nils-herrmann/clean_scirepeval huggingface dataset:
Deblurring Low-Light Images with Light Streaks

Images acquired in low-light conditions with handheld cameras are often blurry, so steady poses and long exposure time are required to alleviate this problem. Although significant advances have been made in image deblurring, state-of-the-art approaches often fail on low-light images, as a sufficient number of salient features cannot be extracted for blur kernel estimation. On the other hand, light streaks are common phenomena in low-light images that have not been extensively explored in existing approaches. In this work, we propose an algorithm that utilizes light streaks to facilitate deblurring low-light images. The light streaks, which commonly exist in the low-light blurry images, contain rich information regarding camera motion and blur kernels. A method is developed in this work to detect light streaks for kernel estimation. We introduce a non-linear blur model that explicitly takes light streaks and corresponding light sources into account, and pose them as constraints for estimating the blur kernel in an optimization framework. For practical applications, the proposed algorithm is extended to handle images undergoing non-uniform blur. Experimental results show that the proposed algorithm performs favorably against the state-of-the-art methods on deblurring real-world low-light images.
Robust Object Tracking via Sparse Collaborative Appearance Model

In this paper, we propose a robust object tracking algorithm based on a sparse collaborative model that exploits both holistic templates and local representations to account for drastic appearance changes. Within the proposed collaborative appearance model, we develop a sparse discriminative classifier (SDC) and sparse generative model (SGM) for object tracking. In the SDC module, we present a classifier that separates the foreground object from the background based on holistic templates. In the SGM module, we propose a histogram-based method that takes the spatial information of each local patch into consideration. The update scheme considers both the most recent observations and original templates, thereby enabling the proposed algorithm to deal with appearance changes effectively and alleviate the tracking drift problem. Numerous experiments on various challenging videos demonstrate that the proposed tracker performs favorably against several state-of-the-art algorithms.
An algebraic approach to affine registration of point sets

This paper proposes a new affine registration algorithm for matching two point sets in ℝ2 or ℝ3. The input point sets are represented as probability density functions, using either Gaussian mixture models or discrete density models, and the problem of registering the point sets is treated as aligning the two distributions. Since polynomials transform as symmetric tensors under an affine transformation, the distributions' moments, which are the expected values of polynomials, also transform accordingly. Therefore, instead of solving the harder problem of aligning the two distributions directly, we solve the softer problem of matching the distributions' moments. By formulating a least-squares problem for matching moments of the two distributions up to degree three, the resulting cost function is a polynomial that can be efficiently optimized using techniques originated from algebraic geometry: the global minimum of this polynomial can be determined by solving a system of polynomial equations. The algorithm is robust in the presence of noises and outliers, and we validate the proposed algorithm on a variety of point sets with varying degrees of deformation and noise.
Unconstrained Face Recognition: Identifying a Person of Interest From a Media Collection

As face recognition applications progress from constrained sensing and cooperative subjects scenarios (e.g., driver's license and passport photos) to unconstrained scenarios with uncooperative subjects (e.g., video surveillance), new challenges are encountered. These challenges are due to variations in ambient illumination, image resolution, background clutter, facial pose, expression, and occlusion. In forensic investigations where the goal is to identify a person of interest, often based on low quality face images and videos, we need to utilize whatever source of information is available about the person. This could include one or more video tracks, multiple still images captured by bystanders (using, for example, their mobile phones), 3-D face models constructed from image(s) and video(s), and verbal descriptions of the subject provided by witnesses. These verbal descriptions can be used to generate a face sketch and provide ancillary information about the person of interest (e.g., gender, race, and age). While traditional face matching methods generally take a single media (i.e., a still face image, video track, or face sketch) as input, this paper considers using the entire gamut of media as a probe to generate a single candidate list for the person of interest. We show that the proposed approach boosts the likelihood of correctly identifying the person of interest through the use of different fusion schemes, 3-D face models, and incorporation of quality measures for fusion and video frame selection.
A multi-channel filtering approach to texture segmentation

Multichannel filtering techniques are presented for obtaining both region- and edge-based segmentations of textured images. The channels are represented by a bank of even-symmetric Gabor filters that nearly uniformly covers the spatial-frequency domain. Feature images are obtained by subjecting each (selected) filtered image to a nonlinear transformation and computing a measure of energy around each pixel. Region-based segmentations are obtained by using a square-error clustering algorithm. Edge-based segmentations are obtained by applying an edge detector to each feature image and combining their magnitude responses. An integrated segmentation technique that combines the strengths of the previous two techniques while eliminating their weaknesses is proposed. The integrated approach is truly unsupervised, since it eliminates the need for knowing the exact number of texture categories in the image.<<ETX>>
Face Matching and Retrieval Using Soft Biometrics

Soft biometric traits embedded in a face (e.g., gender and facial marks) are ancillary information and are not fully distinctive by themselves in face-recognition tasks. However, this information can be explicitly combined with face matching score to improve the overall face-recognition accuracy. Moreover, in certain application domains, e.g., visual surveillance, where a face image is occluded or is captured in off-frontal pose, soft biometric traits can provide even more valuable information for face matching or retrieval. Facial marks can also be useful to differentiate identical twins whose global facial appearances are very similar. The similarities found from soft biometrics can also be useful as a source of evidence in courts of law because they are more descriptive than the numerical matching scores generated by a traditional face matcher. We propose to utilize demographic information (e.g., gender and ethnicity) and facial marks (e.g., scars, moles, and freckles) for improving face image matching and retrieval performance. An automatic facial mark detection method has been developed that uses (1) the active appearance model for locating primary facial features (e.g., eyes, nose, and mouth), (2) the Laplacian-of-Gaussian blob detection, and (3) morphological operators. Experimental results based on the FERET database (426 images of 213 subjects) and two mugshot databases from the forensic domain (1225 images of 671 subjects and 10 000 images of 10 000 subjects, respectively) show that the use of soft biometric traits is able to improve the face-recognition performance of a state-of-the-art commercial matcher.
Online visual vocabulary pruning using pairwise constraints

Given a pair of images represented using bag-of-visual-words and a label corresponding to whether the images are “related”(must-link constraint) or “unrelated” (cannot-link constraint), we address the problem of selecting a subset of visual words that are salient in explaining the relation between the image pair. In particular, a subset of features is selected such that the distance computed using these features satisfies the given pairwise constraints. An efficient online feature selection algorithm is presented based on the dual-gradient descent approach. Side information in the form of pair-wise constraints is incorporated into the feature selection stage, providing the user with flexibility to use an unsupervised or semi-supervised algorithm at a later stage. Correlated subsets of visual words, usually resulting from hierarchical quantization process (called groups), are exploited to select a significantly smaller vocabulary. A group-LASSO regularizer is used to drive as many feature weights to zero as possible. We evaluate the quality of the pruned vocabulary by clustering the data using the resulting feature subset. Experiments on PASCAL VOC 2007 dataset using 5000 visual keywords, resulted in around 80% reduction in the number of keywords, with little or no loss in performance.
Partial Face Recognition: Alignment-Free Approach

Numerous methods have been developed for holistic face recognition with impressive performance. However, few studies have tackled how to recognize an arbitrary patch of a face image. Partial faces frequently appear in unconstrained scenarios, with images captured by surveillance cameras or handheld devices (e.g., mobile phones) in particular. In this paper, we propose a general partial face recognition approach that does not require face alignment by eye coordinates or any other fiducial points. We develop an alignment-free face representation method based on Multi-Keypoint Descriptors (MKD), where the descriptor size of a face is determined by the actual content of the image. In this way, any probe face image, holistic or partial, can be sparsely represented by a large dictionary of gallery descriptors. A new keypoint descriptor called Gabor Ternary Pattern (GTP) is also developed for robust and discriminative face recognition. Experimental results are reported on four public domain face databases (FRGCv2.0, AR, LFW, and PubFig) under both the open-set identification and verification scenarios. Comparisons with two leading commercial face recognition SDKs (PittPatt and FaceVACS) and two baseline algorithms (PCA+LDA and LBP) show that the proposed method, overall, is superior in recognizing both holistic and partial faces without requiring alignment.
Combining multiple clusterings using evidence accumulation

We explore the idea of evidence accumulation (EAC) for combining the results of multiple clusterings. First, a clustering ensemble - a set of object partitions, is produced. Given a data set (n objects or patterns in d dimensions), different ways of producing data partitions are: 1) applying different clustering algorithms and 2) applying the same clustering algorithm with different values of parameters or initializations. Further, combinations of different data representations (feature spaces) and clustering algorithms can also provide a multitude of significantly different data partitionings. We propose a simple framework for extracting a consistent clustering, given the various partitions in a clustering ensemble. According to the EAC concept, each partition is viewed as an independent evidence of data organization, individual data partitions being combined, based on a voting mechanism, to generate a new n × n similarity matrix between the n patterns. The final data partition of the n patterns is obtained by applying a hierarchical agglomerative clustering algorithm on this matrix. We have developed a theoretical framework for the analysis of the proposed clustering combination strategy and its evaluation, based on the concept of mutual information between data partitions. Stability of the results is evaluated using bootstrapping techniques. A detailed discussion of an evidence accumulation-based clustering algorithm, using a split and merge strategy based on the k-means clustering algorithm, is presented. Experimental results of the proposed method on several synthetic and real data sets are compared with other combination strategies, and with individual clustering results produced by well-known clustering algorithms.
On texture in document images

A multichannel filtering-based texture segmentation method is applied to a variety of document image processing problems: text-graphics separation, address-block location, and bar code localization. In each of these segmentation problems, the text context or bar code in the image is considered to define a unique texture. Thus, all three document analysis problems can be posed as texture segmentation problems. Two-dimensional Gabor filters are used to compute texture features. Both supervised and unsupervised methods are used to identify regions of text or bar code in the document images. The performance of the segmentation and classification scheme for a variety of document images demonstrates the generality and effectiveness of the approach.<<ETX>>
Signal Processing for Biometric Systems [DSP Forum]

This IEEE signal processing magazine (SPM) forum discuses signal processing applications, technologies, requirements, and standardization of biometric systems. The forum members bring their expert insights into issues such as biometric security, privacy, and multibiometric and fusion techniques. The invited forum members are Prof. Anil K. Jain of Michigan State University, Prof. Rama Chellappa of the University of Maryland, Dr. Stark C. Draper of theUniversity of Wisconsin in Madison, Prof. Nasir Memon of Polytechnic University, and Dr. P. Jonathon Phillips of the National Institute of Standards and Technology. The moderator of the forum is Dr. Anthony Vetro of Mitsubishi Electric Research Labs, and associate editor of SPM.
Identifying Cohesive Subgroups and Their Correspondences in Multiple Related Networks

Identifying cohesive subgroups in networks, also known as clustering is an active area of research in link mining with many practical applications. However, most of the early work in this area has focused on partitioning a single network or a bipartite graph into clusters/communities. This paper presents a framework that simultaneously clusters nodes from multiple related networks and learns the correspondences between subgroups in different networks. The framework also allows the incorporation of prior information about potential relationships between the subgroups. We have performed extensive experiments on both synthetic and real-life data sets to evaluate the effectiveness of our framework. Our results show superior performance of simultaneous clustering over independent clustering of individual networks.
Bootstrap Techniques for Error Estimation

The design of a pattern recognition system requires careful attention to error estimation. The error rate is the most important descriptor of a classifier's performance. The commonly used estimates of error rate are based on the holdout method, the resubstitution method, and the leave-one-out method. All suffer either from large bias or large variance and their sample distributions are not known. Bootstrapping refers to a class of procedures that resample given data by computer. It permits determining the statistical properties of an estimator when very little is known about the underlying distribution and no additional samples are available. Since its publication in the last decade, the bootstrap technique has been successfully applied to many statistical estimations and inference problems. However, it has not been exploited in the design of pattern recognition systems. We report results on the application of several bootstrap techniques in estimating the error rate of 1-NN and quadratic classifiers. Our experiments show that, in most cases, the confidence interval of a bootstrap estimator of classification error is smaller than that of the leave-one-out estimator. The error of 1-NN, quadratic, and Fisher classifiers are estimated for several real data sets.
Learning Pairwise Similarity for Data Clustering

Each clustering algorithm induces a similarity between given data points, according to the underlying clustering criteria. Given the large number of available clustering techniques, one is faced with the following questions: (a) Which measure of similarity should be used in a given clustering problem? (b) Should the same similarity measure be used throughout the d-dimensional feature space? In other words, are the underlying clusters in given data of similar shape? Our goal is to learn the pairwise similarity between points in order to facilitate a proper partitioning of the data without the a priori knowledge of k, the number of clusters, and of the shape of these clusters. We explore a clustering ensemble approach combined with cluster stability criteria to selectively learn the similarity from a collection of different clustering algorithms with various parameter configurations
Evidential Value of Automated Latent Fingerprint Comparison: An Empirical Approach

Latent prints are routinely recovered from crime scenes and are compared with available databases of known fingerprints for identifying criminals. However, current procedures to compare latent prints to large databases of exemplar (rolled or plain) prints are prone to errors. This suggests caution in making conclusions about a suspect's identity based on a latent fingerprint comparison. A number of attempts have been made to statistically model the utility of a fingerprint comparison in making a correct accept/reject decision or its evidential value. These approaches, however, either make unrealistic assumptions about the model or they lack simple interpretation. We argue that the posterior probability of two fingerprints belonging to different fingers given their match score, referred to as the nonmatch probability (NMP), effectively captures any implicating evidence of the comparison. NMP is computed using state-of-the-art matchers and is easy to interpret. To incorporate the effect of image quality, number of minutiae, and size of the latent on NMP value, we compute the NMP vs. match score plots separately for image pairs (latent and exemplar prints) with different characteristics. Given the paucity of latent fingerprint databases in public domain, we simulate latent prints using two exemplar print databases (NIST SD-14 and Michigan State Police) by cropping regions of three different sizes. We appropriately validate this simulation using four latent databases (NIST SD-27 and three proprietary latent databases) and two state-of-the-art fingerprint matchers to compute their respective match scores. We also discuss a practical scenario where a latent examiner uses the proposed framework to compute the evidential value of a latent-exemplar print pair comparison.
ViSE: Visual Search Engine Using Multiple Networked Cameras

We propose a visual search engine (ViSE) as a semi-automatic component in a surveillance system using networked cameras. The ViSE aims to assist the monitoring operation of huge amounts of captured video streams, which tracks and finds people in the video based on their primitive features with the interaction of a human operator. We address the issues of object detection and tracking, shadow suppression and color-based recognition for the proposed system. The experimental results on a set of video data with ten subjects showed that ViSE retrieves correct candidates with 83% recall at 83% precision
Application of two dimensional spectral estimation in image restoration

In this paper we consider an application of spectral estimation to adaptive restoration of images degraded by additive white noise. Three adaptive techniques of restoration are compared with a non-adaptive technique. In the non-adaptive technique, the whole image is Wiener filtered by assuming a correlation model for the signal. In the adaptive methods, a) the spectral shape of the signal is kept constant, but the variance is adaptively estimated, b) the power spectrum of each block is adaptively estimated, c) the data is adaptively decorrelated in one dimension and filtered along the other.
3D Touchless Fingerprints: Compatibility with Legacy Rolled Images

Fingerprints are traditionally captured based on contact of the finger on paper or a platen surface. This often results in partial or degraded images due to improper finger placement, skin deformation, slippage and smearing, or sensor noise from wear and tear of surface coatings. A new generation of touchless live scan devices that generate 3D representation of fingerprints is appearing in the market. This new sensing technology addresses many of the problems stated above. However, 3D touchless fingerprint images need to be compatible with the legacy rolled images used in automated fingerprint identification systems (AFIS). In order to solve this interoperability issue, we propose an unwrapping algorithm that unfolds the 3D fingerprint in such a way that it resembles the effect of virtually rolling the 3D finger on a 2D plane. Our preliminary experiments show promising results in obtaining touchless fingerprint images that are of high quality and at the same time compatible with legacy rolled fingerprint images.
Image coding by auto regressive synthesis

A new method of image coding by autoregressive (AR) synthesis is presented. The physics of image formation suggests that an image may be considered as a power spectrum. Using this formulation a Cosine transform of the sampled image is shown to yield a set of autocorrelations. These are used to find an equivalent AR model whose parameters are encoded for transmission. Compared to conventional Cosine transform coding, this method is shown to give superior resolution and is shown to suppress the "block-effects" present in block-by-block transform coding methods. Distinction between this method and linear predictive coding (LPC) used for speech data compression is made. Extensions and examples for two dimensional images are given.
Model Based Separation of Overlapping Latent Fingerprints

Latent fingerprints lifted from crime scenes often contain overlapping prints, which are difficult to separate and match by state-of-the-art fingerprint matchers. A few methods have been proposed to separate overlapping fingerprints to enable fingerprint matchers to successfully match the component fingerprints. These methods are limited by the accuracy of the estimated orientation field, which is not reliable for poor quality overlapping latent fingerprints. In this paper, we improve the robustness of overlapping fingerprints separation, particularly for low quality images. Our algorithm reconstructs the orientation fields of component prints by modeling fingerprint orientation fields. In order to facilitate this, we utilize the orientation cues of component fingerprints, which are manually marked by fingerprint examiners. This additional markup is acceptable in forensics, where the first priority is to improve the latent matching accuracy. The effectiveness of the proposed method has been evaluated not only on simulated overlapping prints, but also on real overlapped latent fingerprint images. Compared with available methods, the proposed algorithm is more effective in separating poor quality overlapping fingerprints and enhancing the matching accuracy of overlapping fingerprints.
Image-based form document retrieval

We address the problem of image-based form document retrieval. The essential element of this problem is the definition of a similarity measure that is applicable in real situations, where query images are allowed to differ from the database images. Based on the definition of form signature, we have proposed a similarity measure that is insensitive to translation, scaling, moderate skew (<5/spl deg/) and variations in the geometrical proportions of the form layout. This similarity measure also has a good tolerance to line detection errors. We have developed a prototype form retrieval system which has been tested on a database containing 100 different kinds of forms.