False Matches Research Articles

Restoration and recognition of distant, blurry irises

Raw iris images collected outdoors at standoff distances exceeding 25 m are susceptible to noise and atmospheric blur and even under ideal imaging conditions are too degraded to carry out recognition with high accuracy. Traditionally, atmospherically distorted images have been corrected through the use of unique hardware components such as adaptive optics. Here we apply a pure digital image restoration approach to correct for optical aberrations. Image restoration was applied to both single images and image sequences. We propose both a single-frame denoising and deblurring approach, and a multiframe fusion and deblurring approach. To compare performance of the proposed methods, iris recognitions were carried out using the approach of Daugman. Hamming distances (HDs) of computed binary iris codes were measured before and after the restoration. We found the HD decreased from >0.46 prior to a mean value of <0.39 for random single images. The multiframe fusion approach produced the most robust restoration and achieved a mean HD for all subjects in our data set of 0.33 while known false matches remained at 0.44. These results show that, when used properly, image restoration approaches do significantly increase recognition performance for known true positives with low increase in false positive detections, and irises can be recognized in turbulent atmospheric conditions.

Global Propagation of Affine Invariant Features for Robust Matching

Local invariant features have been successfully used in image matching to cope with viewpoint change, partial occlusion, and clutters. However, when these factors become too strong, there will be a lot of mismatches due to the limited repeatability and discriminative power of features. In this paper, we present an efficient approach to remove the false matches and propagate the correct ones for the affine invariant features which represent the state-of-the-art local invariance. First, a pair-wise affine consistency measure is proposed to evaluate the consensus of the matches of affine invariant regions. The measure takes into account both the keypoint location and the region shape, size, and orientation. Based on this measure, a geometric filter is then presented which can efficiently remove the outliers from the initial matches, and is robust to severe clutters and non-rigid deformation. To increase the correct matches, we propose a global match refinement and propagation method that simultaneously finds a optimal group of local affine transforms to relate the features in two images. The global method is capable of producing a quasi-dense set of matches even for the weakly textured surfaces that suffer strong rigid transformation or non-rigid deformation. The strong capability of the proposed method in dealing with significant viewpoint change, non-rigid deformation, and low-texture objects is demonstrated in experiments of image matching, object recognition, and image based rendering.

A novel spectral library workflow to enhance protein identifications

The innovations in mass spectrometry-based investigations in proteome biology enable systematic characterization of molecular details in pathophysiological phenotypes. However, the process of delineating large-scale raw proteomic datasets into a biological context requires high-throughput data acquisition and processing. A spectral library search engine makes use of previously annotated experimental spectra as references for subsequent spectral analyses. This workflow delivers many advantages, including elevated analytical efficiency and specificity as well as reduced demands in computational capacity. In this study, we created a spectral matching engine to address challenges commonly associated with a library search workflow. Particularly, an improved sliding dot product algorithm, that is robust to systematic drifts of mass measurement in spectra, is introduced. Furthermore, a noise management protocol distinguishes spectra correlation attributed from noise and peptide fragments. It enables elevated separation between target spectral matches and false matches, thereby suppressing the possibility of propagating inaccurate peptide annotations from library spectra to query spectra. Moreover, preservation of original spectra also accommodates user contributions to further enhance the quality of the library. Collectively, this search engine supports reproducible data analyses using curated references, thereby broadening the accessibility of proteomics resources to biomedical investigators. This article is part of a Special Issue entitled: From protein structures to clinical applications.

Spatiotemporal 3D motion vector filtering method for robust visual odometry

Most of the previous visual odometry methods cannot deal with a large independently moving object that takes up over 50% of the image area. To overcome this problem, the spatiotemporal filter is incorporated into the RANSAC method to filter out false match that occurrs by a large independently moving object. This spatiotemporal filter uses the current and previous motion vector's length and direction. Experimental results demonstrate that the proposed method effectively rejects the motion vectors generated from large independently moving objects and improves the visual odometry accuracy.

Bayesian parentage analysis with systematic accountability of genotyping error, missing data and false matching

The goal of any parentage analysis is to identify as many parent-offspring relationships as possible, while minimizing incorrect assignments. Existing methods can achieve these ends, but they require additional information in the form of demographic data, thousands of markers and/or estimates of genotyping error rates. For many non-model systems, it is simply not practical, cost-effective or logistically feasible to obtain this information. Here, we develop a Bayesian parentage method that only requires the sampled genotypes to account for genotyping error, missing data and false matches. Extensive testing with microsatellite and SNP datasets reveals that our Bayesian parentage method reliably controls for the number of false assignments, irrespective of the genotyping error rate. When the number of loci is limiting, our approach maximizes the number of correct assignments by accounting for the frequencies of shared alleles. Comparisons with exclusion and likelihood-based methods on an empirical salmon dataset revealed that our Bayesian method had the highest ratio of correct to incorrect assignments.

Spam Filtering through Multiple Pattern Bit Parallel String Matching Combining Shift AND &amp; OR

Spam refers to unsolicited, unwanted and inappropriate bulk email. Spam filtering has become conspicuous as they consume a lot of network bandwidth, overloads the email server and drops the productivity of global economy. Content based spam filtering is accomplished with the help of multiple pattern string matching algorithm. Traditionally Aho Corasick algorithm was used to filter spam which constructs a trie of the spam keywords. The performance degrades in the context of time as well as space as the size of trie increases with the growing spam keywords count. To counterbalance time and space loss, bit parallel multiple pattern string matching algorithm using Shift OR method is used. The method acts as filter performing approximate string matching . This implies that there are some false matches detected by the filter which requires verification. The proposed method for filtering spams has been developed using a combination of Shift AND and OR operation. The method directly works on spam keywords of equal size whereas for unequal size keywords, a new proposed equal size grouping method is developed. Both method shows improvement over the Aho Corasick algorithm in context of space complexity and also behaves as an efficient filter and reducing the number of false matches as present in Shift OR method.

Lessons learned from developing a drug evidence base to support pharmacovigilance.

This work identified challenges associated with extraction and representation of medication-related information from publicly available electronic sources. We gained direct observational experience through creating and evaluating the Drug Evidence Base (DEB), a repository of drug indications and adverse effects (ADEs), and supplemented this through literature review. We extracted DEB content from the National Drug File Reference Terminology, from aggregated MEDLINE co-occurrence data, and from the National Library of Medicine's DailyMed. To understand better the similarities, differences and problems with the content of DEB and the SIDER Side Effect Resource, and Vanderbilt's MEDI Indication Resource, we carried out statistical evaluations and human expert reviews. While DEB, SIDER, and MEDI often agreed on medication indications and side effects, cross-system shortcomings limit their current utility. The drug information resources we evaluated frequently employed multiple, disparate vaguely related UMLS concepts to represent a single specific clinical drug indication or adverse effect. Thus, evaluations comparing drug-indication and drug-ADE coverage for such resources will encounter substantial numbers of false negative and false positive matches. Furthermore, our review found that many indication and ADE relationships are too complex - logically and temporally - to represent within existing systems. To enhance applicability and utility, future drug information systems deriving indications and ADEs from public resources must represent clinical concepts uniformly and as precisely as possible. Future systems must also better represent the inherent complexity of indications and ADEs.

Template Aging Phenomenon in Iris Recognition

Biometric template aging is defined as an increase in recognition error rate with increased time since enrollment. It is believed that template aging does not occur for iris recognition. Several research groups, however, have recently reported experimental results showing that iris template aging does occur. This template aging effect manifests as a shift in the authentic distribution, resulting in an increased false non-match rate. Analyzing results from a three-year time-lapse data set, we find ~150% increase in the false non-match rate at a decision threshold representing a one in two million false match rate. We summarize several known elements of eye aging that could contribute to template aging, including age-related change in pupil dilation. Finally, we discuss various steps that can control the template aging effect in typical identity verification applications.

Automatic Image Registration Technique of Remote Sensing Images

Image registration is a crucial step in most image processing tasks for which the final result is achieved from a combination of various resources. Automatic registration of remote-sensing images is a difficult task as it must deal with the intensity changes and variation of scale, rotation and illumination of the images. This paper proposes image registration technique of multi-view, multi- temporal and multi-spectral remote sensing images. Firstly, a preprocessing step is performed by applying median filtering to enhance the images. Secondly, the Steerable Pyramid Transform is adopted to produce multi-resolution levels of reference and sensed images; then, the Scale Invariant Feature Transform (SIFT) is utilized for extracting feature points that can deal with the large variations of scale, rotation and illumination between images .Thirdly, matching the features points by using the Euclidian distance ratio; then removing the false matching pairs using the RANdom SAmple Consensus (RANSAC) algorithm. Finally, the mapping function is obtained by the affine transformation. Quantitative comparisons of our technique with the related techniques show a significant improvement in the presence of large scale, rotation changes, and the intensity changes. The effectiveness of the proposed technique is demonstrated by the experimental results.

Research on Real-time Multi-target Tracking Algorithm Based on MSPF

Recently,real-time monitoring multi-target tracking as an important component of intelligent transportation system(ITS) has been paid much attention.The traditional multitarget tracking algorithm has problems that the processing speed is slow and the false matches may happen when vehicles cross.Firstly,the algorithm detects moving targets through modeling a complex background based on Bayesian rules,then introduces a multi-target tracking algorithm based on mean shift particle filter(MSPF).Firstly,the algorithm predicts the extent possible by the use of MSPF for each vehicle in the next frame,uses different detection strategies for simple or multiple targets to avoid a global search and improve the tracking speed;by constructing the importance density function based on the latest observations,the algorithm can achieve an accurate and robust tracking in the part of the block and cross-vehicle.Simulation results verify the proposed algorithm.

An edge-based scale- and affine-invariant algorithm for remote sensing image registration

For remote sensing image registration, we find that affine transformation is suitable to describe the mapping between images. Based on the scale-invariant feature transform (SIFT), affine-SIFT (ASIFT) is capable of detecting and matching scale- and affine-invariant features. Unlike the blob feature detected in SIFT and ASIFT, a scale-invariant edge-based matching operator is employed in our new method. To find the local features, we first extract edges with a multi-scale edge detector, then the distinctive features (we call these ‘feature from edge’ or FFE) with computed scale are detected, and finally a new matching scheme is introduced for image registration. The algorithm incorporates principal component analysis (PCA) to ease the computational burden, and its affine invariance is embedded by discrete sampling as ASIFT. We present our analysis based on multi-sensor, multi-temporal, and different viewpoint images. The operator shows the potential to become a robust alternative for point-feature-based registration of remote-sensing images as subpixel registration consistency is achieved. We also show that using the proposed edge-based scale- and affine-invariant algorithm (EBSA) results in a significant speedup and fewer false matching pairs compared to the original ASIFT operator.

High-Speed and Robust Scene Matching Algorithm Based on ORB for SAR/INS Integrated Navigation System

It is important that scene matching algorithm should satisfy the requirements of real-time, robustness and high-precision for inertial integrated navigation system. And considering the serious distortion and speckle noises of SAR images, we proposed a new scene matching algorithm for the SAR/INS integrated navigation system with high-speed and robustness based on Oriented FAST and Rotated BRIEF (ORB). We started by detecting scale-space FAST-based features in combination with an efficiently computed orientation in the image. Then, we calculated feature point's Rotation-Aware BRIEF descriptor which performs well with rotation and match features by computing Hamming distance between descriptors. Finally, we adopted GroupSAC which are proposed recently to remove the false matching points and the least square algorithm for getting the distortion transformation parameters that are the aircraft position errors and rotation transform parameters between real image and reference image. Experimental results on real SAR images indicate that our algorithm is invariant to various image transformations due to rotation and scale, and also robust to speckle noise and extremely efficient to compute, better than SIFT in many situations. Therefore, our algorithm can meet the high performance needs for matching navigation in the SAR/INS integrated navigation system.

New Methods toward Minimizing the Slow Speed Bias Associated with Atmospheric Motion Vectors

AbstractComparisons between satellite-derived winds and collocated rawinsonde observations often show a pronounced slow speed bias at mid- and upper levels of the atmosphere. A leading cause of the slow speed bias is the improper assignment of the tracer to a height that is too high in the atmosphere. Height errors alone cannot fully explain the slow bias, however. Another factor influencing the speed bias is the size of the target window used in the tracking step. Tracking with a large target window can cause excessive averaging to occur and a smoothing of the instantaneous wind field. Conversely, if too small a window is specified, there is an increased risk of finding a false match. The authors have developed a new “nested tracking” approach that isolates the dominant local motion within a cloud scene and minimizes the smoothing of the motion estimate. A major advantage of the new approach is the ability to identify which pixels within the cloud scene are contributing to the tracking solution. Knowing which pixels contribute to the dominant motion allows for a more representative height to be derived, thereby directly linking the height assignment to the tracking process, which is an important goal for producers of global atmospheric motion vector (AMV) data. When compared with equivalent rawinsondes, the AMVs derived with the new approach show a considerable improvement in the speed bias and root-mean-square error over a control set of AMVs derived with more-conventional methods.

Image Copy-Move Forgery Detection Based on SIFT and Gray Level

In order to reduce the false matching rate when detecting copy-move forgeries, an improved method based on SIFT and gray level was proposed in this study. Firstly, extract SIFT key points, and establish SIFT feature vector for every key point; Secondly, extract the gray level feature and combine it with SIFT feature to found a feature vector with size of 129D; Finally, match the above feature vector between every two different key points and then the copy-move regions would be detected. The experimental results showed that the improved algorithm reduced false matching rate even when an image was distorted by Gaussian blur.

Image copy-move forgery detection based on polar cosine transform and approximate nearest neighbor searching

Copy-move is one of the most commonly used image tampering operation, where a part of image content is copied and then pasted to another part of the same image. In order to make the forgery visually convincing and conceal its trace, the copied part may subject to post-processing operations such as rotation and blur. In this paper, we propose a polar cosine transform and approximate nearest neighbor searching based copy-move forgery detection algorithm. The algorithm starts by dividing the image into overlapping patches. Robust and compact features are extracted from patches by taking advantage of the rotationally-invariant and orthogonal properties of the polar cosine transform. Potential copy-move pairs are then detected by identifying the patches with similar features, which is formulated as approximate nearest neighbor searching and accomplished by means of locality-sensitive hashing (LSH). Finally, post-verifications are performed on potential pairs to filter out false matches and improve the accuracy of forgery detection. To sum up, the LSH based similar patch identification and the post-verification methods are two major novelties of the proposed work. Experimental results reveal that the proposed work can produce accurate detection results, and it exhibits high robustness to various post-processing operations. In addition, the LSH based similar patch detection scheme is much more effective than the widely used lexicographical sorting.

Principal Line-Based Alignment Refinement for Palmprint Recognition

Image alignment is an important step in various biometric authentication applications such as palmprint recognition. Most of the existing palmprint alignment methods make use of some key points between fingers or in palm boundary to establish the local coordinate system for region of interest (ROI) extraction. The ROI is consequently used for feature extraction and matching. Such alignment methods usually yield a coarse alignment of the palmprint images, while many missed and false matches are actually caused by inaccurate image alignments. To improve the palmprint verification accuracy, in this paper, we present an efficient palmprint alignment refinement method. After extracting the principal lines from the palmprint image, we apply the iterative closest point method to them to estimate the translation and rotation parameters between two images. The estimated parameters are then used to refine the alignment of palmprint feature maps for a more accurate palmprint matching. The experimental results show that the proposed method greatly improves the palmprint recognition accuracy and it works in real time.

온톨로지 트리기반 멀티에이전트 세만틱 유사도매칭 알고리즘

Semantic-based information retrieval techniques understand the meanings of the concepts that users specify in their queries, but the traditional semantic matching methods based on the ontology tree have three weaknesses which may lead to many false matches, causing the falling precision. In order to improve the matching precision and the recall of the information retrieval, this paper proposes a multi-agent improved semantic similarity matching algorithm based on the ontology tree, which can avoid the considerable computation redundancies and mismatching during the entire matching process. The results of the experiments performed on our algorithm show improvements in precision and recall compared with the information retrieval techniques based on the traditional semantic similarity matching methods.

Object Recognition Based on Sift Features and A Novel Feature Matching Algorithm

Vision based object recognition is the task of finding object in an image or video sequence by comparing them with an image of that object. Images can be taken from different viewpoints, different scales or even when they are translated or rotated. In this paper, a new algorithm is proposed for object recognition. First the feature points are extracted using scale invariant feature transform (SIFT), then an initial matching is established using photometrical features of the points. A novel geometric based method is used for optimizing matching points. After removing false matches, by using proposed algorithm, remaining key points can be categorized and each distinctive group is assigned to a separated object. The group with maximum likelihood is considered as desirable object. Important innovation in our method is the use of geometrical constraints of feature points between two matching images to find correct matching.

Improved heuristic drift elimination with magnetically-aided dominant directions (MiHDE) for pedestrian navigation in complex buildings

The main problem of pedestrian dead-reckoning (PDR) using only a body-attached inertial measurement unit is the accumulation of heading errors. The heading provided by magnetometers in indoor buildings is in general not reliable and therefore it is commonly not used. Recently, a new method was proposed called heuristic drift elimination (HDE) that minimises the heading error when navigating in buildings. It assumes that the majority of buildings have their corridors parallel to each other, or they intersect at right angles, and consequently most of the time the person walks along a straight path with a heading constrained to one of the four possible directions. In this article we study the performance of HDE-based methods in complex buildings, i.e. with pathways also oriented at 45°, long curved corridors, and wide areas where non-oriented motion is possible. We explain how the performance of the original HDE method can be deteriorated in complex buildings, and also, how severe errors can appear in the case of false matches with the building's dominant directions. Although magnetic compassing indoors has a chaotic behaviour, in this article we analyse large data-sets in order to study the potential use that magnetic compassing has to estimate the absolute yaw angle of a walking person. Apart from these analysis, this article also proposes an improved HDE method called Magnetically-aided Improved Heuristic Drift Elimination (MiHDE), that is implemented over a PDR framework that uses foot-mounted inertial navigation with an extended Kalman filter (EKF). The EKF is fed with the MiHDE-estimated orientation error, gyro bias corrections, as well as the confidence over that corrections. We experimentally evaluated the performance of the proposed MiHDE-based PDR method, comparing it with the original HDE implementation. Results show that both methods perform very well in ideal orthogonal narrow-corridor buildings, and MiHDE outperforms HDE for non-ideal trajectories (e.g. curved paths) and also makes it robust against potential false dominant direction matchings.

A Study of Distortion Effects on Fingerprint Matching

Fingerprint matching often suffers from non-linear distortion. In this paper, we study the distortions of the fingerprints in three public available databases. To simulate the real scenarios of fingerprint matching, we distort fingerprint minutiae templates with the following three methods: 1) Randomly remove certain number of minutiae; 2) Randomly replace certain number of minutiae; 3) Randomly disturb the locations and orientations of the minutiae. Our experimental results show how and to what extent the fingerprint minutiae templates can be distorted without causing increases in false non-match rates and false match rates.