nils-herrmann/clean_scirepeval huggingface dataset:
Activity recognition with volume motion templates and histograms of 3D gradients

We propose a new method for activity recognition based on a view independent representation of human motion. Robust 3D volume motion templates (VMTs) are calculated from tracklets. View independence is achieved through a rotation with respect to a canonical orientation. From this volumes, features based on 3D gradients are extracted, projected to a codebook and pooled into a bags-of-words model classified with an SVM classifier. Experiments show that the method outperforms the original HoG3D method.
Encoding the location of spectral coefficients using quadtrees in transform image compression

A transform image coding method is presented. A discrete cosine transform (DCT) is performed on blocks of 16*16 size. A threshold mask is applied by using a fixed threshold determined by the statistical properties of the DCT coefficients. Selected coefficients are coded with a Huffman code. Three methods for coding the threshold mask are compared. zigzag, orthogonal, and quadtree scan. Three Huffman code tables are presented for coding the runlengths of zeros. A high-quality decoded image is shown at 1 bit/pixel.<<ETX>>
Image understanding and scene models: a generic framework integrating domain knowledge and Gestalt theory

Because of the semantic gap between low-level descriptors and semantic concepts, image understanding systems need to use domain knowledge in order to map regions of a segmented image into semantic objects. However, such systems are often strongly dependent on an application since domain knowledge is not separated from procedures. We therefore argue for a generic grouping mechanism, based on the vision procedures of Gestalt theory, able to evaluate the relevance of its groupings with domain knowledge. In order to be used in a flexible way, such domain knowledge is stored independently in ontologies. We emphasize the need for domain knowledge to be thought as scene knowledge on a multilevel of composition, in order to be efficiently used. We also present the whole framework of our system and an application example within a specific domain: image understanding of thessalian graves' images.
Multiscale colour gradient for image segmentation

We present a new gradient model for colour video images. Video sources have the characteristic, either for transmission or for storage, to present a reduced bandwidth of colour components compared to that of brightness. Traditional multispectral gradient operators amplify the noise from the colour components and they are not very sensitive to colour edges. We propose an adapted vector gradient model to solve this problem and introduce the computation of the partial derivatives at different scales according to the resolution of each component. We show that a weighting function is necessary to control the relative influence of the colour and brightness components to obtain the multiscale colour gradient (MCG). The application of the MCG to colour edge detection illustrates the advantages of the proposed method. Finally, the MCG image is used for the segmentation by active contours of crystals in microscopic images of cement clinker (industrial application).
Multi-component cross entropy segmentation for color image retrieval

This paper presents an adaptive color image segmentation method based on cross entropy minimization. This method is a multi-component approach and provides a hierarchical partitioning of the 3D color space using spherical neighbourhoods. The number of dominant colors (classes) issued from this segmentation is automatically estimated. This avoids an a priori estimation of the number of final classes. The segmentation method is then applied for image retrieval purposes. Local and global descriptors are defined in order to characterize the color feature of these classes. The local descriptors provide information about the local activity in the image class per class, and the global ones evaluate the qualitative image content. Their combination increases significantly the performance of the image retrieval system presented in this paper.
A Comprehensive Survey on Three-Dimensional Mesh Watermarking

Three-dimensional (3-D) meshes have been used more and more in industrial, medical and entertainment applications during the last decade. Many researchers, from both the academic and the industrial sectors, have become aware of their intellectual property protection and authentication problems arising with their increasing use. This paper gives a comprehensive survey on 3-D mesh watermarking, which is considered an effective solution to the above two emerging problems. Our survey covers an introduction to the relevant state of the art, an attack-centric investigation, and a list of existing problems and potential solutions. First, the particular difficulties encountered while applying watermarking on 3-D meshes are discussed. Then we give a presentation and an analysis of the existing algorithms by distinguishing them between fragile techniques and robust techniques. Since attacks play an important role in the design of 3-D mesh watermarking algorithms, we also provide an attack-centric viewpoint of this state of the art. Finally, some future working directions are pointed out especially on the ways of devising robust and blind algorithms and on some new probably promising watermarking feature spaces.
The FG 2015 Kinship Verification in the Wild Evaluation

The aim of the Kinship Verification in the Wild Evaluation (held in conjunction with the 2015  Slovenia) was to evaluate different kinship verification algorithms. For this task, two datasets were made available and three possible experimental protocols (unsupervised, image-restricted, and image-unrestricted) were designed. Five institutions submitted their results to the evaluation: (i) Politecnico di Torino, Italy; (ii) LIRIS-University of Lyon, France; (iii) Universidad de Las Palmas de Gran Canaria, Spain; (iv) Nanjing University of Aeronautics and Astronautics, China; and (v) Bar Ilan University, Israel. Most of the participants tackled the image-restricted challenge and experimental results demonstrated better kinship verification performance than the baseline methods provided by the organizers.
Coding of 3D medical images using 3D wavelet decompositions

The use of a 3-D wavelet transform (WT) for the compression of 3-D medical images is investigated. A 3-D separable WT is applied using, for each direction, a pair of conjugate quadrature filters allowing perfect reconstruction. For each level of resolution, eight subimages are obtained, and a specific coding procedure using a 3-D discrete cosine transform is applied. The first results obtained on 3-D magnetic resonance imaging (MRI) images of a human heart are presented.<<ETX>>
Action recognition in videos

Applications such as video surveillance, robotics, source selection, and video indexing often require the recognition of actions based on the motion of different actors in a video. Certain applications may require assigning activities to several predefined classes, while others may rely on the detection of abnormal or infrequent activities. In this summary we provide a survey of dominant models and methods and discuss recent developments in this domain. We briefly describe two recent contributions: joint level feature and sequence learning, as well as space-time graph matching.
Fast directional fractal coding of subbands using decision-directed clustering for block classification

We propose a new image compression scheme based on fractal coding of wavelet transform coefficients using a fast non-iterative algorithm for the codebook generation. The original image is first decomposed into subbands containing information in different spatial directions and different scales, using an orthogonal wavelet filter bank. Subbands are encoded using local iterated function systems (LIFS) with range and domain blocks presenting horizontal or vertical directionalities. Their sizes are estimated according to the correlation lengths and resolution of each subband. The computational complexity is greatly decreased by using subband decomposition. In addition a fast non-iterative algorithm is implemented for block classification. This algorithm progressively creates the codebook during only one scanning of the training set. We prove the efficiency of the proposed approach both in terms of PSNR/bit rate and computation time.
Adaptive contour sampling and coding using skeleton and curvature

This paper presents a new binary shape sampling and coding method. Within the framework of the MPEG-4 standard, the binary shapes in video object plans have to be coded with a near to lossless approach in order to obtain a perfect spatial and temporal localization. For this purpose, a new sampling method based on the object skeleton is developed. The "r-sampling" is defined function of the local feature size (LFS) which is the distance between a shape point and the closest point on the medial axis of this shape. A weighting convex function /spl phi/ depending on the local curvature is also introduced to increase the local adaptiveness of the sampling ("the r&c-sampling"). This new approach is tested on contours presenting high level details. The first results obtained are satisfactory in terms of both the reduction of samples and the reconstruction error.
Multiresolution adaptive image segmentation based on global and local statistics

In a previous work we have presented an adaptive segmentation algorithm of dirty images in a Bayesian framework. The segmentation problem is formulated as a maximum a posteriori (MAP) estimation problem. The optimization is achieved using Besag's iterated conditional modes algorithm. A multiresolution implementation of the segmentation algorithm, using the discrete wavelet transform, has been used. This work focuses on the adaptive character of the algorithm and discusses how global and local statistics can be taken into account in the segmentation process. We propose an improvement on the adaptivity by introducing an enhancement to control the adaptive properties of the segmentation process. A weighting function taking into account both local and global statistics is introduced in the minimization. The new formulation of the segmentation problem allows us to control the effective contribution of each statistic. Results of segmentation carried out on synthetic images are presented.
Texture based adaptive clustering algorithm for 3D breast lesion segmentation

A specific algorithm is presented for the automatic extraction of breast tumors. This algorithm involves 3D adaptive K-means clustering of the gray-scale and texture features images. The segmentation problem is formulated as a Maximum A Posterior (MAP) estimation problem. The MAP estimation is achieved using Besag's Iterated Conditional Modes algorithm for the minimization of an energy function. This function has three components. The first one constrains the region to be close to the data, the second imposes spatial continuity and the third takes into consideration the texture of the various regions. This segmentation technique is demonstrated on in vivo breast data. The method revealed very efficient. The results are compared with the manual segmentation of lesions by an expert.
Multi-view pose estimation with mixtures-of-parts and adaptive viewpoint selection

We propose a new method for human pose estimation which leverages information from multiple views to impose a strong prior on articulated pose. The novelty of the method concerns the types of coherence modelled. Consistency is maximised over the different views through different terms modelling classical geometric information (coherence of the resulting poses) as well as appearance information which is modelled as latent variables in the global energy function. Moreover, adequacy of each view is assessed and their contributions are adjusted accordingly. Experiments on the HumanEva and UMPM datasets show that the proposed method significantly decreases the estimation error compared to single-view results.
Hierarchical Watermarking of Semiregular Meshes Based on Wavelet Transform

This paper presents a hierarchical watermarking framework for semiregular meshes. Three blind watermarks are inserted in a semiregular mesh with different purposes: a geometrically robust watermark for copyright protection, a high-capacity watermark for carrying a large amount of auxiliary information, and a fragile watermark for content authentication. The proposed framework is based on wavelet transform of the semiregular mesh. More precisely, the three watermarks are inserted in different appropriate resolution levels obtained by wavelet decomposition of the mesh: the robust watermark is inserted by modifying the norms of the wavelet coefficient vectors associated with the lowest resolution level; the fragile watermark is embedded in the high resolution level obtained just after one wavelet decomposition by modifying the orientations and norms of the wavelet coefficient vectors; the high-capacity watermark is inserted in one or several intermediate levels by considering groups of wavelet coefficient vector norms as watermarking primitives. Experimental results demonstrate the effectiveness of the proposed framework: the robust watermark is able to resist all common geometric attacks even with a relatively strong amplitude; the fragile watermark is robust to content-preserving operations, while being sensitive to other attacks of which it can also provide the precise location; the payload of the high-capacity watermark increases rapidly along with the number of watermarking primitives.
An objective performance evaluation tool for color based image retrieval systems

This study addresses the problem of the objective performance evaluation of image retrieval systems. Considering the color feature, a tool for synthetic image databases generation is proposed. This allows to control the number and location of the dominant colors in the Lab space and provides also their spatial coherency. It is then possible to sort exactly the images for the color feature. We also propose a new similarity measure to compare images described by the color feature. Based on the assumption of a Gaussian distribution for each dominant color, each image is modeled by the sum of these Gaussian distributions. The similarity measure performs a Kullman's distance between two modeled distributions. The objective performance evaluation based on the synthetic database is done for comparing our image retrieval system (IMALBUM) which uses the new similarity measure and MPEG7 approach. Experiments on MPEG7 database are also presented as a subjective evaluation and discussed.
Triplet CNN and pedestrian attribute recognition for improved person re-identification

In this paper, we propose a pedestrian attribute recognition approach and a CNN-based person re-identification framework enhanced by pedestrian attributes. The knowledge of person attributes can help video surveillance tasks like person re-identification as well as person search, semantic video indexing and retrieval to overcome viewpoint changes with their robustness to the inherent visual appearance variations. Compared to previous approaches, our attribute recognition method using Local Maximal Occurrence (LOMO) features and a Multi-Label Multi-Layer Perceptron (MLMLP) classifier proves to be more robust to different view points and is computationally more efficient. The experiments on three public benchmarks show that the proposed method improves the state-of-the art on attribute recognition. Furthermore, we integrate our attribute recognition algorithm into a triplet CNN similarity learning framework for person re-identification fusing both learned CNN features and attributes. This fusion leads to an overall improvement, and we achieve state-of-the-art results on person re-identification.
Application of the wavelet packet transform to flaw detection in ultrasound B-scans

An important issue in ultrasonic nondestructive evaluation is the detection of flaw echoes in the presence of coherent background noise associated with the microstructure of materials. Many signal processing techniques have proven to be useful for this purpose. However, when considering B-scan images, it appears that fully two-dimensional robust flaw detection techniques remain desirable. In this paper, we describe a novel automatic flaw detection method based on the wavelet packet transform, which is particularly well adapted to B-scan image analysis. Some experimental detection results in B-scan images of austenitic stainless steel samples comprising artificial flaws are also presented.
3D mirror symmetry detection using Hough transform

This paper describes a simple and efficient means for statistical detection of global and local mirror symmetry. The symmetry planes are selected from all the planes obtained after matching and pairing features extracted on 3D meshes. The original part of the proposed method is the way to extract significant symmetry planes with a 3D Hough transform built from the planes parameters. This method generates faster results compared to existing statistical methods for symmetry detection.
Object categorization using boosting within Hierarchical Bayesian model

In this paper we address the problem of generative object categorization in computer vision. We propose a Bayesian model using Hierarchical Dirichlet Processes mixing AdaBoost learning. Although previous methods trained HDP model for one or two latent themes, our proposed approach uses small-patch-independent-words of appearance-based descriptor and shape information to train a set of intermediate components which are the mixture of visualwords. We then employ AdaBoost weaker learner to find the most related components for classification to handle the variance in intraclass and inter-class information. We show that it performs well for Caltech datasets and with the potential to connect the visual concepts with semantic concepts.
On a new approach of insertion of confidential digital signature into images

In past few years, with the development of telecommunication systems and new multimedia services, the protection of digitally stored information has become an urgent problem. The introduction of a code or watermark into image data (signing the image) is a satisfying technical solution to digital still image protection. This invisible watermark should allow determination of the owner of the image without increasing the quantity of the initial digital information. It should also maintain the visual quality and be difficult to detect and remove. In this study, we propose a new approach to signing digital images based on the slight modification of a limited number of AC coefficients of the discrete cosine transform (DCT).
Design and reconstruction of fractal surfaces

A method for the design and reconstruction of rough surfaces is introduced. A fractal model based on projected IFS attractors allows the definition of free form fractal shapes controlled with a set of points. Ibis flexible model has good fitting properties for recovering surfaces. The approximation is formulated as a non-linear fitting problem and resolved using a modified LEVENBERG-MARQUARDT minimisation method. The main applications are shape design, shape reconstruction and geometric data compression.
3D shape coding with superquadrics

We present a geometric coding method for 3D real and virtual objects transmission and visualization. We describe object surfaces with a set of superellipsoids which are structured with a constructive solid geometry (CSG) tree. An unstructured cloud of 3D points lying on the original object surface is considered as initial data. The tree is obtained using a split and merge algorithm. At each splitting step, the set of points is partitioned into two superellipsoids. The parameters of each ellipsoid is computed with a nonlinear regression technique (Levenberg-Marquardt). This decomposition is stopped when the estimation error reaches a given threshold (the accepted distortion). The objective of the merging algorithm is to reduce the number of superellipsoids used to describe the complex 3D object without increasing the global distortion. This algorithm allows one to merge both connected and non-connected elements of the superellipsoids set obtained from the decomposition step. The whole scheme is applied to synthetic objects.
Pairwise Identity Verification via Linear Concentrative Metric Learning

This paper presents a study of metric learning systems on pairwise identity verification, including pairwise face verification and pairwise speaker verification, respectively. These problems are challenging because the individuals in training and testing are mutually exclusive, and also due to the probable setting of limited training data. For such pairwise verification problems, we present a general framework of metric learning systems and employ the stochastic gradient descent algorithm as the optimization solution. We have studied both similarity metric learning and distance metric learning systems, of either a linear or shallow nonlinear model under both restricted and unrestricted training settings. Extensive experiments demonstrate that with limited training pairs, learning a linear system on similar pairs only is preferable due to its simplicity and superiority, i.e., it generally achieves competitive performance on both the labeled faces in the wild face dataset and the NIST speaker dataset. It is also found that a pretrained deep nonlinear model helps to improve the face verification results significantly.
JPEG dequantization array for regularized decompression

An optimized dequantization array is proposed for the Joint Photographers Expert Group (JPEG) scheme. The reconstruction error is minimized subjected to a regularity constraint. Both the peak-to-peak signal-to-noise ratio (PPSNR) and the visual quality are improved. In addition, the method reduces discontinuities in postprocessing such as edge detection. This approach is in full compliance with the JPEG standard.
On the coding of active quadtree mesh

We consider the coding of a quadrangular mesh structure generated by a multiresolution deformable quadtree algorithm. Such a structure has been found efficient for region based video coding, but few works deal with the coding of the mesh structure. We propose to use the rules of the mesh generation to significantly reduce the number of bits required for the coding of the nodes position defining the mesh structure. This reduction induces an improvement of either the image quality or either the global bit rate.
Progressive transmission of 3D object based on balls and cones union from medial axis transformation

3D discretized objects become more and more present in many applications for computer graphics. The need to store and transmit these data grows permanently. This paper introduces a new scheme of 3D object progressive transmission. It is based on an union of cone sections and balls selected from the medial axis and optimized for a better scalability in terms of bit rates and quality. This representation of a 3D object is a lossless approach and is very efficient for compression and progressive transmission, as shown in the results presented in this paper.
Pairwise Features for Human Action Recognition

Existing action recognition approaches mainly rely on the discriminative power of individual local descriptors extracted from spatio-temporal interest points (STIP), while the geometric relationships among the local features are ignored. This paper presents new features, called pairwise features (PWF), which encode both the appearance and the spatio-temporal relations of the local features for action recognition. First STIPs are extracted, then PWFs are constructed by grouping pairs of STIPs which are both close in space and close in time. We propose a combination of two codebooks for video representation. Experiments on two standard human action datasets: the KTH dataset and the Weizmann dataset show that the proposed approach outperforms most existing methods.