nils-herrmann/clean_scirepeval huggingface dataset:
Discriminative Brain Effective Connectivity Analysis for Alzheimer's Disease: A Kernel Learning Approach upon Sparse Gaussian Bayesian Network

Analyzing brain networks from neuroimages is becoming a promising approach in identifying novel connectivity-based biomarkers for the Alzheimer's disease (AD). In this regard, brain ``effective connectivity" analysis, which studies the causal relationship among brain regions, is highly challenging and of many research opportunities. Most of the existing works in this field use generative methods. Despite their success in data representation and other important merits, generative methods are not necessarily discriminative, which may cause the ignorance of subtle but critical disease-induced changes. In this paper, we propose a learning-based approach that integrates the benefits of generative and discriminative methods to recover effective connectivity. In particular, we employ Fisher kernel to bridge the generative models of sparse Bayesian networks (SBN) and the discriminative classifiers of SVMs, and convert the SBN parameter learning to Fisher kernel learning via minimizing a generalization error bound of SVMs. Our method is able to simultaneously boost the discriminative power of both the generative SBN models and the SBN-induced SVM classifiers via Fisher kernel. The proposed method is tested on analyzing brain effective connectivity for AD from ADNI data, and demonstrates significant improvements over the state-of-the-art work.
Learning a pose lexicon for semantic action recognition

This paper presents a novel method for learning a pose lexicon comprising semantic poses defined by textual instructions and their associated visual poses defined by visual features. The proposed method simultaneously takes two input streams, semantic poses and visual pose candidates, and statistically learns a mapping between them to construct the lexicon. With the learned lexicon, action recognition can be cast as the problem of finding the maximum translation probability of a sequence of semantic poses given a stream of visual pose candidates. Experiments evaluating pre-trained and zero-shot action recognition conducted on MSRC-12 gesture and WorkoutSu-10 exercise datasets were used to verify the efficacy of the proposed method.
2D to pseudo-3D conversion of "head and shoulder" images using feature based parametric disparity maps

This paper presents a method of converting a 2D still photo containing the head & shoulders of a human (e.g. a passport photo) to pseudo-3D, so that the depth can be perceived via stereopsis. This technology has the potential to be included in self-serve photo booths and, also as an added accessory (i.e. software package) for digital still cameras and scanners. The basis of the algorithm is to exploit the ability of the human visual system in combining monoscopic and stereoscopic cues for depth perception. Common facial features are extracted from the 2D photograph, in order to create a parametric depth map that conforms to the available monoscopic depth cues. The original 2D photograph and the created depth map are used to generate left and right views for stereoscopic viewing. The algorithm is implemented in software, and promising results are obtained.
Detection and Separation of Smoke From Single Image Frames

This paper proposes novel methods for detecting and separating smoke from a single image frame. Specifically, an image formation model is derived based on the atmospheric scattering models. The separation of a frame into quasi-smoke and quasi-background components is formulated as convex optimization that solves a sparse representation problem using dual dictionaries for the smoke and background components, respectively. A novel feature is constructed as a concatenation of the respective sparse coefficients for detection. In addition, a method based on the concept of image matting is developed to separate the true smoke and background components from the smoke detection results. Extensive experiments on detection were conducted and the results showed that the proposed feature significantly outperforms existing features for smoke detection. In particular, the proposed method is able to differentiate smoke from other challenging objects (e.g. fog/haze, cloud, and so on) with similar visual appearance in a gray-scale frame. Experiments on smoke separation also demonstrated that the proposed separation method can effectively estimate/separate the true smoke and background components.
Discriminative Sparse Inverse Covariance Matrix: Application in Brain Functional Network Classification

Recent studies show that mental disorders change the functional organization of the brain, which could be investigated via various imaging techniques. Analyzing such changes is becoming critical as it could provide new biomarkers for diagnosing and monitoring the progression of the diseases. Functional connectivity analysis studies the covary activity of neuronal populations in different brain regions. The sparse inverse covariance estimation (SICE), also known as graphical LASSO, is one of the most important tools for functional connectivity analysis, which estimates the interregional partial correlations of the brain. Although being increasingly used for predicting mental disorders, SICE is basically a generative method that may not necessarily perform well on classifying neuroimaging data. In this paper, we propose a learning framework to effectively improve the discriminative power of SICEs by taking advantage of the samples in the opposite class. We formulate our objective as convex optimization problems for both one-class and two-class classifications. By analyzing these optimization problems, we not only solve them efficiently in their dual form, but also gain insights into this new learning framework. The proposed framework is applied to analyzing the brain metabolic covariant networks built upon FDG-PET images for the prediction of the Alzheimer's disease, and shows significant improvement of classification performance for both one-class and two-class scenarios. Moreover, as SICE is a general method for learning undirected Gaussian graphical models, this paper has broader meanings beyond the scope of brain research.
On the step response of the DCT

We show that the discrete cosine transform (DCT) is the best orthogonal transform, in terms of energy packing efficiency, for coding input steps of uniformly distributed random phase. Over sufficiently small block sizes, edges in an image can be modeled as such step inputs. This characteristic of the DCT, coupled with its high energy packing efficiency for highly correlated data, helps explain the impressive performance of the DCT for image compression.
Action Recognition From Depth Maps Using Deep Convolutional Neural Networks

This paper proposes a new method, i.e., weighted hierarchical depth motion maps (WHDMM) + three-channel deep convolutional neural networks (3ConvNets), for human action recognition from depth maps on small training datasets. Three strategies are developed to leverage the capability of ConvNets in mining discriminative features for recognition. First, different viewpoints are mimicked by rotating the 3-D points of the captured depth maps. This not only synthesizes more data, but also makes the trained ConvNets view-tolerant. Second, WHDMMs at several temporal scales are constructed to encode the spatiotemporal motion patterns of actions into 2-D spatial structures. The 2-D spatial structures are further enhanced for recognition by converting the WHDMMs into pseudocolor images. Finally, the three ConvNets are initialized with the models obtained from ImageNet and fine-tuned independently on the color-coded WHDMMs constructed in three orthogonal planes. The proposed algorithm was evaluated on the MSRAction3D, MSRAction3DExt, UTKinect-Action, and MSRDailyActivity3D datasets using cross-subject protocols. In addition, the method was evaluated on the large dataset constructed from the above datasets. The proposed method achieved 2-9% better results on most of the individual datasets. Furthermore, the proposed method maintained its performance on the large dataset, whereas the performance of existing methods decreased with the increased number of actions.
Human detection using local shape and Non-Redundant binary patterns

Motivated by the advantages of using shape matching technique in detecting objects in various postures and viewpoints and the discriminative power of local patterns in object recognition, this paper proposes a human detection method combining both shape and appearance cues. In particular, local shapes of the body parts are detected using template matching. Based on body parts' shapes, local appearance features are extracted. We introduce a novel local binary pattern (LBP) descriptor, called Non-Redundant LBP (NRLBP), to encode local appearance of human. The proposed method was evaluated and compared with other state-of-the-art human detection methods on two commonly used datasets: MIT and INRIA pedestrian test sets. We also performed extensive experiments on selecting appropriate parameters as well as verifying the improvement of the proposed method through all stages of the framework.
Perceived Similarity and Visual Descriptions in Content-Based Image Retrieval

The use of low-level feature descriptors is pervasive in content-based image retrieval tasks and the answer to the question of how well these features describe users' inten- tion is inconclusive. In this paper we devise experiments to gauge the degree of alignment between the description of target images by humans and that implicitly provided by low-level image feature descriptors. Data was collected on how humans perceive similarity in images. Using images judged by humans to be similar, as ground truth, the per- formance of some MPEG-7 visual feature descriptors were evaluated. It is found that various descriptors play different roles in different queries and their appropriate combination can improve the performance of retrieval tasks. This forms a basis for the development of adaptive weight assignment to features depending on the query and retrieval task.
Compact feature vector for natural texture classification

Gabor filter masks are used to extract succinct feature vectors from natural and synthetic textures. Textures from the Brodatz collection and real images, captured through a CCD camera, are used in the reported experiment. The results obtained represent a high accuracy of classification for those textures with pronounced orientation. A classification accuracy of up to 90% is obtained using a 50-feature vector. A lower classification accuracy is obtained with a 20-feature vector (about 70%), due to the fact that the orientation is not finely sampled so as to capture all the possibilities. These results compare well with results obtained using the same method on computer-generated artificial textures.
Detecting humans under occlusion using variational mean field method

This paper proposes a human detection method using variational mean field approximation for occlusion reasoning. In the method, parts of human objects are detected individually using template matching. Initial detection hypotheses with spatial layout information are represented in a graphical model and refined through a Bayesian estimation. In this paper, mean field method is employed for such an estimation. The proposed method was evaluated on the popular CAVIAR-INRIA dataset. Experimental results show that the proposed algorithm is able to detect humans in severe occlusion within reasonable processing time.
Greedy Approximation of Kernel PCA by Minimizing the Mapping Error

In this paper we propose a new kernel PCA (KPCA) speed-up algorithm that aims to find a reduced KPCA to approximate the kernel mapping. The algorithm works by greedily choosing a subset of the training samples that minimizes the mean square error of the kernel mapping between the original KPCA and the reduced KPCA. Experimental results have shown that the proposed algorithm is more efficient in computation and effective with lower mapping errors than previous algorithms.
Pathological Gait Detection of Parkinson's Disease Using Sparse Representation

Parkinson's disease is a progressively degenerative neurological disorder which impacts the control of body movements. While there is no known permanent cure for the disorder, it is possible to monitor the progression and establish management regime that could help the medical team, patients and their family cope with the condition. Gait analysis becomes an attractive quantitative and non-invasive mechanism that can aid early detection and monitoring of the response of patients to the management schedules. In this paper, we model cycles of human gait as a sparsely represented signal using over-complete dictionary. This representation forms the basis of a classification that allows the recognition of symptomatic subjects. Experiments have been conducted using signals of vertical ground reaction force (GRF) from subjects with Parkinson's disease from the publicly available gait database (physionet.org). Our method achieved a classification accuracy of 83% in recognising pathological cases and represents a significant improvement on previously published results that use a selection of the Fourier transform coefficients as features.
A Fast Algorithm for Color Image Segmentation

Based on K-means and a two-layer pyramid structure, a fast algorithm is proposed for color image segmentation. The algorithm employs two strategies. Firstly, a two-layer structure of a color image is established. Then, an improved K-means with integer based lookup table implementation is applied to each layer. The clustering result on the upper layer (lower resolution) is used to guide the clustering in the lower layer (higher resolution). Experiments have shown that the proposed algorithm is significantly faster than the original K-means algorithm while producing comparable segmentation results
An audio representation for content based retrieval

Despite the increasing interest in multimedia data retrieval audio data has received little attention. This is due, not to a lack of interest but rather to unique difficulties posed by the medium. In particular existing unstructured audio representations do not easily lend themselves to content based retrieval and especially browsing. This paper aims to address this oversight by developing an audio representation that provides direct support for browsing and content based retrieval. This support is the result of a structured representation based on psychoacoustic principles in which salient attributes of audio are directly accessible. In addition, the representation is compact thus addressing the requirement for minimisation of storage.
A New Divide and Conquer Algorithm for Graph-based Image and Video Segmentation

The concept of the shortest (or minimum) spanning tree (SST)and recursive SST (RSST) of an undirected weighted graph has been successfully applied in image segmentation and edge detection. This paper presents a divide-and-conquer approach for (R)SST based image segmentation in order to overcome the problem of high computational complexity associated with conventional graph algorithms. In the simplest form, the proposed approach, block-based RSST (BRSST), first divides the image into rectangular blocks, finds the (R)SST of each block individually using conventional graph algorithms and, then, merges the (R)SSTs of all image blocks to form an (R)SST of the entire image. Efficient merging algorithms are presented in this paper. We proved a theorem showing that the (R)SST obtained by the merging algorithms is one of the (R)SST that would be found by applying to the entire image the same algorithm used for finding the (R)SST of each image block. Theoretical analysis and experimental results have shown that BRSST has significantly reduced the computational cost. In addition, an incremental BRSST is proposed for video segmentation and results are presented
High-capacity steganography using a shared colour palette

Seppanen, Makela and Keskinarkaus (SMK) have proposed a high-capacity steganographic technique to conceal information within a colour image. The technique is significant because of the high volume of data that is embedded into pixels but it results in a high level of noise and so the quality of the resulting image is not acceptable. A new type of coding structure is proposed, which maintains a high capacity but lowers the level of noise. Secondly, an adaptive algorithm is used to identify pixel values that have a high capacity to distortion ratio. Also the maximum size of the coding structures is limited to improve the capacity/distortion tradeoff. For the tested images, an average capacity of nearly 6 bits/pixel was achieved with a peak signal to noise ratio of 40 dB.
Local representation of faces through extended NMF

Presented is an extension of the non-negative matrix factorisation (NMF) by imposing an orthogonality constraint on the basis matrix and controlling the sparseness of the coefficient matrix for robust learning of compact local part-based representation of face images. The extended NMF is solved by a projected gradient algorithm with a data-driven initialisation scheme. In addition, an indicator is proposed to objectively measure the locality and compactness of local part-based representation and to quantitatively evaluate the efficiency of the extended NMF. Experimental results on benchmark face databases show that the proposed extended NMF is much more effective in learning local part-based representation and more tolerant to the variations, especially misalignment, of the training samples than conventional NMF and its major extensions.
Method of color interpolation in a single sensor color camera using green channel separation

This paper presents a color interpolation algorithm for a single sensor color camera. The proposed algorithm is especially designed to solve the problem of pixel crosstalk among the pixels of different color channels. Interchannel cross-talk gives rise to blocking effects on the interpolated green plane, and also spreading of false colors into detailed structures. The proposed algorithm separates the green channel into two planes, one highly correlated with the red channel and the other with the blue channel. These separate planes are used for red and blue channel interpolation. Experiments conducted on McBeth color chart and natural images have shown that the proposed algorithm can eliminate or suppress blocking and color artifacts to produce better quality images.
Object detection using Non-Redundant Local Binary Patterns

Local Binary Pattern (LBP) as a descriptor, has been successfully used in various object recognition tasks because of its discriminative property and computational simplicity. In this paper a variant of the LBP referred to as Non-Redundant Local Binary Pattern (NRLBP) is introduced and its application for object detection is demonstrated. Compared with the original LBP descriptor, the NRLBP has advantage of providing a more compact description of object's appearance. Furthermore, the NRLBP is more discriminative since it reflects the relative contrast between the background and foreground. The proposed descriptor is employed to encode human's appearance in a human detection task. Experimental results show that the NRLBP is robust and adaptive with changes of the background and foreground and also outperforms the original LBP in detection task.
Smart Fabrics and Networked Clothing: Recent developments in CNT-based fibers and their continual refinement

Virtually all human cultures routinely wear garments or some sort of attire, both for protection against the elements and as part of a complex social display of everything from class status to gender to the act of belonging to a particular subculture. Smart garments, in addition to performing their social functions, can act as transmitters, sensors, or energy-harvesting entities. Furthermore, such garments may also possess some processing capability, enabling them to perform a variety of tasks, perhaps in conjunction with cloud-based server resources.
A Maximum Likelihood Watermark Decoding Scheme

Based on the observation that an attack applied on a watermarked image, from a decoding point of view, modifies the distribution of the detection values away from the ideal distribution (without attack) for corresponding watermarking scheme, we propose a generic maximum likelihood decoding scheme by approximating the distribution with a finite Gaussian mixture model. The parameters of the model are estimated using expectation-maximization algorithm. The scheme allows the decoding to be automatically adapted to attacks that the watermarked images have undergone and, in consequence, to improve the decoding accuracy. Experiments on a QIM based watermarking system have clearly verified the significant improvement of the decoding accuracy achieved by the proposed maximum likelihood decoding in comparison to conventional threshold decoding.
Image Content Annotation Based on Visual Features

Automatic image content annotation techniques attempt to explore structural visual features of images that describe image content and associate them with image semantics. In this paper, two types of concept spaces, atomic concept and collective concept spaces, are defined and the annotation problems in those spaces are formulated as feature classification and Bayesian inference, respectively. A scheme of image content annotation in this framework is presented and evaluated as an application of photo categorization using MPEG-7 VCE2 dataset and its ground truth. The experimental results show a promising performance
Methods of channel-optimised trellis source coding for the AWGN channel

Improvements to channel-optimised trellis source coding for the AWGN channel are obtained by using, in various forms, real or 'soft' channel information. The proposed 1 bit/sample systems use a channel-optimised encoder matched to (1) a simple decision feedback detector, (2) an expanded codebook with 2-bit quantized information and (3) an optimum non-linear estimator decoder. The third system is further improved by considering vector alphabets and both constant and average energy constrained 2D signal constellations.<<ETX>>
A Phase-Sensitive Approach to Filtering on the Sphere

This paper examines filtering on a sphere, by first examining the roles of spherical harmonic magnitude and phase. We show that phase is more important than magnitude in determining the structure of a spherical function. We examine the properties of linear phase shifts in the spherical harmonic domain, which suggest a mechanism for constructing finite-impulse-response (FIR) filters. We show that those filters have desirable properties, such as being associative, mapping spherical functions to spherical functions, allowing directional filtering, and being defined by relatively simple equations. We provide examples of the filters for both spherical and manifold data.