nils-herrmann/clean_scirepeval huggingface dataset:
An Effective Human Motion Classification Approach using Knowledge Representation in Qualitative Normalised Templates

Classification of human motion in video data is essential in numerous applications. However, problems arise as the human exhibits complex and dynamic motion that is nonlinear and time varying. In this paper, we propose a knowledge-based human motion classification framework that employs fuzzy qualitative reasoning to address these problems. Our approach utilises the rich contextual information (e.g. structural and transitional characteristic of human motion) captured in video sequence to effectively study and recognise human motion. With the aid of domain knowledge, a set of fuzzy rules are defined in the knowledge base. This work is in contrast with previous attempts that depend solely on the trajectories of the body parts. Experimental results on two classes of motion (e.g. walking and running) that result in similar motions; and a comparison with the conventional method has demonstrated and validated the effectiveness of the proposed method in improving the perception of human motion.
Anomalous trajectory detection using the fusion of fuzzy rule and local regression analysis

Motion trajectories provide rich spatio-temporal information about an object activity. In this paper, we present a novel anomaly detection framework to detect anomalous motion trajectory using the fusion of local regression analysis and fuzzy rule-based method. That is, first of all we address the problem by segmenting our moving objects using a Gaussian mixture background model. Secondly, visual tracking using probabilistic appearance manifolds to extract spatio-temporal trajectory. Thirdly, local regression analysis and data quantization are performed on the extracted trajectory such that the anomalous detection can be performed as the incoming data are acquired. Finally, through the accumulative rank of local regression analysis and a fuzzy rule-based anomaly detection framework to detect the anomalous trajectory. Experimental results on various challenging trajectory data has validated the effectiveness of the proposed method.
Enhanced Random Forest with Image/Patch-Level Learning for Image Understanding

Image understanding is an important research domain in the computer vision due to its wide real-world applications. For an image understanding framework that uses the Bag-of-Words model representation, the visual codebook is an essential part. Random forest (RF) as a tree-structure discriminative codebook has been a popular choice. However, the performance of the RF can be degraded if the local patch labels are poorly assigned. In this paper, we tackle this problem by a novel way to update the RF codebook learning for a more discriminative codebook with the introduction of the soft class labels, estimated from the pLSA model based on a feedback scheme. The feedback scheme is performed on both the image and patch levels respectively, which is in contrast to the state-of-the-art RF codebook learning that focused on either image or patch level only. Experiments on 15-Scene and C-Pascal datasets had shown the effectiveness of the proposed method in image understanding task.
Fuzzy action recognition for multiple views within single camera

To be able to perform human action recognition from multiple views is a great challenge in the field of computer vision. State-of-the-art solutions have been focusing on building a 3D action model from multiple views in a multi, calibrated cameras' environment. Promising results were achieved; however, these approaches tend to assume that human action is performed frontal-parallel to each of the multiple cameras. In a real world scenario, this is not always true and the overlapping regions in such systems are very limited. In this paper, we proposed a fuzzy action recognition framework for multiple views within a single camera. We adopted fuzzy quantity space in the framework and introduced a new concept called the Signature Action Behaviour to model an action from multiple views and represent it as fuzzy descriptor. Then, distance measure is applied to deduce an action. Experimental results showed the efficiency of our proposed framework in modeling the actions from different viewpoints and styles.
Total-Text: A Comprehensive Dataset for Scene Text Detection and Recognition

Text in curve orientation, despite being one of the common text orientations in real world environment, has close to zero existence in well received scene text datasets such as ICDAR'13 and MSRA-TD500. The main motivation of Total-Text is to fill this gap and facilitate a new research direction for the scene text community. On top of conventional horizontal and multi-oriented text, it features curved-oriented text. Total-Text is highly diversified in orientations, more than half of its images have a combination of more than two orientations. Recently, a new breed of solutions that casted text detection as a segmentation problem has demonstrated their effectiveness against multi-oriented text. In order to evaluate its robustness against curved text, we fine-tuned DeconvNet and benchmark it on Total-Text. Total-Text with its annotation is available at https://github.com/cs-chan/Total-Text-Dataset.
Zero-Shot Object Recognition System Based on Topic Model

Object recognition systems usually require fully complete manually labeled training data to train classifier. In this paper, we study the problem of object recognition, where the training samples are missing during the classifier learning stage, a task also known as zero-shot learning. We propose a novel zero-shot learning strategy that utilizes the topic model and hierarchical class concept. Our proposed method advanced where cumbersome human annotation stage (i.e., attribute-based classification) is eliminated. We achieve comparable performance with state-of-the-art algorithms in four public datasets: PubFig (67.09%), Cifar-100 (54.85%), Caltech-256 (52.14%), and Animals with Attributes (49.65%), when unseen classes exist in the classification task.
Ceci n'est pas une pipe: A deep convolutional network for fine-art paintings classification

“Ceci n'est pas une pipe” French for “This is not a pipe”. This is the description painted on the first painting in the figure above. But to most of us, how could this painting is not a pipe, at least not to the great Belgian surrealist artist Rene Magritte. He said that the painting is not a pipe, but rather an image of a pipe. In this paper, we present a study on large-scale classification of fine-art paintings using the Deep Convolutional Network. Our objectives are two-folds. On one hand, we would like to train an end-to-end deep convolution model to investigate the capability of the deep model in fine-art painting classification problem. On the other hand, we argue that classification of fine-art collections is a more challenging problem in comparison to objects or face recognition. This is because some of the artworks are non-representational nor figurative, and might requires imagination to recognize them. Hence, a question arose is that does a machine have or able to capture “imagination” in paintings? One way to find out is train a deep model and then visualize the low-level to high-level features learnt. In the experiment, we employed the recently publicly available large-scale “Wikiart paintings” dataset that consists of more than 80,000 paintings and our solution achieved state-of-the-art results (68%) in overall performance.
An inference engine based on Interval Type-2 Fuzzy BK subproduct

Recently, Bandler-Kohout (BK) subproduct based reasoning scheme has been a popular choice in various kind of applications. In this paper, we aim to enhance the BK subproduct based reasoning schemes in two aspects: (1) Extend the BK subproduct in term of Interval Type-2 Fuzzy Sets (IT2FS) instead of the ordinary Type-1 Fuzzy Sets (T1FS), and (2) Introduce weight parameter to the reasoning scheme. Firstly, studies have shown that IT2FS have better capability in handling data with uncertainty compare to the ordinary T1FS. Thus, we extend the BK subproduct in terms of IT2FS theory where subsethood measure based on the fuzzy implication operators for the IT2FS has been developed. Secondly, weight parameter associated to each features is introduced to form a weighted inference scheme with the BK subproduct. The introduction of the weight parameter is to aid in distinguishing the influence of different features in the reasoning process. In here, the Linguistic Weighted Average (LWA) is adopted to solve the outputs of this weighted reasoning scheme. Finally, a case study is employed to demonstrate the capability of the proposed approach.
Monotone operator splitting for optimization problems in sparse recovery

This work focuses on several optimization problems involved in recovery of sparse solutions of linear inverse problems. Such problems appear in many fields including image and signal processing, and have attracted even more interest since the emergence of the compressed sensing (CS) theory. In this paper, we formalize many of these optimization problems within a unified framework of convex optimization theory, and invoke tools from convex analysis and maximal monotone operator splitting. We characterize all these optimization problems, and to solve them, we propose fast iterative convergent algorithms using forward-backward and/or Peaceman/Douglas-Rachford splitting iterations. With non-differentiable sparsity-promoting penalties, the proposed algorithms are essentially based on iterative shrinkage. This makes them very competitive for large-scale problems. We also report some experiments on image reconstruction in CS to demonstrate the applicability of the proposed framework.
Weighted fidelity in non-uniformly quantized compressed sensing

Following the Compressed Sensing (CS) paradigm, this paper studies the problem of recovering sparse or compressible signals from (scalar) non-uniformly quantized measurements. We show that a simple adaptation of the Basis Pursuit De-Quantizer introduced earlier, that is, a sign sensitive weighting of their ℓp-norm fidelity constraint, yields good SNR improvements in the signal reconstruction. As a good indication of this improvement origin, we prove theoretically that a similar decoder, using a particular side-position-to-level oracle, displays a reduction of the reconstruction error when both the number of measurements and the moment p of the constraint increase. This follows the oversampling principle underlined in our previous study for uniformly quantized CS, with an additional gain provided by the non-uniform quantization. We conclude this paper by showing the efficiency of the approach on 1-D and 2-D signal examples.
Data augmentation for galaxy density map reconstruction

The matter density is an important knowledge for today cosmology as many phenomena are linked to matter fluctuations. However, this density is not directly available, but estimated through lensing maps or galaxy surveys. In this article, we focus on galaxy surveys which are incomplete and noisy observations of the galaxy density. Incomplete, as part of the sky is unobserved or unreliable. Noisy as they are count maps degraded by Poisson noise. Using a data augmentation method, we propose a two-step method for recovering the density map, one step for inferring missing data and one for estimating the density. The results show that the missing areas are efficiently inferred and the statistical properties of the maps are preserved.
Stabilizing Nonuniformly Quantized Compressed Sensing With Scalar Companders

This paper addresses the problem of stably recovering sparse or compressible signals from compressed sensing measurements that have undergone optimal nonuniform scalar quantization, i.e., minimizing the common ℓ2-norm distortion. Generally, this quantized compressed sensing (QCS) problem is solved by minimizing the ℓ1-norm constrained by the ℓ2-norm distortion. In such cases, remeasurement and quantization of the reconstructed signal do not necessarily match the initial observations, showing that the whole QCS model is not consistent. Our approach considers instead that quantization distortion more closely resembles heteroscedastic uniform noise, with variance depending on the observed quantization bin. Generalizing our previous work on uniform quantization, we show that for nonuniform quantizers described by the “compander” formalism, quantization distortion may be better characterized as having bounded weighted ℓp-norm (p ≥ 2), for a particular weighting. We develop a new reconstruction approach, termed Generalized Basis Pursuit DeNoise (GBPDN), which minimizes the ℓ1-norm of the signal to reconstruct constrained by this weighted ℓp-norm fidelity. We prove that, for standard Gaussian sensing matrices and K sparse or compressible signals in RN with at least Ω((K logN/K)p/2) measurements, i.e., under strongly oversampled QCS scenario, GBPDN is ℓ2-ℓ1 instance optimal and stable recovers all such sparse or compressible signals. The reconstruction error decreases as O(2-B/√(p+1)) given a budget of B bits per measurement. This yields a reduction by a factor √(p+1) of the reconstruction error compared to the one produced by ℓ2-norm constrained decoders. We also propose an primal-dual proximal splitting scheme to solve the GBPDN program which is efficient for large-scale problems. Interestingly, extensive simulations testing the GBPDN effectiveness confirm the trend predicted by the theory, that the reconstruction error can indeed be reduced by increasing p, but this is achieved at a much less stringent oversampling regime than the one expected by the theoretical bounds. Besides the QCS scenario, we also show that GBPDN applies straightforwardly to the related case of CS measurements corrupted by heteroscedastic generalized Gaussian noise with provable reconstruction error reduction.
Image deconvolution under poisson noise using sparse representations and proximal thresholding iteration

We propose an image deconvolution algorithm when the data is contaminated by Poisson noise. The image to restore is assumed to be sparsely represented in a dictionary of waveforms such as the wavelet or curvelet transform. Our key innovations are: First, we handle the Poisson noise properly by using the Anscombe variance stabilizing transform leading to a non-linear degradation equation with additive Gaussian noise. Second, the deconvolution problem is formulated as the minimization of a convex functional with a data-fidelity term reflecting the noise properties, and a non-smooth sparsity-promoting penalties over the image representation coefficients (e.g. l1-norm). Third, a fast iterative backward-forward splitting algorithm is proposed to solve the minimization problem. We derive existence and uniqueness conditions of the solution, and establish convergence of the iterative algorithm. Experimental results are carried out to show the striking benefits gained from taking into account the Poisson statistics of the noise. These results also suggest that using sparse-domain regularization may be tractable in many deconvolution applications, e.g. astronomy or microscopy.
Image Decomposition and Separation Using Sparse Representations: An Overview

This paper gives essential insights into the use of sparsity and morphological diversity in image decomposition and source separation by reviewing our recent work in this field. The idea to morphologically decompose a signal into its building blocks is an important problem in signal processing and has far-reaching applications in science and technology. Starck , proposed a novel decomposition method-morphological component analysis (MCA)-based on sparse representation of signals. MCA assumes that each (monochannel) signal is the linear mixture of several layers, the so-called morphological components, that are morphologically distinct, e.g., sines and bumps. The success of this method relies on two tenets: sparsity and morphological diversity. That is, each morphological component is sparsely represented in a specific transform domain, and the latter is highly inefficient in representing the other content in the mixture. Once such transforms are identified, MCA is an iterative thresholding algorithm that is capable of decoupling the signal content. Sparsity and morphological diversity have also been used as a novel and effective source of diversity for blind source separation (BSS), hence extending the MCA to multichannel data. Building on these ingredients, we will provide an overview the generalized MCA introduced by the authors in and as a fast and efficient BSS method. We will illustrate the application of these algorithms on several real examples. We conclude our tour by briefly describing our software toolboxes made available for download on the Internet for sparse signal and image decomposition and separation.
Statistical Region-Based Active Contours with Exponential Family Observations

In this paper, we focus on statistical region-based active contour models where image features (e.g. intensity) are random variables whose distribution belongs to some parametric family (e.g. exponential) rather than confining ourselves to the special Gaussian case. Using shape derivation tools, our effort focuses on constructing a general expression for the derivative of the energy (with respect to a domain) and derive the corresponding evolution speed. A general result is stated within the framework of multi-parameter exponential family. More particularly, when using Maximum Likelihood estimators, the evolution speed has a closed-form expression that depends simply on the probability density function, while complicating additive terms appear when using other estimators, e.g. moments method. Experimental results on both synthesized and real images demonstrate the applicability of our approach.
Morphological Diversity and Sparse Image Denoising

Overcomplete representations are attracting interest in image processing theory, particularly due to their potential to generate sparse representations of data based on their morphological diversity. We here consider a scenario of image denoising using an overcomplete dictionary of sparse linear transforms. Rather than using the basic approach where the denoised image is obtained by simple averaging of denoised estimates provided by each sparse transform, we here develop an elegant Bayesian framework to optimally combine the individual estimates. Our derivation of the optimally combined denoiser relies on a scale mixture of Gaussian (SMG) prior on the coefficients in each representation transform. Exploiting this prior, we design a Bayesian ℓ2-risk (mean field) nonlinear estimator and we derive a closed-form for its expression when the SMG specializes to the Bessel K form prior. Experimental results are carried out to show the striking profits gained from exploiting sparsity of data and their morphological diversity.
The Undecimated Wavelet Decomposition and its Reconstruction

This paper describes the undecimated wavelet transform and its reconstruction. In the first part, we show the relation between two well known undecimated wavelet transforms, the standard undecimated wavelet transform and the isotropic undecimated wavelet transform. Then we present new filter banks specially designed for undecimated wavelet decompositions which have some useful properties such as being robust to ringing artifacts which appear generally in wavelet-based denoising methods. A range of examples illustrates the results
Phantom based segmentation assessment for MRI images

This paper describes new effective methods for assessing the reliability of segmentation algorithms applied to encephalon extraction. The methods are based on the use of a brain phantom which provides a valuable global modeling of cerebral structures. Thanks to this realistic model, we create two original discrepancy measurements which are both applied to find the ideal parameters range for the segmentation algorithm proposed and to compare the accuracy of these methods. The most obvious application of this methodology lies in the extension of the results performed on the phantom to real images.
Fast Time-Space Tracking of Smoothly Moving Fine Structures in Image Sequences

We address the problem of temporal tracking fine pointlike and filamentary structures exhibiting smooth motions in image sequences. By taking these specific restrictions into account, we put forward an original tracking method based on the search of integral lines in time-space structure tensor fields. The method is simple and very efficient regarding computation time and tracking precision, which allows a sub-pixel accuracy in both spatial and temporal domains. We suggest a numerical implementation of the algorithm which is based on a modified constrained Runge-Kutta scheme. Its performance and potential applicability are illustrated through two real cases : the tracking of internal linear features in composite materials observed with X-rays and the tracking of granular-shaped objects moving in a gas flow.
3D medical image segmentation approach based on multi-label front propagation

Many practical applications in the field of medical image processing require robust and valid 3D image segmentation results. In this paper, we present a semi-automatic iterative segmentation approach for 3D medical image by combining a 2D boundary tracking algorithm and a boundary mapping process. Upon each of the consecutive slice, the boundary tracking process is accomplished in an alternate procedure of the morphological dilatation and the multi-label front propagation. The multi-label front propagation method is developed based on the minimal path theory and fast sweeping evolution method to ensure the efficiency, and speed of the boundary tracking algorithm. This 3D image segmentation approach can easily extract the close and smooth boundary of the desired object from a 2D medical image series. This approach is efficient and reliable, and requires very limited user intervention. Some experimental results are also presented to demonstrate the efficiency of this approach.
Dequantizing Compressed Sensing: When Oversampling and Non-Gaussian Constraints Combine

In this paper, we study the problem of recovering sparse or compressible signals from uniformly quantized measurements. We present a new class of convex optimization programs, or decoders, coined Basis Pursuit DeQuantizer of moment p (BPDQp), that model the quantization distortion more faithfully than the commonly used Basis Pursuit DeNoise (BPDN) program. Our decoders proceed by minimizing the sparsity of the signal to be reconstructed subject to a data-fidelity constraint expressed in the ℓp-norm of the residual error for 2 ≤ p ≤ ∞. We show theoretically that, (i) the reconstruction error of these new decoders is bounded if the sensing matrix satisfies an extended Restricted Isometry Property involving the Iρ norm, and (ii), for Gaussian random matrices and uniformly quantized measurements, BPDQp performance exceeds that of BPDN by dividing the reconstruction error due to quantization by √(p + 1). This last effect happens with high probability when the number of measurements exceeds a value growing with p, i.e., in an oversampled situation compared to what is commonly required by BPDN = BPDQ2. To demonstrate the theoretical power of BPDQp, we report numerical simulations on signal and image reconstruction problems.
Multivariate statistical modeling of images with the curvelet transform

This paper proposes a theoretical and statistical study to characterize the dependencies of the curvelet coefficients of images across position, scale and orientation. Our study was based on estimated histograms of the marginal and joint distributions to study the statistical properties of curvelet coefficients, and on the mutual information to measure the level of dependence between these coefficients. Finally, a novel multivariate statistical model, namely the anisotropic multivariate generalized gaussian (AMGGD), was proposed to characterize these dependencies.