nils-herrmann/clean_scirepeval huggingface dataset:
Automatic facial expression recognition for affective computing based on bag of distances

In the recent years, the video-based approach is a popular choice for modeling and classifying facial expressions. However, this kind of methods require to segment different facial expressions prior to recognition, which might be a challenging task given real world videos. Thus, in this paper, we propose a novel facial expression recognition method based on extracting discriminative features from a still image. Our method first combines holistic and local distance-based features so that facial expressions could be characterized in more detail. The combined distance-based features are subsequently quantized to form mid-level features using the bag of words approach. The synergistic effect of these steps leads to much improved class separability and thus we can use a typical method, e.g., Support Vector Machine (SVM), to perform classification. We have performed the experiment on the Extended Cohn-Kanade (CK+) dataset. The experiment results show that the proposed scheme is efficient and accurate in facial expression recognition.
Improving the Evaluation Accuracies of Histopathologic Grade and Ki-67 Immunohistochemistry Expression of Breast Carcinoma Using Computer Image Processing (II)

In clinical pathology screening, scoring based on Immunohistochemistry staining ER, PR, Her-2 and Ki-67 are important indicators for evaluations of recurrence risk of breast carcinoma in addition to histological pathology of grading such as scoring based on tubule formation, Pleomorphism, and mitotic count. In this research, we extend the previous research results developed using a series of image processing operations and classification techniques so that an automatic evaluation of IHC4 and primary grading of breast carcinoma can be developed successfully. The propose system not only can improve the problems caused by staining variations at different labs or environments, but also evaluates indicators efficiently discussed previously. Therefore, the proposed system can provide physicians important references in diagnosis, treatments, and prognosis analyses.
Effective fusing the factored matrices in dual tensors for action recognition

In statistics, Canonical Correlation Analysis (CCA) is a kind of method to effectively analyze the correlation between two data. In recent years, some methods reported in literatures treated the action video sequences as the tensors, and calculated the similarity between two video sequences using CCA. Each of these tensors was unfolded into several matrices or vectors in the previous works. And estimated the similarity between two tensors via accumulating the canonical correlations on all of the pairs of the unfolded matrices or vectors. In this paper, we treat each of the unfolded matrices in a tensor as an individual, instead of accumulating the canonical correlations in a whole tensor, such that we can effectively use the characteristic of each unfolded matrix. We also propose an information fusion method to combine the similarities of each of the unfolded matrices between two tensors. Furthermore, we add the Histogram of Oriented Gradients (HOG) features to complement the tensor generated by the pure video sequence. Our method is validated on the UCF sports database, and the experimental result shows that the proposed method can compete with the state-of-the-art methods.
Accurate Joint-Alignment of Indocyanine Green and Fluorescein Angiograph Sequences for Treatment of Subretinal Lesions.

In ophthalmology, aligning images in indocyanine green and fluorescein angiograph sequences is important for the treatment of subretinal lesions. This paper introduces an algorithm that is tailored to align jointly in a common reference space all the images in an angiogram sequence containing both modalities. To overcome the issues of low image contrast and low signal-to-noise ratio for late-phase images, the structural similarity between two images is enhanced using Gabor wavelet transform. Image pairs are pairwise registered and the transformations are simultaneously and globally adjusted for a mutually consistent joint alignment. The joint registration process is incremental and the success depends on the correctness of matches from the pairwise registration. To safeguard the joint process, our system performs the consistency test to exclude incorrect pairwise results automatically to ensure correct matches as more images are jointly aligned. Our dataset consists of 60 sequences of polypoidal choroidal vasculopathy collected by the EVEREST Study Group. On average, each sequence contains 20 images. Our algorithm successfully pairwise registered 95.04% of all image pairs, and joint registered 98.7% of all images, with an average alignment error of 1.58 pixels.
Summation invariant and its applications to shape recognition

A novel summation invariant of curves under transformation group action is proposed. This new invariant is less sensitive to noise than the differential invariant and does not require an analytical expression for the curve as the integral invariant does. We exploit this summation invariant to define a shape descriptor called a semi-local summation invariant and use it as a new feature for shape recognition. Tested on a database of noisy shapes of fish, it was observed that the summation invariant feature exhibited superior discriminating power compared to that of wavelet-based invariant features.
3D Face Recognition Under Expression Variations using Similarity Metrics Fusion

We present a novel 3D face recognition method that incorporates summation invariant features extracted from multiple sub-regions of a facial range images, and optimal fusion of similarity scores between corresponding sub-regions. The key innovation of this paper is the development of the fusion-based face recognition algorithm that delivers significant performance enhancement while requiring very little computation. Experiments on the FRGC (Face Recognition Grand Challenge) version 2 dataset show that our algorithm improves the recognition performance significantly in the presence of facial expressions.
Planar-projective summation invariant features for camera networks

Recently a novel family of geometrically invariant features, called summation invariant, has been developed and applied to object recognition. The range of this family of features is expanded here beyond the Euclidean and affine transformation groups to planar projective transformations. Whereas other methods require small changes in view, or collinear points, this method removes those limitations and allows recognition of general planar objects over wide ranges of viewpoint. The derivation of these new features requires the innovation of deriving the invariants in the homogeneous coordinate space, yet yields results formulated in terms of Cartesian coordinates. Simulations demonstrate the effectiveness of this new approach to object recognition under projective transformations like those encountered in camera networks.
An enhanced biometric score fusion scheme based on the AdaBoost algorithm

Information fusion in biometric systems, either multimodal or intramodal fusion, usually provides an improvement in recognition performance. This paper presents a novel score-level fusion scheme called boosted score fusion. The proposed framework is a two-stage design where an existing fusion algorithm is adopted at the first stage. At the second stage, the weights obtained by the AdaBoost algorithm are utilized to boost the performance of the previously fused results. The experimental results demonstrate that the performance of several score-level fusion methods can be improved by using the presented method.
Face Recognition using 3D Summation Invariant Features

In this paper, we developed a family of 2D and 3D invariant features with applications to 3D human faces recognition. The main contributions of this paper are: (a) systematically deriving a family of novel features, called summation invariant that are invariant to Euclidean transformation in both 2D and 3D; (b) developing an effective method to apply summation invariant to the 3D face recognition problem. Tested with the 3D data from the face recognition grand challenge v1.0 dataset, the proposed new features exhibit achieves a performance that rivals the best 3D face recognition algorithms reported so far
An EM Approach for Time-Variant Poisson-Gaussian Model Parameter Estimation

The problem of estimating the parameters of a Poisson-Gaussian model from experimental data has recently raised much interest in various applications, for instance in confocal fluorescence microscopy. In this context, a field of independent random variables is observed, which is varying both in time and space. Each variable is a sum of two components, one following a Poisson and the other a Gaussian distribution. In this paper, a general formulation is considered where the associated Poisson process is nonstationary in space and also exhibits an exponential decay in time, whereas the Gaussian component corresponds to a stationary white noise with arbitrary mean. To solve the considered parametric estimation problem, we follow an iterative Expectation-Maximization (EM) approach. The parameter update equations involve deriving finite approximation of infinite sums. Expressions for the maximum error incurred in the process are also given. Since the problem is non-convex, we pay attention to the EM initialization, using a moment-based method where recent optimization tools come into play. We carry out a performance analysis by computing the Cramer-Rao bounds on the estimated variables. The practical performance of the proposed estimation procedure is illustrated on both synthetic data and real fluorescence macroscopy image sequences. The algorithm is shown to provide reliable estimates of the mean/variance of the Gaussian noise and of the scale parameter of the Poisson component, as well as of its exponential decay rate. In particular, the mean estimate of the Poisson component can be interpreted as a good-quality denoised version of the data.
Automated heart rate estimation in fish embryo

Transparent organisms such as fish embryos are being increasingly used for environmental toxicology studies. These studies require estimating a number of physiological parameters. These estimations may be diverse in nature and can be a challenge to automate. Among these, an example is the development of reliable and repeatable automated assays for the determination of heart rates. To achieve this, most existing method rely on cyclical luminance variations, since as the heart fills and empties, it become respectively brighter and darker. However, sometimes direct measurement of the heart rate may be difficult, depending on the age of the embryo, its actual transparency, and its aspect under the microscope. It may be easier to seek an indirect measurement. In this article, we estimate the heart function parameters, such as heart frequency, either from measuring the heart motion or from blood flow in arteries. This measurement is more complex from the image analysis point of view, but it is more precise, more physically meaningful and easier to use in practice and to automate than measuring illumination changes. It may also be more informative. We illustrate on medaka embryos.
Global image registration and video color correction in presence of illumination variations

In high-speed video imaging acquisition, artificial lighting can cause non-uniform luminosity and chroma variation between frames, which is linked to the interaction between the frequency of the lighting power source and of the video acquisition. This problem is commonly dubbed “flicker”. Deflickering techniques are also useful for resolving similar problems encountered in other applications for instance in archived film sequences, image-video compression and time-lapse videos. In this context, this paper introduces a rigid registration method using point descriptors that is robust to illumination variations, and followed by a method for stabilizing brightness variations in image sequences. This method is based on a Least Square adjustment approach. Our results demonstrate the efficacy of our method at correcting global brightness fluctuations in image sequences while maintaining image quality and without introducing artifacts.
A non-local chan-vese model for sparse, tubular object segmentation

To deal with the issue of tubular object segmentation, we propose a new model involving a non-local fitting term, in the Chan-Vese framework. This model aims at detecting objects whose intensities are not necessarily piecewise constant, or even composed of multiple piecewise constant regions. Our problem formulation exploits object sparsity in the image domain and a local ordering relationship between foreground and background. A continuous optimization scheme can then be efficiently considered in this context. This approach is validated on both synthetic and real retinal images. The nonlocal data fitting term is shown to be superior to the classical piecewise-constant model, robust to noise and to low contrast.
Curvilinear morpho-Hessian filter

The motivation of this paper is the filtering of thin elongated objects, such as veins, fibres etc. In particular, we focus our attention on detecting thin segments and linking them when disconnection is due to noise. A hybrid Hessian-based and morphological linear filtering method is proposed within the framework of scale space theory. For each pixel the second- derivative response kernel is collected and formed into a Hessian matrix which undergoes eigen analysis. From there, the best eigenvalue response along with the corresponding eigenvector is chosen on different scales. Using the resulting principal directions of linear segment pixels, morphological filters are used to track and connect the linears.
Direction-adaptive grey-level morphology. application to 3D vascular brain imaging

Segmentation and analysis of blood vessels is an important issue in medical imaging. In 3D cerebral angiographic data, the vascular signal is however hard to accurately detect and can, in particular, be disconnected. In this article, we present a procedure utilising both linear, Hessian-based and morphological methods for blood vessel edge enhancement and reconnection. More specifically, multi-scale second-order derivative analysis is performed to detect candidate vessels as well as their orientation. This information is then fed to a spatially-variant morphological filter for reconnection and reconstruction. The result is a fast and effective vessel-reconnecting method.
Path-based morphological openings

A well-known problem in image analysis is the extraction of thin and elongated features such as edges, fractures, fibres, vessels, etc. In many cases, such features are not linear but curved rather than straight. A well-known morphological tool for the extraction of linear features is the opening by straight line segments of a certain length. In this paper we extend this class of morphological filters with openings with narrow structuring elements which are not necessarily straight line segments, but which can be connected paths defined by an adjacency relation.
Poisson-Gaussian noise parameter estimation in fluorescence microscopy imaging

In this paper, we present a new fully automatic approach for noise parameter estimation in the context of fluorescence imaging systems. In particular, we address the problem of Poisson-Gaussian noise modeling in the nonstationary case. In microscopy practice, the nonstationarity is due to the photobleaching effect. The proposed method consists of an adequate moment based initialization followed by Expectation-Maximization iterations. This approach is shown to provide reliable estimates of the mean and the variance of the Gaussian noise and of the scale parameter of Poisson noise, as well as of the photobleaching rates. The algorithm performance is demonstrated on both synthetic and real macro confocal laser scanning microscope image sequences.
Efficient Poisson denoising for photography

In general, image sensor noise is dominated by Poisson statistics, even at high illumination level, yet most standard denoising procedures often assume a simpler additive Gaussian noise, which is in fact a poor approximation. Fortunately, Poisson noise can under some circumstances be simplified via variance stabilizing methods, such as the Anscombe transform, which is well known to statisticians, medical imaging specialists and astronomers. However, in order to use such a procedure effectively, the actual photon count needs to be known and not simply an illumination intensity, which is the main reason why such procedures are not frequently used in the image processing community. In this article, we propose to use Poisson distribution characteristics to estimate the photon count from relative illumination data, under simple hypotheses. This allows us to use variance-stabilizing methods on standard digital photographs. Thanks to this, the noise becomes close to additive Gaussian and standard filtering methods become significantly more effective. As an example we exhibit the level of improvement that can be achieved using the bilateral filter.
A hybrid algorithm for automatic heart segmentation in ct angiography

In this work, we present a hybrid algorithm to automatically delineate the heart volume in 3D cardiac computed tomography (CT) datasets for the visualization of coronary arteries. Our work eliminates the tedious and time consuming step of manually removing obscuring structures around the heart (ribs, sternum, liver...). It quickly provides a clear and well defined view of the coronaries. So far, works related to heart segmentation have mainly focused on heart cavities delineation, which is not suited for coronaries visualization. In contrast, our algorithm extracts the heart cavities, the myocardium and coronaries as a single object. The proposed approach is based on the fitting of a geometric model of the heart to a set of automatically extracted 3D points lying on the heart shell. A novel two-stage fitting scheme is used to improve the robustness to the outliers. The fitting result is further refined using a Random Walker (RW) segmentation approach. Qualitative analysis of results obtained on a 70 exam database shows the efficiency and the accuracy of our approach.
Power Watershed: A Unifying Graph-Based Optimization Framework

In this work, we extend a common framework for graph-based image segmentation that includes the graph cuts, random walker, and shortest path optimization algorithms. Viewing an image as a weighted graph, these algorithms can be expressed by means of a common energy function with differing choices of a parameter q acting as an exponent on the differences between neighboring nodes. Introducing a new parameter p that fixes a power for the edge weights allows us to also include the optimal spanning forest algorithm for watershed in this same framework. We then propose a new family of segmentation algorithms that fixes p to produce an optimal spanning forest but varies the power q beyond the usual watershed algorithm, which we term the power watershed. In particular, when q=2, the power watershed leads to a multilabel, scale and contrast invariant, unique global optimum obtained in practice in quasi-linear time. Placing the watershed algorithm in this energy minimization framework also opens new possibilities for using unary terms in traditional watershed segmentation and using watershed to optimize more general models of use in applications beyond image segmentation.
A Memory Gradient algorithm for ℓ2 — ℓ0 regularization with applications to image restoration

In this paper, we consider a class of differentiable criteria for sparse image recovery problems. The regularization is applied to a linear transform of the target image. As special cases, it includes edge preserving measures or frame analysis potentials. As shown by our asymptotic results, the considered ℓ2 — ℓ0 penalties may be employed to approximate solutions to ℓ0 penalized optimization problems. One of the advantages of the approach is that it allows us to derive an efficient Majorize-Minimize Memory Gradient algorithm. The fast convergence properties of the proposed optimization algorithm are illustrated through image restoration examples.
Image quantization under spatial smoothness constraints

Quantization, defined as the act of attributing a finite number of grey-levels to an image, is an essential task in image acquisition and coding. It is also intricately linked to various image analysis tasks, such as denoising and segmentation. In this paper, we investigate quantization combined with regularity constraints, a little-studied area which is of interest, in particular, when quantizing in the presence of noise or other acquisition artifacts. We present an optimization approach to the problem involving a novel two-step, iterative, flexible, joint quantizing-regularization method featuring both convex and combinatorial optimization techniques. We show that when using a small number of grey-levels, our approach can yield better quality images in terms of SNR, with lower entropy, than conventional optimal quantization methods.
Robust path opening versus path opening for the detection of hedgerows in rural landscapes

The automatic detection of hedgerows in very high resolution remote sensing images is addressed in this paper. In particular, the use of advanced morphological filters, such as path operators, is proposed. Conventional path openings have been already proposed in the literature to discriminate between forest objects and hedge objects in very high resolution optical images. They have shown greater flexibility with respect to geodesic openings. However, path operators are sensitive to noise and in practical situations they are likely to produce missed detections. In particular, path operators are unable to extract long hedgerows as a single object. In order to tackle this limitation, robust path openings are investigated in this work. In the experimental results, robust path opening shows superior performances for the detection of hedgerows.
Multicriteria 3D PET image segmentation

The analysis of images acquired with Positron Emission Tomography (PET) is challenging. In particular, there is no consensus on the best criterion to quantify the metabolic activity for lesion detection and segmentation purposes. Based on this consideration, we propose a versatile knowledge-based segmentation methodology for 3D PET imaging. In contrast to previous methods, an arbitrary number of quantitative criteria can be involved and the experts behaviour learned and reproduced in order to guide the segmentation process. The classification part of the scheme relies on example-based learning strategies, allowing interactive example definition and more generally incremental refinement. The image processing part relies on hierarchical segmentation, allowing vectorial attribute handling. Preliminary results on synthetic and real images confirm the relevance of this methodology, both as a segmentation approach and as an experimental framework for criteria evaluation.
Automated quantification of the epidermal aging process using in-vivo confocal microscopy

Reflectance confocal microscopy (RCM) is a powerful tool to visualize the skin layers at cellular resolution. The epidermal layer appears as a honeycomb pattern, whose regularity decreases with age. Our aim is to provide a method to automatically quantify the regularity of the honeycomb pattern. The proposed strategy relies on a cell-level supervised classification as regular or irregular using spatial information given by a prior segmentation. The aggregated scores defined by the classification results show significant correlation with chronological aging and photo-aging. Thus, our method enables practitioners to more objectively assess the quality of the epidermal layers on large cohort of subjects.