nils-herrmann/clean_scirepeval huggingface dataset:
A link between shape and phase

Signals can be treated as ordered sets and shape as a property of the order. It is shown that the degree of asymmetry or relative contrast of various ordered subsets is a good measure of this property. Statistical measures provide robustness while shape measures provide sensitivity to changes in order. The concept of phase is examined within this framework. What shape information is found in the instantaneous phase? A relationship between instantaneous phase and a measure of shape is derived. Moments, Fourier and wavelet transforms, edges and segments are examined from the new perspective on measures of shape and robustness.
Sequential decision fusion for controlled detection errors

Information fusion in biometrics has received considerable attention. The architecture proposed here is based on the sequential integration of multi-instance and multi-sample fusion schemes. This method is analytically shown to improve the performance and allow a controlled trade-off between false alarms and false rejects when the classifier decisions are statistically independent. Equations developed for detection error rates are experimentally evaluated by considering the proposed architecture for text dependent speaker verification using Hidden Markov Model (HMM) based digit dependent speaker models. The tuning of parameters, n classifiers and m attempts/samples, is investigated and the resultant detection error trade-off performance is evaluated on individual digits. Results show that performance improvement can be achieved even for weaker classifiers (FRR-19.6%, FAR-16.7%). The architectures investigated apply to speaker verification from spoken digit strings such as credit card numbers in telephone or VOIP or internet based applications.
Importance coding of still imagery based on importance maps of visually interpretable regions

The paper proposes a general framework for the importance coding of still images to maximise the interpretability versus bitrate performance. The interpretability of an image to achieve maximum content recognition is important in a diverse range of applications such as surveillance and medical. Importance coding aims to address this problem by prioritisation of the encoded image bit-stream based on the importance of regions in an image. Consequently, the most important features required for interpretability are encoded and transmitted earlier in the encoded image bit-stream. The notion of importance maps, which provide a systematic approach for the assignment of relative importance of regions in an image, are presented and its use in importance coding is developed. One highly desirable advantage of the proposed importance coding framework is that it can be implemented by the EBCOT (embedded block coding with optimised truncation) coder, which has been selected as the core-coding algorithm in JPEG 2000.
White Blood Cell Nuclei Segmentation Using Level Set Methods and Geometric Active Contours

A new method for segmenting white blood cells nuclei in microscopic images is presented. Challenges to accurate segmentation include intra-class variation of the nuclei cell boundaries, non-uniform illumination, and changes in the cell topology due to its orientation and stage of maturity. In this research, level set methods and geometric active contours are used to segment the nucleus of white blood cells from the cytoplasm and the cell wall. Level set methods use morphological operations to estimate an initial cell boundary and are fully automated. Geometric active contours are less computationally complex and adapt better to the curve topology of the cell boundary than parametric active contours, which have been previously used for white blood cell segmentation. Segmentation performance is compared with other segmentation methods using the Berkeley benchmark database and the proposed method is shown to be superior using various indices.
Classifier selection using sequential error ratio criterion for multi-instance and multi-sample fusion

Classifier selection is a problem encountered by multi-biometric systems that aim to improve performance through fusion of decisions. A particular decision fusion architecture that combines multiple instances (n classifiers) and multiple samples (m attempts at each classifier) has been proposed in previous work to achieve controlled trade-off between false alarms and false rejects. Although analysis on text-dependent speaker verification has demonstrated better performance for fusion of decisions with favourable dependence compared to statistically independent decisions, the performance is not always optimal. Given a pool of instances, best performance with this architecture is obtained for certain combination of instances. Heuristic rules and diversity measures have been commonly used for classifier selection but it is shown that optimal performance is achieved for the ‘best combination performance’ rule. As the search complexity for this rule increases exponentially with the addition of classifiers, a measure - the sequential error ratio (SER) - is proposed in this work that is specifically adapted to the characteristics of sequential fusion architecture. The proposed measure can be used to select a classifier that is most likely to produce a correct decision at each stage. Error rates for fusion of text-dependent HMM based speaker models using SER are compared with other classifier selection methodologies. SER is shown to achieve near optimal performance for sequential fusion of multiple instances with or without the use of multiple samples. The methodology applies to multiple speech utterances for telephone or internet based access control and to other systems such as multiple finger print and multiple handwriting sample based identity verification systems.
An approach to statistical lip modelling for speaker identification via chromatic feature extraction

This paper presents a novel technique for the tracking of moving lips for the purpose of speaker identification. In our system, a model of the lip contour is formed directly from chromatic information in the lip region. Iterative refinement of contour point estimates is not required. Colour features are extracted from the lips via concatenated profiles taken around the lip contour. Reduction of order in lip features is obtained via principal component analysis (PCA) followed by linear discriminant analysis (LDA). Statistical speaker models are built from the lip features based on the Gaussian mixture model (GMM). Identification experiments performed on the M2VTS/sup 1/ database, show encouraging results.
Combined 2D/3D Face Recognition Using Log-Gabor Templates

The addition of Three Dimensional (3D) data has the potential to greatly improve the accuracy of Face Recognition Technologies by providing complementary information. In this paper a new method combining intensity and range images and providing insensitivity to expression variation based on Log-Gabor Templates is presented. By breaking a single image into 75 semi-independent observations the reliance of the algorithm upon any particular part of the face is relaxed allowing robustness in the presence of occulusions, distortions and facial expressions. Also presented is a new distance measure based on the Mahalanobis Cosine metric which has desirable discriminatory characteristics in both the 2D and 3D domains. Using the 3D database collected by University of Notre Dame for the Face Recognition Grand Challenge (FRGC), benchmarking results are presented demonstrating the performance of the proposed methods.
Hybrid DWT and MFCC feature warping for noisy forensic speaker verification in room reverberation

The robustness of speaker verification systems is often degraded in real forensic applications, which contain environmental noise and reverberation. Reverberation results in mismatched conditions between enrolment and test speech signals. In this work, we investigate the effectiveness of combining features of discrete wavelet transform (DWT) and feature-warped mel frequency cepstral coefficients (MFCCs) to improve the performance of speaker verification under conditions of reverberation and environmental noises. State of the art intermediate vector (i-vector) and probabilistic linear discriminant analysis (PLDA) were used as a classifier. The algorithm was evaluated by convolving the impulse room response with enrolment speech from an Australian forensic voice comparison database. The test speech signals were combined with car, street, and home noises from the QUT-NOISE database at signal to noise ratios (SNR) ranging from −10 dB to 10 dB. Experimental results indicate that the algorithm achieves a reduction in average equal error rate (EER) ranging from 17.10% to 51.86% over traditional MFCC features when reverberated enrolment data and the test speech signals are corrupted with car, street and home noises at SNRs ranging from −10 dB to 10 dB.
Sequential fusion of decisions from adaptive and random samples for controlled verification errors

Fusion techniques have received considerable attention for achieving performance improvement with biometrics. While a multi-sample fusion architecture reduces false rejects, it also increases false accepts. This impact on performance also depends on the nature of subsequent attempts, i.e., random or adaptive. Expressions for error rates are presented and experimentally evaluated in this work by considering the multi-sample fusion architecture for text-dependent speaker verification using HMM based digit dependent speaker models. Analysis incorporating correlation modeling demonstrates that the use of adaptive samples improves overall fusion performance compared to randomly repeated samples. For a text dependent speaker verification system using digit strings, sequential decision fusion of seven instances with three random samples is shown to reduce the overall error of the verification system by 26% which can be further reduced by 6% for adaptive samples. This analysis novel in its treatment of random and adaptive multiple presentations within a sequential fused decision architecture, is also applicable to other biometric modalities such as finger prints and handwriting samples.
The design and development of an undergraduate signal processing laboratory

The School of Electrical and Electronic Systems Engineering of Queensland University of Technology (like many other universities around the world) has recognised the importance of complementing the teaching of signal processing with computer based experiments. A laboratory has been developed to provide a "hands-on" approach to the teaching of signal processing techniques. The motivation for the development of this laboratory was the cliche "What I hear I remember but what I do I understand." The laboratory has been named as the "Signal Computing and Real-time DSP Laboratory" and provides practical training to approximately 150 final year undergraduate students each year. The paper describes the novel features of the laboratory, techniques used in the laboratory based teaching, interesting aspects of the experiments that have been developed and student evaluation of the teaching techniques.<<ETX>>
A motor controller using field oriented control and Hall effect rotor position sensors: Simulation and implementation

A field oriented control (FOC) algorithm is simulated and implemented for use with a permanent magnet synchronous motor (PMSM). Rotor position is sensed using Hall effect switches on the stator because other hardware position sensors attached to the rotor may not be desirable or cost effective for certain applications. This places a limit on the resolution of position sensing - only a few Hall effect switches can be placed. In this simulation, three sensors are used and the position information is obtained at higher resolution by estimating it from the rotor dynamics, as shown in literature previously. This study compares the performance of the method with an incremental encoder using simulations. The FOC algorithm is implemented using Digital Motor Control (DMC) and IQ Texas Instruments libraries from a Simulink toolbox called Embedded Coder, and downloaded into a TI microcontroller (TMS320F28335) known as the Piccolo via Code Composer Studio (CCS).
Adaptive Optical Flow for Person Tracking

Person tracking systems are dependent on being able to locate a person accurately across a series of frames. Optical flow can be used to segment a moving object from a scene, provided the expected velocity of the moving object is known; but successful detection also relies on being able segment the background. A problem with existing optical flow techniques is that they don’t discriminate the foreground from the background, and so often detect motion (and thus the object) in the background. To overcome this problem, we propose a new optical flow technique, that is based upon an adaptive background segmentation technique, which only determines optical flow in regions of motion. This technique has been developed with a view to being used in surveillance systems, and our testing shows that for this application it is more effective than other standard optical flow techniques.
A Model Integrating Fire Prediction and Detection for Rural-Urban Interface

This paper proposes a model that integrates new smoke detection and fire prediction techniques for the rural-urban interface area. The model aims to predict fire risk from weather parameters, and to detect smoke using video monitoring systems. Further, the fire danger index (FDI) provided by the prediction algorithm would be utilized to enhance the certainty of smoke detection and reduce false alarm rates. Experimental results illustrate that our prediction algorithm successfully predicts fire risk on a five-point scale with mean accuracy of 94.92% and the detection algorithm more effectively detects smoke compared to other algorithms by achieving 97% average accuracy.
On the computation and interpretation of auto- and cross-trispectra

This paper discusses the principal domains of auto- and cross-trispectra. It is shown that the cumulant and moment based trispectra are identical except on certain planes in trifrequency space. If these planes are avoided, their principal domains can be derived by considering the regions of symmetry of the fourth order spectral moment. The fourth order averaged periodogram will then serve as an estimate for both cumulant and moment trispectra. Statistics of estimates of normalised trispectra or tricoherence are also discussed.<<ETX>>
The use of speech and lip modalities for robust speaker verification under adverse conditions

Investigates the use of lip information, in conjunction with speech information, for robust speaker verification in the presence of background noise. We have previously shown ( May 1998) that features extracted from a speaker's moving lips hold speaker dependencies which are complementary with speech features. We demonstrate that the fusion of lip and speech information allows for a highly robust speaker verification system which outperforms either subsystem individually. We present a new technique for determining the weighting to be applied to each modality so as to optimize the performance of the fused system. Given a correct weighting, lip information is shown to be highly effective for reducing the false acceptance and false rejection error rates in the presence of background noise.
Automatic identification of epilepsy by HOS and power spectrum parameters using EEG signals: A comparative study

Epilepsy is characterized by the spontaneous and unforeseeable occurrence of seizures, during which the perception or behavior of patients is disturbed. An automatic system that detects seizure onsets would allow patients or the people near them to take appropriate precautions, and could provide more insight into these phenomena. The use of non-linear features motivated by the higher order spectra (HOS) had been reported to be a promising approach to differentiate between normal, background (pre-ictal) and epileptic EEG signals. In this work, the features are extracted from the power spectrum and the bispectrum. Their performance is studied by feeding them to a Gaussian mixture model (GMM) classifier. Results show that with selected HOS based features, we were able to achieve 93.11% compared to classification accuracy of 88.78% as that of features derived from PSD.
Visual attention based ROI maps from gaze tracking data

The use of visual attention (VA) spatial and temporal characteristics, monitored by a gaze-tracking device, to generate a region of interest (ROI) 'importance' map is proposed. A K-means clustering approach is adopted to group gaze location points into a number of clusters to represent the loci of regions of VA (or ROIs). Several metrics are then derived from the gaze positions and sequences to quantify the relative importance of the K-means clusters. An entropy-weighting strategy is adopted for the combination of these metrics to generate the ROI map. Results show that the ROI map is robust to the number of clusters and different gaze patterns, and can be used in progressive image coding/decoding to enhance the image quality in regions of interest.
Hybrid coding of mixed signals for digital covert audio surveillance

An overview of the requirements of digital covert audio acquisition (DCAA) is provided and a scalable hybrid coder tuned for the coding of intelligible speech in a covert environment is proposed. The codec incorporates linear predictive coding (LPC) and an M-band discrete wavelet transform (DWT) to offer effective intelligible speech coding in the presence of multiple signal sources at bit rates between 8 kbps and 32 kbps. The results of informal intelligibility testing and an analysis of the algorithm's complexity are presented to demonstrate the performance of the proposed coder.
A new view of shape

A new theory that qualitatively and quantitatively describes shape is presented. Signals are treated as ordered sets and shape as a property of the order. Measures of shape are derived starting from simple two element sets. It is shown that the degree of asymmetry of relative contrast at various scales is a sufficient descriptor of shape. The shape of a signal is a composite of the shape of all possible ordered subsets. A distinction is drawn between statistical and shape measures within a unified theoretical framework which may make it possible compare diverse pattern recognition approaches in terms robustness and discrimination power.
Face recognition from 3D data using Iterative Closest Point algorithm and Gaussian mixture models

An approach to face verification from 3D data is presented. The method uses 3D registration techniques designed to work with resolution levels typical of the irregular point cloud representations provided by structured light scanning. Preprocessing using a-priori information of the human face and the Iterative Closest Point algorithm are employed to establish correspondence between test and target and to compensate for the nonrigid nature of the surfaces. Statistical modelling in the form of Gaussian mixture models is used to parameterise the distribution of errors in facial surfaces after registration and is employed to differentiate between intra- and extra-personal comparison of range images. An equal error rate of 2.67% was achieved on the 30 subject manual subset of the 3d/spl I.bar/rma database.
Robust Image Hashing Using Higher Order Spectral Features

Robust image hashing seeks to transform a given input image into a shorter hashed version using a key-dependent non-invertible transform. These image hashes can be used for watermarking, image integrity authentication or image indexing for fast retrieval. This paper introduces a new method of generating image hashes based on extracting Higher Order Spectral features from the Radon projection of an input image. The feature extraction process is non-invertible, non-linear and different hashes can be produced from the same image through the use of random permutations of the input. We show that the transform is robust to typical image transformations such as JPEG compression, noise, scaling, rotation, smoothing and cropping. We evaluate our system using a verification-style framework based on calculating false match, false non-match likelihoods using the publicly available Uncompressed Colour Image database (UCID) of 1320 images. We also compare our results to Swaminathan’s Fourier-Mellin based hashing method with at least 1% EER improvement under noise, scaling and sharpening.
Robustness to expression variations in fractal-based face recognition

Face recognition has developed into a major research area in pattern recognition and computer vision. Face recognition is different from classical pattern recognition problems such as character recognition. In classical pattern recognition, there are relatively few classes, and many samples per class. With many samples per class, algorithms can classify samples not previously seen by interpolating among the training samples. On the other hand, in face recognition, there are many individuals (classes), and only a few images (samples) per person, and algorithms must recognize faces by extrapolating from the training samples. In numerous applications there can be only one training sample (image) of each person (for real time applications). In this paper, we present an approach for expression-invariant face recognition based on fractal features.
Object tracking using multiple motion modalities

This paper presents an object tracking system that utilises a hybrid multi-layer motion segmentation and optical flow algorithm. While many tracking systems seek to combine multiple modalities such as motion and depth or multiple inputs within a fusion system to improve tracking robustness, current systems have avoided the combination of motion and optical flow. This combination allows the use of multiple modes within the object detection stage. Consequently, different categories of objects, within motion or stationary, can be effectively detected utilising either optical flow, static foreground or active foreground information. The proposed system is evaluated using the ETISEO database and evaluation metrics and compared to a baseline system utilising a single mode foreground segmentation technique. Results demonstrate a significant improvement in tracking results can be made through the incorporation of the additional motion information.
Gabor Filter Bank Representation for 3D Face Recognition

In this paper an investigation of the validity of Gabor filter banks for feature extraction in a face recognition context is presented. Using the combined 2D/3D database collected by the Computer Vision Laboratory at the University of Notre Dame and the Colorado State University’s implementation of the Eigenfaces method, a comprehensive evaluation of the Log-Gabor filter bank representation is performed. Analysis of both the spatial and frequency domains is conducted to evaluate the distribution of discriminatory information.