indicate that the proposed fusion framework achieves consistently high performance compared to commonly used score fusion techniques based on score transformation and classification.
Towards automated caricature recognition

This paper addresses the problem of identifying a subject from a caricature. A caricature is a facial sketch of a subject's face that exaggerates identifiable facial features beyond realism, while still conveying his identity. To enable this task, we propose a set of qualitative facial features that encodes the appearance of both caricatures and photographs. We utilized crowdsourcing, through Amazon's Mechanical Turk service, to assist in the labeling of the qualitative features. Using these features, we combine logistic regression, multiple kernel learning, and support vector machines to generate a similarity score between a caricature and a facial photograph. Experiments are conducted on a dataset of 196 pairs of caricatures and photographs, which we have made publicly available. Through the development of novel feature representations and matching algorithms, this research seeks to help leverage the ability of humans to recognize caricatures to improve automatic face recognition methods.
A self-organizing network for hyperellipsoidal clustering (HEC)

We propose a self-organizing network for hyperellipsoidal clustering (HEC). It consists of two layers. The first employs a number of principal component analysis subnetworks to estimate the hyperellipsoidal shapes of currently formed clusters. The second performs competitive learning using the cluster shape information from the first. The network performs partitional clustering using the proposed regularized Mahalanobis distance, which was designed to deal with the problems in estimating the Mahalanobis distance when the number of patterns in a cluster is less than or not considerably larger than the dimensionality of the feature space during clustering. This distance also achieves a tradeoff between hyperspherical and hyperellipsoidal cluster shapes so as to prevent the HEC network from producing unusually large or small clusters. The significance level of the Kolmogorov-Smirnov test on the distribution of the Mahalanobis distances of patterns in a cluster to the cluster center under the Gaussian cluster assumption is used as a compactness measure. The HEC network has been tested on a number of artificial data sets and real data sets, We also apply the HEC network to texture segmentation problems. Experiments show that the HEC network leads to a significant improvement in the clustering results over the K-means algorithm with Euclidean distance. Our results on real data sets also indicate that hyperellipsoidal shaped clusters are often encountered in practice.
PTZ camera assisted face acquisition, tracking & recognition

Face recognition systems typically have a rather short operating distance with standoff (distance between the camera and the subject) limited to 1~2 meters. When these systems are used to capture face images at a larger distance (5~10 m), the resulting images contain only a small number of pixels on the face region, resulting in a degradation in face recognition performance. To address this problem, we propose a camera system consisting of one PTZ camera and two static cameras to acquire high resolution face images up to a distance of 10 meters. We propose a novel camera calibration method based on the coaxial configuration between the static and PTZ cameras. We also use a linear prediction model and camera control to mitigate delays in image processing and mechanical camera motion. The proposed system has a larger standoff in face image acquisition and effectiveness in face recognition test. Experimental results on video data collected at a distance ranging from 5 to 10 meters of 20 different subjects as probe and 10,020 subjects as gallery shows 96.4% rank-1 identification accuracy of the proposed method compared to 0.1% rank-1 accuracy of the conventional camera system using a state-of-the-art matcher.
Online handwritten script recognition

Automatic identification of handwritten script facilitates many important applications such as automatic transcription of multilingual documents and search for documents on the Web containing a particular script. The increase in usage of handheld devices which accept handwritten input has created a growing demand for algorithms that can efficiently analyze and retrieve handwritten data. This paper proposes a method to classify words and lines in an online handwritten document into one of the six major scripts: Arabic, Cyrillic, Devnagari, Han, Hebrew, or Roman. The classification is based on 11 different spatial and temporal features extracted from the strokes of the words. The proposed system attains an overall classification accuracy of 87.1 percent at the word level with 5-fold cross validation on a data set containing 13,379 words. The classification accuracy improves to 95 percent as the number of words in the test sample is increased to five, and to 95.5 percent for complete text lines consisting of an average of seven words.
PhenoTree: Interactive Visual Analytics for Hierarchical Phenotyping From Large-Scale Electronic Health Records

Electronic health records (EHRs) capture comprehensive patient information in digital form from a variety of sources. Increasing availability of EHRs has facilitated development of data and visual analytic tools for healthcare analytics, such as clinical decision support and patient care management systems. Many healthcare analytic tools are used to investigate fundamental problems, such as study of patient population, exploring complicated interactions among patients and their medical histories, and extracting structured phenotypes characterizing the patient population. In this paper, we propose PHENOTREE, a novel data-driven, hierarchical, and interactive phenotyping tool, that enables physicians and medical researchers to participate in the phenotyping process of large-scale EHR cohorts. The proposed visual analytic tool allows users to interactively explore EHR cohorts, and generate, interpret, evaluate, and refine phenotypes by building and navigating a phenotype hierarchy. Specifically, given a cohort or subcohort, PHENOTREE employs sparse principal component analysis (SPCA) to identify key clinical features that characterize the population. The clinical features provide a natural way to generate deeper phenotypes at finer granularities by expanding the phenotype hierarchy. To facilitate the intensive computation required for interactive analytics, we design an efficient SPCA solver based on a variance reduced stochastic gradient technique. The benefits of our method are demonstrated by analyzing two different EHR patient cohorts, a public and a private dataset containing EHRs of 101 767 and 223 076 patients, respectively. Our evaluations show that PHENOTREE can detect clinically meaningful hierarchical phenotypes.
Fingerprint matching using minutiae and texture features

The advent of solid-state fingerprint sensors presents a fresh challenge to traditional fingerprint matching algorithms. These sensors provide a small contact area (/spl ap/0.6"/spl times/0.6") for the fingertip and, therefore, sense only a limited portion of the fingerprint. Thus multiple impressions of the same fingerprint may have only a small region of overlap. Minutiae-based matching algorithms, which consider ridge activity only in the vicinity of minutiae points, are not likely to perform well on these images due to the insufficient number of corresponding points in the input and template images. We present a hybrid matching algorithm that uses both minutiae (point) information and texture (region) information for matching the fingerprints. Results obtained on the MSU-VERIDICOM database shows that a combination of the texture-based and minutiae-based matching scores leads to a substantial improvement in the overall matching performance.
A Radon transform computer for multidimensional signal processing

A high-speed Radon transform computer (RTC) has been designed and simulated that operates on the principles of the parallel projection pipeline engine (PPPE) processor suggested by J.L.C. Sanz, et al. (1988). The computer consists of an array of identical processing elements with fast communication links to each of its neighbors and uses forward- and back-projection formulas tests linear interpolation. Simulation and experimental timing tests have been performed on both a systolic array of 64 transputer processors and on arrays of commercial DSP (digital signal processor) chips. A custom VLSI implementation is currently under design which is expected to perform forward and inverse transforms of 512 by 512 images at or near video rates.<<ETX>>
Testing for Uniformity in Multidimensional Data

Testing for uniformity in multidimensional data is important in exploratory pattern analysis, statistical pattern recognition, and image processing. The goal of this paper is to determine whether the data follow the uniform distribution over some compact convex set in K-dimensional space, called the sampling window. We first provide a simple, computationally efficient method for generating a uniformly distributed sample over a set which approximates the convex hul of the data. We then test for uniformity by comparing this generated sample to the data by using Friedman-Rafsky's minimal spanning tree (MST) based test. Experiments with both simulated and real data indicate that this MST-based test is useful in deciding if data are uniform.
Local weight selection for two-dimensional phase unwrapping

Local weights in phase unwrapping play an important role in guiding the flow of the phase integration. Unwrapping algorithms can proceed in a path-following or a non-path-following fashion. In path-following phase unwrapping algorithms, local weights guide the selection of unwrapping paths. In nonpath-following algorithms, the introduction of predetermined local weights is necessary to accommodate phase inconsistencies in practice. In current unwrapping algorithms, these local weights are extracted either from the quality information (correlation in interferometric synthetic aperture radar and modulation in phase shifting interferometry) or from the information contained in the wrapped phase difference alone. In other words, the crucial issue is how a phase unwrapping algorithm should select these location-associated weights in order to prevent the propagation of phase errors. This paper presents a quantitative scheme for selecting local weights for phase unwrapping opt the basis of both these types of information (the quality map and the wrapped phase). Real objects as well as synthetic ones have been investigated for various unwrapping methods in experiments. Integrated 3D free-form object models demonstrate the validity of the phasor-weighted phase unwrapping approach.
Structure in on-line documents

We present a hierarchical approach for extracting homogeneous regions in on-line documents. The problem of identifying and processing ruled and unruled tables, text and drawings is addressed. The on-line document is first segmented into regions with only text strokes and regions with both text and non-text strokes. The text region is further classified as unruled table or plain text. Stroke clustering is used to segment the non-text regions. Each nontext segment is then classified as drawing, ruled table or underlined keyword using stroke properties. The individual regions are processed and the results are assembled to identify the structure of the on-line document.
Automatic Face Recognition of Newborns, Infants, and Toddlers: A Longitudinal Evaluation

A number of emerging applications requiring reliable identification of children have called attention to whether biometric traits can be utilized as a solution. While biometric traits based on friction ridge patterns (e.g. fingerprints, footprints) have been evaluated to some extent, to our knowledge, no effort has been made to evaluate the efficacy of automatic face recognition of young children over useful durations of time. Additionally, there are some applications where only the face images of a child are available, such as identification of missing or abducted children and children shown in sexually exploitive media sequestered by law enforcement. In this paper, we introduce the Newborns, Infants, and Toddlers Longitudinal (NITL) face image database, which was collected by the authors during four different sessions over a period of one year (March 2015 to March 2016) at the Saran Ashram Hospital, Dayalbagh, India. The NITL database contains 314 subjects in total in the age range of 0 to 4 years old. The aim of this paper is to provide a comprehensive evaluation of a state-of-the-art commercial-off- the-shelf (COTS) face matcher on the NITL face image database to investigate the feasibility of face recognition of children as they age. Experimental results show that while available face recognition technology is not yet ready to reliably recognize very young children, face recognition enrolled at 3 years of age or older may be feasible.
Document Representation and Its Application to Page Decomposition

Transforming a paper document to its electronic version in a form suitable for efficient storage, retrieval, and interpretation continues to be a challenging problem. An efficient representation scheme for document images is necessary to solve this problem. Document representation involves techniques of thresholding, skew detection, geometric layout analysis, and logical layout analysis. The derived representation can then be used in document storage and retrieval. Page segmentation is an important stage in representing document images obtained by scanning journal pages. The performance of a document understanding system greatly depends on the correctness of page segmentation and labeling of different regions such as text, tables, images, drawings, and rulers. We use the traditional bottom-up approach based on the connected component extraction to efficiently implement page segmentation and region identification. A new document model which preserves top-down generation information is proposed based on which a document is logically represented for interactive editing, storage, retrieval, transfer, and logical analysis. Our algorithm has a high accuracy and takes approximately 1.4 seconds on a SGI Indy workstation for model creation, including orientation estimation, segmentation, and labeling (text, table, image, drawing, and ruler) for a 2550/spl  image of a typical journal page scanned at 300 dpi. This method is applicable to documents from various technical journals and can accommodate moderate amounts of skew and noise.
Shape Spectrum Based View Grouping and Matching of 3D Free-Form Objects

We address the problem of constructing view aspects of 3D free-form objects for efficient matching during recognition. We introduce a novel view representation based on "shape spectrum" features, and propose a general and powerful technique for organizing multiple views of objects of complex shape and geometry into compact and homogeneous clusters. Our view grouping technique obviates the need for surface segmentation and edge detection. Experiments on 6,400 synthetically generated views of 20 free-form objects and 100 real range images of 10 sculpted objects demonstrate the good performance of our shape spectrum based model view selection technique.
Face Recognition in Video: Adaptive Fusion of Multiple Matchers

Face recognition in video is being actively studied as a covert method of human identification in surveillance systems. Identifying human faces in video is a difficult problem due to the presence of large variations in facial pose and lighting, and poor image resolution. However, by taking advantage of the diversity of the information contained in video, the performance of a face recognition system can be enhanced. In this work we explore (a) the adaptive use of multiple face matchers in order to enhance the performance of face recognition in video, and (b) the possibility of appropriately populating the database (gallery) in order to succinctly capture intra class variations. To extract the dynamic information in video, the facial poses in various frames are explicitly estimated using active appearance model (AAM) and a factorization based 3D face reconstruction technique. We also estimate the motion blur using discrete cosine transformation (DCT). Our experimental results on 204 subjects in CMU's face-in-action (FIA) database show that the proposed recognition method provides consistent improvements in the matching performance using three different face matchers.
Video-to-video face matching: Establishing a baseline for unconstrained face recognition

Face recognition in video is becoming increasingly important due to the abundance of video data captured by surveillance cameras, mobile devices, Internet uploads, and other sources. Given the aggregate of facial information contained in a video (i.e., a sequence of face images or frames), video-based face recognition solutions can potentially alleviate classic challenges caused by variations in pose, illumination, and expression. However, with this increased focus on the development of algorithms specifically crafted for video-based face recognition, it is important to establish a baseline for the accuracy using state-of-the-art still image matchers. Note that most commercial-off-the-shelf (COTS) offerings are still limited to single frame matching. In order to measure the accuracy of COTS face recognition systems on video data, we first investigate the effectiveness of multi-frame score-level fusion and analyze the consistency across three COTS face matchers. We demonstrate that all three COTS matchers individually are superior to previously published face recognition results on the unconstrained YouTube Faces database. Further, fusion of scores from the three COTS matchers achieves a 20% improvement in accuracy over previously published results. We encourage the use of these results as a competitive baseline for video-to-video face matching on the YouTube Faces database.
Markov Random Field Texture Models

We consider a texture to be a stochastic, possibly periodic, two-dimensional image field. A texture model is a mathematical procedure capable of producing and describing a textured image. We explore the use of Markov random fields as texture models. The binomial model, where each point in the texture has a binomial distribution with parameter controlled by its neighbors and ``number of tries'' equal to the number of gray levels, was taken to be the basic model for the analysis. A method of generating samples from the binomial model is given, followed by a theoretical and practical analysis of the method's convergence. Examples show how the parameters of the Markov random field control the strength and direction of the clustering in the image. The power of the binomial model to produce blurry, sharp, line-like, and blob-like textures is demonstrated. Natural texture samples were digitized and their parameters were estimated under the Markov random field model. A hypothesis test was used for an objective assessment of goodness-of-fit under the Markov random field model. Overall, microtextures fit the model well. The estimated parameters of the natural textures were used as input to the generation procedure. The synthetic microtextures closely resembled their real counterparts, while the regular and inhomogeneous textures did not.
Automatic Construction of 2D Shape Models

A procedure for automated 2D shape model design is presented. The system is given a set of training example shapes defined by contour point coordinates. The shapes are automatically aligned using Procrustes analysis and clustered to obtain cluster prototypes (typical objects) and statistical information about intracluster shape variation. One difference from previous methods is that the training set is first automatically clustered and shapes considered to be outliers are discarded. In this way, cluster prototypes are not distorted by outliers. A second difference is in the manner in which registered sets of points are extracted from each shape contour. We propose a flexible point matching technique that takes into account both pose/scale differences and nonlinear shape differences. The matching method is independent of the objects' initial relative position/scale and does not require any manually tuned parameters. Our shape model design method was used to learn 11 different shapes from contours that were manually traced in MR brain images. The resulting model was then employed to segment several MR brain images that were not included in the shape-training set. A quantitative analysis of our shape registration approach, within the main cluster of each structure, demonstrated results that compare very well to those achieved by manual registration; achieving an average registration error of about 1 pixel. Our approach can serve as a fully automated substitute to the tedious and time-consuming manual 2D shape registration and analysis.
View organization and matching of free-form objects

We address the problem of constructing view aspects of free-form objects for efficient matching during recognition. We introduce a novel view representation based on "shape spectrum" features, and propose a general and powerful technique for organizing multiple views of objects of complex shape and geometry into compact and homogeneous clusters. Our view-grouping technique obviates the need for surface segmentation and edge detection. We also demonstrate that when view-grouping is exploited to structure a large model base of views, even with a relatively flat (two-tiered) arrangement a small set of plausible correct matches can be determined quickly. Experimental results on a database of 3,200 views of 10 objects show that when tested with 1,000 independent views, our matching technique examined, on the average, only 23.5% of the database for correct classification.
A 100 MHz macropipelined CISC CMOS microprocessor

A macropipelined CISC microprocessor implemented in a 0.75- mu m CMOS 3.3-V three-metal-layer technology is described. The 1.3 M-transistor custom chip measures 1.62*1.46 cm/sup 2/, dissipates 18 W (peak), and is packaged in a 339-pin PGA. The chip implements a macroinstruction pipeline to execute the instruction set of a popular CISC minicomputer. A block diagram of the major functional units is shown along with die micrograph.<<ETX>>