nils-herrmann/clean_scirepeval huggingface dataset:
Off-Feature Information Incorporated Metric Learning for Face Recognition

Distance metric learning suppresses the intraclass variation while preserving the inter-class variation between two feature vectors. However, these two types of information are mixed in the feature vectors that need to be separated based on learning from the training data. The limited training data may not be able to well separate these two types of information and hence limits the effectiveness of metric learning. This letter proposes to exploit off-feature information to help suppress the intraclass variation of the feature vectors. For face recognition, some identity-independent information such as pose, expression, and occlusion is extracted from source images and utilized as the off-feature information to enhance the performance of distance metric learning. In training, the algorithm learns how to incorporate the off-feature information to suppress the intraclass variation of features. In recognition, the similarity score of a face image pair is determined by its distance in feature space and that of off-feature space. Extensive experiments demonstrate that the proposed off-feature information incorporated metric learning is helpful to suppress the intraclass variation of feature vectors, which visibly enhances the existing metric learning algorithms.
A vote of confidence based interest point detector

In this paper, a vote of confidence (VC) based detector is proposed to detect bright and dark regions from images. Whether a local region is bright or dark is voted by all the pixels in this region. Compared to the contrast based detectors, such as the popular SIFT detector, the VC detector is invariant to illumination change and robust to abrupt variations. Experiments are conducted on benchmark databases to verify the superior performance of the VC detector in terms of the repeatability and matching score. The proposed detector is also evaluated in the application of face recognition.
A Chi-Squared-Transformed Subspace of LBP Histogram for Visual Recognition

Local binary pattern (LBP) and its variants have been widely used in many recognition tasks. Subspace approaches are often applied to the LBP feature in order to remove unreliable dimensions, or to derive a compact feature representation. It is well-known that subspace approaches utilizing up to the second-order statistics are optimal only when the underlying distribution is Gaussian. However, due to its nonnegative and simplex constraints, the LBP feature deviates significantly from Gaussian distribution. To alleviate this problem, we propose a chi-squared transformation (CST) to transfer the LBP feature to a feature that fits better to Gaussian distribution. The proposed CST leads to the formulation of a two-class classification problem. Due to its asymmetric nature, we apply asymmetric principal component analysis (APCA) to better remove the unreliable dimensions in the CST feature space. The proposed CST-APCA is evaluated extensively on spatial LBP for face recognition, protein cellular classification, and spatial-temporal LBP for dynamic texture recognition. All experiments show that the proposed feature transformation significantly enhances the recognition accuracy.
Geiger-mode APD single-photon cameras for 3D laser radar imaging

We describe the design and performance of short-wave infrared 3D imaging cameras with focal plane arrays (FPAs) based on Geiger-mode avalanche photodiodes (GmAPDs) with single photon sensitivity for laser radar imaging applications. The FPA pixels incorporate InP/InGaAs(P) GmAPDs for the detection of single photons with high efficiency and low dark count rates. Based on the design of the GmAPD detectors, FPAs have been optimized for source wavelengths near either 1.0 μm or 1.5 μm. We present results and attributes of fully integrated camera sub-systems with 32 × 32 and 128 × 32 formats, which have 100 μm pitch and 50 μm pitch, respectively. We also address the sensitivity of the fundamental GmAPD detectors to radiation exposure, including recent results that correlate detector active region volume to sustainable radiation tolerance levels. For GmAPD detector designs that are compatible with our existing FPA platform, these discrete device results indicate that sensors with our current level of performance can be designed to tolerate radiation exposure of at least 8 krad with acceptable levels of dark count rate elevation.
A Color Channel Fusion Approach for Face Recognition

Due to high dimensionality of images or generated color features, different color channels are usually processed separately and then concatenated together into a feature vector for classification. This makes channel fusion a crucial step in color face recognition (FR) systems. However, existing methods simply concatenate channel-wise color features without identifying the importance or reliability of features in different color channels. In this paper, we propose a color channel fusion (CCF) approach using jointly dimension reduction algorithms to select more features from reliable and discriminative channels. Experiments using two different dimension reduction approaches, two different types of features on three image datasets show that CCF achieves consistently better performance than color channel concatenation (CCC) method which deals with different color channels equally.
Fundamental frequency estimation by higher order spectrum

This paper proposes a new approach to estimate the fundamental frequency of periodic non-sinusoidal signals by higher order spectrum (HOS). The power of a periodic non-sinusoidal signal is distributed to its fundamental frequency and harmonics. Properly defined higher order spectrum can enhance the fundamental frequency component of spectrum by using the harmonics and therefore has the signal more easily detected from the noise. A specific form of higher order spectrum other than the traditional bispectrum or trispectrum is proposed to improve the reliability of the fundamental frequency estimation for unknown, periodic non-sinusoidal signals. The results of applying the proposed method to weak, high noise signals are surprisingly good. The performances of the various techniques are visually, quantitatively and statistically compared for some signals with different signal-to-noise ratios.
A compact mobile FM Transmitter with automatic embedded antenna tuning and low spurious emission in 65nm CMOS

A 76-108MHz FM Transmitter emits 120dBμVrms output signal using an embedded antenna on a PCB. A proposed on-chip calibration scheme digitally tunes an on-chip capacitor array to centre the embedded antenna resonance circuit with an external inductor at the desired FM channel. The transmitter achieves a 60dB audio SINAD, better than -45dB LO leakage and -50dB sideband suppression with -30/-55dB ACLR for 200kHz/400kHz offsets, respectively. The out of band spurious emission level for coexistence with other radio bands is very low and mostly around -102 dBm or less. A new on-chip T/R switch capable of handling 4Vpp signal is used to allow both FM TX and RX to share the same embedded antenna. The transmitter adds only 0.2mm^2 die area overhead to an FM TRX SoC in 65nm CMOS and draws 10mA from a 4.2V battery.
Image watermarking using dual-tree complex wavelet by coefficients swapping and group of coefficients quantization

In this paper, we present two watermarking schemes for images using the low-pass frequency coefficients and high-pass complex frequency coefficients of the Dual-Tree Complex Wavelet Transform (DT-CWT) of the image, the distinctive characteristics of the two schemes can lead to different applications. We investigate the problem of embedding binary watermark sequence in DT-CWT domain, which is a challenging problem. The coefficients swapping is done by carefully selecting those 2×2 blocks with high-energy in the low frequency and swap the coefficients that lie in the median range in the block to embed the watermark. Whereas the group of coefficients quantization quantizes a group of high-pass complex frequency coefficients to make the quantized coefficients lie in the middle of the quantization range and distribute the changes among the coefficients. Experimental results conducted on 100 standard images achieve about 96% and 92% detection rate for the low-pass and high-pass frequency coefficients-based schemes, respectively. Robustness tests against some common signal processing attacks such as additive noise, median filtering and lossy JPEG compression confirm the superior performance of the proposed schemes.
Fingerprint image ridge frequency estimation by higher order spectrum

This paper proposes a new approach for estimating the ridgeline frequency of fingerprint images by using higher order spectrum. The higher order spectrum enhances the fundamental frequency component of the ridgeline by using its harmonics and therefore suppresses the noise. A specific form of higher order spectrum other than the traditional bispectrum or trispectrum is employed to improve the reliability of the fingerprint ridge frequency estimation. This approach provides a reliable ridgeline frequency estimation for heavy noised fingerprint images. Experimental results are presented to compare this approach with the traditional approaches of power spectrum, bispectrum and trispectrum.
Binarization of Low-Quality Barcode Images Captured by Mobile Phones Using Local Window of Adaptive Location and Size

It is difficult to directly apply existing binarization approaches to the barcode images captured by mobile device due to their low quality. This paper proposes a novel scheme for the binarization of such images. The barcode and background regions are differentiated by the number of edge pixels in a search window. Unlike existing approaches that center the pixel to be binarized with a window of fixed size, we propose to shift the window center to the nearest edge pixel so that the balance of the number of object and background pixels can be achieved. The window size is adaptive either to the minimum distance to edges or minimum element width in the barcode. The threshold is calculated using the statistics in the window. Our proposed method has demonstrated its capability in handling the nonuniform illumination problem and the size variation of objects. Experimental results conducted on 350 images captured by five mobile phones achieve about 100% of recognition rate in good lighting conditions, and about 95% and 83% in bad lighting conditions. Comparisons made with nine existing binarization methods demonstrate the advancement of our proposed scheme.
Fingerprint Retrieval by Complex Filter Responses

This paper proposes an approach of fingerprint retrieval based on the continuous classification of two complex filter responses. Two complex filters are introduced and applied on the fingerprint orientation field to extract the local singularities, the similarities to the singular points. A numerical feature vector from the aligned fingerprint local singularities is constructed as the global feature for fingerprint retrieval. The continuous classification is employed to retrieve a subset of fingerprints similar to the query fingerprint for the finer matching. Experimental results on NIST fingerprint database 4 (NIST-4) shows the effectiveness of the proposed fingerprint retrieval approach
Human Body Part Selection by Group Lasso of Motion for Model-Free Gait Recognition

Gait recognition is an emerging biometric technology that identifies people through the analysis of the way they walk. The challenge of model-free based gait recognition is to cope with various intra-class variations such as clothing variations, carrying conditions and angle variations that adversely affect the recognition performance. This paper proposes a method to select the most discriminative human body part based on group Lasso of motion to reduce the intra-class variation so as to improve the recognition performance. The proposed method is evaluated using CASIA Gait Dataset B. Experimental results demonstrate that the proposed technique gives promising results.
Investigation of a multistack voltage-tunable four-color quantum-well infrared photodetector for mid- and long-wavelength infrared detection

A four-color quantum-well infrared photodetector (QWIP) for mid- and long-wavelength infrared (MWIR and LWIR) detection has been demonstrated in this work. Four stacks of QW structures with four different detection wavelengths are sandwiched between three highly doped contact layers. Peak detection wavelengths of this device are centered at 4.7, 8.5, 9, and 12.3 /spl mu/m, respectively. The 4.7- and 8.5-/spl mu/m stacks are separated from the 9- and 12.3-/spl mu/m stacks by a middle contact layer, and the peak detection wavelength within these two double-stack QWIP's can be tuned by the applied bias. Four different combinations of two-color simultaneous readings can be achieved. By using a small number of QW's and balancing the impedance between the stacks, we are able to use all four stacks for voltage-tunable multicolor detection with two terminals. The bias and temperature dependence of the dark current and peak responsivity as well as the dynamic impedance in this four-color QWIP were systematically studied over a wide range of bias and temperature. In spite of using four different stacks of strained InGaAs-AlGaAs and GaAs-AlGaAs materials, the device shows excellent material quality and performance.
Eigenfeature Regularization and Extraction in Face Recognition

This work proposes a subspace approach that regularizes and extracts eigenfeatures from the face image. Eigenspace of the within-class scatter matrix is decomposed into three subspaces: a reliable subspace spanned mainly by the facial variation, an unstable subspace due to noise and finite number of training samples, and a null subspace. Eigenfeatures are regularized differently in these three subspaces based on an eigenspectrum model to alleviate problems of instability, overfitting, or poor generalization. This also enables the discriminant evaluation performed in the whole space. Feature extraction or dimensionality reduction occurs only at the final stage after the discriminant assessment. These efforts facilitate a discriminative and a stable low-dimensional feature representation of the face image. Experiments comparing the proposed approach with some other popular subspace methods on the FERET, ORL, AR, and GT databases show that our method consistently outperforms others.
Robust face recognition using trimmed linear regression

In this work, we focus on the problem of partially occluded face recognition. Using a robust estimator, we detect and trim the contaminated pixels from query sample. The corresponding pixels in the training samples are trimmed as well. The linear regression is applied to the trimmed images. Finally, the query image is labeled to the class with minimum normalized reconstruction error. Extensive experiments on benchmark face datasets demonstrate that the proposed approach is much more robust than state-of-the-art methods in dealing with occluded faces.
Eye detection based on rank order filter

A novel eye detection algorithm based on rank order filter is proposed in this paper. Features such as eyeball is round and darker than surrounding pixels are widely used in eye detection. However quite often eyeball is distorted by iris reflection or other obstacles around eyeball. Rank order filter pair is designed to tackle these problems. One rank order filter is applied on central pixels to emphasize the darkness of the eyeball pixels and another rank order filter is applied on surrounding pixels to emphasize the brightness of the surrounding pixels. Then the difference of those two filter outputs is an important clue for eye detection, since regions near eyeball will yield large response. Those pixels of large response are selected as eyeball candidates, grouped and further verified by a series of geometric constraints. It is tested for 4095 face images with large variations in illuminations, hair styles, facial expressions and partial occlusions and detection rate as high as 98.97% is achieved.
Visual object detection by parts-based modeling using extended histogram of gradients

In this paper, we present a parts-based modeling framework using Extended Histogram of Gradients (ExHoG) for object detection. Visual object detection is a challenging issue in computer vision where objects need to be detected in varying illumination and contrast environments. Furthermore, objects belonging to the same class exhibit large intra-class variations. Here, we propose using ExHoG with the discriminatively trained deformable part models of Felzenszwalb et. al. . This framework is based on mixtures of multiscale deformable part models. ExHoG is a novel feature proposed earlier for the purpose of human detection and has shown promising results against other state-of-the-art approaches. The proposed approach is tested on INRIA Human data set and the PASCAL VOC 2007 data set. Results demonstrate superior performance on INRIA compared to existing state-of-the-art approaches and improved performance on PASCAL VOC 2007.
Minutiae extraction by adaptive tracing the gray level ridge of the fingerprint image

This paper presents an improved approach of minutiae detection that adaptively traces the gray level ridge of the originals fingerprint image with piecewise linear lines of different length. While tracing the ridges, the fingerprint image is smoothed with an oriented smoothing filter only at selected pixels where smoothing is necessary. After tracing all the ridges, a piece-wise linear skeleton image is obtained. Each ridge in the skeleton is labeled with a number so that each minutiae is associated with one or two ridge numbers. The post-processing is based not only on the location relationship of the minutiae, but also the associated ridge relationship and the certainty level of the minutiae. The performance of this approach is objectively assessed by using two large fingerprint databases.
LBP-Structure Optimization With Symmetry and Uniformity Regularizations for Scene Classification

Local binary pattern (LBP) and its variants have been widely used in many visual recognition tasks. Most existing approaches utilize predefined LBP structures to extract LBP features. Recently, data-driven LBP structures have shown promising results. However, due to the limited number of training samples, data-driven structures may overfit the training samples, hence could not generalize well on the novel testing samples. To address this problem, we propose two structural regularization constraints for LBP-structure optimization: symmetry constraint and uniformity constraint. These two constraints are inspired by predefined LBP structures, which convey the human prior knowledge on designing LBP structures. The LBP-structure optimization is casted as a binary quadratic programming problem and solved efficiently via the branch-and-bound algorithm. The evaluation on two scene-classification datasets demonstrates the superior performance of the proposed approach compared with both predefined LBP structures and unconstrained data-driven LBP structures.
Face Recognition Based on Discriminant Evaluation in the Whole Space

This paper proposes a face recognition approach that performs linear discriminant analysis in the whole eigenspace. It decomposes the eigenspace into two subspaces: a reliable subspace spanned mainly by the facial variation and an unstable subspace due to finite number of training samples. Eigenvalues in the unstable subspace are replaced by a constant. This alleviates the over-fitting problem and enables the discriminant evaluation in the whole space. Feature extraction or dimensionality reduction occurs only at the final stage after the discriminant assessment. These efforts facilitate a discriminative and stable low-dimensional feature representation of the face image. Experimental results comparing some popular subspace methods on FERET and ORL databases show that our approach consistently outperforms others.
Analyzing of tri-axis differential in-pipe robot with adaptive differential capability for moving inside pipelines

In this paper, a novel called Tri-axis Differential In-pipe Robot without tether cable, featured with adaptive differential capability is developed as a mobile platform for long distance inspection of moving inside pipelines. The robot has several remarkable features superior to the others such as an differential mechanism with adaptive differential capability in elbow. Consequently the in-pipe robot can equip with single driving motor to traverse through various pipeline elements smoothly with high efficiency without sophisticated sensory information. To confirm the effectiveness of the proposed robotic differential method, experiments are performed to quantify the differential precision of the respective driving wheel as well as the robotic driving efficiency.
Extended Histogram of Gradients feature for human detection

Unsigned Histogram of Gradients (UHoG) is a popular feature used for human detection. Despite its superior performance as reported in recent literature, an inherent limitation of UHoG is that gradients of opposite directions in a cell are mapped into the same histogram bin. This is undesirable as it will produce the same UHoG feature for two different patterns. To address this problem, we propose a new feature named the Extended Histogram of Gradients (ExHoG) in this paper. It comprises two components: UHoG and a histogram of absolute bin value differences of opposite gradient directions computed from Histogram of Gradients (HoG). Our experimental results show that the proposed ExHoG consistently outperforms the standard HoG and UHoG for human detection.
InGaAs/InP single photon avalanche diodes with negative feedback

We have designed and fabricated various NFAD devices, and they show reasonable dark count rate and afterpulse probability at 10% PDE. These NFAD devices have great promise to reduce charge flow compared to passively quenched SPADs with hybridly integrated resistance and purely active quenched SPADs, and therefore significantly reduce afterpulse effects. Operating the NFAD is very simple compared to actively quenched free-running SPAD devices. The NFAD matrix devices have the potential of resolving photon numbers. These characteristics of NFAD devices make them attractive for many applications that require single photon sensitivity with free-running operation at SWIR wavelengths.
Extended Histogram of Gradients with Asymmetric Principal Component and Discriminant Analyses for Human Detection

Asymmetry in training sets of humans and non-humans and high dimensionality of existing features are problems plaguing human detection. As classification of humans tends to be one class versus all other classes, existing classification methods do not consider this asymmetry in the training sets which leads to sub optimal classifier performance. Furthermore, the high dimensionality of existing features hampers real-time performance of human detection and classification. In this paper, we address these 2 issues by considering Asymmetric Principal Component and Discriminant Analyses (APCDA) for use with modified Extended Histogram of Gradients (ExHoG) with Mahalanobis distance classifiers. APCDA is a dimensionality reduction and feature extraction technique which specifically addresses the asymmetrical training sets problem and improves classification performance. We modify ExHoG by performing normalization on the Histogram of Gradients block features to suppress any large peaks in gradient magnitudes. Our experimental results, using the INRIA Human data set, show that the modified ExHoG results in an improved performance over the original ExHoG. Furthermore, using APCDA and Mahalanobis distance classifiers, our method with the modified ExHoG outperforms state-of-the-art methods on the Daimler Pedestrian Classification Benchmark data set.