nils-herrmann/clean_scirepeval huggingface dataset:
Boolean operations with implicit and parametric representation of primitives using R-functions

We present a new and efficient algorithm to accurately polygonize an implicit surface generated by multiple Boolean operations with globally deformed primitives. Our algorithm is special in the sense that it can be applied to objects with both an implicit and a parametric representation, such as superquadrics, supershapes, and Dupin cyclides. The input is a constructive solid geometry tree (CSG tree) that contains the Boolean operations, the parameters of the primitives, and the global deformations. At each node of the CSG tree, the implicit formulations of the subtrees are used to quickly determine the parts to be transmitted to the parent node, while the primitives' parametric definition are used to refine an intermediary mesh around the intersection curves. The output is both an implicit equation and a mesh representing its solution. For the resulting object, an implicit equation with guaranteed differential properties is obtained by simple combinations of the primitives' implicit equations using R-functions. Depending on the chosen R-function, this equation is continuous and can be differentiable everywhere. The primitives' parametric representations are used to directly polygonize the resulting surface by generating vertices that belong exactly to the zero-set of the resulting implicit equation. The proposed approach has many potential applications, ranging from mechanical engineering to shape recognition and data compression. Examples of complex objects are presented and commented on to show the potential of our approach for shape modeling.
Genetic Algorithms for Gielis Surface Recovery from 3D Data Sets

In this paper, we apply genetic algorithms to reconstruct Gielis surfaces from 3D data sets. The Levenberg-Marquardt method has been used as a standard for superquadrics recovery and has recently been extended to Gielis surfaces. Unfortunately, the non homogeneity of the Gielis surface parameters requires additional heuristic to determine discrete parameters such as the number of symmetries. Genetic algorithms overcome this issue and provide a more general framework for Gielis surface reconstruction.
A Geometric Algorithm for Ray/Bézier Surfaces Intersection Using Quasi-Interpolating Control Net

In this paper, we present a new geometric algorithm to compute the intersection between a ray and a rectangular Bezier patch. The novelty of our approach resides in the use of bounds of the difference between a Bezier patch and its quasi-interpolating control net. The quasi-interpolating polygon of a Bezier surface of arbitrary degree approximates the limit surface within a precision that is function of the second order difference of the control points, which allows for very simple projections and 2D intersection tests to determine sub-patches containing a potential intersection. Our algorithm is simple, because it only determines a 2D parametric interval containing the solution, and efficient because the quasi-interpolating polygon is directly computed, which avoids both minimum or maximum evaluations of the basis functions or complex envelops construction.
Static and Dynamic Objects Analysis as a 3D Vector Field

In the context of scene modelling, understanding, and landmark-based robot navigation, the knowledge of static scene parts and moving objects with their motion behaviours plays a vital role. We present a complete framework to detect and extract the moving objects to reconstruct a high quality static map. For a moving 3D camera setup, we propose a novel 3D Flow Field Analysis approach which accurately detects the moving objects using only 3D point cloud information. Further, we introduce a Sparse Flow Clustering approach to effectively and robustly group the motion flow vectors. Experiments show that the proposed Flow Field Analysis algorithm and Sparse Flow Clustering approach are highly effective for motion detection and segmentation, and yield high quality reconstructed static maps as well as rigidly moving objects of real-world scenarios.
SUSAN 3D operator, principal saliency degrees and directions extraction and a brief study on the robustness to noise

In order to complete the work on the 3D SUSAN operator, an extraction of principal saliency degrees and direction is proposed. This will lead the proposed operator to a complete classification of vertices in five different classes: cavity points, valley points, flat points, prong points and salient points. To assess the operator, we propose two different studies. The robustness to the resolution and the regularity of meshes is presented for three different operators. To complete the evaluation, the robustness to noise is analysed, for the Stokely and the SUSAN operator. Our conclusion presents the benefits, drawbacks of the technique and future works.
Kolmogorov Superposition Theorem and Its Application to Multivariate Function Decompositions and Image Representation

In this paper, we present the problem of multivariate function decompositions into sums and compositions of monovariate functions. We recall that such a decomposition exists in the Kolmogorov's superposition theorem, and we present two of the most recent constructive algorithms of these monovariate functions. We first present the algorithm proposed by Sprecher, then the algorithm proposed by Igelnik, and we present several results of decomposition for gray level images. Our goal is to adapt and apply the superposition theorem to image processing, i.e. to decompose an image into simpler functions using Kolmogorov superpositions. We synthetise our observations, before presenting several research perspectives.
Enhancement and assessment of WKS variance parameter for intelligent 3D shape recognition and matching based on MPSO

This paper presents an improved wave kernel signature (WKS) using the modified particle swarm optimization (MPSO)-based intelligent recognition and matching on 3D shapes. We select the first feature vector from WKS, which represents the 3D shape over the first energy scale. The choice of this vector is to reinforce robustness against non-rigid 3D shapes. Furthermore, an optimized WKS-based method for extracting key-points from objects is introduced. Due to its discriminative power, the associated optimized WKS values with each point remain extremely stable, which allows for efficient salient features extraction. To assert our method regarding its robustness against topological deformations, experiments show that the method is discriminative and robust to data perturbed by various noises. The algorithm is evaluated by its capability to differentiate between the salient feature points and to match efficiently between similar geometric structures for the same shape in different poses.
A more distinctive representation for 3D shape descriptors using principal component analysis

Many researchers have used the Heat Kernel Signature (or HKS) for characterizing points on non-rigid three-dimensional shapes and Classical Multidimensional Scaling (Classical MDS) method in object classification which we quote, in particular, the example of Jian Sun et al. (2009) . However, in this paper, the main focuses on classification that we propose a concise and provably factorial method by invoking Principal Component Analysis (PCA) as a classifier to improve the scheme of 3D shape classification. To avoid losing or disordering information after extracting features from the mesh, PCA is used instead of the Classical MDS to discriminate-as much as possible-feature points for each 3D shape in several poses. To demonstrate the practical relevance of this scheme, we present, illustrate and compare several assessments of the two proposed methods for non-rigid three-dimensional shapes classification based on heat diffusion. Across a collection of shapes, our results analysis show that the proposed contribution outperforms the classification method without PCA.
Static-Map and Dynamic Object Reconstruction in Outdoor Scenes Using 3-D Motion Segmentation

This letter aims to build the static-map of a dynamic scene using a mobile robot equipped with 3-D sensors. The sought static-map consists of only the static scene parts, which has a vital role in scene understanding and landmark based navigation. Building static-map requires the categorization of moving and static objects. In this work, we propose a Sparse subspace clustering-based motion segmentation method that categories the static scene parts and the multiple moving objects using their 3D motion trajectories. Our motion segmentation method uses the raw trajectory data, allowing the objects to move in direct 3-D space, without any projection model assumption or whatsoever. We also propose a complete pipeline for static-map building, which estimates the interframe motion parameters by exploiting the minimal 3-point random sample consensus algorithm on the feature correspondences only from the static scene parts. The proposed method has been especially designed and tested for large scene in real outdoor environments. On one hand, our 3-D motion segmentation approach outperforms its 2-D-based counterparts, for extensive experiments on KITTI dataset. On the other hand, separately reconstructed static-maps and moving objects for various dynamic scenes are very satisfactory.
Depth from dynamic (de)focused projection

This paper presents a novel 3D reconstruction approach using dynamic (de)focused light. The method combines both depth from focus (DFF) and depth from defocus (DFD) techniques. To overcome drawbacks of surface reflectivity found in traditional methods, different optimized illumination patterns are projected on the object in order to enforce strong dominant texture and provide the projection covering the object surface as much as possible. The image acquisition system is specifically constructed to keep the whole object sharp in all captured images. Therefore, only projected patterns experience the different defocused deformation according to the object depths. The light pattern is projected onto the object at certain focused ranges similar to DFF approach, while the blur levels used to generate depth map are calculated basing on Point Spread Function (PSF) strategy as in DFD method. The final depth is then assigned to specific pixel coordinates by assumption of pre-defined overlapping pixel weights. With this approach, the final reconstruction is supposed to be prominent to the one obtained from DFD because at least one focus or near-focus image within depth of field exists in the computation. Moreover, it is also less computational extensive comparing to DFF that requires numerous input images. Experimental results on real images demonstrate effective performance of our method in which it provides reliable depth estimation and compromised time consumption.
G1-Blend between a Differentiable Superquadric of Revolution and a Plane or a Sphere Using Dupin Cyclides

In this article, we present a method to perform G1-continuous blends between a differentiable superquadric of revolution and a plane or a sphere using Dupin cyclides. These blends are patches delimited by four lines of curvature. They allow to avoid parameterization problems that may occur when parametric surfaces are used. Rational quadratic Bezier curves are used to approximate the principal circles of the Dupin cyclide blends and thus a complex 3D problem is now reduced to a simpler 2D problem. We present the necessary conditions to be satisfied to create the blending patches and illustrate our approach by a number of superellipsoid/plane and superellipsoid/sphere blending examples.
Face alignment via an ensemble of random ferns

This paper proposes a simple but efficient shape regression method for face alignment using an ensemble of random ferns. First, a classification method is used to obtain several mean shapes for initialization. Second, an ensemble of local random ferns is learned based on the correlation between the projected regression targets and local pixel-difference matrix for each landmark. Third, the ensemble of random ferns is used to generate local binary features. Finally, the global projection matrix is learned based on concatenated binary features using ridge regression. The results demonstrate that the proposed method is efficient and accurate when compared with the state-of-the-art face alignment methods and achieve the best performance on LFPW and Helen datasets.
Model-based estimation of 3D human motion with occlusion based on active multi-viewpoint selection

We present a new method for the 3D model-based tracking of human body parts. To mitigate the difficulties arising due to occlusion among body parts, we employ multiple calibrated cameras in a mutually orthogonal configuration. In addition, we develop criteria for a time varying active selection of a set of cameras to track the motion of a particular human part. In particular, at every frame, each camera tracks a number of parts depending on the visibility of these parts and the observability of their predicted motion from the specific camera. To relate points on the occluding contours of the parts to points on their models we apply concepts from projective geometry. Then, within the physics-based framework we compute the generalized forces applied from the parts' occluding contours to model points of the body parts. These forces update the translational and rotational degrees of freedom of the model, such as to minimize the discrepancy between the sensory data and the estimated model state. We present initial tracking results from a series of experiments involving the recovery of complex 3D motions in the presence of significant occlusion.
3D face recognition for partial data using Semi-Coupled Dictionary Learning

3D face recognition for partial data is a very challenging task. The task is even more challenging when the gallery sample originates from one side of the face while the probe sample originates from the other. We present a new method for computing the similarity of partial 3D data for the purpose of face recognition. This method improves upon an existing Semi-Coupled Dictionary Learning method by computing a jointly-optimized solution that incorporates the reconstruction cost, the discrimination cost and the semi-coupling cost. Our experiments demonstrate that this method can improve the recognition performance of existing state-of-the-art wavelet signatures used for 3D face recognition.
Which parts of the face give out your identity?

We present a Markov Random Field model for the analysis of lattices (e.g., images or 3D meshes) in terms of the discriminative information of their vertices. The proposed method provides a measure field that estimates the probability of each vertex to be “discriminative” or “non-discriminative”. As an application of the proposed framework, we present a method for the selection of compact and robust features for 3D face recognition. The resulting signature consists of 360 coefficients, based on which we are able to build a classifier yielding better recognition rates than currently reported in the literature. The main contribution of this work lies in the development of a novel framework for feature selection in scenarios in which the most discriminative information is known to be concentrated along piece-wise smooth regions of a lattice.
Data mining in situ gene expression patterns at cellular resolution

In the post-genomic era, large-scale efforts have begun to characterize the role of gene products. Several of these efforts aim to systematically discover the activity of all /spl sim/20,000 genes throughout functionally complex tissue specimens such as embryo and the mature brain. By applying a subdivision-based deformable model of the brain, we rapidly organize spatial gene expression data into a common coordinate system. Doing this enables powerful queries, comparisons, and associations of the data.
Understanding diagrams in technical documents

The development of a knowledge base of biological research papers that supports the Scientist's Assistant, an intelligent system that will provide a scientist with interactive access to the research results, methods, and reasoning in a collection of scientific papers, is discussed. The document understanding system uses graphics constraint grammars for describing and analyzing diagrams, spatial indexing in diagram analysis and understanding, and extension of natural-language processing techniques to complex scientific text. The graphics constraint grammars of the diagram-understanding system are described in detail. Example diagrams and grammars are presented.<<ETX>>
Adaptive SVM+: Learning with Privileged Information for Domain Adaptation

Incorporating additional knowledge in the learning process can be beneficial for several computer vision and machine learning tasks. Whether privileged information originates from a source domain that is adapted to a target domain, or as additional features available at training time only, using such privileged (i.e., auxiliary) information is of high importance as it improves the recognition performance and generalization. However, both primary and privileged information are rarely derived from the same distribution, which poses an additional challenge to the recognition task. To address these challenges, we present a novel learning paradigm that leverages privileged information in a domain adaptation setup to perform visual recognition tasks. The proposed framework, named Adaptive SVM+, combines the advantages of both the learning using privileged information (LUPI) paradigm and the domain adaptation framework, which are naturally embedded in the objective function of a regular SVM. We demonstrate the effectiveness of our approach on the publicly available Animals with Attributes and INTERACT datasets and report state-of-the-art results in both of them.
Exploiting privileged information for facial expression recognition

Most of the facial expression recognition methods consider that both training and testing data are equally distributed. As facial image sequences may contain information for heterogeneous sources, facial data may be asymmetrically distributed between training and testing, as it may be difficult to maintain the same quality and quantity of information. In this work, we present a novel classification method based on the learning using privileged information (LUPI) paradigm to address the problem of facial expression recognition. We introduce a probabilistic classification approach based on conditional random fields (CRFs) to indirectly propagate knowledge from privileged to regular feature space. Each feature space owns specific parameter settings, which are combined together through a Gaussian prior, to train the proposed t-CRF+ model and allow the different tasks to share parameters and improve classification performance. The proposed method is validated on two challenging and publicly available benchmarks on facial expression recognition and improved the state-of-the-art methods in the LUPI framework.
Automatic segmentation of time-lapse microscopy images depicting a live Dharma embryo

Biological inferences about the toxicity of chemicals reached during experiments on the zebrafish Dharma embryo can be greatly affected by the analysis of the time-lapse microscopy images depicting the embryo. Among the stages of image analysis, automatic and accurate segmentation of the Dharma embryo is the most crucial and challenging. In this paper, an accurate and automatic segmentation approach for the segmentation of the Dharma embryo data obtained by fluorescent time-lapse microscopy is proposed. Experiments performed in four stacks of 3D images over time have shown promising results.
Facial landmark detection in uncontrolled conditions

Facial landmark detection is a fundamental step for many tasks in computer vision such as expression recognition and face alignment. In this paper, we focus on the detection of landmarks under realistic scenarios that include pose, illumination and expression challenges as well as blur and low-resolution input. In our approach, an n-point shape of point-landmarks is represented as a union of simpler polygonal sub-shapes. The core idea of our method is to find the sequence of deformation parameters simultaneously for all sub-shapes that transform each point-landmark into its target landmark location. To accomplish this task, we introduce an agglomerate of fern regressors. To optimize the convergence speed and accuracy we take advantage of search localization using component-landmark detectors, multi-scale analysis and learning of point cloud dynamics. Results from extensive experiments on facial images from several challenging publicly available databases demonstrate that our method (ACFeR) can reliably detect landmarks with accuracy comparable to commercial software and other state-of-the-art methods.
Benchmarking asymmetric 3D-2D face recognition systems

Asymmetric 3D-2D face recognition (FR) aims to recognize individuals from 2D face images using textured 3D face models in the gallery (or vice versa). This new FR scenario has the potential to be readily deployable in field applications while still keeping the advantages of 3D FR solutions of being more robust to pose and lighting variations. In this paper, we propose a new experimental protocol based on the UHDB11 dataset for benchmarking 3D-2D FR algorithms. This new experimental protocol allows for the study of the performance of a 3D-2D FR solution under pose and/or lighting variations. Furthermore, we also benchmarked two state of the art 3D2D FR algorithms. One is based on the Annotated Deformable Model (using manually labeled landmarks in this paper) using manually labeled landmarks whereas the other makes use of Oriented Gradient Maps along with an automatic pose estimation through random forest.
Adaptive Frames-Based Denoising of Confocal Microscopy Data

In this paper, we present a novel frames-based denoising algorithm. Using a general result on lifting frames, we construct a non-separable 3D frame capable of robust edge detection. This frame detects edge information by ensemble thresholding of the filtered data. The denoising uses a hysteresis thresholding step and an affine thresholding function, which are filter-adaptive and take full advantage of the threshold bounds. The threshold bounds are statistically determined from the given data for each directional filter. We compare our denoising method with other methods based on separable 3D wavelets and 3D median filtering, and report very encouraging results on applications to both synthetic and real confocal microscopy data
Hierarchical multi-label framework for robust face recognition

In this paper, we propose a patch based face recognition framework. First, a face image is iteratively divided into multi-level patches and assigned hierarchical labels. Second, local classifiers are built to learn the local prediction of each patch. Third, the hierarchical relationships defined between local patches are used to obtain the global prediction of each patch. We develop three ways to learn the global prediction: majority voting, ℓ1-regularized weighting, and decision rule. Last, the global predictions of different levels are combined as the final prediction. Experimental results on different face recognition tasks demonstrate the effectiveness of our method.