nils-herrmann/clean_scirepeval huggingface dataset:
Segmentation of Document Images

Several methods for segmentation of document images (maps, drawings, etc.) are explored. The segmentation operation is posed as a statistical classification task with two pattern classes: print and background. A number of classification strategies are available. All require some prior information about the distribution of gray levels for the two classes. Training (either supervised or unsupervised) is employed to form these initial density estimates. Automatic updating of the class-conditional densities is performed within subregions in the image to adapt these global density estimates to the local image area. After local class-conditional densities have been obtained, each pixel is classified within the window using several techniques: a noncontextual Bayes classifier, Besag's classifier, relaxation, Owen and Switzer's classifier, and Haslett's classifier. Four test images were processed. In two of these, the relaxation method performed best, and in the other two, the noncontextual method performed best. Automatic updating improved the results for both classifiers.
A 20Gbaud/s PAM-4 65nm CMOS optical receiver using 3D solenoid based bandwidth enhancement

This paper presents an optical receiver (RX) suitable for amplifying a high-speed PAM-4 signal. To achieve a high gain-bandwidth product at low power consumption, a triple inductively peaked regulated cascode (RGC) transimpedance amplifier is used. The inductors are implemented as small 3D solenoids to reduce chip area and optimized for minimum group delay variation. The receiver further includes a Cherry Hooper variable gain amplifier, a continuous time linear equalizer and a 50Ω differential buffer, and achieves a gain of 65 dBΩ at 14 GHz bandwidth. The chip was fabricated using a 65nm CMOS technology and integrated with an 80fF Germanium photodiode by a microbump flip-chip process onto a Silicon Photonic chip. The input referred noise current density is 23.4 pA/VHz. Clear eye diagrams up to 20Gbaud/s (40Gbit/s) have been measured. At this baud rate, the RX's power consumption is 80mW, corresponding to an energy efficiency of 2pJ/bit.
Learning rules for 3D object recognition

A fully autonomous computer-vision system needs the ability to automatically learn, or discover, from training views of objects the object information (models) needed for recognition. One approach to object recognition uses an evidence rulebase composed of objects with corresponding degrees of evidence for the various objects in the databases. A technique for automatically deriving the evidence rulebase from training views of objects is presented. This generated rulebase is shown to provide successful recognition of new views of those objects.<<ETX>>
A Network of Dynamic Probabilistic Models for Human Interaction Analysis

We propose a novel method of analyzing human interactions based on the walking trajectories of human subjects, which provide elementary and necessary components for understanding and interpretation of complex human interactions in visual surveillance tasks. Our principal assumption is that an interaction episode is composed of meaningful small unit interactions, which we call “sub-interactions”. We model each sub-interaction by a dynamic probabilistic model and propose a modified factorial hidden Markov model (HMM) with factored observations. The complete interaction is represented with a network of dynamic probabilistic models (DPMs) by an ordered concatenation of sub-interaction models. The rationale for this approach is that it is more effective in utilizing common components, i.e., sub-interaction models, to describe complex interaction patterns. By assembling these sub-interaction models in a network, possibly with a mixture of different types of DPMs, such as standard HMMs, variants of HMMs, dynamic Bayesian networks, and so on, we can design a robust model for the analysis of human interactions. We show the feasibility and effectiveness of the proposed method by analyzing the structure of network of DPMs and its success on four different databases: a self-collected dataset, Tsinghua University's dataset, the public domain CAVIAR dataset, and the Edinburgh Informatics Forum Pedestrian dataset.
Integrating Faces and Fingerprints for Personal Identification

An automatic personal identification system based solely on fingerprints or faces is often not able to meet the system performance requirements. We have developed a prototype biometrics system which integrates faces and fingerprints. The system overcomes the limitations of face recognition systems as well as fingerprint verification systems. The integrated prototype system operates in the identification mode with an admissible response time. The identity established by the system is more reliable than the identity established by a face recognition system. In addition, the proposed decision fusion scheme enables performance improvement by integrating multiple cues with different confidence measures. Experimental results demonstrate that our system performs very well. It meets the response time as well as the accuracy requirements.
Optimal Registration of Object Views Using Range Data

This paper deals with robust registration of object views in the presence of uncertainties and noise in depth data. Errors in registration of multiple views of a 3D object severely affect view integration during automatic construction of object models. We derive a minimum variance estimator (MVE) for computing the view transformation parameters accurately from range data of two views of a 3D object. The results of our experiments show that view transformation estimates obtained using MVE are significantly more accurate than those computed with an unweighted error criterion for registration.
Crowd powered latent Fingerprint Identification: Fusing AFIS with examiner markups

Automatic matching of poor quality latent fingerprints to rolled/slap fingerprints using an Automated Fingerprint Identification System (AFIS) is still far from satisfactory. Therefore, it is a common practice to have a latent examiner mark features on a latent for improving the hit rate of the AFIS. We propose a synergistic crowd powered latent identification framework where multiple latent examiners and the AFIS work in conjunction with each other to boost the identification accuracy of the AFIS. Given a latent, the candidate list output by the AFIS is used to determine the likelihood that a hit at rank-1 was found. A latent for which this likelihood is low is crowdsourced to a pool of latent examiners for feature markup. The manual markups are then input to the AFIS to increase the likelihood of making a hit in the reference database. Experimental results show that the fusion of an AFIS with examiner markups improves the rank-1 identification accuracy of the AFIS by 7.75% (using six markups) on the 500 ppi NIST SD27, 11.37% (using two markups) on the 1000 ppi ELFT-EFS public challenge database, and by 2.5% (using a single markup) on the 1000 ppi RS&A database against 250,000 rolled prints in the reference database.
Classification of text documents

We investigate four different classification methods for document classification: the naive Bayes classifier, nearest neighbor classifier, decision tree classifier, and subspace method. The classifiers were applied to seven-class Yahoo newsgroups individually and in combination. We study three classifier combination approaches: simple voting, dynamic classifier selection, and adaptive classifier combination. Our experimental results indicate that the naive Bayes classifier and the subspace method outperform the other two classification methods on our data sets. Combinations of multiple classifiers did not always improve classification accuracy. Among the three different combination approaches, the adaptive classifier combination method proposed here performed the best.
Two-dimensional phase unwrapping using a block least-squares method

We present a block least-squares (BLS) method for two-dimensional (2-D) phase unwrapping. The method works by tessellating the input image into small square blocks with only one phase wrap. These blocks are unwrapped using a simple procedure, and the unwrapped blocks are merged together using one of two proposed block merging algorithms. By specifying a suitable mask, the method can easily handle objects of any shape. This approach is compared with the Ghiglia-Romero method and the Marroquin-Rivera method. On synthetic images with different noise levels, the BLS method is shown to be superior, both with respect to the resulting gray values in the unwrapped image as well as visual inspection. The method is also shown to successfully unwrap synthetic and real images with shears, fiber-optic interferometry images, and medical magnetic resonance images. We believe the new method has the potential to improve the present quality of phase unwrapped images of several different image modalities.
/spl lambda//spl tau/-space representation of images and generalized edge detector

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

A custom IC processor that traces contours in black-and-white images is described. The CMOS IC contains a finite state machine, an arithmetic logic unit (ALU), an input delay line, and RAM. Each process extracts the edges from a rectangular portion of the input image and traces the edges, producing a description of the contours. A complete tracing system employs an array of these processors. The array architecture allows expansion to handle arbitrarily large images. The processors trace independently, thereby providing fully parallel high-speed operation. Partial contours are generated as a result of the partitioning of the image among the processors. A postprocessor links any partial contours created by the subdivision of the input image. The contour-tracing algorithm and simulation results for compression ratio and number of operations are presented. The architecture and measured performance of the IC are described.
Sketch based face recognition: Forensic vs. composite sketches

Facial sketches are widely used by law enforcement agencies to assist in the identification and apprehension of suspects involved in criminal activities. Sketches used in forensic investigations are either drawn by forensic artists (forensic sketches) or created with computer software (composite sketches) following the verbal description provided by an eyewitness or the victim. These sketches are posted in public places and in media in hopes that some viewers will provide tips about the identity of the suspect. This method of identifying suspects is slow and tedious and may not lead to apprehension of the suspect. Hence, there is a need for a method that can automatically and quickly match facial sketches to large police mugshot databases. We address the problem of automatic facial sketch to mugshot matching and, for the first time, compare the effectiveness of forensic sketches and composite sketches. The contributions of this paper include: (i) a database containing mugshots and corresponding forensic and composite sketches that will be made available to interested researchers; (ii) a comparison of holistic facial representations versus component based representations for sketch to mugshot matching; and (iii) an analysis of the effect of filtering a mugshot gallery using three sources of demographic information (age, gender and race/ethnicity). Our experimental results show that composite sketches are matched with higher accuracy than forensic sketches to the corresponding mugshots. Both of the face representations studied here yield higher sketch to photo matching accuracy compared to a commercial face matcher.
Interacting multiple model (IMM) Kalman filters for robust high speed human motion tracking

Accurate and robust tracking of humans is of growing interest in the image processing and computer vision communities. The ability of a vision system to track the subjects and accurately, predict their future locations is critical to many surveillance and camera control applications. Further, an inference of the type of motion as well as to rapidly detect and switch between motion models is critical since in some applications the switching time between motion models can be extremely small. The interacting multiple model (IMM) Kalman filter provides a powerful framework for performing the tracking of both the motion as well as the shape of these subjects. The tracking system utilizes a simple geometric shape primitive such as an ellipse to define a bounding extent of the subject. The utility of the IMM paradigm for rapid model switching and behaviour detection is shown for a passenger airbag suppression system in an automobile. The simplicity, of the methods and the robustness of the underlying IMM filtering make the framework well suited for low-cost embedded real-time motion sequence analysis systems.
Pores and Ridges: Fingerprint Matching Using Level 3 Features

Fingerprint friction ridge details are generally described in a hierarchical order at three levels, namely, Level 1 (pattern), Level 2 (minutiae points) and Level 3 (pores and ridge shape). Although high resolution sensors (~1000 dpi) have become commercially available and have made it possible to reliably extract Level 3 features, most automated fingerprint identification systems (AFIS) employ only Level 1 and Level 2 features. As a result, increasing the scan resolution does not provide any matching performance improvement (1998). We develop a matcher that utilizes Level 3 features, including pores and ridge contours, for 1000 dpi fingerprint matching. Level 3 features are automatically extracted using wavelet transform and Gabor filters and are locally matched using the ICP algorithm. Our experiments on a median-sized database show that Level 3 features carry significant discriminatory information. EER values are reduced (relatively ~20%) when Level 3 features are employed in combination with Level 1 and 2 features
Learning Texture Discrimination Masks

A neural network texture classification method is proposed in this paper. The approach is introduced as a generalization of the multichannel filtering method. Instead of using a general filter bank, a neural network is trained to find a minimal set of specific filters, so that both the feature extraction and classification tasks are performed by the same unified network. The authors compute the error rates for different network parameters, and show the convergence speed of training and node pruning algorithms. The proposed method is demonstrated in several texture classification experiments. It is successfully applied in the tasks of locating barcodes in the images and segmenting a printed page into text, graphics, and background. Compared with the traditional multichannel filtering method, the neural network approach allows one to perform the same texture classification or segmentation task more efficiently. Extensions of the method, as well as its limitations, are discussed in the paper.
Content-based image retrieval: An application to tattoo images

Tattoo images on human body have been routinely collected and used in law enforcement to assist in suspect and victim identification. However, the current practice of matching tattoos is based on keywords. Assigning keywords to individual tattoo images is both tedious and subjective. We have developed a content-based image retrieval system for a tattoo image database. The system automatically extracts image features based on the Scale Invariant Feature Transform (SIFT). Side information, i.e., body location of tattoos and tattoo classes, is utilized to improve the retrieval time and retrieval accuracy. Geometrical constraints are also introduced in SIFT keypoint matching to reduce false retrievals. Experimental results on 1,000 queries against an operational database of 63,593 tattoo images show a rank-20 accuracy of 94.2%; the average matching time per query is 2.9 sec. on Intel Core 2, 2.66 GHz, 3 GB RAM processor.
Shape spectra based view grouping for free-form objects

The concept of "shape spectrum" of an object view is introduced for constructing view aspects of free-form objects. The shape spectrum captures the shape characteristics of an object by aggregating the areas of the surfaces of the object at each value of the shape index. We derive a novel view representation based on shape spectral features and propose a general and powerful technique for organizing multiple views of objects of complex shape and geometry into compact and homogeneous clusters. Our view-grouping technique obviates the need for surface segmentation and edge detection. We demonstrate with experimental results on a database of 3,200 views of 10 objects that view aspects can be determined for sculpted objects easily and effectively.
Registration and Integration of Multiple Object Views for 3D Model Construction

Automatic 3D object model construction is important in applications ranging from manufacturing to entertainment, since CAD models of existing objects may be either unavailable or unusable. We describe a prototype system for automatically registering and integrating multiple views of objects from range data. The results can then be used to construct geometric models of the objects. New techniques for handling key problems such as robust estimation of transformations relating multiple views and seamless integration of registered data to form an unbroken surface have been proposed and implemented in the system. Experimental results on real surface data acquired using a digital interferometric sensor as well as a laser range scanner demonstrate the good performance of our system.
On the evidential value of fingerprints

Fingerprint evidence is routinely used by forensics and law enforcement agencies worldwide to apprehend and convict criminals, a practice in use for over 100 years. The use of fingerprints has been accepted as an infallible proof of identity based on two premises: (i) permanence or persistence, and (ii) uniqueness or individuality. However, in the absence of any theoretical results that establish the uniqueness or individuality of fingerprints, the use of fingerprints in various court proceedings is being questioned. This has raised awareness in the forensics community about the need to quantify the evidential value of fingerprint matching. A few studies that have studied this problem estimate this evidential value in one of two ways: (i) feature modeling, where a statistical (generative) model for fingerprint features, primarily minutiae, is developed which is then used to estimate the matching error and (ii) match score modeling, where a set of match scores obtained over a database is used to estimate the matching error rates. Our focus here is on match score modeling and we develop metrics to evaluate the effectiveness and reliability of the proposed evidential measure. Compared to previous approaches, the proposed measure allows explicit utilization of prior odds. Further, we also incorporate fingerprint image quality to improve the reliability of the estimated evidential value.
On matching altered fingerprints

Fingerprint alteration refers to changes made in a person's finger ridge structure by means of abrading, cutting, or performing plastic surgery on the fingertips. Fingerprint alteration is a serious attack on Automated Fingerprint Identification Systems (AFIS) since it can reduce the similarity between fingerprint impressions from the same finger due to the loss of genuine minutiae, increase in spurious minutiae and distortion in spatial distribution of the minutiae. We investigate the capability of a state-of-the-art commercial fingerprint matcher to match altered fingerprints to their pre-altered mates by removing spurious minutiae in the altered region. We also attempt to restore minutiae structure in a well-known type of fingerprint alteration, called `Z'-cut, which tears local skin patches of a finger and switches them with each other. Nonrigid skin distortion introduced during switching of local patches is modeled by thin-plate spline (TPS). Experimental results show that removing spurious minutiae in the altered region and relocating the minutiae in the restored local patches improve the matching performance and help to retrieve correct mates from a large background fingerprint database.
Design and Fabrication of 3D Fingerprint Targets

Standard targets are typically used for structural (white-box) evaluation of fingerprint readers, e.g., for calibrating imaging components of a reader. However, there is no standard method for behavioral (black-box) evaluation of fingerprint readers in operational settings where variations in finger placement by the user are encountered. The goal of this research is to design and fabricate 3D targets for repeatable behavioral evaluation of fingerprint readers. 2D calibration patterns with known characteristics (e.g., sinusoidal gratings of pre-specified orientation and frequency, and fingerprints with known singular points and minutiae) are projected onto a generic 3D finger surface to create electronic 3D targets. A state-of-the-art 3D printer (Stratasys Objet350 Connex) is used to fabricate wearable 3D targets with materials similar in hardness and elasticity to the human finger skin. The 3D printed targets are cleaned using 2M NaOH solution to obtain evaluation-ready 3D targets. Our experimental results show that: 1) features present in the 2D calibration pattern are preserved during the creation of the electronic 3D target; 2) features engraved on the electronic 3D target are preserved during the physical 3D target fabrication; and 3) intra-class variability between multiple impressions of the physical 3D target is small. We also demonstrate that the generated 3D targets are suitable for behavioral evaluation of three different (500/1000 ppi) PIV/Appendix F certified optical fingerprint readers in the operational settings.