nils-herrmann/clean_scirepeval huggingface dataset:
Face detection and synthesis using Markov random field models

Markov random fields (MRFs) are proposed as viable stochastic models for the spatial distribution of gray levels for images of human faces. These models are trained using data bases of face and non-face images. The trained MRF models are then used for detecting human faces in test images. We investigate the performance of the face detection algorithm for two classes of MRFs given by the first- and second-order neighborhood systems. From the cross validation results and from actual detection in real images, it is shown that the second-order model makes fewer false detections. We also investigate the possibility of increasing our training data base of faces by simulating face-like images from the trained MRFs. The performance of the re-trained MRFs based on added face-like images is compared to the original training data base.
Data capture from maps based on gray scale topographic analysis

There is a large number of documents, including hand-printed maps, where useful information is lost if binarization is performed on the scanned image before further processing. For such documents, methods which utilize the gray scale values must be used in order to extract as much of the available information as possible. Topographic analysis has been used in the literature to recognize characters directly in the gray scale images. The authors extend the topographic analysis method, so that characters and lines can be extracted from gray scale map images where methods using only the information in the binary image fail.
Hiding Biometric Data

With the wide spread utilization of biometric identification systems, establishing the authenticity of biometric data itself has emerged as an important research issue. The fact that biometric data is not replaceable and is not secret, combined with the existence of several types of attacks that are possible in a biometric system, make the issue of security/integrity of biometric data extremely critical. We introduce two applications of an amplitude modulation-based watermarking method, in which we hide a user's biometric data in a variety of images. This method has the ability to increase the security of both the hidden biometric data (e.g., eigen-face coefficients) and host images (e.g., fingerprints). Image adaptive data embedding methods used in our scheme lead to low visibility of the embedded signal. Feature analysis of host images guarantees high verification accuracy on watermarked (e.g., fingerprint) images.
Compression of fingerprint images using hybrid image model

We present an efficient model-based fingerprint image compression scheme based on a hybrid image model. Our model is based on extracting ridge and valley contours and then reconstructing a hybrid surface by using the gray values on these contours. The hybrid model we utilized is the convex combination of the membrane and plate functionals used for surface reconstruction by regularization. Two parameters of this model is determined to obtain a good approximation of the original fingerprint image given the sparse data, on ridges and valleys. In this compression scheme, the ridge contours are coded efficiently by using a differential chain code, while the differences between consecutive gray values along the chains are encoded using Huffman coding. Also included in the compressed image are the mean value of each valley segment and two parameters of the hybrid model. One advantage of our approach as compared to transform based algorithms and wavelet-based algorithms is that features such as delta and core points, end points, bifurcation points can be extracted directly from compressed image even for very high values of the compression ratio. The algorithm has been applied to various fingerprint images, and high compression ratios like 45:1 have been obtained while keeping all the important features in the images.
On-line fingerprint verification

We describe the design and implementation of an online fingerprint verification system which operates in two stages: (i) minutia extraction and (ii) minutia matching. An improved minutia extraction algorithm that is much faster and more accurate than our earlier algorithm has been implemented. For minutia matching, an alignment-based elastic matching algorithm has been developed. This algorithm is capable of finding the correspondences between input minutiae and the stored template without resorting to exhaustive search and has the ability to adaptively compensate for the nonlinear deformations and inexact pose transformations between finger prints. The system has been tested on two sets of finger print images captured with inkless scanners. The verification accuracy is found to be over 99% with a 15% reject rate. Typically, a complete fingerprint verification procedure takes, on an average, about 8 seconds on a SPARC 20 workstation. It meets the response time requirements of on-line verification with high accuracy.
F2ID: a personal identification system using faces and fingerprints

A real-time automatic personal identification system should meet the conflicting dual requirements of accuracy and response time. In addition, it also should be user-friendly. We introduce a medium-size realtime automatic personal identification system, F2ID, which integrates faces and fingerprints to make a personal identification. F2ID overcomes some of the limitations of face recognition systems and fingerprint verification systems and can achieve a desirable identification accuracy with a tolerable response time. We have tested our system on a limited set of face and fingerprint images collected in a laboratory environment. Experimental results show that that our system meets both the identification accuracy as well as the speed requirements.
Page segmentation using texture discrimination masks

We propose a new texture-based page segmentation algorithm which automatically extracts the text, halftone, and line-drawing regions from input greyscale document images. This approach utilizes a neural network to train a set of masks which is optimal for discriminating the three main texture classes in the page segmentation problem: halftone, background, and text and line-drawing regions. The test and line-drawing regions are further discriminated based on connectivity analysis. We have applied the algorithm to successfully segment English and Chinese document images. We also demonstrate that the masks can perform language separation (English/Chinese) when appropriately trained.
Coupled Discriminant Analysis for Heterogeneous Face Recognition

Coupled space learning is an effective framework for heterogeneous face recognition. In this paper, we propose a novel coupled discriminant analysis method to improve the heterogeneous face recognition performance. There are two main advantages of the proposed method. First, all samples from different modalities are used to represent the coupled projections, so that sufficient discriminative information could be extracted. Second, the locality information in kernel space is incorporated into the coupled discriminant analysis as a constraint to improve the generalization ability. In particular, two implementations of locality constraint in kernel space (LCKS)-based coupled discriminant analysis methods, namely LCKS-coupled discriminant analysis (LCKS-CDA) and LCKS-coupled spectral regression (LCKS-CSR), are presented. Extensive experiments on three cases of heterogeneous face matching (high versus low image resolution, digital photo versus video image, and visible light versus near infrared) validate the efficacy of the proposed method.
Multi-label learning with incomplete class assignments

We consider a special type of multi-label learning where class assignments of training examples are incomplete. As an example, an instance whose true class assignment is (c1, c2, c3) is only assigned to class c1 when it is used as a training sample. We refer to this problem as multi-label learning with incomplete class assignment. Incompletely labeled data is frequently encountered when the number of classes is very large (hundreds as in MIR Flickr dataset) or when there is a large ambiguity between classes (e.g., jet vs plane). In both cases, it is difficult for users to provide complete class assignments for objects. We propose a ranking based multi-label learning framework that explicitly addresses the challenge of learning from incompletely labeled data by exploiting the group lasso technique to combine the ranking errors. We present a learning algorithm that is empirically shown to be efficient for solving the related optimization problem. Our empirical study shows that the proposed framework is more effective than the state-of-the-art algorithms for multi-label learning in dealing with incompletely labeled data.
Cluster validation using a probabilistic attributed graph

We propose a new cluster validity index. A data partition is described by a set of disjoint sub-graphs, each corresponding to the minimum spanning tree of a cluster, taking as edge weight the dissimilarity between linked objects. Based on the assumption that each cluster has a characteristic parametric distribution of dissimilarity increments, graph probabilities are estimated. The validity index is defined as the minimum description length for both estimated model parameters and data partition, according to this probabilistic model. This new index can be used to evaluate various partitions of a given data set obtained by: (i) a single clustering algorithm, (ii) different clustering algorithms, or (iii) cluster ensemble methods. Experimental evaluation of the proposed index on synthetic and real data taken from the UCI repository confirms the usefulness of the method in selecting good clustering solutions.
Biometric recognition: Sensor characteristics and image quality

Biometric recognition, or simply biometrics, refers to recognizing a person based on one or more of his anatomical or behavioral characteristics. A good biometric trait should be measurable, distinctive (different for every person) and stable over time. The sensing method should not be intrusive or socially unacceptable, and the system should be easy to use. A biometric system based on this trait should be accurate, fast, robust and inexpensive. In this article, we discuss the signal acquisition aspects of fingerprint and iris biometrics-two of the most widely used biometric traits.Personal recognition of people is necessary to conduct many social and economic activities. Besides visual recognition of acquaintances, checking a person's government issued photo ID is the most common procedure. In electronic access or transactions, passwords and security tokens are commonly used. These credentials are surrogates of a person's identity Their major shortcoming is that they can be easily compromised by being lost, stolen, or given to someone else. For better security, it is necessary to link the digital identity of a person to his body's characteristics.
Frame-to-frame restoration of diffusion images

Frame-to-frame image data are acquired in many applications such as Radar, Biomedical, and Television imaging. In many situations, the imaging phenomenon can be modeled by a diffusion process. Here, we consider the problem of recursive filtering of such images on a frame-to-frame basis. Also, a two-dimensional problem of restoration of blurred images can be solved by imbedding it in this three-dimensional reeursive filtering problem without blur. The model structure leads to a computationally feasible filtering algorithm achieving large reduction of dimensionality and is useful in real-time hardware simulation or generation of such blurred image data as might occur in a forward looking radar (FLR).
Image restoration methods based on quantum field models

The multispectral image restoration method presented is based on an analogy between an image with N pixels and a 2-D solid with N particles on a regular grid. Each pixel is associated with a single particle (SP). The movement of each particle is described using SP models from quantum mechanics. The SP analogy is created by associating the observed feature vectors of each pixel and the noise covariance in the multispectral image with the potential energy function and the total energy of the physical system, respectively. The approach is used to design data-dependent image restoration filters applicable to multispectral images. The filter weights are iteratively updated using a deterministic relaxation scheme to reach a global equilibrium state. The restoration of simulated gray-scale images led to significantly better segmentations than typical spatial image enhancement procedures reported in the literature. The restoration of one real multispectral magnetic resonance image gave a substantial reduction in the noise while enhancing all real structures.<<ETX>>
Learning 2D shape models

A new fully automated shape learning method is presented. It is based on clustering a set of training shapes in the original shape space (defined by the coordinates of the contour points) and performing a Procrustes analysis on each cluster to obtain cluster prototypes and information about shape variation. The main difference from previously reported methods is that the training set is first automatically clustered and those shapes considered to be outliers are discarded. The second difference is in the manner in which registered sets of points are extracted from each shape contour. As a direct application of our shape learning method, an 11-structure shape model of brain substructures was extracted from MR image data, an eigen-shape model was automatically trained, and employed to segment several MR brain images not present in the shape-training set. A quantitative analysis of our shape registration approach, within the main cluster of each structure, shows that our results compare very well to those achieved by manual registration; achieving an average rms error of about 1 pixel. Our approach can serve as a fully automated substitute to the tedious and time-consuming manual shape registration and analysis.
Heterogeneous Face Attribute Estimation: A Deep Multi-Task Learning Approach

Face attribute estimation has many potential applications in video surveillance, face retrieval, and social media. While a number of methods have been proposed for face attribute estimation, most of them did not explicitly consider the attribute correlation and heterogeneity (e.g., ordinal versus nominal and holistic versus local) during feature representation learning. In this paper, we present a Deep Multi-Task Learning (DMTL) approach to jointly estimate multiple heterogeneous attributes from a single face image. In DMTL, we tackle attribute correlation and heterogeneity with convolutional neural networks (CNNs) consisting of shared feature learning for all the attributes, and category-specific feature learning for heterogeneous attributes. We also introduce an unconstrained face database (LFW+), an extension of public-domain LFW, with heterogeneous demographic attributes (age, gender, and race) obtained via crowdsourcing. Experimental results on benchmarks with multiple face attributes (MORPH II, LFW+, CelebA, LFWA, and FotW) show that the proposed approach has superior performance compared to state of the art. Finally, evaluations on a public-domain face database (LAP) with a single attribute show that the proposed approach has excellent generalization ability.
Prostate Cancer Grading: Use of Graph Cut and Spatial Arrangement of Nuclei

Tissue image grading is one of the most important steps in prostate cancer diagnosis, where the pathologist relies on the gland structure to assign a Gleason grade to the tissue image. In this grading scheme, the discrimination between grade 3 and grade 4 is the most difficult, and receives the most attention from researchers. In this study, we propose a novel method (called nuclei-based method) that 1) utilizes graph theory techniques to segment glands and 2) computes a gland-score (based on the spatial arrangement of nuclei) to estimate how similar a segmented region is to a gland. Next, we create a fusion method by combining this nuclei-based method with the lumen-based method presented in our previous work to improve the performance of grade 3 versus grade 4 classification problem (the accuracy is now improved to 87.3% compared to 81.1% of the lumen-based method alone). To segment glands, we build a graph of nuclei and lumina in the image, and use the normalized cut method to partition the graph into different components, each corresponding to a gland. Unlike most state-of-the-art lumen-based gland segmentation method, the nuclei-based method is able to segment glands without lumen or glands with multiple lumina. Moreover, another important contribution in this research is the development of a set of measures to exploit the difference in nuclei spatial arrangement between grade 3 images (where nuclei form closed chain structure on the gland boundary) and grade 4 image (where nuclei distribute more randomly in the gland). These measures are combined to generate a single gland-score value, which estimates how similar a segmented region (which is a set of nuclei and lumina) is to a gland.
Automatic latent value determination

Latent fingerprints are the most frequently encountered and reliable crime scene evidence used in forensics investigations. Automatic methods for quantitative assessment of a latent in terms of (i) value for individualization (VID), (ii) value for exclusion only (VEO), and (iii) no value (NV), are needed to minimize the workload of latent examiners so that they can pay more attention to challenging prints (VID and NV latents). Current value determination is either made by examiners or predicted given manually annotated features. Because both of these approaches depend on human markup, they are subjective and time consuming. We propose a fully automatic method for latent value determination based on the number, reliability, and compactness of the minutiae, ridge quality, ridge flow, and the number of core and delta points. Given the small number of latents with VEO and NV labels in two latent databases available to us (NIST SD27 and WVU), only a two-class value determination is considered, namely VID and VID̅, where the VID̅ class contains VEO and NV latents. Experimental results show that the value determination by the proposed method (i) obviates the need for examiner markup while maintaining the accuracy of value determination and (ii) can predict the AFIS performance better than examiners.
lamda-tau-Space Representation of Images and Generalized Edge Detector

An image and surface representation based on regularization theory is introduced in this paper. This representation is based on a hybrid model derived from the physical membrane and plate models. The representation, called the /spl lambda//spl tau/-representation, has two dimensions; one dimension represents smoothness or scale while the other represents the continuity of the image or surface. It contains images/surfaces sampled both in scale space and the weighted Sobolev space of continuous functions. Thus, this new representation can be viewed as an extension of the well-known scale space representation. We have experimentally shown that the proposed hybrid model results in improved results compared to the two extreme constituent models, i.e., the membrane and the plate models. Based on this hybrid model, a generalized edge detector (GED) which encompasses most of the well-known edge detectors under a common framework is developed. The existing edge detectors can be obtained from the generalized edge detector by simply specifying the values of two parameters, one of which controls the shape of the filter (/spl tau/) and the other controls the scale of the filter (/spl lambda/). By sweeping the values of these two parameters continuously, one can generate an edge representation in the /spl lambda//spl tau/ space, which is very useful for developing a goal-directed edge detection scheme for a specific task. The proposed representation and the edge detector have been evaluated qualitatively and quantitatively on several different types of image data such as intensity, range, and stereo images.
Large scale feature selection using modified random mutation hill climbing

Feature selection is a critical component of many pattern recognition applications. There are two distinct mechanisms for feature selection, namely the wrapper methods and the filter methods. The filter methods are generally considered inferior to the wrapper method, however wrapper methods are computationally more demanding than filter methods. One of the popular methods for wrapper-based feature selection is random mutation hill climbing. It performs a random search over the feature space to derive the optimal set of features. We would describe two enhancements to this algorithm, one that would improve its convergence time, and the other that would allow us to bias the results towards either higher accuracy or lower final feature space dimensionality. We would apply the algorithm to a real-world massive-scale feature selection problem involving the image classification problem associated with suppressing automobile airbags for children. We would provide classification results on an image database of nearly 4,000 images that indicate the advantages of the proposed method.
Robust motion-based image segmentation using fusion

To support real-time tracking of objects in video sequences, there has been considerable effort directed at developing optical flow and general motion-based image segmentation algorithms. The goal is to segment multiple moving objects in the image based on their relative motion. This task can be complicated by the presence of lighting variations. Furthermore, a combination of multiple motions and complex lighting effects can lead to dramatic image variations that may not be adequately accounted for by a single motion-based segmentation algorithm. We propose to fuse the results of multiple motion segmentation algorithms to improve the system robustness. Our approach uses the expectation maximization (EM) algorithm as a fusion engine. It also uses principal components analysis (PCA) to perform dimensionality reduction to improve the performance of the EM algorithm and reduce the processing burden. The performance of the proposed fusion algorithm has been demonstrated in the "smart airbag" application of monitoring occupants in a moving automobile to determine if they are too close to the instrument panel (airbag). Through fusion of the outputs of multiple algorithms we are able to reduce the percentage of pixels missed on the target by 35.
Surface classification: hypothesis testing and parameter estimation

A 3-D surface classification method based on the quadric surface model is described. This technique does not require the points from the surface to lie on a grid. A sample of surface points is classified as planar or nonplanar through two hypothesis tests. If the sample is nonplanar, curvature features are evaluated at each point to classify the sample as spherical, cylindrical, or conical. A nonlinear optimization technique is then used to refine the parameters (e.g. radius, orientation) of the resulting surface type.<<ETX>>