nils-herrmann/clean_scirepeval huggingface dataset:
CAD-based computer vision: from CAD models to relational graphs

The authors outline their approach for automatic translation of geometric entities produced by a CAD system into a relational graph structure. They have developed a system which uses 3-D object descriptions created on a commercial CAD system and expressed in the industry-standard IGES form, and performs geometric inferencing to object a relational graph representation of the object which can be stored in a database of models of object recognition. Details of the IGES standard, the geometric inference engine, and some formal properties of 3-D models are discussed. In addition to the process of translation from one data format to another, the interference engine extracts higher-level information from the CAD model and stores it explicitly in the new data structure. The higher-level features will allow the search space explored during the object recognition stage to be pruned early.
Face retriever: Pre-filtering the gallery via deep neural net

Face retrieval is an enabling technology for many applications, including automatic face annotation, deduplication, and surveillance. In this paper, we propose a face retrieval system which combines a k-NN search procedure with a COTS matcher (PittPatt1) in a cascaded manner. In particular, given a query face, we first pre-filter the gallery set and find the top-k most similar faces for the query image by using deep facial features that are learned with a deep convolutional neural network. The top-k most similar faces are then re-ranked based on score-level fusion of the similarities between deep features and the COTS matcher. To further boost the retrieval performance, we develop a manifold ranking algorithm. The proposed face retrieval system is evaluated on two large-scale face image databases: (i) a web face image database, which consists of over 3, 880 query images of 1, 507 subjects and a gallery of 5, 000, 000 faces, and (ii) a mugshot database, which consists of 1, 000 query images of 1, 000 subjects and a gallery of 1, 000, 000 faces. Experimental results demonstrate that the proposed face retrieval system can simultaneously improve the retrieval performance (CMC and precision-recall) and scalability for large-scale face retrieval problems.
Demonstration of 10 Gbit/s burst-mode transmission using a linear burst-mode receiver and burst-mode electronic equalization [invited]

We report on the feasibility of using burst-mode electronic dispersion compensation (BM-EDC) combined with a linear burst-mode receiver (LBMRx) to transmit 10 Gbit/s bursts with a loud/soft ratio of up to 15 dB over a reach greater than 100 km. The experimental demonstration used a LBMRx fabricated using a 250 nm SiGe BiCMOS technology and a BM-EDC implemented by offline processing of sampled data. BM-EDC means that the EDC tap coefficients are adapted based on a training sequence embedded in the preamble at the start of each burst. BM-EDC can be used to increase the total and differential reach of passive optical networks limited by chromatic dispersion or improve link performance for other impairments, especially if these vary from burst to burst. The training sequence must be kept short in order to ensure high traffic efficiency (defined as the ratio of the effective number of transmitted data bits to the total number of transmitted bits). Here we show that, using gear-shifted least-mean-squares adaptation, the required number of training bits in the preamble can be as short as 250 bits.
A Fast Karhunen-Loeve Transform for Digital Restoration of Images Degraded by White and Colored Noise

The Karhunen-Loeve (KL) transform is known to have certain properties which make it "optimal" for many "mean-square" signal processing applications [1]-[4]. Recently, it has been shown that a class of digital images may be represented by a set of boundary value stochastic difference equations in two dimensions [5]-[7]. If the boundary conditions of this class of images are fixed, then these equations lead to a fast KL transform algorithm. Here this fast KL transform is used for Wiener filtering of images degraded by white or colored noise. Comparisons with "Generalized Wiener Filtering"  and conventional Fourier domain filtering are made. It is shown that the two-dimensional Wiener filter is nonseparable so that two-dimensional generalized Wiener filtering is more elaborate than reported in . It is also shown that certain fast KL filters give better signal-to-noise ratio than the conventional Fourier domain Wiener filter and enable determination of an easily computable performance bound. Recursive filtering equations for implementing the fast KL filter on two-dimensional images including both white and colored noise cases are given. These results show that recursive filtering algorithms for images are faster than the transform-domain algorithms and the one-step interpolator algorithm performs very close to the smoothing filter and can be implemented online by introducing a one-step delay.
Retrieval of on-line hand-drawn sketches

Sketch matching algorithms are commonly used for indexing and retrieval of documents based on printed or hand-drawn sketches. One could use a hand-held computer to do sketch-based queries to a database containing hand-drawn and printed sketches. We present an on-line hand-drawn sketch matching algorithm based on a line-based representation of sketches. A distance measure is defined for comparing two sketches based on this representation. The algorithm is computationally efficient and achieves a recall rate of 88.44% at the same precision, when tested on a database of 150 sketches collected from 5 users.
Radon transform theory for random fields and optimum image reconstruction from noisy projections

In this paper we present some new results on Radon transform theory for stationary random fields. In particular we present a new projection theorem which gives the relation between the power spectrum density of one dimensional projections of a stationary random field and its two dimensional power spectrum density. This result yields the optimum mean square reconstruction filter from noisy projections and is useful in other problems such as multidimensional spectral estimation from one dimensional projections, noise analysis in computed tomography, etc. Example are given to demonstrate the usefulness of these results.
On the utility of extended fingerprint features: A study on pores

Extended fingerprint features are routinely used by latent examiners in forensic applications. They are now being considered for inclusion in automatic fingerprint identification systems (AFIS), particularly with the adoption of 1000ppi resolution in the Next Generation Identification (NGI) system. Earlier studies on this topic suffered from two limitations: (i) experiments were based on live scan images that are generally of good quality and contain smaller intra-class variations compared to ink images and (ii) the baseline minutiae matcher used to measure the additive value of extended features was not a state-of-the-art matcher. In this paper, we study the utility of pores, one of the most prevalent extended fingerprint features, on rolled ink fingerprint images at both 500ppi and 1000ppi resolution in the NIST SD30 database. The results show that the fingerprint image quality significantly affects the automatic extraction and matching accuracy of pores. Furthermore, the contribution of pores to the overall fingerprint recognition accuracy is miniscule when a COTS matcher is used for 500ppi rolled ink fingerprint images. The fusion between pore matcher and COTS minutiae matcher is a bit more effective on 1000ppi good quality rolled ink fingerprint images. We believe that these results will be useful in the design of next generation AFIS.
Securing Fingerprint Template: Fuzzy Vault with Helper Data

An important issue gaining attention in biometrics community is the security and privacy of biometric systems: How robust are these systems against attacks? What happens if the biometric template is lost or stolen? Can the privacy of the users be preserved even when a security breach occurs? Among the numerous attacks that can be launched against these systems, protecting the user template that is stored either locally (e.g., on a smart card) or centrally (e.g., on the server) is a major concern. As a possible solution to this problem, a new class of algorithms, termed biometric cryptosystems has been proposed. These systems do not store the original template but only a transformed version of the template within a cryptographic framework. An example of such systems is the fuzzy vault construct proposed by Juels and Sudan. In this construct, the biometric template is converted to a 2D point cloud, containing a secret such as a symmetric encryption key. The operation of the vault requires some "helper" data. In this paper, we present an implementation of the fuzzy fingerprint vault based on orientation field based helper data that is automatically extracted from the fingerprints. We further show that this helper data does not leak any information about fingerprint minutiae, hence complementing the increased user privacy afforded by the fuzzy fingerprint vault. We demonstrate the vault performance on a public domain fingerprint database.
A wrapper-based approach to image segmentation and classification

The traditional processing flow of segmentation followed by classification in computer vision assumes that the segmentation is able to successfully extract the object of interest. This is extremely difficult without any prior knowledge about the object that is being extracted from the scene. We propose a method of segmentation that uses the classification subsystem as an integral part of the segmentation, which provide contextual information regarding the objects to be segmented. We note that traditional segmentation can then be viewed as a filter operating on the image independently of the classifier, much like the filter methods for feature selection. Our motivation for integrating segmentation and classification follows the wrapper methods of feature selection. In the wrapper methods for feature selection, the classifier is an integral part of the selection process and serves as the metric to decide the best feature set. In the same way, we wrap the segmentation and classification together, and use the classification accuracy as the metric to determine the best segmentation. We show the performance of wrapper-based segmentation on real-world and complex images of automotive vehicle occupants.
A hybrid fingerprint matcher

We describe a hybrid fingerprint matching scheme that uses both minutiae and ridge flow information to represent and match fingerprints. A set of 8 Gabor filters, whose spatial frequencies correspond to the average inter-ridge spacing in fingerprints, is used to capture the ridge strength at equally spaced orientations. A square tessellation of the filtered images is then used to construct an eight-dimensional feature map, called the. ridge feature map. The ridge feature map along with the minutiae set of a fingerprint image is used for matching purposes. The genuine accept rate of the hybrid matcher is observed to be /spl sim/10% higher than that of a minutiae-based matcher at low false accept rates. Fingerprint verification using the hybrid matcher on a Pentium III (800 MHz) processor takes /spl sim/1.4 seconds.
Learning Face Image Quality From Human Assessments

Face image quality can be defined as a measure of the utility of a face image to automatic face recognition. In this paper, we propose (and compare) two methods for learning face image quality based on target face quality values from: 1) human assessments of face image quality (matcher-independent) and 2) quality values computed from similarity scores (matcher-dependent). A support vector regression model trained on face features extracted using a deep convolutional neural network (ConvNet) is used to predict the quality of a face image. The proposed methods are evaluated on two unconstrained face image databases, Labeled Faces in the Wild and IARPA Janus Benchmark-A (IJB-A), which both contain facial variations encompassing a multitude of quality factors. Evaluation of the proposed automatic face image quality measures shows we are able to reduce the false non-match rate at 1% false match rate by at least 13% for two face matchers (a commercial off-the-shelf matcher and a ConvNet matcher) by using the proposed face quality to select subsets of face images and video frames for matching templates (i.e., multiple faces per subject) in the IJB-A protocol. To the best of our knowledge, this is the first work to utilize human assessments of face image quality in designing a predictor of unconstrained face quality that is shown to be effective in cross-database evaluation.
Independence, Measurement Complexity, and Classification Performance

If f(x) and g(x) are the densities for the N-dimensional measurement vector x, conditioned on the classes c1 and c2, and if finite sets of samples from the two classes are available, then a decision function based on estimates f(x) and ¿(x) can be used to classify future observations. In general, however, when the measurement complexity (the dimensionality N) is increased arbitrarily and the sets of training samples remain finite, a ``peaking phenomenon'' of the following kind is observed: classification accuracy improves at first, peaks at a finite value of N, called the optimum measurement complexity, and starts deteriorating thereafter. We derive, for the case of statistically independent measurements, general conditions under which it can be guaranteed that the peaking phenomenon will not occur, and the correct classification probability will keep increasing to value unity as N ¿ ¿. Several applications are considered which together indicate, contrary to general belief, that independence of measurements alone does not guarantee the absence of the peaking phenomenon.
Indexing and retrieval of on-line handwritten documents

Recent advances in on-line data capturing technologiesand its widespread deployment in devices like PDAsand notebook PCs is creating large amounts of handwrittendata that need to be archived and retrieved efficiently.Word-spotting, which is based on a direct comparison ofa handwritten keyword to words in the document, is commonlyused for indexing and retrieval. We propose a stringmatching-based method for word-spotting in on-line documents.The retrieval algorithm achieves a precision of92.3% at a recall rate of 90% on a database of 6,672 wordswritten by 10 different writers. Indexing experiments showan accuracy of 87.5% using a database of 3,872 on-linewords.
Automatic caption localization in compressed video

We present a method to automatically locate captions in MPEG video. Caption text regions are segmented from the background using their distinguishing texture characteristics. This method first locates candidate text regions directly in the DCT compressed domain, and then reconstructs the candidate regions for further refinement in the spatial domain. Therefore, only a small amount of decoding is required. The proposed algorithm achieves about 4.0% false reject rate and less than 5.7% false positive rate on a variety of MPEG compressed video containing more than 42,000 frames.
Fingerprint indexing and matching: An integrated approach

Large scale fingerprint recognition systems have been deployed worldwide not only in law enforcement but also in many civilian applications. Thus, it is of great value o identify a query fingerprint in a large background finger-print database both effectively and efficiently based on indexing strategies. The published indexing algorithms do not meet the requirements, especially at low penetrate rates, because of the difficulty in extracting reliable minutiae and other features in low quality fingerprint images. We propose a Convolutional Neural Network (ConvNet) based fingerprint indexing algorithm. An orientation field dictionary is learned to align fingerprints in a unified coordinate system and a large longitudinal fingerprint database, where each finger has multiple impressions over time, is used to train the ConvNet. Experimental results on NIST SD4 and NIST SD14 show that the proposed approach outperforms state-of-the-art fingerprint indexing techniques reported in the literature. Further indexing results on an augmented gallery set of 250K rolled prints demonstrate the scalability of the proposed algorithm. At a penetrate rate of 1%, a score-level fusion of the proposed indexing and a state-of-the-art COTS SDK provides 97.8% rank-1 identification accuracy with a 100-fold reduction in the search space.
Combining classifiers for face recognition

Current two-dimensional face recognition approaches can obtain a good performance only under constrained environments. However, in the real applications, face appearance changes significantly due to different illumination, pose, and expression. Face recognizers based on different representations of the input face images have different sensitivity to these variations. Therefore, a combination of different face classifiers which can integrate the complementary information should lead to improved classification accuracy. We use the sum rule and RBF-based integration strategies to combine three commonly used face classifiers based on PCA, ICA and LDA representations. Experiments conducted on a face database containing 206 subjects (2,060 face images) show that the proposed classifier combination approaches outperform individual classifiers.
MRF model-based segmentation of range images

Consideration is given to the application of Markov random field (MRF) models to the problem of edge labeling in range images. The authors propose a segmentation algorithm which handles both jump and crease edges. The jump and crease edge likelihoods at each edge site are computed using special local operators. These likelihoods are then combined in a Bayesian framework with a MRF prior distribution on the edge labels to derive the a posterior distribution of labels. An approximation to the maximum a posteriori estimate is used to obtain the edge labelings. The edge-based segmentation has been integrated with a region-based segmentation scheme resulting in a robust surface segmentation method.<<ETX>>
Recognizing infants and toddlers using fingerprints: Increasing the vaccination coverage

One of the major goals of most national, international and non-governmental health organizations is to eradicate the occurrence of vaccine-preventable childhood diseases (e.g., polio). Without a high vaccination coverage in a country or a geographical region, these deadly diseases take a heavy toll on children. Therefore, it is important for an effective immunization program to keep track of children who have been immunized and those who have received the required booster shots during the first 4 years of life to improve the vaccination coverage. Given that children, as well as the adults, in low income countries typically do not have any form of identification documents which can be used for this purpose, we address the following question: can fingerprints be effectively used to recognize children from birth to 4 years? We have collected 1,600 fingerprint images (500 ppi) of 20 infants and toddlers captured over a 30-day period in East Lansing, Michigan and 420 fingerprints of 70 infants and toddlers at two different health clinics in Benin, West Africa. We devised the following strategies to improve the fingerprint recognition accuracy when comparing the acquired fingerprints against an extended gallery database of 32,768 infant fingerprints collected by VaxTrac in Benin: (i) upsample the acquired fingerprint image to facilitate minutiae extraction, (ii) match the query print against templates created from each enrollment impression and fuse the match scores, (iii) fuse the match scores of the thumb and index finger, and (iv) update the gallery with fingerprints acquired over multiple sessions. A rank-1 (rank-10) identification accuracy of 83.8% (89.6%) on the East Lansing data, and 40.00% (48.57%) on the Benin data is obtained after incorporating these strategies when matching infant and toddler fingerprints using a commercial fingerprint SDK. This is an improvement of about 38% and 20%, respectively, on the two datasets without using the proposed strategies. A state-of-the-art latent finger-print SDK achieves an even higher rank-1 (rank-10) identification accuracy of 98.97% (99.39%) and 67.14% (71.43%) on the two datasets, respectively, using these strategies; an improvement of about 23% and 24%, respectively, on the two datasets without using the proposed strategies.
The FaceSketchID System: Matching Facial Composites to Mugshots

Facial composites are widely used by law enforcement agencies to assist in the identification and apprehension of suspects involved in criminal activities. These composites, generated from witness descriptions, are posted in public places and media with the hope that some viewers will provide tips about the identity of the suspect. This method of identifying suspects is slow, tedious, and may not lead to the timely apprehension of a suspect. Hence, there is a need for a method that can automatically and efficiently match facial composites to large police mugshot databases. Because of this requirement, facial composite recognition is an important topic for biometrics researchers. While substantial progress has been made in nonforensic facial composite (or viewed composite) recognition over the past decade, very little work has been done using operational composites relevant to law enforcement agencies. Furthermore, no facial composite to mugshot matching systems have been documented that are readily deployable as standalone software. Thus, the contributions of this paper include: 1) an exploration of composite recognition use cases involving multiple forms of facial composites; 2) the FaceSketchID System, a scalable, and operationally deployable software system that achieves state-of-the-art matching accuracy on facial composites using two algorithms (holistic and component based); and 3) a study of the effects of training data on algorithm performance. We present experimental results using a large mugshot gallery that is representative of a law enforcement agency's mugshot database. All results are compared against three state-of-the-art commercial-off-the-shelf face recognition systems.
Address block location on complex mail pieces

Unlike simple letter envelopes which have a high degree of global spatial structure among a limited number of entities, many mail pieces such as magazines usually have an address block printed on a label which can be pasted in an arbitrary position and orientation among text, graphics and images on a magazine cover, which is often shrink wrapped. This work concentrates on address block location for complex mail pieces with an arbitrary layout of printed entities. Using a bottom-up approach, a pyramid model is created for the address block. Based on this model, some features are extracted for assigning a confidence value to the located address blocks. The typical processing time for locating a destination address block is 0.1 seconds on a SGI Indy workstation.