nils-herrmann/clean_scirepeval huggingface dataset:
Contrast Invariant Interest Point Detection by Zero-Norm LoG Filter.

The Laplacian of Gaussian (LoG) filter is widely used in interest point detection. However, low-contrast image structures, though stable and significant, are often submerged by the high-contrast ones in the response image of the LoG filter, and hence are difficult to be detected. To solve this problem, we derive a generalized LoG filter, and propose a zero-norm LoG filter. The response of the zero-norm LoG filter is proportional to the weighted number of bright/dark pixels in a local region, which makes this filter be invariant to the image contrast. Based on the zero-norm LoG filter, we develop an interest point detector to extract local structures from images. Compared with the contrast dependent detectors, such as the popular scale invariant feature transform detector, the proposed detector is robust to illumination changes and abrupt variations of images. Experiments on benchmark databases demonstrate the superior performance of the proposed zero-norm LoG detector in terms of the repeatability and matching score of the detected points as well as the image recognition rate under different conditions.
Dark Count Statistics in Geiger-Mode Avalanche Photodiode Cameras for 3-D Imaging LADAR

We describe cameras incorporating focal plane arrays of Geiger-mode avalanche photodiodes (GmAPDs) that enable 3-D imaging in laser radar (LADAR) systems operating at wavelengths near 1.0 and 1.5 μm. GmAPDs based on the InGaAsP material system achieve single-photon sensitivity at every pixel of the array and are hybridized to custom CMOS ROICs providing 0.25 ns timing resolution. We present camera-level performance for photon detection efficiency and dark count rate, along with a survey of the evolution of performance for a substantial number of 32 × 32 cameras. We then describe a temporal statistical analysis of the array-level dark count behavior that distinguishes between Poissonian intrinsic dark count rate and non-Poissonian crosstalk counts. We also report the spatial analysis of crosstalk events to complement the statistical temporal analysis. Differences between cameras optimized for the two different wavelengths-1.0 and 1.5 μm-are noted, particularly with regard to crosstalk behavior.
An interactive and secure user authentication scheme for mobile devices

Graphical password (i.e., image based authentication) is considered as a promising alternative to traditional textual password for mobile devices, to achieve better tradeoff between usability and security. However, previous proposals of graphical password have the limitation of limited entropy. In this paper, we propose a new scheme incorporating user face based authentication into the association-based graphical password solution we proposed before, aiming at achieving higher security without compromising user-friendliness for mobile application scenarios. System performance analysis and comparisons with other schemes are presented to validate our scheme.
A 4-in-1 (WiFi/BT/FM/GPS) connectivity SoC with enhanced co-existence performance in 65nm CMOS

In recent years, the increasing popularity of mobile devices, such as smart- phones and tablets, is driving the demand for integrating multiple radios on a single SoC to reduce cost, form factor and external BOM. These devices require ubiquitous wireless connectivity, which means concurrent operation with different radios. While concurrent operation of multiple radios brings excellent user experiences, there exist great challenges in dealing with radio co-existence in an SoC. For example, concurrent operation between WiFi and BT, both oper- ating in the 2.4GHz ISM band, sets additional requirements in RF front-end cir- cuits and system control. In addition, thermal effect of the integrated WiFi PA needs to be compensated to minimize its impact on the frequency-precise GPS system.
LDA enhanced moments

Moments and functions of moments are powerful tools in a vast number of fields, particularly image signal processing. In this paper, a method for obtaining a set of orthogonal, noise-robust, and distribution-adaptive moments, called Fishermoments (FM), is presented. FM are obtained by performing linear discriminant analysis (LDA) in the moment space resided by geometric moments (GM). The moment space is transformed into the feature space where a separability criterion is maximized. Experiments performed to gauge the performance of FM show significant improvements in terms of accuracy and noise robustness as predicted by the theoretical framework.
Accurate localization of four extreme corners for barcode images captured by mobile phones

In this paper, we propose a novel method to locate the four extreme corners of barcodes in the images captured by mobile phones. To achieve this goal, the two nearly-parallel outer boundary lines are firstly localized by utilizing the prior knowledge of the relative distances and angles between the lines, which are subsequently employed to obtain the initially localized corners. A novel post-localization process based on edge tracing is also proposed to further validate the initially localized corners. This is achieved by setting the constraints in maximum direction change when tracing from the current to the next candidate corners. The distinctive feature of the proposed algorithm lies in the capability of handling those curved barcode images taken by the mobile phones. Experiments conducted on a data set of 1410 images show the accuracy of the corner localization is about 92.3% and 90.5% for indoor and outdoor barcode images, respectively. The processing time taken is about 0.8 second using Matlab.
Exploiting global and local decisions for multimodal biometrics verification

In this paper, we address the multimodal biometric decision fusion problem. By exploring into the user-specific approach for learning and threshold setting, four possible paradigms for learning and decision making are investigated. Since each user requires a decision hyperplane specific to him in order to achieve good verification accuracy, those tedious iterative training methods like the neural network approach would not be suitable. We propose to use a model that requires only a single training step for this application. The four global and local learning and decision paradigms are then explored to observe their decision capability. Besides the proposal of a relevant receiver operating characteristic performance for the local decision, extensive experiments were conducted to observe the verification performance for fusion of two and three biometrics.
LBP-Based Edge-Texture Features for Object Recognition

This paper proposes two sets of novel edge-texture features, Discriminative Robust Local Binary Pattern (DRLBP) and Ternary Pattern (DRLTP), for object recognition. By investigating the limitations of Local Binary Pattern (LBP), Local Ternary Pattern (LTP) and Robust LBP (RLBP), DRLBP and DRLTP are proposed as new features. They solve the problem of discrimination between a bright object against a dark background and vice-versa inherent in LBP and LTP. DRLBP also resolves the problem of RLBP whereby LBP codes and their complements in the same block are mapped to the same code. Furthermore, the proposed features retain contrast information necessary for proper representation of object contours that LBP, LTP, and RLBP discard. Our proposed features are tested on seven challenging data sets: INRIA Human, Caltech Pedestrian, UIUC Car, Caltech 101, Caltech 256, Brodatz, and KTH-TIPS2-a. Results demonstrate that the proposed features outperform the compared approaches on most data sets.
On critical condition under adiabatic shear in high speed machining

A linear perturbation analysis is performed to derive an adiabatic shear critical condition in high speed machining and the effects of strain hardening and thermal softening on adiabatic shear are investigated for thermal softening induced plastic instability in rate dependent materials. The criterion indicates that the materials became unstable once the thermal softening overcomes the strain hardening, regardless of the strain rate. The adiabatic shear is likely to form under the conditions of smaller strain hardening index, larger thermal softening index.
Common-Mode Cancellation in Sinusoidal Gating With Balanced InGaAs/InP Single Photon Avalanche Diodes

We demonstrate a sinusoidal-gated InGaAs/InP single photon avalanche diode (SPAD) pair with high photon detection efficiency (PDE) and low dark count rate (DCR). The photodiode pair is biased in a balanced configuration with only one of the SPADs illuminated. The advantage of balanced detectors is cancellation of the common component of the output signal, which in this case arises from sinusoidal gating. In conventional sinusoidal gating, narrow-band RF filters are used to eliminate the gating signal while imparting minimal change to the avalanche pulses. A disadvantage of this approach is that the requisite filters fix the operating frequency, whereas the balanced SPAD receiver is frequency agile. At a laser repletion rate of 1 MHz and a temperature of 240 K, the DCR and PDE are 58 kHz and 43%, respectively. The afterpulse probability is lower than a single sinusoidal-gated SPAD. Jitter of 240 ps is achieved with one photon per pulse and an excess bias of 1.6%.
Discriminative sparsity preserving embedding for face recognition

Over the past few years, sparse representation (SR) becomes a hotspot and applied in many research fields. Sparsity preserving projections (SPP) utilizes SR to dimensionality reduction (DR) for face classification. However, as the original framework of SR is unsupervised, SPP can not employ the class information, which is very crucial for classification. To address this problem, we propose an algorithm, namely supervised SR (SSR), to cooperate with label information. Furthermore, we also propose a DR method, discriminative sparsity preserving embedding (DSPE), in this paper. DSPE learns the discriminative sparse structure with SSR and finds the low dimensional subspace that reduces the within class distances and keeps the between class distances. Compared with the related state-of-the-art methods, experimental results on benchmark face databases verify the advancement of the proposed method.
Linear Subspace Learning-Based Dimensionality Reduction

The ultimate goal of pattern recognition is to discriminate the class membership of the observed novel objects with the minimum misclassification rate. An observed object is often represented by a high dimensional real-valued vector after some preprocessing while its class membership can be represented by a much lower dimensional binary vector. Thus, in the discriminating process, a pattern recognition system intrinsically reduces the dimensionality of the input data into the number of classes.
Research on the relation between Printed Log-Periodic Antenna's feed and bandwidth

According to the problems appeared during the design, simulation and actual making of Log-Periodic Antenna, this paper compared between different material of pad, calculation of parameters and method of simulation. Through selection, A Printed Log-Periodic Antenna was made using the material of TB-73. The antenna could be used in the channel of 700MHz to 3GHz from the results of actual measuring, the average of Gain was better then 9dB, and the VSWR was less than 1.2 from 700MHz to 2GHz. Adding covering which was made of PEI and changing the feed structure were also measured and analyzed. From the results, the operating frequency became narrow after adding covering, and the bandwidth in high frequency points dropped 200MHz. With the change of feeder, VSWR was higher than 2 in high frequency points and reduce to less than 1.2 in low frequency. This paper has some guidance ideas to the design of LPDA.
Feature extraction for image recognition and computer vision

Feature extraction and classifier design are two main processing blocks in all pattern recognition and computer vision systems. For visual patterns, extracting robust and discriminative features from image is the most difficult yet the most critical step. Several typical and advanced approaches of feature extraction from image are explored, some of which are analyzed in depth. Various techniques of feature extraction from image are organized in four categories: human expert knowledge based methods, image local structure based approaches, image global structure based techniques and machine learning based statistical approaches. We will show examples of applying these feature extraction approaches to solve problems of the image based biometrics, including fingerprint verification/identification and face detection/recognition. These illustrative application examples unveil the ideas, principles and advancements of feature extraction techniques and demonstrate their effectiveness and limitations in solving real-world problems.
Fingerprint Retrieval for Identification

This paper presents a front-end filtering algorithm for fingerprint identification, which uses orientation field and dominant ridge distance as retrieval features. We propose a new distance measure that better quantifies the similarity evaluation between two orientation fields than the conventional Euclidean and Manhattan distance measures. Furthermore, fingerprints in the data base are clustered to facilitate a fast retrieval process that avoids exhaustive comparisons of an input fingerprint with all fingerprints in the data base. This makes the proposed approach applicable to large databases. Experimental results on the National Institute of Standards and Technology data base-4 show consistent better retrieval performance of the proposed approach compared to other continuous and exclusive fingerprint classification methods as well as minutia-based indexing schemes
Sparse and Dense Hybrid Representation via Dictionary Decomposition for Face Recognition

Sparse representation provides an effective tool for classification under the conditions that every class has sufficient representative training samples and the training data are uncorrupted. These conditions may not hold true in many practical applications. Face identification is an example where we have a large number of identities but sufficient representative and uncorrupted training images cannot be guaranteed for every identity. A violation of the two conditions leads to a poor performance of the sparse representation-based classification (SRC). This paper addresses this critic issue by analyzing the merits and limitations of SRC. A sparse- and dense-hybrid representation (SDR) framework is proposed in this paper to alleviate the problems of SRC. We further propose a procedure of supervised low-rank (SLR) dictionary decomposition to facilitate the proposed SDR framework. In addition, the problem of the corrupted training data is also alleviated by the proposed SLR dictionary decomposition. The application of the proposed SDR-SLR approach in face recognition verifies its effectiveness and advancement to the field. Extensive experiments on benchmark face databases demonstrate that it consistently outperforms the state-of-the-art sparse representation based approaches and the performance gains are significant in most cases.
Further Properties and a Fast Realization of the Iterative Truncated Arithmetic Mean Filter

The iterative truncated arithmetic mean (ITM) filter has been recently proposed. It possesses merits of both the mean and median filters. In this brief, the Cramer-Rao lower bound is employed to further analyze the ITM filter. It shows that this filter outperforms the median filter in attenuating not only the short-tailed Gaussian noise but also the long-tailed Laplacian noise. A fast realization of the ITM filter is proposed. Its computational complexity is studied. Experimental results demonstrate that the proposed algorithm is faster than the standard median filter.
Frontal infrared human face detection by distance from centroid method

Recently, thermal imaging has been receiving much attention owing to its application to face recognition in rapid mass fever detection. However, a non-frontal view of the face captured by the camera leads to erroneous temperature measurements. The paper proposes an automated thermal imaging system that is able to discriminate frontal from non-frontal face views with the assumption that at any one time, there is only one person in the field of view of the camera and no other heat-emitting objects are detected by the camera. The relationship between temperature and grey level values, various methods of enhancement, restoration, and segmentation of infrared images, and a way to determine the pose of a face are explained. A boundary signature, i.e. distance from centroid (DFC) of the human face to the lower part of the face outline is used. This measure has been used for the representation of objects, but it is now used to compare the degree of symmetry of the lower face outline. Experimental results are given to verify the validity of the method.
Robust eyeball segmentation in noisy iris images using fourier spectral density

In this paper, a new eyeball segmentation approach based on the Fourier spectral density is proposed for noisy iris images. The design of an accurate segmentation method for noisy iris images could make non-cooperative iris recognition possible. The proposed segmentation method aims to achieve high segmentation accuracy in defocused, reflection-contained and eyelid-occluded iris images. The proposed method could extract the eyeball region correctly in a significant number of noisy iris images from the UBIRIS database [16].
A study of fingerprint image filtering

Fingerprint image enhancement is a crucial step in automatic fingerprint recognition. A large number of approaches for filtering the fingerprint image have been suggested. Most of them perform oriented band pass filtering. However, such filters, for example, Gabor filters, may create spurious ridge structure information. This is harmful for the feature extraction and therefore is harmful for the automatic fingerprint recognition. This paper examines the properties of applying the Gabor filter in the fingerprint image enhancement. It shows that the nonsinusoidal-shaped ridge structure, the ridge frequency estimation error, and the small filter size are the causes of creating spurious ridge structures. As a solution, we suggest an adaptive oriented low pass filter instead of the Gabor filter to avoid producing undesired harmful side effects for the automatic fingerprint recognition.
Reference point detection for fingerprint recognition

A fingerprint recognition algorithm needs to recover the pose transformation between the input fingerprint and the template. One solution is to determine a unique reference point for fingerprint alignment. This work develops a new algorithm to detect a unique reference point consistently for all types of fingerprints. Our detection algorithm works on the orientation field smoothed with an adaptively varying neighborhood. The adaptive window is used to attenuate the noise of orientation field effectively while maintaining the detailed orientation information in the high curvature area. A new approach of reference point localization is proposed that is based on hierarchical analysis of the orientation coherence. Experiments demonstrate that our developed algorithm consistently locates a unique reference point with high accuracy for all types of fingerprints.
Feature Weighting and Regularization of Common Spatial Patterns in EEG-Based Motor Imagery BCI

Electroencephalography signals have very low spatial resolution and electrodes capture signals that are overlapping each other. To extract the discriminative features and alleviate overfitting problem for motor imagery brain–computer interface (BCI), spatial filtering is widely applied but often only very few common spatial patterns (CSP) are selected as features while ignoring all others. However, using only few CSP features, though alleviates overfitting problem, loses the discriminating information, which limits the BCI performance. This letter proposes a novel feature weighting and regularization (FWR) method that utilizes all CSP features to avoid information loss. The proposed method can be applied in all CSP-based approaches. Experiments of this letter show the effect of the proposed method applied in the standard CSP and its two extensions, common spatio-spectral patterns and regularized CSP. Results on BCI Competition III Dataset IIIa and IV Dataset IIa demonstrate that the proposed FWR method enhances the classification accuracy comparing to the conventional feature selection approaches.
Dynamic window construction for the binarization of barcode images captured by mobile phones

It is difficult to directly apply existing binarization methods to the barcode images captured by mobile devices under uncontrolled lighting conditions. Employing a fix-sized window to locally binarize the image cannot handle the situation when small and large objects co-exist in an image. This paper proposes a novel scheme to dynamically determine the size of the binarization window, which changes with the presence of the high gradient pixels in a search window centering the processing pixel. The proposed method has demonstrated its capability in handling objects of different sizes and the uneven illumination problem. Further, it is not constrained to certain barcode types. Experimental results conducted on 330 images captured using five mobile phones in indoor and outdoor environments achieve about 93% recognition rate evaluated using a barcode decoder. Comparisons with some existing approaches show its superior binarization performance.
Minutiae data synthesis for fingerprint identification applications

In this paper, we address the false rejection problem due to the small solid state sensor area available for fingerprint image capture. We propose a minutiae data synthesis approach to circumvent this problem. The main advantages of this approach over the existing image mosaicing approach include low memory storage requirements and low computational complexity. Moreover, the possible matching search overhead due to data redundancy can be reduced. Extensive experiments are conducted to determine the best transformation suitable for minutiae alignment. Among the three transformations presented, affine transformation is found to be most suited for minutiae alignment. We demonstrate the idea of synthesis with an example using physical fingerprint images. The proposed synthesis system is also shown to reduce the number of false rejects caused by the use of different fingerprint regions for matching.
Shadow detection using double-threshold pulse coupled neural networks

A novel double-threshold pulse coupled neural networks (DT-PCNN) is proposed and applied to shadow detection. It attempts to reduce the false detection of shadows in a single image where the hue and brightness of some non-shadow regions are similar to or even lower than those of shadows. Shadows whose intensity and hue fall in between those of the scene and objectives are often viewed as non-shadows by the single dynamic threshold of PCNN. Moreover, entities with similar or darker hue and intensity may be wrongly classified as shadows. To solve this problem, two different dynamic thresholds that iteratively alter are designed. The upper and lower limits of detecting shadows are determined respectively by a higher threshold that decreases iteratively and a lower one that increases iteratively. The detection result is obtained by a fusion of two detection components. Experimental results demonstrate that compared to other tested methods, the misclassifications are significantly reduced and the shadows are more accurately extracted.