nils-herrmann/clean_scirepeval huggingface dataset:
Detecting kangaroos in the wild: the first step towards automated animal surveillance

Recent studies in computer vision have provided new solutions to real-world problems. In this paper, we focus on using computer vision methods to assist in the study of kangaroos in the wild. In order to investigate the feasibility, we built a kangaroo image dataset from collected data from several national parks across the State of Queensland. To achieve reasonable detection accuracy, we explored a multi-pose approach and proposed a framework based on the state-of-the-art Deformable Part Model (DPM). Experiments show that the proposed framework outperformed the state-of-the-art methods on the proposed dataset. Also, the proposed vision tools are able to help our field biologists in studying kangaroo related problems such as population tracking for activity analysis.
Gaussian Probabilistic Confidence Score for Biometric Applications

We propose a quick and widely applicable approach for converting biometric identification match scores to probabilistic confidence scores, resulting in increased discrimination accuracy. This approach builds on a confidence scoring approach for Binomial distributions resulting from Hamming distances (commonly used in iris recognition). We derive a Gaussian confidence scoring approach that is three orders of magnitude faster than the Binomial approach while still resulting in higher recognition rates. Gaussian distributions are also more common and thus more widely applicable to different biometric systems. For probe-to-gallery (1-to-N) identification of the face recognition system tested, this approach has been shown to improve the identification rate from 25.66% to 68.05% at 1.00% false alarm rate for a CCTV video matching dataset, and from 63.34% to 73.14% for images from the LFW dataset. A sensitivity analysis demonstrates that modeling errors in genuine and impostor distributions only negatively impacts discrimination when the distribution means are modelled to be closer together than the true underlying distributions. For the reverse case where the distribution means are modeled to be further apart than the true distributions, discrimination accuracy is improved.
Directed Random Subspace Method for Face Recognition

With growing attention to ensemble learning, in recent years various ensemble methods for face recognition have been proposed that show promising results. Among diverse ensemble construction approaches, random subspace method has received considerable attention in face recognition. Although random feature selection in random subspace method improves accuracy in general, it is not free of serious difficulties and drawbacks. In this paper we present a learning scheme to overcome some of the drawbacks of random feature selection in the random subspace method. The proposed learning method derives a feature discrimination map based on a measure of accuracy and uses it in a probabilistic recall mode to construct an ensemble of subspaces. Experiments on different face databases revealed that the proposed method gives superior performance over the well-known benchmarks and state of the art ensemble methods.
Classification of cervical cell nuclei using morphological segmentation and textural feature extraction

This paper presents preliminary results for the classification of Pap Smear cell nuclei, using gray level co-occurrence matrix (GLCM) textural features. We outline a method of nuclear segmentation using fast morphological gray-scale transforms. For each segmented nucleus, features derived from a modified form of the GLCM are extracted over several angle and distance measures. Linear discriminant analysis is performed on these features to reduce the dimensionality of the feature space, and a classifier with hyper-quadric decision surface is implemented to classify a small set of normal and abnormal cell nuclei. Using 2 features, we achieve a misclassification rate of 3.3% on a data set of 61 cells.<<ETX>>
Combining Classifiers in Rotated Face Space

Face recognition is a very complex classification problem due to nuisance variations in different conditions. Normally no single classifier can discriminate patterns well when unpredictable variations and a huge number of classes are involved. Combining multiple classifiers can improve discriminability over the best single classifier. In this paper, we present a way to combine classifiers for face recognition problem based on APCA classifiers. The proposed combinator generates various classifiers by rotating various face spaces and fusing them by applying a weighted distance measure. The combined classifier is tested on the Asian Face Database with 856 images. Experiments show a 30% reduction in classification error rate of our combined classifier and illustrates that combining classifiers from different face spaces may perform better than those based on a single face space.
Kernel analysis over Riemannian manifolds for visual recognition of actions, pedestrians and textures

A convenient way of analysing Riemannian manifolds is to embed them in Euclidean spaces, with the embedding typically obtained by flattening the manifold via tangent spaces. This general approach is not free of drawbacks. For example, only distances between points to the tangent pole are equal to true geodesic distances. This is restrictive and may lead to inaccurate modelling. Instead of using tangent spaces, we propose embedding into the Reproducing Kernel Hilbert Space by introducing a Riemannian pseudo kernel. We furthermore propose to recast a locality preserving projection technique from Euclidean spaces to Riemannian manifolds, in order to demonstrate the benefits of the embedding. Experiments on several visual classification tasks (gesture recognition, person re-identification and texture classification) show that in comparison to tangent-based processing and state-of-the-art methods (such as tensor canonical correlation analysis), the proposed approach obtains considerable improvements in discrimination accuracy.
On intelligent surveillance systems and face recognition for mass transport security

We describe a project to trial and develop enhanced surveillance technologies for public safety. A key technology is robust recognition of faces from low-resolution CCTV footage where there may be as few as 12 pixels between the eyes. Current commercial face recognition systems require 60-90 pixels between the eyes as well as tightly controlled image capture conditions. Our group has thus concentrated on fundamental face recognition issues such as robustness to low resolution and image capture conditions as required for uncontrolled CCTV surveillance. In this paper, we propose a fast multi-class pattern classification approach to enhance PCA and FLD methods for 2D face recognition under changes in pose, illumination, and expression. The method first finds the optimal weights of features pairwise and constructs a feature chain in order to determine the weights for all features. Computational load of the proposed approach is extremely low by design, in order to facilitate usage in automated surveillance. The method is evaluated on PIE, FERET, and Asian Face databases, with the results showing that the method performs remarkably well compared to several benchmark appearance-based methods. Moreover, the method can reliably recognise faces with large pose angles from just one gallery image.
Invited paper: Embedded face and biometric technologies for national and border security

The CCTV surveillance industry is undergoing a sea change due to the adoption of IP technologies. This is allowing the integration of a plethora of new cameras and other sensors into huge integrated networks. Adoption of IP technologies is presenting opportunities for scalable visual analytics that has the potential to add enormous value to entire camera networks. One such technology is scalable robust face search to identify persons of interest in large crowds. Not only are such systems required to work robustly in a wide variety of conditions, they must also be extremely fast and scalable to hundreds, if not thousands, of high definition camera nodes. Developing and testing such technology is challenging and requires a combination of fast algorithms, distributed databases, mobile platform integration, parallel processing using distributed middleware such as ROS, and GPU acceleration using tools such as CUDA and OpenCL. In this paper we cover emerging system trends such as super-megapixel cameras, post incident digital PTZ, integration and fusion of video and non-video sensors, multimodal remote biometrics including face and iris on the move. Finally, recognition results will be presented from a formal face recognition trial in early 2011 in one of Asia's largest International airports.
Robust object tracking using local oriented energy features and its hardware/software implementation

This paper presents the use of local oriented energy features for real-time object tracking on embedded vision systems. Local oriented energy features are extracted using complex Gabor filters. Filtering is carried out across multiple channels with different frequencies and orientations. The effectiveness of the chosen feature set is tested using a mean-shift tracker. Our experiments show that adding local oriented energy features can significantly enhance the performance of the tracker in presence of photometric variations and geometric transformation. The realtime implementation of the system is also described in this paper. To achieve the desired performance, a hardware/software co-design approach is pursued. Multi-channel Gabor filtering, Local Oriented Energy Feature and Feature histogram Computation is implemented on hardware while mean-shift vector calculation is performed on a processor. The system was synthesized onto a Xilinx Virtex-5 XC5VSX50T using Xilinx ML506 development board and the implementation results are presented.
Pattern Recognition, Part 1 [Guest editors' introduction]

This special issue reports the advances in pattern recognition theory and applications, particularly, the basic issues of pattern classification and image analysis and their applications. The selected articles address image feature representation, contextual pattern analysis, compact low-rank sparse representation for abnormal event detection in video, customer churn prediction using ensemble learning, online text-independent writer identification using deep convolutional neural network, image depth ordering reasoning, and brain MR image tumor segmentation, respectively.
K-tangent spaces on Riemannian manifolds for improved pedestrian detection

For covariance-based image descriptors, taking into account the curvature of the corresponding feature space has been shown to improve discrimination performance. This is often done through representing the descriptors as points on Riemannian manifolds, with the discrimination accomplished on a tangent space. However, such treatment is restrictive as distances between arbitrary points on the tangent space do not represent true geodesic distances, and hence do not represent the manifold structure accurately. In this paper we propose a general discriminative model based on the combination of several tangent spaces, in order to preserve more details of the structure. The model can be used as a weak learner in a boosting-based pedestrian detection framework. Experiments on the challenging INRIA and DaimlerChrysler datasets show that the proposed model leads to considerably higher performance than methods based on histograms of oriented gradients as well as previous Riemannian-based techniques.
Combining Generative and Discriminative Learning for Face Recognition

Face recognition is a very complex classification problem and most existing methods are classified into two categories: generative classifiers and discriminative classifiers. Generative classifiers are optimized for description and representation which is not optimal for classification. Discriminative classifiers may achieve less asymptotic errors but are inefficient to train and may overfit to training data. In this paper, we present a hybrid learning algorithm that combines both generative learning and discriminative learning to find a trade-off between these two approaches. Experiments on Asian Face Database show a reduction in classification error rate for our hybrid learning method.
The GIST of aligning faces

We propose a novel supervised initialization scheme for cascaded face alignment by searching nearest neighbors based on global image descriptors. Unlike existing schemes which resort to additional large training data sets for learning features, our method does not require additional training steps; thus making our method low computational. Moreover, we found that it is sufficient to use a simple low-dimensional global image descriptor that is easy to extract. In particular, in this work we use the GIST features as our global image descriptor. The proposed initialization scheme outperforms existing initialization schemes for face alignment and improves on the state-of-the-art methods on two challenging datasets, 300-W and COFW.
Dictionary Learning and Sparse Coding on Grassmann Manifolds: An Extrinsic Solution

Recent advances in computer vision and machine learning suggest that a wide range of problems can be addressed more appropriately by considering non-Euclidean geometry. In this paper we explore sparse dictionary learning over the space of linear subspaces, which form Riemannian structures known as Grassmann manifolds. To this end, we propose to embed Grassmann manifolds into the space of symmetric matrices by an isometric mapping, which enables us to devise a closed-form solution for updating a Grassmann dictionary, atom by atom. Furthermore, to handle non-linearity in data, we propose a kernelised version of the dictionary learning algorithm. Experiments on several classification tasks (face recognition, action recognition, dynamic texture classification) show that the proposed approach achieves considerable improvements in discrimination accuracy, in comparison to state-of-the-art methods such as kernelised Affine Hull Method and graph-embedding Grassmann discriminant analysis.
Detection of skin lesions by fuzzy entropy based texel identification

This paper proposes automated detection of skin lesions by unsupervised feature based clustering based on a new fuzzy entropy function for characterizing texture. The parameterized entropy function is optimized using the Bacterial Foraging algorithm. The clustering of the entropy function of the image is done using the popular Fuzzy C-means algorithm (FCM). The experimental results obtained after the clustering process indicate a very good segregation of texture clusters with satisfactory visual results. The results also provide us with the normalized entropy values needed for texel identification.
Multi-Modal Joint Clustering With Application for Unsupervised Attribute Discovery

Utilizing multiple descriptions/views of an object is often useful in image clustering tasks. Despite many works that have been proposed to effectively cluster multi-view data, there are still unaddressed problems such as the errors introduced by the traditional spectral-based clustering methods due to the two disjoint stages: 1) eigendecomposition and 2) the discretization of new representations. In this paper, we propose a unified clustering framework which jointly learns the two stages together as well as utilizing multiple descriptions of the data. More specifically, two learning methods from this framework are proposed: 1) through a graph construction from different views and 2) through combining multiple graphs. Furthermore, benefiting from the separability and local graph preserving properties of the proposed methods, a novel unsupervised automatic attribute discovery method is proposed. We validate the efficacy of our methods on five data sets, showing that the proposed joint learning clustering methods outperform the recent state-of-the-art methods. We also show that it is possible to derive a novel method to address the unsupervised automatic attribute discovery tasks.
Method for accurate unsupervised cell nucleus segmentation

To achieve the extreme accuracy rates demanded by applications in unsupervised automated cytology, it is frequently necessary to supplement the primary segmentation algorithm with a segmentation quality control system. The more robust the segmentation strategy, the less severe the data pruning need be at the segmentation validation stage. These issues are addressed as we describe our cell nucleus segmentation strategy which is able to achieve 100% accurate segmentation from a data set of 19946 cell nucleus images by automatically discarding the most difficult cell images. The automatic quality checking is applied to enhance-the performance of a robust energy minimisation based segmentation scheme which already achieved a 99.47% accurate segmentation rate.
Adaptive Patch-Based Background Modelling for Improved Foreground Object Segmentation and Tracking

A robust foreground object segmentation technique is proposed, capable of dealing with image sequences containing noise, illumination variations and dynamic backgrounds. The method employs contextual spatial information by analysing each image on an overlapping patch-by-patch basis and obtaining a low-dimensional texture descriptor for each patch. Each descriptor is passed through an adaptive multi-stage classifier, comprised of a likelihood evaluation, an illumination robust measure, and a temporal correlation check. A probabilistic foreground mask generation approach integrates the classification decisions by exploiting the overlapping of patches, ensuring smooth contours of the foreground objects as well as effectively minimising the number of errors. The parameter settings are robust against wide variety of sequences and post-processing of foreground masks is not required. Experiments on the difficult Wallflower and I2R datasets show that the proposed method obtains considerably better results (both qualitatively and quantitatively) than methods based on Gaussian mixture models, feature histograms, and normalised vector distances. Further experiments on the CAVIAR dataset (using several tracking algorithms) indicate that the proposed method leads to considerable improvements in object tracking accuracy.
Modelling and classification of shapes in two-dimensions using vector quantization

This paper is an extension of work by He and Kundu (1991) in which the task of object recognition is performed on silhouettes or outlines. He and Kundu reduce a 2-dimensional image to a 1-dimensional segment sequence and use the autoregressive (AR) model for feature extraction and hidden Markov model (HMM) for classification. We show that due to the AR model's inability to estimate abrupt changes (i.e. where the signal is not bandlimited), poor image modelling is obtained. By direct application of vector quantization (VQ) to the normalized data segments, the image features are retained better than with AR modelling. Furthermore, by replacing the HMM classification with VQ distortion, better recognition results are obtained with reduced training times as compared to the HMM algorithm.<<ETX>>
Classification of Human Epithelial type 2 cell indirect immunofluoresence images via codebook based descriptors

The Anti-Nuclear Antibody (ANA) clinical pathology test is commonly used to identify the existence of various diseases. A hallmark method for identifying the presence of ANAs is the Indirect Immunofluorescence method on Human Epithelial (HEp-2) cells, due to its high sensitivity and the large range of antigens that can be detected. However, the method suffers from numerous shortcomings, such as being subjective as well as time and labour intensive. Computer Aided Diagnostic (CAD) systems have been developed to address these problems, which automatically classify a HEp-2 cell image into one of its known patterns (eg., speckled, homogeneous). Most of the existing CAD systems use handpicked features to represent a HEp-2 cell image, which may only work in limited scenarios. In this paper, we propose a cell classification system comprised of a dual-region codebook-based descriptor, combined with the Nearest Convex Hull Classifier. We evaluate the performance of several variants of the descriptor on two publicly available datasets: ICPR HEp-2 cell classification contest dataset and the new SNPHEp-2 dataset. To our knowledge, this is the first time codebook-based descriptors are applied and studied in this domain. Experiments show that the proposed system has consistent high performance and is more robust than two recent CAD systems.
Improved Person Re-Identification Using Statistical Approximation

Person re-identification on image sets in which each image is taken from a different angle and lighting condition is a very challenging task. This task becomes even more difficult when images are low resolution and carrying image compression artifacts. The accuracy of the existing re- identification techniques are relatively low on the challenging evaluation grounds such as VIPeR and iLIDS image datasets. In these datasets, distortions in shape and colour make the re- identification task difficult and uncertain for both machine and human. In this paper, we propose a new approach to address the uncertainty in low resolution images for person re-identification by using statistical approximation. We first show that the distribution within a patch on person's image does not fit a normal distribution via Kolmogorov- Smirnov test. Then we simplify the Kolmogorov- Smirnov statistic by using only the mean and standard deviation of the distribution. These values are used as descriptors for per region per channel, and concatenated for comparison of image pairs. Experiments show that the proposed approach outperforms the state-of-the-art on person re- identification methods. The small memory foot print and the low computational cost of the proposed technique make it suitable for person re- identification in large scale surveillance applications.
An Automatic Image Based Single Dilution Method for End Point Titre Quantitation of Antinuclear Antibodies Tests Using HEp-2 Cells

Indirect Immunofluorescence (IIF) on Human epithelial (HEp-2) cells test has been the golden standard for identifying the presence of Anti-Nuclear Antibodies (ANA) due to its high sensitivity and the large range of antigens that can be detected. Furthermore, IIF ANA test allows the positive sample strength (sample end point titre) to be reported. Despite its advantages, the IIF ANA test needs to be performed manually, and therefore it is perceived as an expensive and laborious process. This also applies to determining the strength of positive samples (end point titre) which traditionally is done by serially diluting the specimen. In this paper, we present an image-based method which is able to automatically determine the end point titre of positive samples based only on a single screening dilution. This can be done by simulating the manual titration process using a mathematical model of the exposure-density curve. Technically, a new Image Titration Endpoint (ITE) unit based on the model is introduced. Each specimen image is then measured in terms of this unit. Finally, the end point titre for the specimen is determined through a standard curve which specifies the end point titre given an ITE unit. This process is fully automated which would give an advantage over the current digital titration methods. The overall endpoint titre agreement between the proposed approach and the manual serial dilution method in the evaluation of 134 positive samples was 100%. This high agreement demonstrates that the proposed approach is suitable for routine ANA IIF testing in the clinical settings.