nils-herrmann/clean_scirepeval huggingface dataset:
Design and Implementation of Multisteerable Matched Filters

Image analysis problems such as feature tracking, edge detection, image enhancement, or texture analysis require the detection of multi-oriented patterns which can appear at arbitrary orientations. Direct rotated matched filtering for feature detection is computationally expensive, but can be sped up with steerable filters. So far, steerable filter approaches were limited to only one direction. Many important low-level image features are, however, characterized by more than a single orientation. We therefore present here a framework for efficiently detecting specific multi-oriented patterns with arbitrary orientations in grayscale images. The core idea is to construct multisteerable filters by appropriate combinations of single-steerable filters. We exploit that steerable filters are closed under addition and multiplication. This allows to derive a design guide for multisteerable filters by means of multivariate polynomials. Furthermore, we describe an efficient implementation scheme and discuss the use of weighting functions to reduce angular oscillations. Applications in camera calibration, junction analysis of images from plant roots, and the discrimination of L, T, and X-junctions demonstrate the potential of this approach.
Automatic 3D modeling of pleural thickening through thin plate spline interpolation

Pleuramesothelioma is malignant tumor on the pleura, caused by asbestos exposure. Computer-assisted diagnosis system shall support physicians to assess the growth rate of detected pleural thickenings from CT data. This paper describes a new, improved method to automatically assess the size of detected thickening using thin plate spline interpolation which then leads to the 3D modeling of thickening. First, we detect each pleural thickening. Second, an automatic coordinate transformation is applied to enable the numeric calculation of the spline. Next, the appropriate landmark points are then automatically selected by using the newly applied chain-code concavity analysis and used as the constraint points for the interpolation. Numerical integration is applied to calculate area by slice. In the final step, the spline interpolation between layers is applied to calculate volume of each thickening. The results show that the thin plate spline interpolated boundary is suitable for 3D modeling of the thickening.
Inertial navigation system for bladder endoscopy

The usage of video endoscopes in cystoscopic interventions of the urinary bladder impedes an intuitive navigation. Although image-based solutions such as panorama images can provide extended views of the surgical field, a real-time 3-D navigation is not supported. Furthermore, the integration of common tracking systems in ambulant clinics is often hindered due to low usability and high costs. Thus, we discuss in this paper a first low-cost inertial navigation system. Our evaluation results show that in spite of lower sensor accuracies, mean errors between < 1° and 4° are achieved for solid angles. Using endoscopes with different view angles we apply an extended endoscope model for an adaptive displacement correction. Furthermore, we implement a first guided navigation tool for tumor re-identification in real-time.
Comparative Analysis of Shift Variance and Cyclostationarity in Multirate Filter Banks

Multirate filter banks introduce periodic time-varying phenomena into their subband signals. The nature of these effects depends on whether the signals are regarded as deterministic or as random signals. We analyze the behavior of deterministic and wide-sense stationary (WSS) random signals in multirate filter banks in a comparative manner. While aliasing in the decimation stage causes subband energy spectra of deterministic signals to become shift-variant, imaging in the interpolation stage causes WSS random signals to become WS cyclostationary (WSCS). We provide criteria to quantify both shift variance and cyclic nonstationarity. For shift variance, these criteria separately assess the shift dependence of energy and of energy spectra. Similarly for nonstationarity, they separately assess the nonstationary behavior of signal power and of power spectra. We show that, under aliasing cancellation and perfect reconstruction constraints of paraunitary and biorthogonal filter banks, these criteria evaluate the behavior of deterministic and WSS random signals in a consistent, dual way. We apply our criteria to paraunitary and biorthogonal filter banks as well as to orthogonal block transforms, and show that, for critical signals such as lines or edges in image data, the biorthogonal 9/7 filters perform best among these
Top-down image segmentation using object detection and contour relaxation

A novel segmentation technique that starts with the whole image being a single region is presented. First, an object detection scheme, which marks those locations where local statistics deviate significantly from the overall statistics, provides location and approximate shapes of the major objects (regions) in the scene. Exact boundaries are subsequently obtained by a contour relaxation algorithm, which includes a general model for typical region shapes. Object detection and contour relaxation are repeated recursively until a stable segmentation result is achieved. Segmentation results are presented.<<ETX>>
Detection and recognition of moving objects using statistical motion detection and Fourier descriptors

Object recognition, i.e. classification of objects into one of several known object classes, generally is a difficult task. In this paper we address the problem of detecting and classifying moving objects in image sequences from traffic scenes recorded with a static camera. In the first step, a statistical, illumination invariant motion detection algorithm is used to produce binary masks of the scene-changes. Next, Fourier descriptors of the shapes from the refined masks are computed and used as feature vectors describing the different objects in the scene. Finally, a feedforward neural net is used to distinguish between humans, vehicles, and background clutter.
Analysis of Superimposed Oriented Patterns

Estimation of local orientation in images may be posed as the problem of finding the minimum gray-level variance axis in a local neighborhood. In bivariate images, the solution is given by the eigenvector corresponding to the smaller eigenvalue of a 2times2 tensor. For an ideal single orientation, the tensor is rank-deficient, i.e., the smaller eigenvalue vanishes. A large minimal eigenvalue signals the presence of more than one local orientation, what may be caused by non-opaque additive or opaque occluding objects, crossings, bifurcations, or corners. We describe a framework for estimating such superimposed orientations. Our analysis is based on the eigensystem analysis of suitably extended tensors for both additive and occluding superpositions. Unlike in the single-orientation case, the eigensystem analysis does not directly yield the orientations, rather, it provides so-called mixed-orientation parameters (MOPs). We, therefore, show how to decompose the MOPs into the individual orientations. We also show how to use tensor invariants to increase efficiency, and derive a new feature for describing local neighborhoods which is invariant to rigid transformations. Applications are, e.g., in texture analysis, directional filtering and interpolation, feature extraction for corners and crossings, tracking, and signal separation
Noise in high dynamic range imaging

High dynamic range (HDR) imaging is more and more widely used to increase the limited dynamic range of digital cameras and, in turn, to cover the dynamic range of the acquired scene. This image acquisition process can be subdivided into two steps. The first step is the measurement or estimation of the mostly non-linear camera transfer function (CTF). This is followed by the second step, the combination of a set of differently exposed images of the same scene into one HDR image after linearization with the inverse CTF. Each of the individual images in such an exposure set contains noise from the image acquisition process. Consequently, the calculated HDR image will as well contain noise, which fortunately is reduced by the weighted average of the images from the exposure set. We analyze the achieved gain in SNR for different weighting functions proposed in the literature and compare these with a plain average. Although these functions are based on reasonable intuitions, we show that the highest SNRgain is achieved with the plain average.
Vision-based Auto-Teaching for automated PCB depaneling

Machines for automated PCB depaneling have greatly improved the industrial production efficiency of electronic products. But the preparation for automated depaneling could be very complex and time-consuming. In this paper, we propose a novel systematic solution for this problem. Using a vision-based assistant system, called ”Auto-Teaching”, all connection tabs that should be milled off panels are detected automatically. Highly accurate milling curves are generated with respect to the geometry of the corresponding tabs. Then they could be easily converted into CNC code which drive milling cutters later. Moreover, placement suggestions of supporting pins used to fix panels are obtained. All image analysis functions are implemented in C++ and optimized to meet the minimal hardware and time requirements. Thus the whole process of the preparation is simplified for users and the PCB manufactures can benefit from reduced idle running of machines and labor costs.
A two-stage-classifier for defect classification in optical media inspection

In this paper we address the problem of inspecting optical media like compact disks and digital versatile disks. Here, defective disks have to be identified during production. For optimizing the production process and in order to be able to decide how critical a certain defect is, the defects found have to be classified. As this has to be done online, the classification algorithm has to work very fast. With regard to speed, the well known minimum distance classifier is usually a good choice. However, when training data are not well clustered in the feature-space this classifier becomes rather unreliable. To trade-off speed and reliability we propose a two-stage-algorithm. It combines the fast minimum distance classification with a reliable fuzzy k-nearest neighbor classifier. The resulting two-stage-classifier is considerably faster than the fuzzy k-nearest neighbor classifier. Its classification rates are in the range of the fuzzy k-nearest neighbor classifier and far better than those of the minimum distance classifier. To evaluate the results, we compare them to the results obtained using various standard classifiers.
Analysing superimposed oriented patterns

Estimation of local orientation in images is often posed as the task of finding the minimum variance axis in a local neighborhood. The solution is given as the eigenvector belonging to the smaller eigenvalue of a 2/spl times/2 tensor. Ideally, the tensor is rank-deficient, i.e., the smaller eigenvalue is zero. A large minimal eigenvalue signals the presence of more than one local orientation. We describe a framework for estimating such superimposed orientations. Our analysis of superimposed orientations is based on the eigensystem analysis of a suitably extended tensor. We show how to carry out the eigensystem analysis efficiently using tensor invariants. Unlike in the single orientation case, the eigensystem analysis does not directly yield the orientations, rather, it provides so-called mixed orientation parameters. We therefore show how to decompose the mixed orientation parameters into the individual orientations. These, in turn, allow the superimposed patterns to be separated.
HDR-Microscopy of Cell Specimens: Imaging and Image Analysis

Bright field microscopy is applied in both biomedical research and diagnostics. The cellular material is generally imaged with cameras mounted onto the microscope, allowing thus to measure cell properties. However, these cameras exhibit only a limited dynamic range, which makes the quantification of these properties difficult or even impossible. We therefore show how the dynamic range can be increased by acquiring a set of differently exposed images of a specimen. These high dynamic range (HDR) images allow to measure features, which are otherwise difficult to capture, if at all. We show that HDR-microscopy not only increases the dynamic range, but furthermore reduces noise and improves the measurements of colors. We develop HDR microscopy-based algorithms for three different applications essential for early cancer diagnostics, viz, detection of positive cells in immunocytochemical marker stained material, nuclei segmentation in low contrast morphologically stained material, and detection of certain subcellular features, so- called AgNORs, in silver (Ag) stained specimens.
Repetitive Control Design for MIMO Systems With Saturating Actuators

This paper addresses the problem of tracking and rejection of periodic signals for linear multi-input, multi-output systems subject to control saturation. To ensure the periodic tracking/rejection, a modified state-space repetitive control structure is considered. Conditions in a “quasi” linear matrix inequality form are proposed to simultaneously compute a stabilizing state feedback gain and an anti-windup gain. Provided that the references and disturbances belong to a certain admissible set, these gains guarantee that the trajectories of the closed-loop system starting in a certain ellipsoidal set contract to the linearity region of the closed-loop system, where the presence of the repetitive controller ensures the periodic tracking/rejection.
Large Scale Distributed Acoustic Modeling With Back-Off ${\rm N}$ -Grams

The paper revives an older approach to acoustic modeling that borrows from n-gram language modeling in an attempt to scale up both the amount of training data and model size (as measured by the number of parameters in the model), to approximately 100 times larger than current sizes used in automatic speech recognition. In such a data-rich setting, we can expand the phonetic context significantly beyond triphones, as well as increase the number of Gaussian mixture components for the context-dependent states that allow it. We have experimented with contexts that span seven or more context-independent phones, and up to 620 mixture components per state. Dealing with unseen phonetic contexts is accomplished using the familiar back-off technique used in language modeling due to implementation simplicity. The back-off acoustic model is estimated, stored and served using MapReduce distributed computing infrastructure. Speech recognition experiments are carried out in an N-best list rescoring framework for Google Voice Search. Training big models on large amounts of data proves to be an effective way to increase the accuracy of a state-of-the-art automatic speech recognition system. We use 87 000 hours of training data (speech along with transcription) obtained by filtering utterances in Voice Search logs on automatic speech recognition confidence. Models ranging in size between 20-40 million Gaussians are estimated using maximum likelihood training. They achieve relative reductions in word-error-rate of 11% and 6% when combined with first-pass models trained using maximum likelihood, and boosted maximum mutual information, respectively. Increasing the context size beyond five phones (quinphones) does not help.
A framework for networked motion control

A framework the representation and control of networked vehicle systems is presented. From set theory, this framework borrowed the representational power of the language of sets to capture the relations among vehicles and controllers in a way that is consistent with control design. From dynamic optimization, this framework has inherited the concepts and theories of optimality, reach set computation and control, the techniques to operate on sets and dynamic systems, and the motivation to improve the performance of increasingly complex physical processes.
Pointer disambiguation via strict inequalities

The design and implementation of static analyses that disambiguate pointers has been a focus of research since the early days of compiler construction. One of the challenges that arise in this context is the analysis of languages that support pointer arithmetics, such as C, C++ and assembly dialects. This paper contributes to solve this challenge. We start from an obvious, yet unexplored, observation: if a pointer is strictly less than another, they cannot alias. Motivated by this remark, we use abstract interpretation to build strict less-than relations between pointers. We construct a program representation that bestows the Static Single Information (SSI) property onto our dataflow analysis. SSI gives us a sparse algorithm, whose correctness is easy to ensure. We have implemented our static analysis in LLVM. It runs in time linear on the number of program variables, and, depending on the benchmark, it can be as much as six times more precise than the pointer disambiguation techniques already in place in that compiler.
Motion Planning and Control of Coordinated Systems

Two problems of motion planning for controlled systems which are required to attain a given target set while satisfying coordinated constraints are formulated and solved using dynamic optimization techniques. Constraint coordination arises from the fact that the state of each system is mapped onto state constraints for the other systems. The problems are formulated in terms of backward reach sets which are the sub-zero level sets of appropriate value functions for nonstandard cost functions. The value functions are the solutions of Hamilton-Jacobi-Bellman type PDEs. For linear dynamics and ellipsoidal constraints the value functions are calculated through duality techniques from convex analysis.
In vitro ultrasound characterization of coronary arteries using temperature dependent wave speed

Temperature induced time shift of backscattered ultrasound is used to probe the temperature dependence of the speed of sound, /spl part/c//spl part/T. An Ultrasound Biomicroscope (UBM), with a PVDF transducer (center frequency=50.0 MHz), and a temperature controlled micropositioning sample cell was used. RF-backscattered signals were collected at 5 fixed temperatures of the bath, starting with 36/spl deg/C and in steps of 1/spl deg/C. The experimental results for the mean (/spl plusmn/ one standard deviation) of /spl part/c//spl part/T, in m/s/spl middot//spl deg/C, from the wall of human coronary arteries obtained at autopsy of 8 different samples from 4 individuals, were 2.20 (/spl plusmn/1.95), 4.32 (/spl plusmn/0.84) and -2.26 (/spl plusmn/1.33) for intima, media and adventitia layers, respectively. At the intima layer, /spl part/c//spl part/T appeared to decrease, from 4.29 to -0.05 m/s/spl middot//spl deg/C, as the disease progressed from mild intimal thickening to a more atherosclerotic state.
Computer-assisted evaluation of undergraduate courses in frequency-domain techniques for system control

An innovative computer-assisted teaching technique is proposed as an effective way to help in the assessment of undergraduate courses in classical control theory. Using an iterative Matlab/Simulink based algorithm, the task of identifying an unknown linear system from its frequency response is given to the students. At each step, the student improves his/her approximation to minimize the frequency response of the mismatch and decides whether the estimate is sufficient. The proposed self-evaluation task gives the students a means to analyze Bode diagrams and system identification techniques, nontrivial issues in introductory courses in control systems theory
Nondegenerate Necessary Conditions of Optimality for Problems Without Normality Assumptions

Nondegenerate second-order necessary conditions of optimality for general nonlinear optimization problems are presented and discussed in this article. Besides functional equality and inequality constraints, we also consider constraints in the form of an inclusion into a given closed set. Without assuming a priori normality, our conditions remain informative for abnormal points, and, under very general assumptions also take into account the second order effect of the curvature of the set in the inclusion constraints.
An Ecore based Petri net type definition for PNML IOPT models

In this paper we present a new Input-Output Place-Transition (IOPT) Petri net metamodel and its Petri Net Type Definition (PNTD) using a RELAX NG grammar. This new version introduces changes to the previous metamodel to ensure that the model representation follows the International Standard guidelines to extend PNML and to fully support automatic generation of models state space. The validation of this revised metamodel and the corresponding PNTD were made through case studies. The new concepts introduced in the metamodel have demonstrated their validity and usefulness, in the automatic generation of executable code and the state space. The correctness of the RELAX NG grammar was also validated through case studies.
Adaptive shape and texture intra refreshment schemes for improved error resilience in object-based video coding

Video encoders may use several techniques to improve error resilience. In particular, for video encoders that rely on predictive (inter) coding to remove temporal redundancy, intra coding refreshment is especially useful to stop temporal error propagation when errors occur in the transmission or storage of the coded streams, since these errors may cause the decoded quality to decay very rapidly. In the context of object-based video coding, intra coding refreshment can be applied to both the shape and texture data. In this paper, novel shape and texture intra refreshment schemes are proposed which can be used by object-based video encoders, such as MPEG-4 video encoders, independently or combined. These schemes allow to adaptively determine when the shape and texture of the various video objects in a scene should be refreshed in order to maximize the decoded video quality for a certain total bit rate.
Planning transport of crude oil derivatives with simultaneous auctions

In supply chains of the petroleum industry, maintaining a balance between production and consumption of petroleum derivatives is a crucial issue. Basically, this kind of chain has several elements like producer bases, consumer bases, intermediary terminals that are linked by a transport network. These elements should cooperate to reach the global balance of the system with a minimum transportation cost. In this context, this paper proposes and compares two solutions based on auctions carried out by agents, which represent the elements of the aforementioned chain. These solutions are characterized respectively by the execution of sequential auctions and simultaneous auctions in order to consumers bid for batches of crude oil derivatives. In the comparative tests, the solution based on simultaneous auctions is better than the sequential one mainly because of the intense cooperation among agents.