nils-herrmann/clean_scirepeval huggingface dataset:
Advances in mathematical models for image processing

Several state-of-the-art mathematical models useful in image processing are considered. These models include the traditional fast unitary transforms, autoregessive and state variable models as well as two-dimensional linear prediction models. These models introduced earlier [51], [52] as low-order finite difference approximations of partial differential equations are generalized and extended to higher order in the framework of linear prediction theory. Applications in several image Processing problems, including image restoration, smoothing, enhancement, data compression, spectral estimation, and filter design, are discussed and examples given.
A Rule Based Approach for Visual Pattern Inspection

The authors investigate the use of a priori knowledge about a scene to coordinate and control bilevel image segmentation, interpretation, and shape inspection of different objects in the scene. The approach is composed of two main steps. The first step consists of proper segmentation and labeling of individual regions in the image for subsequent ease in interpretation. General as well as scene-specific knowledge is used to improve the segmentation and interpretation processes. Once every region in the image has been identified, the second step proceeds by testing different regions to ensure they meet the design requirements, which are formalized by a set of rules. Morphological techniques are used to extract certain features from the previously processed image for rule verification purposes. As a specific example, results for detecting defects in printed circuit boards are presented.
3D Fingerprint Phantoms

One of the critical factors prior to deployment of any large scale biometric system is to have a realistic estimate of its matching performance. In practice, evaluations are conducted on the operational data to set an appropriate threshold on match scores before the actual deployment. These performance estimates, though, are restricted by the amount of available test data. To overcome this limitation, use of a large number of 2D synthetic fingerprints for evaluating fingerprint systems had been proposed. However, the utility of 2D synthetic fingerprints is limited in the context of testing end-to-end fingerprint systems which involve the entire matching process, from image acquisition to feature extraction and matching. For a comprehensive evaluation of fingerprint systems, we propose creating 3D fingerprint phantoms (phantoms or imaging phantoms are specially designed objects with known properties scanned or imaged to evaluate, analyze, and tune the performance of various imaging devices) with known characteristics (e.g., type, singular points and minutiae) by (i) projecting 2D synthetic fingerprints with known characteristics onto a generic 3D finger surface and (ii) printing the 3D fingerprint phantoms using a commodity 3D printer. Preliminary experimental results show that the captured images of the 3D fingerprint phantoms can be successfully matched to the 2D synthetic fingerprint images (from which the phantoms were generated) using a commercial fingerprint matcher. This demonstrates that our method preserves the ridges and valleys during the 3D fingerprint phantom creation process ensuring that the synthesized 3D phantoms can be utilized for comprehensive evaluations of fingerprint systems.
Generating Discriminating Cartoon Faces Using Interacting Snakes

As a computational bridge between the high-level a priori knowledge of object shape and the low-level image data, active contours (or snakes) are useful models for the extraction of deformable objects. We propose an approach for manipulating multiple snakes iteratively, called interacting snakes, that minimizes the attraction energy functionals on both contours and enclosed regions of individual snakes and the repulsion energy functionals among multiple snakes that interact with each other. We implement the interacting snakes through explicit curve (parametric active contours) representation in the domain of face recognition. We represent human faces semantically via facial components such as eyes, mouth, face outline, and the hair outline. Each facial component is encoded by a closed (or open) snake that is drawn from a 3D generic face model. A collection of semantic facial components form a hypergraph, called semantic face graph, which employs interacting snakes to align the general facial topology onto the sensed face images. Experimental results show that a successful interaction among multiple snakes associated with facial components makes the semantic face graph a useful model for face representation, including cartoon faces and caricatures, and recognition.
Large-Scale Image Annotation by Efficient and Robust Kernel Metric Learning

One of the key challenges in search-based image annotation models is to define an appropriate similarity measure between images. Many kernel distance metric learning (KML) algorithms have been developed in order to capture the nonlinear relationships between visual features and semantics of the images. One fundamental limitation in applying KML to image annotation is that it requires converting image annotations into binary constraints, leading to a significant information loss. In addition, most KML algorithms suffer from high computational cost due to the requirement that the learned matrix has to be positive semi-definitive (PSD). In this paper, we propose a robust kernel metric learning (RKML) algorithm based on the regression technique that is able to directly utilize image annotations. The proposed method is also computationally more efficient because PSD property is automatically ensured by regression. We provide the theoretical guarantee for the proposed algorithm, and verify its efficiency and effectiveness for image annotation by comparing it to state-of-the-art approaches for both distance metric learning and image annotation.
Multibiometric Template Security Using Fuzzy Vault

Template security is a critical issue in biometric systems because biometric templates cannot be easily revoked and reissued. While multibiometric systems overcome limitations such as non-universality and high error rates that affect unibiometric systems, they require storage of multiple templates for the same user. Securing the different templates of a user separately is not optimal in terms of security. Hence, we propose a scheme for securing multiple templates of a user as a single entity. We derive a single multibiometric template from the individual templates and secure it using the fuzzy vault framework. We demonstrate that a multibiometric vault provides better recognition performance and higher security compared to a unibiometric vault. For example, our multibiometric vault based on fingerprint and iris achieves a GAR of 98.2% at a FAR of ap 0.01%, while the corresponding GAR values of the individual iris and fingerprint vaults are 88% and 78.8%, respectively. Further, we also show that the security of the system is only 41 bits when the iris and fingerprint vaults are stored separately. On the other hand, the multibiometric vault based on fingerprint and iris provides 49 bits of security.
PILL-ID: Matching and Retrieval of Drug Pill Imprint Images

Automatic illicit drug pill matching and retrieval is becoming an important problem due to an increase in the number of tablet type illicit drugs being circulated in our society. We propose an automatic method to match drug pill images based on the imprints appearing on the tablet. This will help identify the source and manufacturer of the illicit drugs. The feature vector extracted from tablet images is based on edge localization and invariant moments. Instead of storing a single template for each pill type, we generate multiple templates during the edge detection process. This circumvents the difficulties during matching due to variations in illumination and viewpoint. Experimental results using a set of real drug pill images (822 illicit drug pill images and 1,294 legal drug pill images) showed 76.74% (93.02%) rank one (rank-20) matching accuracy.
Multiple Kernel Learning for Visual Object Recognition: A Review

Multiple kernel learning (MKL) is a principled approach for selecting and combining kernels for a given recognition task. A number of studies have shown that MKL is a useful tool for object recognition, where each image is represented by multiple sets of features and MKL is applied to combine different feature sets. We review the state-of-the-art for MKL, including different formulations and algorithms for solving the related optimization problems, with the focus on their applications to object recognition. One dilemma faced by practitioners interested in using MKL for object recognition is that different studies often provide conflicting results about the effectiveness and efficiency of MKL. To resolve this, we conduct extensive experiments on standard datasets to evaluate various approaches to MKL for object recognition. We argue that the seemingly contradictory conclusions offered by studies are due to different experimental setups. The conclusions of our study are: (i) given a sufficient number of training examples and feature/kernel types, MKL is more effective for object recognition than simple kernel combination (e.g., choosing the best performing kernel or average of kernels); and (ii) among the various approaches proposed for MKL, the sequential minimal optimization, semi-infinite programming, and level method based ones are computationally most efficient.
A Structural Approach To Identify Defects In Textured Images

From a structural point of view, a textured image is considered to be composed of skeleton and background primitives which occur repeatedly according to certain placement rule. Identification of defects in textured images is an important topic in computer vision. In this paper, an approach to defect detection is developed. A textured image is first thresholded using histogram analysis. Then it is mapped into a special data structure called the skeleton representation. Based on both location and length histograms from the data structure, several statistical measurements are defined. These measurements are used to identify and locate defects such as fluctuation, mean jump, and end influency in the textured image. The experimental results show satisfactory performance of this approach and its potential usefulness in industrial environments.
A Hybrid Approach for Generating Secure and Discriminating Face Template

Biometric template protection is one of the most important issues in deploying a practical biometric system. To tackle this problem, many algorithms, that do not store the template in its original form, have been reported in recent years. They can be categorized into two approaches, namely biometric cryptosystem and transform-based. However, most (if not all) algorithms in both approaches offer a trade-off between the template security and matching performance. Moreover, we believe that no single template protection method is capable of satisfying the security and performance simultaneously. In this paper, we propose a hybrid approach which takes advantage of both the biometric cryptosystem approach and the transform-based approach. A three-step hybrid algorithm is designed and developed based on random projection, discriminability-preserving (DP) transform, and fuzzy commitment scheme. The proposed algorithm not only provides good security, but also enhances the performance through the DP transform. Three publicly available face databases, namely FERET, CMU-PIE, and FRGC, are used for evaluation. The security strength of the binary templates generated from FERET, CMU-PIE, and FRGC databases are 206.3, 203.5, and 347.3 bits, respectively. Moreover, noninvertibility analysis and discussion on data leakage of the proposed hybrid algorithm are also reported. Experimental results show that, using Fisherface to construct the input facial feature vector (face template), the proposed hybrid method can improve the recognition accuracy by 4%, 11%, and 15% on the FERET, CMU-PIE, and FRGC databases, respectively. A comparison with the recently developed random multispace quantization biohashing algorithm is also reported.
A Markov random field model for classification of multisource satellite imagery

A general model for multisource classification of remotely sensed data based on Markov random fields (MRF) is proposed. A specific model for fusion of optical images, synthetic aperture radar (SAR) images, and GIS (geographic information systems) ground cover data is presented in detail and tested. The MRF model exploits spatial class dependencies (spatial context) between neighboring pixels in an image, and temporal class dependencies between different images of the same scene. By including the temporal aspect of the data, the proposed model is suitable for detection of class changes between the acquisition dates of different images. The performance of the proposed model is investigated by fusing Landsat TM images, multitemporal ERS-1 SAR images, and GIS ground-cover maps for land-use classification, and on agricultural crop classification based on Landsat TM images, multipolarization SAR images, and GIS crop field border maps. The performance of the MRF model is compared to a simpler reference fusion model. On an average, the MRF model results in slightly higher (2%) classification accuracy when the same data is used as input to the two models. When GIS field border data is included in the MRF model, the classification accuracy of the MRF model improves by 8%. For change detection in agricultural areas, 75% of the actual class changes are detected by the MRF model, compared to 62% for the reference model. Based on the well-founded theoretical basis of Markov random field models for classification tasks and the encouraging experimental results in our small-scale study, the authors conclude that the proposed MRF model is useful for classification of multisource satellite imagery.
Multimedia content protection via biometrics-based encryption

We propose a multimedia content protection framework that is based on biometric data of the users and a layered encryption/decryption scheme. Password-only encryption schemes are vulnerable to illegal key exchange problems. By using biometric data along with hardware identifiers as keys, it is possible to alleviate fraudulent usage of protected content. A combination of symmetric and asymmetric key systems is utilized for this purpose. The computational requirements and applicability of the proposed method are addressed. The results of encryption and decryption experiments related to time measurements are included. Watermarking systems can be used to complement the proposed method to permit novel uses of protected multimedia data.
Fast inversion of banded Toeplitz matrices by circular decompositions

Banded Toeplitz matrices of large size occur in many practical problems [1]-[6]. Here the problem of inversion as well as the problem of solving simultaneous equations of the type Hx = y, when H is a large banded Toeplitz matrix, are considered. It is shown via certain circular decompositions of H that such equations may be exactly solved in O(N \log_{2} N) rather than in O(N2) computations as in Levinson-Trench algorithms. Furthermore, the algorithms of this paper are nonrecursive (as compared to the Levinson-Trench algorithms), and afford parallel processor architectures and others such as transversal filters [17] where the computation time becomes proportional to N rather than to N \log N . Finally, a principle of matrix decomposition for fast inversion of matrices is introduced as a generalization of the philosophy of this paper.
Vehicle segmentation using deformable templates

The paper proposes a segmentation algorithm using deformable template models to segment a vehicle of interest from the stationary complex background and other moving vehicles in an image sequence. We define a polygonal template to characterize a general model of a vehicle and derive a prior probability density function to constrain the template to be deformed within a set of allowed shapes. We propose a likelihood probability density function which combines motion information and edge directionality to ensure that the deformable template is contained within the moving areas in the image and its boundary coincide with strong edges with the same orientation in the image. The segmentation problem is reduced to a minimization problem and solved by the Metropolis algorithm. The system was successfully tested on 287 image sequences containing multiple moving vehicles on a highway.
Query by video clip

Typical video search is based on queries involving a single shot. We generalize this problem by allowing queries that involve a video clip. We propose two schemes for query by video clip. In the first scheme, retrieval based on key frames where the database and query video are segmented into shots which are represented via key frames. For every query key frame, a similarity value (using color, texture, and motion) is associated with the key frames in the database video clip. Boundaries marking highly similar consecutive shots are then used to generate the set of retrieved video sub-clips. In the second scheme, in retrieval using sub-sampled frames, we uniformly sub-sample the query clip as well as the database video. The retrieval is based on matching color and texture features of the sub-sampled frames. Initial experiments on two video databases show promising results.
Quantization Complexity and Independent Measurements

It is known that, in general, the number of measurements in a pattern classification problem cannot be increased arbitrarily, when the class-conditional densities are not completely known and only a finite number of learning samples are available. Above a certain number of measurements, the performance starts deteriorating instead of improving steadily. It was earlier shown by one of the authors that an exception to this "curse of finite sample size" is constituted by the case of binary independent measurements if a Bayesian approach is taken and uniform a priori on the unknown parameters are assumed. In this paper, the following generalizations are considered: arbitrary quantization and the use of maximum likelihood estimates. Further, the existence of an optimal quantization complexity is demonstrated, and its relationship to both the dimensionality of the measurement vector and the sample size are discussed. It is shown that the optimum number of quantization levels decreases with increasing dimensionality for a fixed sample size, and increases with the sample size for fixed dimensionality.
Parameter estimation in MRF line process models

A scheme for the estimation of the Markov random field (MRF) line process parameters that uses geometric CAD models of the objects in the scene is presented. The models are used to generate synthetic images of the objects from random viewpoints. The edge maps computed from the synthesized images are used as training samples to estimate the line process parameters using a least squares method. It is shown that this parameter estimation method is useful for detecting edges in range as well as intensity images.<<ETX>>
Multi-resolution image representation using Markov random fields

This paper presents a new method for representing the spatial information present in digital grey-tone images. The method is based on using multi-resolution decompositions (MRDs) and Markov random fields (MRFs) concurrently. A given image is represented by a MRD of it, along with an optimally estimated set of Gaussian MRF (GMRF) parameters. Since the GMRF parameters are very small in number, this addition to the usual MRD results in only a small increase in the number of bits in the representation. It is shown, however, that such a minor addition helps when reconstructing the (given) original image from its MRD. Experimental results are presented to illustrate the usefulness of this new method.<<ETX>>
A self-organizing network for hyperellipsoidal clustering (HEC)

We propose a self-organizing network (HEC) for hyper-ellipsoidal clustering. The HEC network performs a partitional clustering using the regularized Mahalanobis distance. This regularized Mahalanobis distance measure is proposed to deal with the problems in estimating the Mahalanobis distance when the number of patterns in a cluster is less than (ill-posed problem) or not considerably larger than (poorly-posed problem) the dimensionality of the feature space in clustering multidimensional data. This regularized distance also achieves a tradeoff between hyperspherical and hyperellipsoidal cluster shapes so as to prevent the HEC network from producing unusually large or unusually small clusters. The significance level of the Kolmogrov-Smirnov test on the distribution of the Mahalanobis distances of patterns in a cluster to the cluster center under the multivariate Gaussian assumption is used as a measure of cluster compactness. The HEC network has been tested on a number of artificial data sets and real data sets. Experiments show that the HEC network gives better clustering results compared to the well-known K-means algorithm with the Euclidean distance metric.<<ETX>>
Dental Biometrics: Alignment and Matching of Dental Radiographs

Dental biometrics utilizes dental radiographs for human identification. The dental radiographs provide information about teeth, including tooth contours, relative positions of neighboring teeth, and shapes of the dental work (e.g., crowns, fillings, and bridges). The proposed system has two main stages: feature extraction and matching. The feature extraction stage uses anisotropic diffusion to enhance the images and a mixture of Gaussians model to segment the dental work. The matching stage has three sequential steps: tooth-level matching, computation of image distances, and subject identification. In the tooth-level matching step, tooth contours are matched using a shape registration method and the dental work is matched on overlapping areas. The distance between the tooth contours and the distance between the dental works are then combined using posterior probabilities. In the second step, the tooth correspondences between the given query (postmortem) radiograph and the database (antemortem) radiograph are established. A distance based on the corresponding teeth is then used to measure the similarity between the two radiographs. Finally, all the distances between the given postmortem radiographs and the antemortem radiographs that provide candidate identities are combined to establish the identity of the subject associated with the postmortem radiographs.
3D object recognition using invariant feature indexing of interpretation tables

The authors propose a technique for 3D object recognition from range data; the technique collects partial interpretations from precompiled tables indexed by invariant features of pairs of surface patches. This technique was motivated by some recent work in so-called geometric hashing procedures. A technique for automated construction of invariant feature indexed interpretation tables from CAD models is described. Recognition using these tables consists of an interpretation creation phase and an interpretation extension phase. The authors describe both procedures, and test them on some real images. The use of precompiled tables of hypotheses offers the potential for greater efficiency in the recognition step than techniques such as constrained search.