nils-herrmann/clean_scirepeval huggingface dataset:
Comprehensive Analysis on the Effects of Noise Estimation Strategies on Image Noise Artifact Suppression Performance

In this paper, the effects of employing different noise estimation strategies on the performance of noise artifact suppression techniques in achieving high image quality has been investigated. Most literature on the subject tends to use the true noise level of the noisy image when performing noise artifact suppression. However, this approach does not reflect how such techniques would be used in practical situations where the true noise level is unknown, which is common in most image and video processing applications. Therefore, in practical situations, the noise level must first be estimated before a noise artifact suppression technique can be applied using the estimated noise level. Through a comprehensive analysis of different noise estimation strategies using empirical testing on a variety of images with different characteristics, the MAD wavelet noise estimation technique was found to be the overall preferred noise estimation technique for all popular noise artifact suppression techniques investigated (BM3D, bilateral, Neigh Shrink, BLS-GSM and non-local means). Furthermore, the BM3D noise artifact suppression technique, combined with the MAD wavelet noise estimation technique, was found to offer the best performance in achieving high image quality in situations where the noise level is unknown and must be estimated. The outcome of this research is clear recommendations that can be used in practise when suppressing noise artifacts exhibited in digital imagery and video.
Curvelet based feature extraction of dynamic ice from SAR imagery

Synthetic Aperture Radar (SAR) images of sea ice have proven to be very useful toward classification of ice cover into ice types. However, using SAR images to separate the marginal ice zone (MIZ) from consolidated ice and open water has not been explicitly considered before. One typical feature of MIZ is that it is more dynamic than consolidated ice, and includes floes, fast and thin ice or ice eddies. The current paper utilizes the dynamic feature of MIZ to investigate a curvelet-based feature extraction method in order to classify a SAR image into open water, dynamic ice and consolidated ice, as a first step toward using SAR imagery to identify the MIZ. An experiment of 10-fold cross validation is conducted to demonstrate that the proposed feature extraction method is effective. Finally, an SVM classifier is used on a SAR image to test the performance of the curvelet-based feature. The result shows that curvelet-based feature can classify the dynamic ice accurately.
Correlated wavelet shrinkage: models of local random fields across multiple resolutions

This paper proposes a novel correlated shrinkage method based on wavelet joint statistics. Our objective is to demonstrate effectiveness of the wavelet correlation models [Z. Azimifar et al., 2004] in estimating the original signal from a noising observation. Simulation results are given to show the advantage of the new correlated shrinkage function. In comparison with the popular nonlinear shrinkage algorithms, it improves the denoised results.
Fast retrieval methods for images with significant variations

Fast image retrieval is the key to success for operations on large image databases, and a great many techniques have been developed for efficient retrieval. However, most of these methods are tailored to visual scenes or to images having limited variations. We investigate the searching of enormous databases (of up to 10/sup 7/ images) for the matching and identification of precious stones (principally diamonds). Because of the size of the database, we propose a hierarchy of classifiers, which successively prune candidate images such that the more complex classifiers are required to test only tiny portions of the data. The new classifier developed here applies a wavelet transform to image histograms and is capable of rejecting 99.9% of bad matches.
Image saliency detection via multi-scale statistical non-redundancy modeling

A multi-scale statistical non-redundancy modeling approach is introduced for saliency detection in images. The statistical non-redundancy of pixels at different wavelet sub-bands is characterized using a multi-dimensional lattice of non-parametric statistical models, thus taking into account image saliency at multiple scales. This identifies saliency in image attributes at multiple scales, and makes saliency detection strongly robust against noisy input images. Results based on images from a public database show that the proposed approach outperforms existing single and multi-scale approaches, particularly when dealing with noisy images.
Improved interactive medical image segmentation using Enhanced Intelligent Scissors (EIS)

A novel interactive approach called Enhanced Intelligent Scissors (EIS) is presented for segmenting regions of interest in medical images. The proposed interactive medical image segmentation algorithm addresses the issues associated with segmenting medical images and allows for fast, robust, and flexible segmentation without requiring accurate manual tracing. A robust complex wavelet phase-based representation is used as an external local cost to address issues associated with contrast non-uniformities and noise typically found in medical images. The boundary extraction problem is formulated as a Hidden Markov Model (HMM) and the novel approach to the second-order Viterbi algorithm with state pruning is used to find the optimal boundary in a robust and efficient manner based on the extracted external and internal local costs, thus handling much inexact user boundary definitions than existing methods. Experimental results using MR and CT images show that the proposed algorithm achieves accurate segmentation in medical images without the need for accurate boundary definition as per existing Intelligent Scissors methods. Furthermore, usability testing indicate that the proposed algorithm requires significantly less user interaction than Intelligent Scissors.
Cross modality label fusion in multi-atlas segmentation

Multi-atlas label fusion is a widely used approach in medical image analysis that has improved the accuracy of segmentation. Majority voting, as the most common combination strategy, weighs each candidate in the atlas database equally. More sophisticated methods rely on the intensity similarity of each atlas to the target volume. However, these methods cannot handle those cases in which the atlases and the target image are in different modalities. A new method for label fusion is proposed, based on a structural similarity measure, relying on the structural relationships of features extracted from an undecimated wavelet transform instead of explicit image intensities. The new label fusion method has been tested on simulated and real MR images; segmentation results are promising, and open the door to a wider range of multi-modal approaches.
BRINT: Binary Rotation Invariant and Noise Tolerant Texture Classification

In this paper, we propose a simple, efficient, yet robust multiresolution approach to texture classification-binary rotation invariant and noise tolerant (BRINT). The proposed approach is very fast to build, very compact while remaining robust to illumination variations, rotation changes, and noise. We develop a novel and simple strategy to compute a local binary descriptor based on the conventional local binary pattern (LBP) approach, preserving the advantageous characteristics of uniform LBP. Points are sampled in a circular neighborhood, but keeping the number of bins in a single-scale LBP histogram constant and small, such that arbitrarily large circular neighborhoods can be sampled and compactly encoded over a number of scales. There is no necessity to learn a texton dictionary, as in methods based on clustering, and no tuning of parameters is required to deal with different data sets. Extensive experimental results on representative texture databases show that the proposed BRINT not only demonstrates superior performance to a number of recent state-of-the-art LBP variants under normal conditions, but also performs significantly and consistently better in presence of noise due to its high distinctiveness and robustness. This noise robustness characteristic of the proposed BRINT is evaluated quantitatively with different artificially generated types and levels of noise (including Gaussian, salt and pepper, and speckle noise) in natural texture images.
An adaptive Monte Carlo approach to nonlinear image denoising

This paper introduces a novel stochastic approach to image denoising using an adaptive Monte Carlo scheme. Random samples are generated from the image field using a spatially-adaptive importance sampling approach. Samples are then represented using Gaussian probability distributions and a sample rejection scheme is performed based on a chi2 statistical hypothesis test. The remaining samples are then aggregated based on Pearson Type VII statistics to create a non-linear estimate of the denoised image. The proposed method exploits global information redundancy to suppress noise in an image. Experimental results show that the proposed method provides superior noise suppression performance both quantitatively and qualitatively when compared to the state-of-the-art image denoising methods.
Probabilistic estimation of Braille document parameters

Braille is the most common means of reading and study for visually handicapped people. The need for converting Braille documents into a computer-readable format has motivated research into the implementation of Braille recognition systems. The most fundamental steps in such recognition are estimating the scaling, spacing, and skewness properties of a given Braille document. In this paper we propose a statistical method for estimating these parameters, where we model an entire Braille image using a parameterized probability density function and find the maximum-likelihood (ML) estimates using Expectation-Maximization (EM) algorithm. The proposed method is robust against noise and other image artifacts, and has a modest computational complexity for straightforward execution on ordinary personal computers.
Thermography spatial resolution enhancement by non-rigid registration with visible imagery

Over the last decade Carbon-Fiber reinforced polymers (CFRP) have become increasingly important in industry and consumer oriented products due to their physical robustness and shape adaptivity. Defect localization and characterization is key to assuring the quality and durability of CFRPs. Active thermography has become an important non-invasive technology for the detection of hidden defects within CFRP materials. However current active thermographers exhibit resolutions of only up to 640 × 512 pixels, whereas visible spectrum imagers routinely have resolutions of 10 megapixels and higher. We therefore propose a fusion of active thermography and visible imagery data, capable of producing higher-resolution thermography images based on the physical boundaries in the visible domain. Our approach handles the disparity between the two imagers without the need for stereo or individual calibration.
A Medical Texture Local Binary Pattern For TRUS Prostate Segmentation

Prostate cancer diagnosis and treatment rely on segmentation of Transrectal Ultrasound (TRUS) prostate images. This is a challenging and difficult task dut to weak prostate boundaries, speckle noise and the short range of gray levels. Advances in digital imaging techniques have made it possible the acquisition of large volumes of TRUS prostate images so that there is considerable demand for automated segmentation systems. Local binary pattern (LBP) has been used for texture segmentation and analysis. Despite its promising performance for texture classification it has not yet been considered for TRUS prostate segmentation. In this paper we introduce a medical texture local binary pattern operator designed for applications of medical imaging where different tissues or micro organisms might maintain extremely weak underlying textures that make it impossible or very difficult for ordinary texture analysis approaches to classify them. In the proposed method the deformations of a level set contour are controlled based on the medical texture local binary pattern operator.
From active contours to active surfaces

Identifying the surfaces of three-dimensional static objects or of two-dimensional objects over time are key to a variety of applications throughout computer vision. Active surface techniques have been widely applied to such tasks, such that a deformable spline surface evolves by the influence of internal and external (typically opposing) energies until the model converges to the desired surface. Present deformable model surface extraction techniques are computationally expensive and are not able to reliably identify surfaces in the presence of noise, high curvature, or clutter. This paper proposes a novel active surface technique, decoupled active surfaces, with the specific objectives of robustness and computational efficiency. Motivated by recent results in two-dimensional object segmentation, the internal and external energies are treated separately, which leads to much faster convergence. A truncated maximum likelihood estimator is applied to generate a surface consistent with the measurements (external energy), and a Bayesian linear least squares estimator is asserted to enforce the prior (internal energy). To maintain tractability for typical three-dimensional problems, the density of vertices is dynamically resampled based on curvature, a novel quasi-random search is used as a substitute for the ML estimator, and sparse conjugate-gradient is used to execute the Bayesian estimator. The performance of the proposed method is presented using two natural and two synthetic image volumes.
Probabilistic continuous edge detection using local symmetry

We describe a new model for the detection of edges in a given image. The model takes the invariance of local features of the image w.r.t translational symmetry operations into account. This is done by expressing the symmetries as a local Lie group and their associated Lie algebras in the regularizer of our model. Central to our work is the formulation of an energy density for the regularizer which itself is invariant under the action of a Lie algebra. Formulated as a Gaussian Markov Random Field, the parameters of the model are estimated by the EM principle.
Two-dimensional ultrasonic flaw detection based on the wavelet packet transform

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, but fully 2-D 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. After a brief review of the essential elements of the theory of wavelets and wavelet packets, a detailed description of the method is provided. The detection process operates on a set of spatially oriented frequency channels, i.e., detail images, obtained from successive wavelet packet decompositions of the initial B-scan. A statistical selection procedure based on the modeling of the detail image histograms retains the useful information-bearing frequency channels. The flaw information is then extracted from these selected channels by means of a specific thresholding scheme. Some experimental detection results in B-scan images of austenitic stainless steel samples comprising artificial flaws are presented.
A Benchmark for 3D Mesh Watermarking

This paper presents a benchmarking system for the evaluation of robust mesh watermarking methods. The proposed benchmark has three different components: a ''standard'' mesh model collection, a software tool and two application-oriented evaluation protocols. The software tool integrates both geometric and perceptual measurements of the distortion induced by watermark embedding, and also the implementation of a variety of attacks on watermarked meshes. The two evaluation protocols define the main steps to follow when conducting the evaluation experiments. The efficiency of the benchmark is demonstrated through the evaluation and comparison of two recent robust algorithms.
Content-Based Image Retrieval: on the Way to Object Features

Content-based systems retrieve images based on low-level features (color, texture) while the user usually seeks some objects from real world. As segmentation is never accurate, such systems do not allow the use of powerful feature during retrieval (shape, structure). We propose a new system that relies on a hierarchy of segmentations, in order to handle some artifacts. Besides, it allows using some object-related features for indexing (shape, structure). We also present some result on a 600 images database from Corel
Fractal coding of shapes based on a projected IFS model

This paper addresses the problem of approximation of natural complex shapes. Using MPEG-7 terminology, this problem can be considered as the search of a descriptor for a shape feature. This shape can be defined either as a frontier between image regions or a natural curve. For this purpose, an original descriptor which combines iterated function system (IFS) model and the notion of free form curves is proposed. A set of control points allows one to define the IFS model in a barycentric space. This generalization adds a real flexibility to fractal approximation techniques enriching the set of contractive operators which are candidate to model the self-similarity. This new descriptor named projected IFS model allows the reconstruction of a shape using a projection via the control points. It is adapted to the representation of both smooth shapes (man-made objects, body,..) and fractal shapes (mountain, cloud, tree,..). Results on synthetic shapes and a real mountain shape are presented.
Improving Zernike Moments Comparison for Optimal Similarity and Rotation Angle Retrieval

Zernike moments constitute a powerful shape descriptor in terms of robustness and description capability. However the classical way of comparing two Zernike descriptors only takes into account the magnitude of the moments and loses the phase information. The novelty of our approach is to take advantage of the phase information in the comparison process while still preserving the invariance to rotation. This new Zernike comparator provides a more accurate similarity measure together with the optimal rotation angle between the patterns, while keeping the same complexity as the classical approach. This angle information is particularly of interest for many applications, including 3D scene understanding through images. Experiments demonstrate that our comparator outperforms the classical one in terms of similarity measure. In particular the robustness of the retrieval against noise and geometric deformation is greatly improved. Moreover, the rotation angle estimation is also more accurate than state-of-the-art algorithms.
Recognizing and Localizing Individual Activities through Graph Matching

In this paper we tackle the problem of detecting individualhuman actions in video sequences. While the most successfulmethods are based on local features, which proved thatthey can deal with changes in background, scale and illumination,most existing methods have two main shortcomings:first, they are mainly based on the individual power ofspatio-temporal interest points (STIP), and therefore ignorethe spatio-temporal relationships between them. Second,these methods mainly focus on direct classification techniquesto classify the human activities, as opposed to detectionand localization. In order to overcome these limitations,we propose a new approach, which is based on agraph matching algorithm for activity recognition. In contrastto most previous methods which classify entire videosequences, we design a video matching method from twosets of ST-points for human activity recognition. First,points are extracted, and a hyper graphs are constructedfrom them, i.e. graphs with edges involving more than 2nodes (3 in our case). The activity recognition problemis then transformed into a problem of finding instances ofmodel graphs in the scene graph. By matching local featuresinstead of classifying entire sequences, our methodis able to detect multiple different activities which occursimultaneously in a video sequence. Experiments on twostandard datasets demonstrate that our method is comparableto the existing techniques on classification, and that itcan, additionally, detect and localize activities.
3D Object Detection and Viewpoint Selection in Sketch Images Using Local Patch-Based Zernike Moments

In this paper we present a new approach to detect and recognize 3D models in 2D storyboards which have been drawn during the production process of animated cartoons. Our method is robust to occlusion, scale and rotation. The lack of texture and color makes it difficult to extract local features of the target object from the sketched storyboard. Therefore the existing approaches using local descriptors like interest points can fail in such images. We propose a new framework which combines patch-based Zernike descriptors with a method enforcing spatial constraints for exactly detecting 3D models represented as a set of 2D views in the storyboards. Experimental results show that the proposed method can deal with partial object occlusion and is suitable for poorly textured objects.
Toward a near optimal quad/triangle subdivision surface fitting

In this paper we present a new framework for subdivision surface fitting of arbitrary surfaces (not closed objects) represented by polygonal meshes. Our approach is particularly suited for output surfaces from a mechanical or CAD object segmentation for a piecewise subdivision surface approximation. Our algorithm produces a mixed quadrangle-triangle control mesh, near optimal in terms of face and vertex numbers while remaining independent of the connectivity of the input mesh. The first step approximates the boundaries with subdivision curves and creates an initial subdivision surface by optimally linking the boundary control points with respect to the lines of curvature of the target surface. Then, a second step optimizes the initial control polyhedron by iteratively moving control points and enriching regions according to the error distribution. Experiments conducted on several surfaces and on a whole segmented mechanical object, have proven the coherency and the efficiency of our algorithm, compared with existing methods.
Hierarchical Blind Watermarking of 3D Triangular Meshes

An original hierarchical watermarking scheme for three-dimensional triangular meshes is proposed in this paper. A geometrically robust watermark and a high-capacity watermark are inserted in different resolution levels of the wavelet decomposition of a semi-regular mesh by modifying the norms of wavelet coefficients. Both watermarks are blind and invariant to similarity transformations. The robustness of the first watermark is achieved by synchronizing and quantizing watermark primitives according to edges lengths of the coarsest level, which are quite insensible to geometrical attacks. The high capacity of the second watermark is obtained by considering the permutation of the norms of a group of wavelet coefficients. Experiments have proven the high robustness of the first watermark under common geometrical attacks. To our knowledge, the capacity of the second method, which can attain the factorial of the candidate coefficients number, is the highest for 3D meshes in the literature.