nils-herrmann/clean_scirepeval huggingface dataset:
Automatic classification of tennis video for high-level content-based retrieval

We present our techniques and results on automatic analysis of tennis video to facilitate content-based retrieval. Our approach is based on the generation of an image model for the tennis court-lines. We derive this model by using the knowledge about dimensions and connectivity (form) of a tennis court and typical camera geometry used when capturing a tennis video. We use this model to develop: a court line detection algorithm; and a robust player tracking algorithm to track the tennis players over the image sequence. We also present a color-based algorithm to select tennis court clips from an input raw footage of tennis video. Automatically extracted tennis court lines and the players' location information are analyzed in a high-level reasoning module and related to useful high-level tennis play events. Results on real tennis video data are presented demonstrating the validity and performance of the approach.
Parsimonious network design and feature selection through node pruning

Proposes a node saliency measure and a backpropagation type of algorithm to compute the node saliencies. A node-pruning procedure is then presented to remove insalient nodes in the network to create a parsimonious network. The optimal/suboptimal subset of features are simultaneously selected by the network. The performance of the proposed approach for feature selection is compared with Whitney's feature selection method. One advantage of the node-pruning procedure over classical feature selection methods is that the node-pruning procedure can simultaneously "optimize" both the feature set and the classifier, while classical feature selection methods select the "best" subset of features with respect to a fixed classifier.
Detecting straight edges in millimeter-wave images

This paper presents two new methods for detecting edges in millimeter-wave radar images. The first method is based on a deformable template model of edge shapes and a random field model of the millimeter-wave imaging process. The second method is similar to the first, except that the imaging model component is replaced by one that is based on the magnitude and direction of the image gradient. Experimental results are shown to illustrate the advantages of using these methods over traditional edge detectors.
Periocular Biometrics in the Visible Spectrum

The term periocular refers to the facial region in the immediate vicinity of the eye. Acquisition of the periocular biometric is expected to require less subject cooperation while permitting a larger depth of field compared to traditional ocular biometric traits (viz., iris, retina, and sclera). In this work, we study the feasibility of using the periocular region as a biometric trait. Global and local information are extracted from the periocular region using texture and point operators resulting in a feature set for representing and matching this region. A number of aspects are studied in this work, including the 1) effectiveness of incorporating the eyebrows, 2) use of side information (left or right) in matching, 3) manual versus automatic segmentation schemes, 4) local versus global feature extraction schemes, 5) fusion of face and periocular biometrics, 6) use of the periocular biometric in partially occluded face images, 7) effect of disguising the eyebrows, 8) effect of pose variation and occlusion, 9) effect of masking the iris and eye region, and 10) effect of template aging on matching performance. Experimental results show a rank-one recognition accuracy of 87.32% using 1136 probe and 1136 gallery periocular images taken from 568 different subjects (2 images/subject) in the Face Recognition Grand Challenge (version 2.0) database with the fusion of three different matchers.
Partial face recognition: An alignment free approach

Many approaches have been developed for holistic face recognition with impressive performance. However, few studies have addressed the question of how to recognize an arbitrary image patch of a holistic face. In this paper we address this problem of partial face recognition. Partial faces frequently appear in unconstrained image capture environments, particularly when faces are captured by surveillance cameras or handheld devices (e.g. mobile phones). The proposed approach adopts a variable-size description which represents each face with a set of keypoint descriptors. In this way, we argue that a probe face image, holistic or partial, can be sparsely represented by a large dictionary of gallery descriptors. The proposed method is alignment free and we address large-scale face recognition problems by a fast filtering strategy. Experimental results on three public domain face databases (FRGCv2.0, AR, and LFW) show that the proposed method achieves promising results in recognizing both holistic and partial faces.
Automatic image orientation detection

We present an algorithm for automatic image orientation estimation using a Bayesian learning framework. We demonstrate that a small codebook (the optimal size of codebook is selected using a modified MDL criterion) extracted from a learning vector quantizer (LVQ) can be used to estimate the class-conditional densities of the observed features needed for the Bayesian methodology. We further show how principal component analysis (PCA) and linear discriminant analysis (LDA) can be used as a feature extraction mechanism to remove redundancies in the high-dimensional feature vectors used for classification. The proposed method is compared with four different commonly used classifiers, namely k-nearest neighbor, support vector machine (SVM), a mixture of Gaussians, and hierarchical discriminating regression (HDR) tree. Experiments on a database of 16 344 images have shown that our proposed algorithm achieves an accuracy of approximately 98% on the training set and over 97% on an independent test set. A slight improvement in classification accuracy is achieved by employing classifier combination techniques.
A 100-MHz macropipelined VAX microprocessor

A macropipelined CISC microprocessor was implemented in a 0.75- mu m CMOS 3.3-V technology. The 1.3-million-transistor custom chip measures 1.62*1.46 cm/sup 2/ and dissipates 16.3 W. The 100-MHz parts were benchmarked at 50 SPEC marks. The on-chip clocking system and several high-performance logic and circuit techniques are described. Macroinstruction handling, micropipeline management, and control store structures highlight the design architecture. The hierarchical array organization and fast tag comparison technique of the primary cache are discussed. Power estimation procedures are outlined, and the results are compared to measurements. Physical design and verification methods, and CAD tools are also described. After extensive functional verification efforts are described, chip and system test results are presented.
Asynchronous Multi-task Learning

Many real-world machine learning applications involveseveral learning tasks which are inter-related. For example, in healthcare domain, we need to learn a predictive model of a certain disease for many hospitals. The models for each hospital may be different because of the inherent differences in the distributions of the patient populations. However, the models are also closely related because of the nature of the learning tasks modeling the same disease. By simultaneously learning all the tasks, multi-task learning (MTL) paradigm performs inductive knowledge transfer among tasks to improve the generalization performance. When datasets for the learning tasks are stored at different locations, it may not always be feasible to transfer the data to provide a data centralized computing environment due to various practical issues such as high data volume and privacy. In this paper, we propose a principled MTL framework for distributed and asynchronous optimization to address the aforementioned challenges. In our framework, gradient update does not wait for collecting the gradient information from all the tasks. Therefore, the proposed method is very efficient when the communication delay is too high for some task nodes. We show that many regularized MTL formulations can benefit from this framework, including the low-rank MTL for sharedsubspace learning. Empirical studies on both synthetic and realworld datasets demonstrate the efficiency and effectiveness of the proposed framework.
Image databases: a case study in Norwegian silver authentication

Establishing the identity of the craftsmen of old Norwegian silver objects can be accomplished through the recognition of master-marks. Manually searching through the 6000 master-marks and 250 city-marks is tedious and unreliable. Our-goal is to develop an efficient retrieval of images of master- and city-marks from large databases based on the shape content of the image. We are building an automatic retrieval system to browse through the entire database. The first step is to extract primitive visual features from the images and to retrieve images on the basis of these features. We have developed a prototype image database of master-marks from a catalog by scanning 171 of these marks which appear on silver tankards. We have successfully extracted shape features from these marks and matched master-marks in the database against the unknown query images. Initial experiments on master-marks are encouraging. The same shape-based matching techniques are used to match city-marks. Our future work will involve building an expert system for a stylistic analysis of the silver tankards.
Robust Keypoint Detection Using Higher-Order Scale Space Derivatives: Application to Image Retrieval

Image retrieval has been extensively studied over the last two decades due to the increasing demands for the effective use of multimedia data. Among various approaches to image retrieval, scale space representation and local keypoint descriptors have been shown to be a promising approach. Even though the concept of scale space representation has been known for a long time, it has now gained prominence as a powerful method for image retrieval mostly due to the invention of the Scale Invariant Feature Transform (SIFT). We will review the characteristics of the scale space operation and provide an extended method of scale space operation that significantly improves the image matching accuracy in the context of image retrieval. We use an operational tattoo image database containing 1,000 near duplicate images to show the superior retrieval performance of the proposed method compared to SIFT keypoints.
Extrapolation algorithms for discrete signals with application in spectral estimation

In this paper we present some new algorithms useful for extrapolation and spectral estimation of band-limited sequences in one and two dimensions. First we show that many of the existing extrapolation algorithms for noiseless observations are unified under the criterion of minimum norm least squares (MNLS) extrapolation. For example, the iterative algorithms proposed in  and [8]-[10] are shown to be special cases of a one-step gradient algorithm which has linear convergence. Convergence and other numerical properties are improved by going to a conjugate gradient algorithm. For noisy observations, these algorithms could be extended by considering a mean-square extrapolation criterion which gives rise to a mean-square extrapolation filter and also to a recursive extrapolation filter. Examples and application of these methods are given. Extension of these algorithms is made for problems where the signal is known to be periodic. A new set of functions called the periodic-discrete prolate spheroidal sequences (P-DPSS), analogous to DPSS [21], [22], are introduced and their properties are studied. Finally, several of these algorithms are generalized to two dimensions and the relevant equations are given.
Fast Fourier transform algorithms for linear estimation, smoothing and Riccati equations

In the past two decades since the advent of Kalman's recursive filter, numerous algorithms for linear estimation have emerged. Most of these algorithms are recursive and rely on solving a Riccati equation or equivalent recursive equations. It will be shown how some of the classical problems such as linear smoothing, Riccati equations, boundary value problems, and recursive block filtering problems can be solved exactly by some new nonrecursive algorithms which are based on the fast Fourier transform (FFT). In the context of modern digital signal processing these algorithms have a highly parallel structure and are well suited for VLSI implementations.
Gold Fingers: 3D Targets for Evaluating Capacitive Readers

With capacitive fingerprint readers being increasingly used for access control as well as for smartphone unlock and payments, there is a growing interest among metrology agencies (e.g., the National Institute of Standards and Technology) to develop standard artifacts (targets) and procedures for repeatable evaluation of capacitive readers. We present our design and fabrication procedures to create conductive 3D targets (gold fingers) for capacitive readers. Wearable 3D targets with known feature markings (e.g., fingerprint ridge flow and ridge spacing) are first fabricated using a high-resolution 3D printer. A sputter coating process is subsequently used to deposit a thin layer (~300 nm) of conductive materials (titanium and gold) on 3D printed targets. The wearable gold finger targets are used to evaluate a PIV-certified single-finger capacitive reader as well as small-area capacitive readers embedded in smartphones and access control terminals. In additional, we show that a simple procedure to create 3D printed spoofs with conductive carbon coating is able to successfully spoof a PIV-certified single-finger capacitive reader as well as a capacitive reader embedded in an access control terminal.
Page segmentation using document model

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 document model is necessary to solve this problem. Document modeling involves techniques of thresholding, skew detection, geometric layout analysis and logical layout analysis. The derived model can then be used in document storage and retrieval. 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.
Occupant classification system for automotive airbag suppression

The introduction of airbags into automobiles has significantly improved the safety of the occupants. Unfortunately, airbags can also cause fatal injuries if the occupant is a child smaller (in weight) than a typical 6 year old. In response to this, The National Highway Transportation and Safety Administration (NHTSA) has mandated that starting in the 2006 model year all automobiles be equipped with an automatic suppression system to detect the presence of a child or infant and suppress the airbag. The classification problem we address is a four-class problem with the classes being rear-facing infant seat, child, adult, and empty seat. We describe a machine vision-based occupant classification system using a single grayscale camera and a digital signal processor that can perform this function in "real time" (< 5 seconds). The system has been extensively tested on a database of over 21,000 real-world images collected over a period of 4 months in moderate lighting conditions with a wide variety of passengers in eight different vehicles. We have achieved a classification accuracy of /spl sim/ 95%. We believe this system serves the need for a low-cost, high reliability embedded real-time airbag suppression system. Additional testing and improvements of the classification system are currently underway.
Learning prototypes for online handwritten digits

A writer independent handwriting recognition system must be able to recognize a wide variety of handwriting styles, while attempting to obtain a high degree of accuracy when recognizing data from any one of those styles. As the number of writing styles increases, so does the variability of the data's distribution. We then have an optimization problem: how to best model the data, while keeping the representation as simple as possible? If we can identify N different styles of writing individual characters (referred to as lexemes), these can then be modeled as N relatively simple independent distributions. We describe here a template-based system using a string-matching distance measure for the recognition of online handwriting which takes advantage of lexemes to reduce the number of templates that must be stored. A method of identifying lexemes and lexeme representatives is shown, and experimental results are given for a set of handwritten digits taken from 21 different writers. The use of lexeme representatives reduces classification time by 90.2% while retaining approximately 98% of the recognition accuracy.
Periocular biometrics in the visible spectrum: A feasibility study

Periocular biometric refers to the facial region in the immediate vicinity of the eye. Acquisition of the periocular biometric does not require high user cooperation and close capture distance unlike other ocular biometrics (e.g., iris, retina, and sclera). We study the feasibility of using periocular images of an individual as a biometric trait. Global and local information are extracted from the periocular region using texture and point operators resulting in a feature set that can be used for matching. The effect of fusing these feature sets is also studied. The experimental results show a 77% rank-1 recognition accuracy using 958 images captured from 30 different subjects.
Small sample size effects in statistical pattern recognition: recommendations for practitioners and open problems

The authors discuss the effects of sample size on the feature selection and error estimation for several types of classifiers. In addition to surveying prior work in this area, they give practical advice to today's designers and users of statistical pattern recognition systems. It is pointed out that one needs a large number of training samples if a complex classification rule with many features is being utilized. In many pattern recognition problems, the number of potential features is very large and not much is known about the characteristics of the pattern classes under consideration: thus, it is difficult to determine a priori the complexity of the classification rule needed. Therefore, even when the designer believes that a large number of training samples has been selected, they may not be enough for designing and evaluating the classification problem at hand. It is further noted that a small sample size can cause many problems in the design of a pattern recognition system.<<ETX>>
Deformable matching of hand shapes for user verification

We present a method for personal authentication based on deformable matching of hand shapes. Authentication systems are already employed in domains that require some sort of user verification. Unlike previous methods on hand shape based verification, our method aligns the hand shapes before extracting a feature set. We also base the verification decision on the shape distance which is automatically computed during the alignment stage. The shape distance proves to be a more reliable classification criterion than the handcrafted feature sets used by previous systems. Our verification system attained a high level of accuracy: 96.5% genuine accept rate vs. false accept rate. This performance is further improved by learning an enrolment template shape for each user.
Rank-based distance metric learning: An application to image retrieval

We present a novel approach to learn distance metric for information retrieval. Learning distance metric from a number of queries with side information, i.e., relevance judgements, has been studied widely, for example pairwise constraint-based distance metric learning. However, the capacity of existing algorithms is limited, because they usually assume that the distance between two similar objects is smaller than the distance between two dissimilar objects. This assumption may not hold, especially in the case of information retrieval when the input space is heterogeneous. To address this problem explicitly, we propose rank-based distance metric learning. Our approach overcomes the drawback of existing algorithms by comparing the distances only among the relevant and irrelevant objects for a given query. To avoid over-fitting, a regularizer based on the Burg matrix divergence is also introduced. We apply the proposed framework to tattoo image retrieval in forensics and law enforcement application domain. The goal of the application is to retrieve tattoo images from a gallery database that are visually similar to a tattoo found on a suspect or a victim. The experimental results show encouraging results in comparison to the standard approaches for distance metric learning.
Facial marks: Soft biometric for face recognition

We propose to utilize micro features, namely facial marks (e.g., freckles, moles, and scars) to improve face recognition and retrieval performance. Facial marks can be used in three ways: i) to supplement the features in an existing face matcher, ii) to enable fast retrieval from a large database using facial mark based queries, and iii) to enable matching or retrieval from a partial or profile face image with marks. We use Active Appearance Model (AAM) to locate and segment primary facial features (e.g., eyes, nose, and mouth). Then, Laplacian-of-Gaussian (LoG) and morphological operators are used to detect facial marks. Experimental results based on FERET (426 images, 213 subjects) and Mugshot (1,225 images, 671 subjects) databases show that the use of facial marks improves the rank-1 identification accuracy of a state-of-the-art face recognition system from 92.96% to 93.90% and from 91.88% to 93.14%, respectively.
Modeling and Analysis of DC Traction System in Light of Recent Innovations from HPC and Virtual Reality

We have developed a library for simulation of DC traction system. The modeling, installation and reliability aspects of Traction system in purview of current developments in HPC and Virtual reality are being implemented. We have proposed a model to predict and analyze traction system more comprehensively and put a computational structure regarding the same. Distribution of various computations is shown in an HPC setup and has been implemented. The advantages of Virtual reality in installation in city and intercity, driving has been implemented on C# for an HPC setup. Installation of VR framework for installation of DC traction thus has been implemented in the current technologies of HPC and VR.