nils-herrmann/clean_scirepeval huggingface dataset:
Depth Pooling Based Large-Scale 3-D Action Recognition With Convolutional Neural Networks

This paper proposes three simple, compact yet effective representations of depth sequences, referred to respectively as dynamic depth images (DDI), dynamic depth normal images (DDNI), and dynamic depth motion normal images (DDMNI), for both isolated and continuous action recognition. These dynamic images are constructed from a segmented sequence of depth maps using hierarchical bidirectional rank pooling to effectively capture the spatial-temporal information. Specifically, DDI exploits the dynamics of postures over time, and DDNI and DDMNI exploit the 3-D structural information captured by depth maps. Upon the proposed representations, a convolutional neural network (ConvNet)-based method is developed for action recognition. The image-based representations enable us to fine-tune the existing ConvNet models trained on image data without training a large number of parameters from scratch. The proposed method achieved the state-of-art results on three large datasets, namely, the large-scale continuous gesture recognition dataset (means the Jaccard index 0.4109), the large-scale isolated gesture recognition dataset (<inline-formula> <tex-math notation="LaTeX">$\text{59.21}\%$</tex-math></inline-formula>), and the NTU RGB+D dataset (<inline-formula> <tex-math notation="LaTeX">$\text{87.08}\%$</tex-math></inline-formula> cross-subject and <inline-formula> <tex-math notation="LaTeX">$\text{84.22}\%$</tex-math></inline-formula> cross-view) even though only the depth modality was used.
Creating Simplified 3D Models with High Quality Textures

This paper presents an extension to the KinectFusion algorithm which allows creating simplified 3D models with high quality RGB textures. This is achieved through (i) creating model textures using images from an HD RGB camera that is calibrated with Kinect depth camera, (ii) using a modified scheme to update model textures in an asymmetrical colour volume that contains a higher number of voxels than that of the geometry volume, (iii) simplifying dense polygon mesh model using quadric-based mesh decimation algorithm, and (iv) creating and mapping 2D textures to every polygon in the output 3D model. The proposed method is implemented in real-time by means of GPU parallel processing. Visualization via ray casting of both geometry and colour volumes provides users with a real-time feedback of the currently scanned 3D model. Experimental results show that the proposed method is capable of keeping the model texture quality even for a heavily decimated model and that, when reconstructing small objects, photorealistic RGB textures can still be reconstructed.
Semi-supervised maximum a posteriori probability segmentation of brain tissues from dual-echo magnetic resonance scans using incomplete training data

This study presents a stochastic framework in which incomplete training data are used to boost the accuracy of segmentation and to optimise segmentation when images under consideration are corrupted by inhomogeneities. The authors propose a semi-supervised maximum a posteriori probability (ssMAP) segmentation method that is able to utilise any amount of training data that are usually insufficient for supervised segmentation. The ssMAP unifies supervised and unsupervised segmentation and takes the two as its special cases. To deal with inhomogeneities, the authors propose to incorporate a bias field into the ssMAP and present an algorithm (referred to as ssMAPe) for simultaneous maximum a posteriori probability (MAP) estimation of the inhomogeneity field and segmentation of brain tissues. Experiments on both simulated and real magnetic resonance (MR) images have shown that ssMAP with only a very small quantity of training data improves the segmentation accuracy substantially (up to 30%) compared to both fully supervised and unsupervised methods. The proposed ssMAPe estimates the inhomogeneity field effectively and further improves the segmentation if the MR images are corrupted by inhomogeneity.
Face recognition from single sample based on human face perception

Although research show that human recognition performance for unfamiliar faces is relatively poor, when the sample is always available for analysis and becomes ”familiar”, people are able to recognize a previous unknown face from single sample. In this paper, a method is proposed to deal with the one sample per person face recognition problem based on the process how unfamiliar faces become familiar to people. Particularly, quantized local features which learnt from generic face dataset are used in the proposed method to mimic the prototype effect of human face recognition. Furthermore, a landmark-based scheme is introduced to quantify the distinctiveness of each facial component for the sample face, then the difference between the sample and the average face is emphasized by weighting face regions according to the gained distinctiveness. The experiments on ORL and FERET face databases demonstrate the efficiency of the proposed method.
Similarity measures for compressed image databases

For image database applications it is desirable that functions such as searching, browsing and partial recall be done without the need to totally decompress the image. This has the advantage of alleviating possible burden and degradation that the network may suffer. Edge images derived from wavelet-compressed images are considered as an index that can be queried by example. Zernike moment invariants are used as descriptors for the index edge image and the query sketch image. The descriptions are compared for the purpose of database searching. The query images were allowed to undergo translation, rotation, scaling and some deformation. Simulation results gave a 90% recognition rate.
Coding gain and spatial localisation properties of discrete wavelet transform filter banks for image coding

We consider coding gain and spatial localisation properties of DWT filters for still image compression. A high coding gain, relative to a highly correlated image source model, ensures that slowly varying image areas are coded efficiently. A low spatial width ensures that anomalies such as edges can also be coded efficiently. By considering an image model we design filters that simultaneously exhibit a high coding gain and low spatial width. Several existing filters are also examined. Several filter sets are then compared for image compression using a DWT and a JPEG type quantisation method. The filters with the best spatial width and coding gain properties outperform the other filters at low bit rates, especially with regard to ringing distortion.
Kernel PCA of HOG features for posture detection

Motivated by the non-linear manifold learning ability of the Kernel Principal Component Analysis (KPCA), we propose in this paper a method for detecting human postures from single images by employing KPCA to learn the manifold span of a set of HOG features that can effectively represent the postures. The main contribution of this paper is to apply the KPCA as a non-linear learning and open-set classification tool, which implicitly learns a smooth manifold from noisy data that scatter over the feature space. For a new instance of HOG feature, its distance to the manifold that is measured by its reconstruction error when mapping into the kernel space serves as a criterion for detection. And by combining with a newly developed KPCA approximation technique, the detector can achieve almost real-time speed with neglectable loss of performance. Experimental results have shown that the proposed method can achieve promising detection rate with relatively small size of positive training dataset.
Signal analysis using a multiresolution form of the singular value decomposition

This paper proposes a multiresolution form of the singular value decomposition (SVD) and shows how it may be used for signal analysis and approximation. It is well-known that the SVD has optimal decorrelation and subrank approximation properties. The multiresolution form of SVD proposed here retains those properties, and moreover, has linear computational complexity. By using the multiresolution SVD, the following important characteristics of a signal may he measured, at each of several levels of resolution: isotropy, sphericity of principal components, self-similarity under scaling, and resolution of mean-squared error into meaningful components. Theoretical calculations are provided for simple statistical models to show what might be expected. Results are provided with real images to show the usefulness of the SVD decomposition.
Joint Geometrical and Statistical Alignment for Visual Domain Adaptation

This paper presents a novel unsupervised domain adaptation method for cross-domain visual recognition. We propose a unified framework that reduces the shift between domains both statistically and geometrically, referred to as Joint Geometrical and Statistical Alignment (JGSA). Specifically, we learn two coupled projections that project the source domain and target domain data into low-dimensional subspaces where the geometrical shift and distribution shift are reduced simultaneously. The objective function can be solved efficiently in a closed form. Extensive experiments have verified that the proposed method significantly outperforms several state-of-the-art domain adaptation methods on a synthetic dataset and three different real world cross-domain visual recognition tasks.
Age estimation based on extended non-negative matrix factorization

Previous studies suggested that local appearance-based methods are more efficient than geometric-based and holistic methods for age estimation. This is mainly due to the fact that age information are usually encoded by the local features such as wrinkles and skin texture on the forehead or at the eye corners. However, the variations of theses features caused by other factors such as identity, expression, pose and lighting may be larger than that caused by aging. Thus, one of the key challenges of age estimation lies in constructing a feature space that could successfully recovers age information while ignoring other sources of variations. In this paper, non-negative matrix factorization (NMF) is extended to learn a localized non-overlapping subspace representation for age estimation. To emphasize the appearance variation in aging, one individual extended NMF subspace is learned for each age or age group. The age or age group of a given face image is then estimated based on its reconstruction error after being projected into the learned age subspaces. Furthermore, a coarse to fine scheme is employed for exact age estimation, so that the age is estimated within the pre-classified age groups. Cross-database tests are conducted using FG-NET and MORPH databases to evaluate the proposed method. Experimental results have demonstrated the efficacy of the method.
An MPEG tolerant authentication system for video data

We propose a secure video authentication algorithm that is tolerant to visual degradation due to MPEG lossy compression to a designed level. The authentication process generates a tag that is sent with video data and the level of protection can be adjusted so that longer tags are used for higher security, and that the protection is distributed such that higher security is provided for regions of interest in the image. The computation required for authentication and verification can be largely performed as part of MPEG compression and so generation and verification of the tag can be integrated into the compression system. Calculation of the tag can be parallelized and so made fast
Modelling of color cross-talk in CMOS image sensors

This paper presents a way to model the cross-talk effect in CMOS image sensors. Two algorithms are derived from the model; both of them work on the Bayer raw data and have low computational complexity. Experiments on Macbeth color chart and real images have shown the effectiveness of the modeling to eliminate the cross-talk effect and produce better quality images with traditional color interpolation and correction algorithms designed for CCD image sensors.
On the Combination of Local Texture and Global Structure for Food Classification

This paper proposes a food image classification method using local textural patterns and their global structure to describe the food image. In this paper, a visual codebook of local textural patterns is created by employing Scale Invariant Feature Transformation (SIFT) interest point detector with the Local Binary Pattern (LBP) feature. In addition to describing the food image using local texture, the global structure of the food object is represented as the spatial distribution of the local textural structures and encoded using shape context. We evaluated the proposed method on the Pittsburgh Fast-Food Image (PFI) dataset. Experimental results showed that the proposed method could obtain better performance than the baseline experiment on the PFI dataset.
Coding gain and spatial localisation properties of discrete wavelet transform filter banks for image coding

We consider coding gain and spatial localisation properties of DWT filters for still image compression. A high coding gain, relative to a highly correlated image source model, ensures that slowly varying image areas are coded efficiently. A low spatial width ensures that anomalies such as edges can also be coded efficiently. By considering an image model we design filters that simultaneously exhibit a high coding gain and low spatial width. Several existing filters are also examined. Several filter sets are then compared for image compression using a DWT and a JPEG type quantisation method. The filters with the best spatial width and coding gain properties outperform the other filters at low bit rates, especially with regard to ringing distortion.<<ETX>>
Learning Discriminative Bayesian Networks from High-Dimensional Continuous Neuroimaging Data

Due to its causal semantics, Bayesian networks (BN) have been widely employed to discover the underlying data relationship in exploratory studies, such as brain research. Despite its success in modeling the probability distribution of variables, BN is naturally a generative model, which is not necessarily discriminative. This may cause the ignorance of subtle but critical network changes that are of investigation values across populations. In this paper, we propose to improve the discriminative power of BN models for continuous variables from two different perspectives. This brings two general discriminative learning frameworks for Gaussian Bayesian networks (GBN). In the first framework, we employ Fisher kernel to bridge the generative models of GBN and the discriminative classifiers of SVMs, and convert the GBN parameter learning to Fisher kernel learning via minimizing a generalization error bound of SVMs. In the second framework, we employ the max-margin criterion and build it directly upon GBN models to explicitly optimize the classification performance of the GBNs. The advantages and disadvantages of the two frameworks are discussed and experimentally compared. Both of them demonstrate strong power in learning discriminative parameters of GBNs for neuroimaging based brain network analysis, as well as maintaining reasonable representation capacity. The contributions of this paper also include a new Directed Acyclic Graph (DAG) constraint with theoretical guarantee to ensure the graph validity of GBN.
A novel template matching method for human detection

This paper proposes a novel weighted template matching method. It employs a generalized distance transform (GDT) and an orientation map (OM). The GDT allows us to weight the distance transform more on the strong edge points and the OM provides supplementary local orientation information for matching. Based on the matching method, a two-stage human detection method consisting of template matching and Bayesian verification is developed. Experimental results have shown that the proposed method can effectively reduce the false positive and false negative detection rates and perform superiorly in comparison to the conventional Chamfer matching method.
Content-based retrieval from compressed-image databases

There is an enormous amount of multimedia data including images, video, speech, audio and text, distributed among the various computer nodes on the Internet. The extent to which a user will be able to derive useful information from these data depends largely on the ease with which required data can be retrieved from the databases. The volume of the data also poses a storage constraint on the databases; hence these data will need to exist in the compressed form on the databases. We concentrate on image data and propose a new paradigm in which a compressed-image database can be searched for its contents. In this paradigm, the need for a separate index is obviated by utilising image compression schemes that can support some form of object search in the compressed be domain. The central idea is to store the image in layers of different resolutions and to be able to synthesise an edge image from a subset of the layers. This edge image then constitutes a model of the image that can be used as a searchable index. The implication of this approach is that the index is inherent in the compressed image file and does not occupy any additional storage space as would be the case in a conventional index. The preliminary results obtained from the system simulated in our experiments indicate the feasibility of the proposed paradigm.
Large-scale Isolated Gesture Recognition using Convolutional Neural Networks

This paper proposes three simple, compact yet effective representations of depth sequences, referred to respectively as Dynamic Depth Images (DDI), Dynamic Depth Normal Images (DDNI) and Dynamic Depth Motion Normal Images (DDMNI). These dynamic images are constructed from a sequence of depth maps using bidirectional rank pooling to effectively capture the spatial-temporal information. Such image-based representations enable us to fine-tune the existing ConvNets models trained on image data for classification of depth sequences, without introducing large parameters to learn. Upon the proposed representations, a convolutional Neural networks (ConvNets) based method is developed for gesture recognition and evaluated on the Large-scale Isolated Gesture Recognition at the ChaLearn Looking at People (LAP) challenge 2016. The method achieved 55.57% classification accuracy and ranked 2nd place in this challenge but was very close to the best performance even though we only used depth data.
Index-compressed vector quantisation based on index mapping

The authors introduce a novel coding technique which significantly improves the performance of the traditional vector quantisation (VQ) schemes at low bit rates. High interblock correlation in natural images results in a high probability that neighbouring image blocks are mapped to small subsets of the VQ codebook, which contains highly correlated codevectors. If, instead of the whole VQ codebook, a small subset is considered for the purpose of encoding neighbouring blocks, it is possible to improve the performance of traditional VQ schemes significantly. The performance improvement obtained with the new method is about 3 dB on average when compared with traditional VQ schemes at low bit rates. The method provides better performance than the JPEG coding standard at low bit rates, and gives comparable results with much less complexity than address VQ.
Scene Flow to Action Map: A New Representation for RGB-D Based Action Recognition with Convolutional Neural Networks

Scene flow describes the motion of 3D objects in real world and potentially could be the basis of a good feature for 3D action recognition. However, its use for action recognition, especially in the context of convolutional neural networks (ConvNets), has not been previously studied. In this paper, we propose the extraction and use of scene flow for action recognition from RGB-D data. Previous works have considered the depth and RGB modalities as separate channels and extract features for later fusion. We take a different approach and consider the modalities as one entity, thus allowing feature extraction for action recognition at the beginning. Two key questions about the use of scene flow for action recognition are addressed: how to organize the scene flow vectors and how to represent the long term dynamics of videos based on scene flow. In order to calculate the scene flow correctly on the available datasets, we propose an effective self-calibration method to align the RGB and depth data spatially without knowledge of the camera parameters. Based on the scene flow vectors, we propose a new representation, namely, Scene Flow to Action Map (SFAM), that describes several long term spatio-temporal dynamics for action recognition. We adopt a channel transform kernel to transform the scene flow vectors to an optimal color space analogous to RGB. This transformation takes better advantage of the trained ConvNets models over ImageNet. Experimental results indicate that this new representation can surpass the performance of state-of-the-art methods on two large public datasets.
Weakly structured information aggregation for upper-body posture assessment using ConvNets

Posture assessment aims to determine the risk associated with poor posture and thus avoid injury in subjects. Upper-body posture assessment from images offers an attractive alternative to manual methods by directly extracting relevant features for classification. A deep convolutional neural network is proposed to extract structured features from different body parts and learn shared features that are used to determine the appropriate assessment. The structured features are learned with triplet-based rank constraints based on head and torso separately. The shared feature and assessment function are learned with soft-max constraints based on posture risk measurements. Experimental evaluation on a self-collected upper-body posture dataset has verified the efficacy of the proposed method and network architecture.
Head pose estimation based on extended non-negative matrix factorization

One popular solution to head pose estimation is to formulate it as a pattern classification problem, and treat the holistic facial appearance as the input to classifiers. However, since the face appearance contains all kinds of information, the variation caused by other factors such as identity, expression and lighting may be larger than that caused by different head poses. Thus, the key challenge of these appearance based methods lies in constructing a feature subspace that could successfully recovers head pose while ignoring other sources of image variation. In this paper, following the intuition of combining parts to form a whole face, non-negative matrix factorization (NMF) is extended to learn a localized non-overlapping subspace representation for head pose estimation. To emphasize the appearance variation in head poses, one individual extended NMF subspace is learned for each pose. The head pose of a given face image is then estimated based on its reconstruction error after being projected into the learned pose subspaces. Experiments based on benchmark face database demonstrate the efficiency of the proposed method.