nils-herrmann/clean_scirepeval huggingface dataset:
Organ Recognition Using Gabor Filters

The aim of this research is to investigate the possibility of using medical image information to extract unique features and classify different patients’ organ tissues, such as the prostate, based on concepts related to what is already done in iris recognition. This paper therefore presents a new approach in medical imaging, an organ recognition system, tested on a standard database of grey scale prostate images in order to validate its performance. In this research, features of the prostate image were encoded by convolving the normalized organ region with a 2D Gabor filter and then quantizing its phase in order to produce a bit-wise biometric template. Our experiments prove that prostate patterns have a low degree of freedom to be used in organ recognition systems and inter-class and intraclass distributions are highly correlated. However, there are still open issues that need to be addressed for future work on organ recognition, including precise segmentation and intensive computation cost.
Wavelet shrinkage with correlated wavelet coefficients

This paper investigates the statistical characterization of multiscale wavelet coefficients corresponding to random signals and images. Virtually all approaches to wavelet shrinkage model the wavelet coefficients as independent; we challenge that assumption and demonstrate several cases where substantial correlations may be present in the wavelet domain. In particular, the correlation between scales can be surprisingly substantial, even for pixels separated by several scales. Our goal is to develop an efficient random field model describing these statistical correlations, and demonstrate its effectiveness in the context of Bayesian wavelet shrinkage for signal and image denoising.
Shape-guided active contour based segmentation and tracking of lumbar vertebrae in video fluoroscopy using complex wavelets

This paper presents a novel shape-guided active contour based approach for segmenting and tracking lumbar vertebrae in video fluoroscopy using complex-valued wavelets. representations. Due to low radiation exposure levels, fluoroscopic images are characterized by low signal-to-noise ratios, low contrast resolution, and illumination non-homogeneities both spatially and temporally, making current methods ill-suited for segmenting and tracking lumbar vertebrae based on existing energy functionals. Furthermore, current methods perform poorly in situations characterized by high curvature as found in the structure of lumbar spine vertebrae. In this paper, a novel iterative estimation approach is used to determine an external energy functional based on complex wavelets. A shaped-guided algorithm is used to evolve the contour around a lumbar spine vertebra based on the complex wavelet energy. The high curvature exhibited by the lumbar spine vertebra is addressed through a novel importance sampling scheme. Experimental results show that the proposed algorithm achieves significantly better segmentation and tracking performance for lumbar spine vertebrae in fluoroscopic images when compared to existing techniques.
Embedded Motion Detection via Neural Response Mixture Background Modeling

Recent studies have shown that deep neural networks (DNNs) can outperform state-of-the-art algorithms for a multitude of computer vision tasks. However, the ability to leverage DNNs for near real-time performance on embedded systems have been all but impossible so far without requiring specialized processors or GPUs. In this paper, we present a new motion detection algorithm that leverages the power of DNNs while maintaining low computational complexity needed for near real-time embedded performance without specialized hardware. The proposed Neural Response Mixture (NeRM) model leverages rich deep features extracted from the neural responses of an efficient, stochastically-formed deep neural network (StochasticNet) for constructing Gaussian mixture models to detect motion in a scene. NeRM was implemented embedded on an Axis surveillance camera, and results demonstrated that the proposed NeRM approach can achieve strong motion detection accuracy while operating at near real-time performance.
Robust Non-saliency Guided Watermarking

A non-saliency guided watermarking approach is introduced where the watermark is embedded in natural images based on three measures, namely, non-saliency, heterogeneity and brightness. The three measures aim to exploit the main characteristics of the human visual system (HVS) for the purpose of embedding watermarks in images. The proposed method has the following advantages: 1) increasing the imperceptibility of the watermark through modeling HVS, 2) robustness to different attacks through embedding the data into non-salient areas with strong features and 3)increasing the watermarking capacity of labeling image pixels as appropriate or inappropriate candidates by using the continuous non-saliencyheterogeneity-brightness spectrum as opposed to binary labelingschemes as suggested by other methods. A variety of experimentsdemonstrate the superiority of the proposed method over existing state-of-the-art methods in terms of robustness and imperceptibility.
Decoupled Active Surface for Volumetric Image Segmentation

Finding the surface of a volumetric 3D object is a fundamental problem in computer vision. Energy minimizing splines, such as active surfaces, have been used to carry out such tasks, evolving under the influence of internal and external energies until the model converges to a desired surface. The present deformable model based surface extraction techniques are computationally expensive and are generally unreliable in identifying the surfaces of noisy, high-curvature and cluttered 3D objects. This paper proposes a novel decoupled active surface (DAS) for identifying the surface of volumetric 3D objects. The proposed DAS introduces two novel aspects which leads to robust, efficient and accurate convergence. First, rather than a parameterized surface, which leads to difficulties with complex shapes and parameter singularities, the DAS uses a conforming triangular mesh to represent the surface. Second, motivated by earlier successes in two-dimensional segmentation, the DAS treats the two energy components separately and uses novel solution techniques to efficiently minimize the two energy terms separately. The performance of DAS in segmenting static 3D objects is presented using several natural and synthetic volumetric images, with excellent convergence results.
Tensor vector field based active contours

Among the main limitations of active contours are their high noise sensitivity and poor capture range from the target object. One of the most promising approaches for addressing these limitations is the concept of Vector Field Convolution (VFC). However, due to its isotropic vector field kernel, VFC does not take full advantage of the underlying image structural characteristics. By specifically addressing this idea, a novel local tensor vector field approach is developed to adaptively account for these structural characteristics. Experimental results demonstrate that the proposed adaptive method leads to more accurate segmentation.
Multi-scale 3D representation via volumetric quasi-random scale space

A novel nonlinear volumetric scale-space framework is proposed for multi-scale volumetric data representation. The problem is formulated as a Bayesian least-squares estimator, and a quasi-random density estimation approach is introduced for estimating the posterior distribution between consecutive volumetric scale space realizations. Experimental results using both synthetic and real MR volumetric data demonstrate the effectiveness of the proposed scale-space framework for three-dimensional representation with significantly better structural separation and localization across all scales when compared to existing volumetric scale-space frameworks such as volumetric anisotropic diffusion and volumetric linear Gaussian scale-space, especially under scenarios with high noise levels.
Sparse Reconstruction of Breast MRI Using Homotopic $L_0$ Minimization in a Regional Sparsified Domain

The use of MRI for early breast examination and screening of asymptomatic women has become increasing popular, given its ability to provide detailed tissue characteristics that cannot be obtained using other imaging modalities such as mammography and ultrasound. Recent application-oriented developments in compressed sensing theory have shown that certain types of magnetic resonance images are inherently sparse in particular transform domains, and as such can be reconstructed with a high level of accuracy from highly undersampled k-space data below Nyquist sampling rates using homotopic L0 minimization schemes, which holds great potential for significantly reducing acquisition time. An important consideration in the use of such homotopic L0 minimization schemes is the choice of sparsifying transform. In this paper, a regional differential sparsifying transform is investigated for use within a homotopic L0 minimization framework for reconstructing breast MRI. By taking local regional characteristics into account, the regional differential sparsifying transform can better account for signal variations and fine details that are characteristic of breast MRI than the popular finite differential transform, while still maintaining strong structure fidelity. Experimental results show that good breast MRI reconstruction accuracy can be achieved compared to existing methods.
Texture Classification from Random Features

Inspired by theories of sparse representation and compressed sensing, this paper presents a simple, novel, yet very powerful approach for texture classification based on random projection, suitable for large texture database applications. At the feature extraction stage, a small set of random features is extracted from local image patches. The random features are embedded into a bag--of-words model to perform texture classification; thus, learning and classification are carried out in a compressed domain. The proposed unconventional random feature extraction is simple, yet by leveraging the sparse nature of texture images, our approach outperforms traditional feature extraction methods which involve careful design and complex steps. We have conducted extensive experiments on each of the CUReT, the Brodatz, and the MSRC databases, comparing the proposed approach to four state-of-the-art texture classification methods: Patch, Patch-MRF, MR8, and LBP. We show that our approach leads to significant improvements in classification accuracy and reductions in feature dimensionality.
Image synthesis using Conditional Random Fields

Methods of scientific imaging and image analysis have become pervasive in a great variety of fields, including the properties of porous media. To study the large-scale morphological properties of porous media, high resolution random (Monte Carlo) samples are required. The purpose of this paper is to propose a novel approach for the statistical synthesis of scientific images, based on the concept of Conditional Random Fields. We explore two different sets of potential functions are used to model the pore-structure characteristics, and Monte Carlo Markov chain methods are also used to sample the high resolution images from the trained model. The resulting images are of high quality, and show the performance of the proposed framework.
A Gabor based technique for image denoising

As an alternative to the wavelet, Gabor function has been used as an efficient representation of two dimensional signals. We are interested in BayesShrink techniques for image denoising, and have shown in our previous work that BayesShrink Ridgelet performs better than VisuShrink ridgelet and VisuShrink wavelet. In this paper, a dyadic Gabor filter bank is combined with BayesShrink method for image denoising. In the proposed method, the noisy image is decomposed to different channels in several levels by a dyadic Gabor filter bank. To recover the image, the corrupting noise is removed by applying the proposed BayesShrink method on the noisy Gabor coefficients. The noise variance is estimated in Gabor domain and the estimated noise is then used to dynamically calculate an individual threshold for each spatio-frequency channel. Finally denoised coefficients are transformed back to reconstruct the image
Accurate Boundary Localization using Dynamic Programming on Snakes

The extraction of contours using deformable models, such as snakes, is a problem of great interest in computer vision, particular in areas of medical imaging and tracking. Snakes have been widely studied, and many methods are available. In most cases, the snake converges towards the optimal contour by minimizing a sum of internal (prior) and external (image measurement) energy terms. This approach is elegant, but frequently mis-converges in the presence of noise or complex contours. To address these limitations, a novel discrete snake is proposed which treats the two energy terms separately. Essentially, the proposed method is a deterministic iterative statistical data fusion approach, in which the visual boundaries of the object are extracted, ignoring any prior, employing a hidden Markov model (HMM) and Viterbi search, and then applying importance sampling to the boundary points, on which the shape prior is asserted. The proposed implementation is straightforward and achieves dramatic speed and accuracy improvement. Compared to four other published methods and across six different images (two original, four published), the proposed method is demonstrated to be, on average, 7 times faster with a 45 percent reduction in the mean square error. Only the proposed method was able to successfully segment the desired object in each test image.
Quasi-Random Scale Space Approach to Robust Keypoint Extraction in High-Noise Environments

A novel multi-scale approach is presented for the purpose of robust keypoint extraction in high-noise environments. A multi-scale representation of the noisy scene is computed using quasi-random scale space theory. A gradient second-order moment analysis is employed at each quasi random scale to identify initial keypoint candidates. Final keypoints and their characteristic scales are selected based on the local Hessian trace extrema over all quasi-random scales. The proposed keypoint extraction method is designed to reduce noise sensitivity by taking advantage of the structural localization and noise robustness gained through the use of quasi-random scale space theory. Experimental results using scenes under different high noise conditions, as well as real synthetic aperture sonar imagery, show the effectiveness of the proposed method for noise robust keypoint extraction when compared to existing keypoint extraction techniques.
Efficient interpolation of large image sequences

Dynamic estimation of large-scale remote-sensing image sequences is important in a variety of scientific applications. However, the growing size of such sensed images makes conventional dynamic estimation methods, for example the Kalman and related filters, impractical. In this paper we present an approach that emulates the Kalman filter, but with considerably reduced computational and storage requirements. Our approach is illustrated in the context of a large (512/spl times/512) image sequence of ocean surface temperature.
Adaptive Nonlinear Image Denoising and Restoration Using a Cooperative Bayesian Estimation Approach

A novel nonlinear cooperative approach to image denoising and restoration is presented. Samples from the image field with similar characteristics are first grouped into clusters by first performing image decomposition based on the Mumford-Shah model using a total variational framework and performing fuzzy c-means clustering within each image partition. Samples within each cluster are then aggregated using an cooperative Bayesian estimation method based on information from all the samples to provide a nonlinear estimate of the original image. The proposed method exploits information redundancy within each cluster to denoise and restore the original image. Furthermore, the proposed cooperative Bayesian estimation method is capable of suppressing noise and reducing image degradation while preserving image detail by utilizing intra-cluster statistics. The experimental results using different types of images demonstrate that the proposed algorithm provides state-of-the-art image denoising performance in terms of both peak signal-to-noise ratio (PSNR) and subjective visual quality.
Background estimation for microscopic cellular images

modelling is a key task in tracking applications. Our interest in this paper is the accurate estimation of static backgrounds in scientific imaging, such as those in automated stem cell tracking. In this paper, an effective background estimation method is proposed. First, the segmentation results are used to remove the foreground objects, then the background is robustly estimated over the resultant 3-Dimensional residual image sequence. We do spatio-temporal background estimation over a local neighbourhood with a robust trimmed mean. The experimental results generated by the proposed method are quite promising.
Seam tracking in automated welding

With the growing number of automated welding systems present throughout manufacturing, achieving high precision is naturally a key objective. The alignment of weld tip to weld seam, particularly in very long welds (such as in pipes), is a technical challenge in which computer vision has much to offer. This paper introduces a real-time methodology for weld-seam tracking. The key challenge associated with the weld tracking problem is to accurately locate the seam and welding tip; that is, to define image features which allow the seam and tip edges to be identified. An efficient edge detection method based on steerable filters is proposed to have a robust edge extraction in different conditions of noise and illumination. The image processing precision is experimentally verified through comparison with ground truth data.
Hierarchical Region Mean-Based Image Segmentation

Gibbs Random Fields (GRFs), which produce elegant models, but which have very poor computational speed have been widely applied to image segmentation. In contrast to block-based hierarchies usually constructed for GRFs, the irregular region-based approach is a more natural model in segmenting real images. In this paper, we show that the fineto- coarse region-based hierarchical regions framework for the well-known Potts model can be extended to non-edge based interactions. By deliberately oversegmenting at the finer scale, the method proceeds conservatively by avoiding the construction of regions which straddle a region boundary by computing region mean differences. This demonstrates the hierarchical method is able to model region interactions through new generalizations at higher levels in the hierarchy which represent regions. Promising results are presented.
Adaboost and Support Vector Machines for White Matter Lesion Segmentation in MR Images

The use of two powerful classification techniques (boosting and SVM) is explored for the segmentation of white-matter lesions in the MRI scans of human brain. Simple features are generated from proton density (PD) scans. Radial basis function (RBF) based Adaboost technique and support vector machines (SVM) are employed for this task. The classifiers are trained on severe, moderate and mild cases. The segmentation is performed in T1 acquisition space rather than standard space (with more slices). Hence, the proposed approach requires less time for manual verification. The results indicate that the proposed approach can handle MR field inhomogeneities quite well and is completely independent from manual selection process so that it can be run under batch mode. Segmentation performance comparison with manual detection is also provided
External forces for active contours using the undecimated wavelet transform

A limitation of active contours models (both parametric and geometric) is their sensitivity to noise. Many solutions to noise sensitivity have been proposed in the literature, with the current state-of-the-art based on image blurring and multiresolution processing. However a significant drawback of both approaches is the side effect of edge delocalization. In this paper, gradient information extracted from all resolutions of the undecimated wavelet transform is used to build the external force map for the active contour. The new map accurately drives the active contour and improves edge localization. The proposed method builds on both Gradient Vector Flow and Vector Field Convolution active contours. Comparisons to classical and state-of-the-art methods show a dramatic improvement in active contour convergence for all levels of noise.
Temporal Conditional Random Fields: A conditional state space predictor for visual tracking

We present a modified Temporal Conditional Random Fields framework for modeling and predicting object motion. To facilitate such a powerful graphical model with prediction and come up with a CRF-based predictor, we propose a set of new temporal relations for object tracking, with feature functions such as optical flow (calculated among consequent frames). We evaluate our proposed Temporal Conditional Random Field method with real and synthetic data sequences and will show that the TCRF prediction is nearly equivalent with result of template matching. Experimental results show that our proposed method estimates future target state with zero error until target dynamic changes. Our proposed modified CRF method with simple and easy to implement feature functions, can learn any target dynamic, thus, it can predict next state of target with zero error.
Data fusion of SSM/I channels using multiresolution wavelet transform

This paper presents an approach to the fusion of SSM/I (Special Sensor Microwave/Imager) data from different resolutions, based on the prior statistical information about the data. The result is an estimated field that lives in a finer scale than any of the measurements. We apply a Wavelet Transform that increases speed and decreases memory requirements by sparsifying and preconditioning the statistics. This approach makes feasible the use of reprogrammable FPGA implementations for onboard satellite data processing, which greatly enhances flexibility and, most importantly, reduces communication burdens by limiting the extent to which raw, unprocessed data are transmitted to the ground.
Watershed deconvolution for cell segmentation

Cell segmentation and/or localization is the first stage of a (semi)automatic tracking system. We addressed the cell localization problem in our previous work where we characterized a typical blood stem cell in a microscopic image as an approximately circular object with dark interior and bright boundary. We also addressed the modelling of adjacent and dividing cells in our previous work as a deconvolution method to model individual blood stem cell as well as adjacent and dividing blood stem cells where an optimization algorithm was combined with a template matching method to segment cell regions and locate the cell centers. Our previous cell deconvolution method is capable of modelling different cell types with changes in the model parameters. However in cases where either a complex parameterized shape is needed to model a specific cell type, or in place of cell center localization, an exact cell segmentation is needed, this method will not be effective. In this paper we propose a method to achieve cell boundary segmentation. Considering cell segmentation as an inverse problem, we assume that cell centers are located in advance. Then, the cell segmentation will be solved by finding cell regions for optimal representation of cell centers while a template matching method is effectively employed to localize cell