nils-herrmann/clean_scirepeval huggingface dataset:
Time-frequency signal analysis and instantaneous frequency estimation: methodology, relationships and implementations

A procedure is described for the time-frequency analysis of signals, based on time-frequency distributions (TFDs) and instantaneous frequency (IF) estimation. First, a suitable TFD is used to determine the number of signal components. Then, if the signal is monocomponent, the IF law can be estimated directly. For multicomponent signals, two-dimensional windowing in the time-frequency domain (a form of time-varying filtering) is used to isolate each component; IF estimation is then applied to the individual components. The periodic first moment of a TFD is used to estimate the IF. A suitable definition and of the periodic first moment is proposed, and the relationship of these estimators to others based on the central finite difference of the phase of the analytic signal is given. A TFD such as the Wigner-Ville distribution can be used to represent both IF and amplitude variations in the individual signal components at each stage of the analysis.<<ETX>>
Optimizing Resources of an FPGA-based Smart Camera Architecture

The acceptance of reconfigurable platforms specifically FPGAs in embedded system design is becoming more apparent. While there are varieties of platforms available for smart camera implementation, our interest is mainly on reconfigurable platforms, specifically FPGAs. In this paper we discuss the research opportunity and the importance of hardware/software partitioning on FPGA-based smart camera platform. An overview of our current research work on the above mentioned problem is describe in this paper.
Towards Miss Universe automatic prediction: The evening gown competition

Can we predict the winner of Miss Universe after watching how they stride down the catwalk during the evening gown competition? Fashion gurus say they can! In our work, we study this question from the perspective of computer vision. In particular, we want to understand whether existing computer vision approaches can be used to automatically extract the qualities exhibited by the Miss Universe winners during their catwalk. This study can pave the way towards new vision-based applications for the fashion industry. To this end, we propose a novel video dataset, called the Miss Universe dataset, comprising 10 years of the evening gown competition selected between 1996–2010. We further propose two ranking-related problems: (1) Miss Universe Listwise Ranking and (2) Miss Universe Pairwise Ranking. In addition, we also develop an approach that simultaneously addresses the two proposed problems. To describe the videos we employ the recently proposed Stacked Fisher Vectors in conjunction with robust local spatio-temporal features. From our evaluation we found that although the addressed problems are extremely challenging, the proposed system is able to rank the winner in the top 3 best predicted scores for 5 out of 10 Miss Universe competitions.
Understanding HMM training for video gesture recognition

When developing a video gesture recognition system to recognise letters of the alphabet based on hidden Markov model (HMM) pattern recognition, we observed that by carefully selecting the model structure we could obtain greatly improved recognition performance. This led us to the questions: Why do some HMMs work so well for pattern recognition? Which factors affect the HMM training process? In an attempt to answer these fundamental questions of learning, we used simple triangle and square video gestures where good HMM structure can be deduced analytically from knowledge of the physical process. We then compared these analytic models to models estimated from Baum-Welch training on the video gestures. This paper shows that with appropriate constraints on model structure, Baum-Welch reestimation leads to good HMMs which are very similar to those obtained analytically. These results corroborate earlier work where we show that the LR banded HMM structure is remarkably effective in recognising video gestures when compared to fully-connected (ergodic) or LR HMM structures.
Face Recognition Robust to Head Pose from One Sample Image

Most face recognition systems only work well under quite constrained environments. In particular, the illumination conditions, facial expressions and head pose must be tightly controlled for good recognition performance. In 2004, we proposed a new face recognition algorithm, adaptive principal component analysis (APCA) (Blanz and Vetter, 1999), which performs well against both lighting variation and expression change. But like other eigenface-derived face recognition algorithms, APCA only performs well with frontal face images. The work presented in this paper is an extension of our previous work to also accommodate variations in head pose. Following the approach of Cootes et al., we develop a face model and a rotation model which can be used to interpret facial features and synthesize realistic frontal face images when given a single novel face image. We use a Viola-Jones based face detector to detect the face in real-time and thus solve the initialization problem for our active appearance model search. Experiments show that our approach can achieve good recognition rates on face images across a wide range of head poses. Indeed recognition rates are improved by up to a factor of 5 compared to standard PCA
The circular nature of discrete-time frequency estimates

It is shown that the conventional (linear) definitions of mean and variance can yield absurd results when applied to data defined on a circular domain. Appropriate definitions for the mean, variance, and moments of circular data are given, and these concepts are applied to reformulate a recently proposed frequency estimator to avoid bias and elevated threshold effects. The maximum likelihood frequency estimator is shown to be equivalent to least-squares regression on phase estimates. These results demonstrate the importance of appreciating the true circular nature of many quantities commonly encountered in digital signal processing.<<ETX>>
Face Recognition on Consumer Devices: Reflections on Replay Attacks

Widespread deployment of biometric systems supporting consumer transactions is starting to occur. Smart consumer devices, such as tablets and phones, have the potential to act as biometric readers authenticating user transactions. However, the use of these devices in uncontrolled environments is highly susceptible to replay attacks, where these biometric data are captured and replayed at a later time. Current approaches to counter replay attacks in this context are inadequate. In order to show this, we demonstrate a simple replay attack that is 100% effective against a recent state-of-the-art face recognition system; this system was specifically designed to robustly distinguish between live people and spoofing attempts, such as photographs. This paper proposes an approach to counter replay attacks for face recognition on smart consumer devices using a noninvasive challenge and response technique. The image on the screen creates the challenge, and the dynamic reflection from the person's face as they look at the screen forms the response. The sequence of screen images and their associated reflections digitally watermarks the video. By extracting the features from the reflection region, it is possible to determine if the reflection matches the sequence of images that were displayed on the screen. Experiments indicate that the face reflection sequences can be classified under ideal conditions with a high degree of confidence. These encouraging results may pave the way for further studies in the use of video analysis for defeating biometric replay attacks on consumer devices.
Region-Based Anomaly Localisation in Crowded Scenes via Trajectory Analysis and Path Prediction

In this paper, we propose an approach for locating anomalies in crowded scene for surveillance videos. In contrast to the previous approaches, the proposed approach does not rely on traditional tracking techniques which tend to fail in crowed scenes. Instead the anomalies are tracked based on the information taken from a set of anomaly classifiers. To this end, each video frame is divided into non- overlapping regions wherein a set of low-level features are extracted. After that, we apply the anomaly classifiers which determine whether there is anomaly in each region. We then derive the anomaly trajectory by connecting the anomalous regions temporarily across the video frames. Finally, we propose path prediction using linear Support Vector Machine (SVM) to smooth the trajectory. By doing this, we will able to better locate them in the crowded scene. We tested our approach on UCSD Anomaly Detection dataset which contains crowded scenes and achieved notable improvement over the state-of-the-art results without sacrificing computational simplicity.
Classifying Anti-nuclear Antibodies HEp-2 Images: A Benchmarking Platform

There has been an ongoing effort in improving reliability and consistency of pathology test results due to their critical role in making an accurate diagnosis. One way to do this is by applying image-based Computer Aided Diagnosis (CAD) systems. This paper proposes a comprehensive benchmarking platform comprising over 1,000 images to evaluate CAD systems for the Anti-Nuclear Antibody (ANA) test via the Indirect Immunofluorescence (IIF) protocol applied on Human Epithelial Type 2 (HEp-2) cells. While prior works in this domain have primarily focussed on classifying individual cell images derived from ANA IIF HEp-2 images, our proposed benchmarking platform goes beyond this by considering the ANA IIF HEp-2 image classification problem. Generally the existing works derive an ANA IIF HEp-2 image label from the dominant pattern of the cell images (we call this approach baseline). In this work, we argue that this approach cannot be used to achieve an acceptable performance, thus, the problem of classifying ANA IIF HEp-2 images (or ANA images in short) is still largely unexplored. To demonstrate that, we propose a simple-yet-effective CAD system which is inspired from the recent success of object bank representation in the object classification domain. We evaluate the proposed system, the baseline and a recent CAD system and show that our proposed system considerably outperforms the others.
Random projections on manifolds of Symmetric Positive Definite matrices for image classification

Recent advances suggest that encoding images through Symmetric Positive Definite (SPD) matrices and then interpreting such matrices as points on Riemannian manifolds can lead to increased classification performance. Taking into account manifold geometry is typically done via (1) embedding the manifolds in tangent spaces, or (2) embedding into Reproducing Kernel Hilbert Spaces (RKHS). While embedding into tangent spaces allows the use of existing Euclidean-based learning algorithms, manifold shape is only approximated which can cause loss of discriminatory information. The RKHS approach retains more of the manifold structure, but may require non-trivial effort to kernelise Euclidean-based learning algorithms. In contrast to the above approaches, in this paper we offer a novel solution that allows SPD matrices to be used with unmodified Euclidean-based learning algorithms, with the true manifold shape well-preserved. Specifically, we propose to project SPD matrices using a set of random projection hyperplanes over RKHS into a random projection space, which leads to representing each matrix as a vector of projection coefficients. Experiments on face recognition, person re-identification and texture classification show that the proposed approach outperforms several recent methods, such as Tensor Sparse Coding, Histogram Plus Epitome, Riemannian Locality Preserving Projection and Relational Divergence Classification.
Biometric Authentication Based on Infrared Thermal Hand Vein Patterns

Hand Vein patterns have been adjudged to be one of the safest biometric modalities due to their strong resilience against the impostor attacks. This paper presents a new approach for biometric authentication using infrared thermal hand vein patterns. In contrast to the existing features for hand vein patterns which are based solely on edge detection, we propose Box and branch point based approaches for multiple feature representations. A robust peg free camera set up is employed for infrared thermal imaging. A region of interest (ROI) is extracted from the vein patterns and is convolved with Gabor filter. The real part of this convolution is only preserved for further processing. Multiple features are extracted from the real parts of the convolved images using the proposed branch point based feature extraction techniques. The multiple features are then integrated at the decision level. AND and OR fusion rules are employed to combine the decisions taken by the individual matcher. Experiments conducted on a database of 100 users result in a False Acceptance Rate (FAR) of 0.1% for the Genuine Acceptance Rate (GAR) of 99% for decision level fusion.
An efficient and robust sequential algorithm for background estimation in video surveillance

Many computer vision algorithms such as object tracking and event detection assume that a background model of the scene under analysis is known. However, in many practical circumstances it is unavailable and must be estimated from cluttered image sequences. We propose a sequential technique for background estimation in such conditions, with low computational and memory requirements. The first stage is somewhat similar to that of the recently proposed agglomerative clustering background estimation method, where image sequences are analysed on a patch by patch basis. For each patch location a representative set is maintained which contains distinct patches obtained along its temporal line. The novelties lie in iteratively filling in background areas by selecting the most appropriate candidate patches according to the combined frequency responses of extended versions of the candidate patch and its neigh-bourhood. It is assumed that the most appropriate patch results in the smoothest response, indirectly enforcing the spatial continuity of structures within a scene. Experiments on real-life surveillance videos demonstrate the efficacy of the proposed method.
Dynamic Amelioration of Resolution Mismatches for Local Feature Based Identity Inference

While existing face recognition systems based on local features are robust to issues such as misalignment, they can exhibit accuracy degradation when comparing images of differing resolutions. This is common in surveillance environments where a gallery of high resolution mugshots is compared to low resolution CCTV probe images, or where the size of a given image is not a reliable indicator of the underlying resolution (e.g. poor optics). To alleviate this degradation, we propose a compensation framework which dynamically chooses the most appropriate face recognition system for a given pair of image resolutions. This framework applies a novel resolution detection method which does not rely on the size of the input images, but instead exploits the sensitivity of local features to resolution using a probabilistic multi-region histogram approach. Experiments on a resolution-modified version of the "Labeled Faces in the Wild" dataset show that the proposed resolution detector frontend obtains a 99% average accuracy in selecting the most appropriate face recognition system, resulting in higher overall face discrimination accuracy (across several resolutions) compared to the individual baseline face recognition systems.
Video face matching using subset selection and clustering of probabilistic Multi-Region Histograms

Balancing computational efficiency with recognition accuracy is one of the major challenges in real-world video-based face recognition. A significant design decision for any such system is whether to process and use all possible faces detected over the video frames, or whether to select only a few ‘best’ faces. This paper presents a video face recognition system based on probabilistic Multi-Region Histograms to characterise performance trade-offs in: (i) selecting a subset of faces compared to using all faces, and (ii) combining information from all faces via clustering. Three face selection metrics are evaluated for choosing a subset: face detection confidence, random subset, and sequential selection. Experiments on the recently introduced MOBIO dataset indicate that the usage of all faces through clustering always outperformed selecting only a subset of faces. The experiments also show that the face selection metric based on face detection confidence generally provides better recognition performance than random or sequential sampling. Moreover, the optimal number of faces varies drastically across selection metric and subsets of MOBIO. Given the trade-offs between computational effort, recognition accuracy and robustness, it is recommended that face feature clustering would be most advantageous in batch processing (particularly for video-based watchlists), whereas face selection methods should be limited to applications with significant computational restrictions.
A unified approach to the STFT, TFDs, and instantaneous frequency

Cohen's class of time-frequency representations (TFRs) is reformulated into a discrete-time, discrete-frequency, computer-implementable form. It is shown how, in this form, many of the properties of the continuous-time, continuous-frequency formulation are either lost or altered. Intuitions applicable in the continuous-time case do not necessarily carry over to the discrete-time case examined. The properties of the discrete variable formulation examined are the presence and form of cross-terms, instantaneous frequency estimation, and relationships between Cohen's class of TFRs. A parameterized class of distributions which is a blending between the short-time Fourier transform (STFT) and the Wigner-Ville distribution. The two main conclusions are that all TFRs of Cohen's class implementable in the given form (which includes all commonly used TFRs) possess cross-terms and that instantaneous frequency estimation using periodic moments of these TFRs is purposeless, since simpler methods obtain the same result.
A high resolution smart camera with GigE Vision extension for surveillance applications

Intelligent video surveillance is currently a hot topic in computer vision research. The goal of intelligent video surveillance is to process the captured video from the monitored area, extract specific information and take appropriate action based on that information. Due to the high computational complexity of vision tasks and the real-time nature of these systems, current software-based intelligent video surveillance systems are unable to perform sophisticated operations. Smart cameras are a key component for future intelligent surveillance systems. They use embedded processing to offload computationally intensive vision tasks from the host processing computers and increasingly reduce the required communication bandwidth and data flows over the network. This paper reports on the design of a high resolution smart camera with a GigE vision extension for automated video surveillance systems. The features of the new camera interface standard, GigE Vision will be introduced and its suitability for video surveillance systems will be described. The surveillance framework for which the GigE vision standard has been developed is presented as well as a brief overview of the proposed smart camera.
Face detection system design for real time high resolution smart camera

Recognizing faces in a crowd in real-time is a key feature which would significantly enhance Intelligent Surveillance Systems. Using a smart camera as a tool to extract faces for recognition would greatly reduce the computational load on the main processing unit. Main processing unit would not be overloaded by the demands of the high data rates of the video and could be designed solely for face recognition. The challenge is with the increasing speed and resolution of the camera sensors, a fast and robust face detection system is required for real time operation. In this paper we report on a multiple-stage face detection system that is designed for implementation on an FPGA based high resolution smart camera system. The system consist of filter stages to greatly reduce the region of interest in video image, followed by a face detection stage to accurately locate the faces. For filter stage, the algorithm is designed to be very fast so that it can be processed in real time. Meanwhile, for face detection stage, a hardware and software co-design technique is utilised to accelerate it.
Improved Shadow Removal for Robust Person Tracking in Surveillance Scenarios

Shadow detection and removal is an important step employed after foreground detection, in order to improve the segmentation of objects for tracking. Methods reported in the literature typically have a significant trade-off between the shadow detection rate (classifying true shadow areas as shadows) and the shadow discrimination rate (discrimination between shadows and foreground). We propose a method that is able to achieve good performance in both cases, leading to improved tracking in surveillance scenarios. Chromacity information is first used to create a mask of candidate shadow pixels, followed by employing gradient information to remove foreground pixels that were incorrectly included in the mask. Experiments on the CAVIAR dataset indicate that the proposed method leads to considerable improvements in multiple object tracking precision and accuracy.
Bayesian analysis of cell nucleus segmentation by a Viterbi search based active contour

An image segmentation scheme is shown to be exceptionally successful through the application of high-level knowledge of the required image objects (cell nuclei). By tuning the algorithm's single parameter it is shown that the performance can be maximised for the dataset, but leads to individual failures that may require alternative choices. A second stage is introduced to process each of the resulting segmentations obtained by varying the parameter over the working range. This stage gives a Bayesian interpretation of the results which indicates the probable accuracy of each of the segmentations that can then be used to make a decision upon whether to accept or reject the segmentation.
Blotch Detection in Pigmented Skin Lesions Using Fuzzy Co-clustering and Texture Segmentation

The ‘Fuzzy Co-Clustering Algorithm for Images (FCCI)’ technique has been successfully applied to colour segmentation of medical images. The goal of this work is to extend this technique by the inclusion of texture features as a clustering parameter for detecting blotches in skin lesions based on colour information. The objective function is optimized using the bacterial foraging algorithm which gives image specific values to the parameters involved in the algorithm. Experiments show the efficacy of the proposed method in extracting malignant blotches from different types of pigmented skin lesion images.
Representative feature chain for single gallery image face recognition

Under the constraint of using only a single gallery image per person, this paper proposes a fast multi-class pattern classification approach to 2D face recognition robust to changes in pose, illumination, and expression (PIE). This work has three main contributions: (1) we propose a representative face space method to extract robust features, (2) we apply a learning method to weight features in pairs, (3) we combine the feature pairs into a feature chain in order to find the weights for all features. The approach is evaluated for face recognition under PIE changes on three public databases. Results show that the method performs considerably better than several other appearance-based methods and can reliably recognise faces at large pose angles without the need for fragile pose estimation pre-processing. Moreover, computational load is low (comparable to standard eigenface methods), which is a critical factor in wide-area surveillance applications.