nils-herrmann/clean_scirepeval huggingface dataset:
Tracking of persons for video surveillance of unattended environments

This paper describes a visual surveillance system for remote monitoring of unattended environments. For the purpose of efficiently tracking multiple people in the presence of occlusions, we propose: (i) to combine blob matching with particle filtering, and (ii) to augment these tracking algorithms with a novel colour appearance model. The proposed system efficiently counteracts the shortcomings of the two algorithms by switching from one to the other during occlusions. Results on public datasets as well as real surveillance videos from a metropolitan railway station demonstrate the efficacy of the proposed system.
Landmark manifold: Revisiting the Riemannian manifold approach for facial emotion recognition

Automatically recognising facial emotions has drawn increasing attention in computer vision. Facial landmark based methods are one of the most widely used approaches to perform this task. However, these approaches do not provide good performance. Thus, researchers usually tend to combine more information such as textural and audio information to increase the recognition rate. In this paper we propose a novel method, here called the landmark manifold, that shows the possibility to achieve competitive performance by facial landmark information alone. Through experiments on the well-known dataset: marked Cohn-Kanade extended facial emotion dataset (CK+), we show that with accurate facial landmarks, our simple approach is fast to run and can achieve competitive performance with enormously expensive methods.
Illumination and expression invariant face recognition with one sample image

Most face recognition approaches either assume constant lighting condition or standard facial expressions, thus cannot deal with both kinds of variations simultaneously. This problem becomes more serious in applications when only one sample images per class is available. In this paper, we present a linear pattern classification algorithm, adaptive principal component analysis (APCA), which first applies PCA to construct a subspaces for image representation; then warps the subspace according to the within-class co-variance and between-class covariance of samples to improve class separability. This technique performed well under variations in lighting conditions. To produce insensitivity to expressions, we rotate the subspace before warping in order to enhance the representativeness of features. This method is evaluated on the Asian face image database. Experiments show that APCA outperforms PCA and other methods in terms of accuracy, robustness and generalization ability.
Binary watermarks: a practical method to address face recognition replay attacks on consumer mobile devices

Mobile devices (laptops, tablets, and smart phones) are ideal for the wide deployment of biometric authentication, such as face recognition. However, their uncontrolled use and distributed management increases the risk of remote compromise of the device by intruders or malicious programs. Such compromises may result in the device being used to capture the user's face image and replay it to gain unauthorized access to their online accounts, possibly from a different device. Replay attacks can be highly automated and are cheap to launch worldwide, as opposed to spoofing attacks which are relatively expensive as they must be tailored to each individual victim. In this paper, we propose a technique to address replay attacks for a face recognition system by embedding a binary watermark into the captured video. Our monochrome watermark provides high contrast between the signal states, resulting in a robust signal that is practical in a wide variety of environmental conditions. It is also robust to different cameras and tolerates relative movements well. In this paper, the proposed technique is validated on different subjects using several cameras in a variety of lighting conditions. In addition, we explore the limitations of current devices and environments that can negatively impact on performance, and propose solutions to reduce the impact of these limitations.
On robust biometric identity verification via sparse encoding of faces: Holistic vs local approaches

In the field of face recognition, Sparse Representation (SR) has received considerable attention during the past few years. Most of the related literature focuses on holistic descriptors in closed-set identification applications. The underlying assumption in identification is that the gallery always has sufficient samples per subject to linearly reconstruct a query image. Unfortunately, such assumption is easily violated in the more challenging and realistic face verification scenario. A verification algorithm is required to determine if two faces (where one or both have not been seen before) belong to the same person, while explicitly taking into account the possibility of impostor attacks. In this paper, we first discuss why most of the SR literature is not applicable to verification problems. Motivated by the success of bag-of-words methods in the field of object recognition, which describe an image as a set of local patches or interest points, we then propose to tackle the verification problem by encoding each local face patch through SR. The locally encoded sparse vectors are pooled to form regional descriptors, where each descriptor covers a relatively large portion of the face. Experiments in various challenging conditions show that the proposed method achieves high and robust verification performance.
An energy minimisation approach to stereo-temporal dense reconstruction

We propose a novel energy minimisation framework for the dense reconstruction of stereo image sequences that incorporates data fidelity as well as spatial and temporal regularity. An iterated dynamic programming scheme is proposed to minimise the energy function. We also present an efficient implementation of the minimisation scheme by introducing morphological decomposition techniques to solve the dynamic programming subproblem. Our proposed method is capable of reconstructing dynamic scenes with complex motion. Results are presented demonstrating the strength of our proposed algorithm.
Evaluation of HMM training algorithms for letter hand gesture recognition

The paper introduces an application using computer vision for letter hand gesture recognition. A digital camera records a video stream of hand gestures. The hand is automatically segmented, the position of the hand centroid is calculated in each frame, and a trajectory of the hand is determined. After smoothing the trajectory, a sequence of angles of motion along the trajectory is calculated and quantized to form a discrete observation sequence. Hidden Markov models (HMMs) are used to recognize the letters. Baum Welch and Viterbi path counting algorithms are applied for training the HMMs. Our system recognizes all 26 letters from A to Z and the database contains 30 example videos of each letter gesture. We achieve an average recognition rate of about 90 percent. A motivation for the development of this system is to provide an alternate text input mechanism for camera enabled handheld devices, such as video mobile phones and PDAs.
Domain Adaptation on the Statistical Manifold

In this paper, we tackle the problem of unsupervised domain adaptation for classification. In the unsupervised scenario where no labeled samples from the target domain are provided, a popular approach consists in transforming the data such that the source and target distributions become similar. To compare the two distributions, existing approaches make use of the Maximum Mean Discrepancy (MMD). However, this does not exploit the fact that probability distributions lie on a Riemannian manifold. Here, we propose to make better use of the structure of this manifold and rely on the distance on the manifold to compare the source and target distributions. In this framework, we introduce a sample selection method and a subspace-based method for unsupervised domain adaptation, and show that both these manifold-based techniques outperform the corresponding approaches based on the MMD. Furthermore, we show that our subspace-based approach yields state-of-the-art results on a standard object recognition benchmark.
Improved Image Set Classification via Joint Sparse Approximated Nearest Subspaces

Existing multi-model approaches for image set classification extract local models by clustering each image set individually only once, with fixed clusters used for matching with other image sets. However, this may result in the two closest clusters to represent different characteristics of an object, due to different undesirable environmental conditions (such as variations in illumination and pose). To address this problem, we propose to constrain the clustering of each query image set by forcing the clusters to have resemblance to the clusters in the gallery image sets. We first define a Frobenius norm distance between subspaces over Grassmann manifolds based on reconstruction error. We then extract local linear subspaces from a gallery image set via sparse representation. For each local linear subspace, we adaptively construct the corresponding closest subspace from the samples of a probe image set by joint sparse representation. We show that by minimising the sparse representation reconstruction error, we approach the nearest point on a Grassmann manifold. Experiments on Honda, ETH-80 and Cambridge-Gesture datasets show that the proposed method consistently outperforms several other recent techniques, such as Affine Hull based Image Set Distance (AHISD), Sparse Approximated Nearest Points(SANP) and Manifold Discriminant Analysis (MDA).
Feature Space Hausdorff Distance for Face Recognition

We propose a novel face image similarity measure based on Hausdorff distance (HD). In contrast to conventional HD-based measures, which are generally applied in the image space (such as edge maps or gradient images), the proposed HD-based similarity measure is applied in the feature space. By extending the concept of HD using a variable radius and reference set, we can generate a neighbourhood set for HD measures in feature space and then apply this concept for classification. Experiments on the ‘Labeled Faces in the Wild’ and FRGC datasets show that the proposed measure improves the overall classification performance quite dramatically, especially under the highly desirable low false acceptance rate conditions.
Discovering discriminative cell attributes for HEp-2 specimen image classification

Recently, there has been a growing interest in developing Computer Aided Diagnostic (CAD) systems for improving the reliability and consistency of pathology test results. This paper describes a novel CAD system for the Anti-Nuclear Antibody (ANA) test via Indirect Immunofluorescence protocol on Human Epithelial Type 2 (HEp-2) cells. While prior works have primarily focused on classifying cell images extracted from ANA specimen images, this work takes a further step by focussing on the specimen image classification problem itself. Our system is able to efficiently classify specimen images as well as producing meaningful descriptions of ANA pattern class which helps physicians to understand the differences between various ANA patterns. We achieve this goal by designing a specimen-level image descriptor that: (1) is highly discriminative; (2) has small descriptor length and (3) is semantically meaningful at the cell level. In our work, a specimen image descriptor is represented by its overall cell attribute descriptors. As such, we propose two max-margin based learning schemes to discover cell attributes whilst still maintaining the discrimination of the specimen image descriptor. Our learning schemes differ from the existing discriminative attribute learning approaches as they primarily focus on discovering image-level attributes. Comparative evaluations were undertaken to contrast the proposed approach to various state-of-the-art approaches on a novel HEp-2 cell dataset which was specifically proposed for the specimen-level classification. Finally, we showcase the ability of the proposed approach to provide textual descriptions to explain ANA patterns.
Fuzzy Co-Clustering of medical images using bacterial foraging

A novel modification of the Fuzzy Clustering for Categorical Multivariate date (FCCM) algorithm termed as dasiaFuzzy Co-Clustering Algorithm for Images (FCCI)psila is proposed for clustering of medical images. The main aim of this work is to segment regions of interest in histo-pathological images which consist of groups of similar cells indicating some form of abnormality in the animal tissue. The proposed method relies on improved colour clustering results when FCCI is applied on images as compared to the conventional clustering techniques. The method also categorizes different types of lesions based on the co-clustering results. The objective function is optimized using the bacterial foraging algorithm which gives image specific values to the parameters involved in the algorithm. The colour segmentation results are found to be more accurate, producing well formed and valid clusters having ldquocrisppsila values of membership function with lesser number of iterations. The algorithm results in distinct co-clusters ranked in the order of their memberships.
Kernelised orthonormal random projection on grassmann manifolds with applications to action and gait-based gender recognition

Video surveillance systems require both accurate and efficient operations for biometric classification tasks. Recent research has shown that modelling video data on manifold space leads to significant improvement on classification accuracy. However, processing manifold points directly often requires computationally expensive operations since manifolds are non-Euclidean. In this work, we tackle this problem by projecting the manifold points into a random projection space constructed by orthonormal hyperplanes. As the projection notion in manifold space is generally not well defined, the random projection is done indirectly via the Reproducing Kernel Hilbert Space (RKHS). There are at least two reasons that make random projection for manifold points attractive: (1) by random projection, manifold points can be projected into lower dimensional space while preserving most of the structure in the RKHS; and (2) after random projection, the classification of manifold points can be solved via scalable linear classifiers. Our formulation is novel compared to the previous work in the way that we use an orthogonality constraint in the hyperplane generation. By orthogonalising the hyperplanes, the mutual information between the dimensions in the projected space is maximised; a desirable property for addressing classification problems. Experimental results in two biometric applications such as action and gait-based gender recognition, show that we can achieve better accuracy than the state-of-the-art random projection method for manifold points. Further, comparisons with kernelised classifiers show that our method achieves nearly 3-fold speed up on average whilst maintaining the accuracy.
A Benchmarking Platform for Mitotic Cell Classification of ANA IIF HEp-2 Images

Anti-nuclear antibody (ANA) indirect immunofluoresence (IIF) human epithelial type-2 (HEp-2) testing provides important clinical information for the diagnosis of systemic autoimmune rheumatic diseases (SARD). Recent developments in computer aided diagnosis (CAD) systems aim to improve the reliability and reproducibility of ANA IIF laboratory testing by providing ANA classification. The limited prior work into ANA IIF HEp-2 mitotic classification derives its protocol from clinically abstract performance metrics dependent on a single data set of FITC channel images. In this paper, we propose an extensive benchmarking platform considering both the DAPI and FITC channel images for mitotic classification. Furthermore, we argue that pedestrian detection metrics better define the mitotic classification problem. To support these propositions, we design a simple classifier on the DAPI channel and use meaningful performance metrics to demonstrate that it significantly outperforms the most recent state-of-the-art approach.
An Optimization Approach to Scanning Skin Direct Immunofluorescence Specimens

We propose an optimization framework for developing a fully automated scanning system. The framework allows us to have effective design choices in developing the system as every choice should be based on optimizing the objective function. We apply this framework in developing a fully automated scanning system for skin Direct Immunofluorescence (DIF) test. To that end, we introduce both non-algorithmic and algorithmic methods to optimize the objective function. Whilst the non-algorithmic methods comprise various design choices that could indirectly optimize the framework, the algorithmic methods primarily aim to optimize the objective by computing an optimal scan plan. In this work, we explore two algorithmic methods: (1) a heuristic sliding region approach and (2) a quad-tree approach. To our knowledge, this is one of the first works to describe a fully automated scanning system for skin DIF tests. As such, we propose a novel dataset that is hoped to stimulate the research interest in developing digitizing systems for skin DIF tests. All the described methods were evaluated on this novel dataset. Our scanning system is now part of a digital pathology system which has been fully deployed and routinely used within a pathology laboratory.
Automatic Image Attribute Selection for Zero-Shot Learning of Object Categories

Recently the use of image attributes as image descriptors has drawn great attention. This is because the resulting descriptors extracted using these attributes are human understandable as well as machine readable. Although the image attributes are generally semantically meaningful, they may not be discriminative. As such, prior works often consider a discriminative learning approach that could discover discriminative attributes. Nevertheless, the resulting learned attributes could lose their semantic meaning. To that end, in the present work, we study two properties of attributes: discriminative power and reliability. We then propose a novel greedy algorithm called Discriminative and Reliable Attribute Learning (DRAL) which selects a subset of attributes which maximises an objective function incorporating the two properties. We compare our proposed system to the recent state-of-the-art approach, called Direct Attribute Prediction (DAP) for the zero-shot learning task on the Animal with Attributes (AwA) dataset. The results show that our proposed approach can achieve similar performance to this state-of-the-art approach while using a significantly smaller number of attributes.
Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition

In video based face recognition, face images are typically captured over multiple frames in uncontrolled conditions, where head pose, illumination, shadowing, motion blur and focus change over the sequence. Additionally, inaccuracies in face localisation can also introduce scale and alignment variations. Using all face images, including images of poor quality, can actually degrade face recognition performance. While one solution it to use only the ‘best’ of images, current face selection techniques are incapable of simultaneously handling all of the abovementioned issues. We propose an efficient patch-based face image quality assessment algorithm which quantifies the similarity of a face image to a probabilistic face model, representing an ‘ideal’ face. Image characteristics that affect recognition are taken into account, including variations in geometric alignment (shift, rotation and scale), sharpness, head pose and cast shadows. Experiments on FERET and PIE datasets show that the proposed algorithm is able to identify images which are simultaneously the most frontal, aligned, sharp and well illuminated. Further experiments on a new video surveillance dataset (termed ChokePoint) show that the proposed method provides better face subsets than existing face selection techniques, leading to significant improvements in recognition accuracy.
Summarisation of surveillance videos by key-frame selection

We propose two novel techniques for automatic summarisation of lengthy surveillance videos, based on selection of frames containing scenes most informative for rapid perusal and interpretation by humans. In contrast to other video summarisation methods, the proposed methods explicitly focus on foreground objects, via edge histogram descriptor and a localised foreground information quantity (entropy) measurement. Frames are iteratively pruned until a preset summarisation rate is reached. Experiments on the publicly available CAVIAR dataset, as well as our own dataset focused on people walking through natural choke points (such as doors), suggest that the proposed method obtains considerably better results than methods based on optical flow, entropy differences and colour spatial distribution characteristics.
Real-Time Quantitative Bronchoscopy

The determination of motion within a sequence of images remains one of the fundamental problems in computer vision after more than 30 years of research. Despite this work, there have been relatively few application of these techniques to practical problems outside the fields of robotics and video encoding. In this paper, we present the continuing work to apply optical flow and egomotion recovery to the problem of measuring and navigating through the airway using a bronchoscope during a standard procedure, without the need for any additional data, localization systems or other external components. The current implementation uses a number of techniques to provide a range of numerical measurements and estimations to physicians in real time, using standard computer hardware.
International Contest on Pattern Recognition techniques for indirect immunofluorescence images analysis

This contest is a joint initiative organized by the University of Salerno (Italy) and the University of Queensland (Australia) with the support of the Sullivan Nicolaides Pathology (SNP), Australia. The contest primarily aims to provide a platform for scientists and practitioners for performing research to develop Computer Aided Diagnosis (CAD) systems for pathology tests utilizing indirect immunofluorescence protocol. In particular, the contest considers the Antinuclear Antibodies (ANA) test using Human Epithelial type 2 (HEp-2) cells. The competition is divided into four tasks that address specific problems: (1) HEp-2 cell classification; (2) Patient specimen classification; (3) HEp-2 mitotic cell identification and (4) Cell segmentation.
Exploiting Bayesian Belief Network for Adaptive IP-Reuse Decision

A smart camera processor has to perform substantial amount of processing of data-intensive operations. Hence, it is vital to identify critical segments of the processing load by involving HW/SW codesign in smart camera system design. This paper presents a novel fully automatic hybrid framework that combines heuristic and knowledge-based approaches to partition, allocate and schedule IP modules efficiently. In this work, the concept of Bayesian Belief Network (BBN) is utilised and incorporated into the proposed framework. In the experiment section of this paper, we report a comparison of our proposed framework with three previously published work: A BBN based method proposed by a research group from the University of Arizona, the exhaustive algorithm and finally the with greedy algorithms.