nils-herrmann/clean_scirepeval huggingface dataset:
Shape from Shading for Hybrid Surfaces

This paper presents a new method for recovering the shape of hybrid surfaces that have both diffuse reflection and specular reflection using shape from shading (SFS). The image irradiance equation has been derived as an explicit partial differential equation (PDE) under the assumptions of orthographic camera projection and distant point light source. The reflectance model of Ward has been used to express the hybrid reflection as a linear combination from the diffuse and specular components. The resulting PDE is solved using the Lax-Friedrichs sweeping method. The proposed algorithm is evaluated by using both synthetic data and real images and the experimental results show the efficiency of the approach.
Modified fuzzy c-mean in medical image segmentation

This paper describes the application of fuzzy set theory in medical imaging, namely the segmentation of brain images. We propose a fully automatic technique to obtain image clusters. A modified fuzzy c-mean (FCM) classification algorithm is used to provide a fuzzy partition. Our new method, inspired from the Markov random field (MRF), is less sensitive to noise as it filters the image while clustering it, and the filter parameters are enhanced in each iteration by the clustering process. We applied the new method on a noisy CT scan and on a single channel MRI scan. We recommend using a methodology of over segmentation to the textured MRI scan and a user guided-interface to obtain the final clusters. One of the applications of this technique is TBI recovery prediction in which it is important to consider the partial volume. It is shown that the system stabilizes after a number of iterations with the membership value of the region contours reflecting the partial volume value. The final stage of the process is devoted to decision making or the defuzzification process.
3D segmentation and labeling using self-organizing Kohonen network for volumetric measurements on brain CT imaging to quantify TBI recovery

In this paper, we present a new system to segment and label CT brain slices using a self-organizing Kohonen network. Our aim is to extract reliable and robust measures from CT images of Traumatic Brain Injury (TBI) patients that can accurately describe the morphological changes in the brain as recovery progresses. Segmentation is performed by assigning a feature pattern to each voxel, consisting of a scaled family of differential geometrical invariant features. The invariant feature pattern is input to Kohonen network for an unsupervised classification of the voxels into regions.
Automatic extraction of interdental gingiva regions for accurate statistical shape from shading-based reconstruction of human jaw

The three-dimensional (3D) reconstruction of visible part of the human jaw is becoming required for many of diagnostics and treatment procedures. In our previous work , we proposed a novel approach for 3D reconstruction of human jaw using Statistical Shape from Shading (SSFS). The input of this approach has to be a single optical image of the jaw augmented manually with several requirements; anatomical landmark points, jaw status and the number of teeth per jaw. In this paper, we boost our SSFS framework by proposing a novel approach to automatically extract these prior information. This two-step framework consists of: Interdental gingiva's regions extraction for each individual tooth and detects the centerline across the jaw span. These two steps help us to extract the anatomical landmark points and detect the status of the jaw. Experimental results highlight the accuracy of the extracted prior information and how this information boosts recovering 3D models of the human jaw.
New Deformable Registration Technique Using Scale Space and Curve Evolution Theory and A Finite Element Based Validation Framework

In this paper, we present a novel and accurate approach for nonrigid registration. New feature descriptors are built as voxel signatures using scale space theory. These descriptors are used to capture the global motion of the imaged object. Local deformations are modelled through an evolution process of equi-spaced closed curves/surfaces (iso-contours/surfaces) which are generated using fast marching level sets and are matched using the built feature descriptors. The performance of the proposed approach is validated using the finite element method. Both 2D and 3D tissue deformations cases are simulated, and the registration accuracy is quantified by co-registering the deformed images with the original ones and comparing the recovered mesh point displacements with the simulated ones. The evaluation results show the potential of the proposed approach in handling local deformation better than some conventional approaches
Shape-from-shading using sensor and physical object characteristics applied to human teeth surface reconstruction

Image formation involves understanding the sensors characteristics and object reflectance. In dentistry, for example an accurate three-dimensional (3D) representation of the human jaw may be used for diagnostic and treatment purposes. Photogrammetry can offer a flexible, cost-effective solution in that regard. Nonetheless there are several challenges, such as non-friendly image acquisition environment inside the human mouth, problems with lighting (specularity effects because of saliva, gum discolourisation, and occlusion because of the tongue in the lower jaw), and errors because of the data acquisition sensors (e.g. camera calibration errors, lens distortion and so on). In this study, the authors focus on the 3D surface reconstruction aspect for human jaw modelling based on physical surface characteristics and sensor properties. Owing to apparent lens distortion imposed by near-field imaging, the authors propose a new flexible calibration for lens radial distortion based on a single image of a sphere. The authors propose a non-Lambertian shape-from-shading (SFS) algorithm under perspective projection which benefits from camera calibration parameters. Our experiments provide quantitative metric results for the proposed approach. The reflectance of the tooth surface is modelled by the Oren–Nayar reflectance model for rough surfaces whose roughness parameter is physically computed from an optical surface profiler measurements. As compared to state-of-the-art SFS approaches, our approach is able to recover geometric details of tooth occlusal surface. This work is fundamental for establishing an optical-based approach for reconstructing the human jaw, that is inexpensive and does not use ionising radiation.
A comparative study of statistical and neural methods for remote-sensing image classification and decision fusion

This paper focuses on evaluating a number of statistical and neural methods for supervised, pixel-wise remote-sensing image classification and decision fusion. Despite the enormous progress in the analysis of remote sensing imagery over the past three decades, still much is desired in the area of image classification as no specific algorithm is known to provide accurate results under all circumstances. Decision fusion may be pursued to combine the outputs of different classifiers applied on the same data, in the hope of combining the best of what each approach provides. We report the results of the comparison between several classification and fusion methods on two real datasets, one of which is the standard benchmark Satimage dataset. It is shown that the fusion approaches can indeed outperform the performance of the best classifier.
Modeling image irradiance under natural illumination and isotropic surface reflectance

Discounting subsurface scattering and surface emittance, this work seeks to address the question; how to accurately represent image irradiance under unknown general illumination and reflectance, given that a surface point sees its surrounding world through the local upper hemisphere oriented by the surface normal at this point. In this paper, we formulate the image formation process of isotropic surfaces under arbitrary distant illumination in the frequency space while addressing the physical compliance of hemispherical basis for representing surface reflectance, e.g. Helmholtz reciprocity and isotropy. This representation is further reduced in dimensionality through analytically deriving the principal components of the image irradiance. A database of natural illumination and real world surface materials are used to compute a general-purpose basis which captures the full behavior of illumination and reflectance in a lower-dimensional subspace for image irradiance representation. Compared to similar basis in literature, our proposed basis achieves higher accuracy levels at lower illumination orders.
Fully automated 3D colon segmentation for early detection of colorectal cancer based on convex formulation of the active contour model

Accurate colon segmentation would play a vital role in a virtual colonoscopy system, amounting for reliable polyp detection; a colon cancer indicator. In this paper, we propose a fully automated framework for 3D colon segmentation based on the global/convex continuous minimization of the active contour model in 3D space. For optimal results, 3D region growing and some colon anatomical features, e.g. size, persistence and curvature have been incorporated for post processing. The proposed framework, applied on 12 colon data sets, is compared with graph cuts (discrete optimization) and adaptive level sets (non-convex continuous optimization). Our results outperform the other two in different aspects including speed of convergence, sensitivity and specificity with overall accuracy of 99%.
A silhouette-contour based 3-D registration methodology as a pre-evaluation step of 3-D reconstruction techniques

Data registration is a crucial step in performance evaluation procedures that aim at localizing errors in given measured data. Under the performance evaluation topic, the probably corrupted measured data should be accurately registered/aligned to the ground truth data such that the registration process would not affect the accuracy of the consequent evaluation steps. To cope with this problem, another registration methodology that can go beyond the conventional ones should be used. Here we propose a novel approach for 3-D data registration. The performance of the approach is totally independent of the measured data set. The key advantage of this approach is that it keeps the registration process away from being affected by the probably corrupted data sets, hence it permits confidential evaluation results.
Image registration in multispectral data sets

The paper discusses the matching of two multispectral data sets using the genetic algorithm (GA) as a search technique for the global optimum estimates of the transformation parameters. It uses the simplest form of the affine transformation, where the scaling and rotation are ignored, to explain how the GAs, which are used in the registrations' processes for the different bands, can cooperate. This cooperation between the GAs improves the speed of the matching process. The cooperation depends on the exchange of the intermediate results between the GAs. Therefore, the proposed approach demands the parallel realization of GAs. In addition, the proposed approach can be considered as a fusion method for the different decisions resulting form the registrations for the different bands. Landsat 7-band and 4-band aerial data set types are considered. The aerial data set type is used to explain how to the proposed approach speeds the matching process.
Effect of minicolumnar disturbance on dyslexic brains: an MRI study

The minicolumn is generally considered the basic unit of the neocortex in all the mammalian brains. Enlargement of the cortical surface is believed to occur through the addition of minicolumns rather than a single neuron. This study aims at testing the hypothesis that brain developmental disorders can be diagnosed and analyzed in terms of the minicolumnar disturbance. To do this, we propose to correlate the pathological findings in terms of the minicolumnar structure to the MRI findings in terms of volumetric analysis
Multiresolution Approach for Non-Contact Measurements of Arterial Pulse using Thermal Imaging

This paper presents a novel, multiresolution (MR) approach for non-contact passive measurements of the arterial pulse along the arterial tree using thermal imaging of the most accessible regions of human face and neck. The method automatically detects Region/s Of Measurement (ROM) of the arterial pulse using the periodic nature of arterial thermal patterns. This ROM constitutes a skin area where the heat pulsation produced by an artery is most pronounced. The output of the algorithm is a descriptive arterial pulse waveform. To the best of our knowledge, our results are novel and have not been reported before. Some potential applications of this work are in the field of non-contact health state/ physiology monitoring, magnetic resonance imaging, smart rooms, polygraph testing, and intent identification.
A Non-invasive Method for Measuring Blood Flow Rate in Superficial Veins from a Single Thermal Image

In this paper, we propose a thermal image based measurement technique for the volumetric flow rate of a liquid inside a thin tube. Our technique makes use of the convection heat transfer dependency between the flow rate and the temperature of the flowing liquid along the tube. The proposed method can be applied to diagnose superficial venous disease non-invasively by measuring the volumetric blood flow rate from a FLIR LWIR single thermal image.
Non-metric calibration of camera lens distortion

This paper addresses the problem of calibrating camera lens distortion, which can be significant in medium to wide angle lenses. Our approach is based on the analysis of distorted images of straight lines. We derive a new distortion measure that can be optimized using nonlinear search techniques to find the best distortion parameters that straighten these lines. Unlike the other existing approaches, we also show how to use this measure to find fast, closed-form solutions to the distortion coefficients. Some experiments to evaluate the performance of this approach on synthetic and real data are reported.
Free-form object recognition and registration using surface signatures

This paper presents a new concept for 3-D free-form surface registration and object recognition using a new surface representation scheme. This representation scheme captures the 3-D curvature information of any free-form surface and encodes it into a 2-D image corresponding to a certain point on the surface. This image is unique for this point and is independent from the object translation or orientation in space. For this reason we called this image “Surface Point Signature” (SPS). This scheme can be used as a global representation of the surface as well as a local one and also in a scale independent surface matching. It performs faster registration than existing registration approaches. We applied this technique in object registration, multimodal medical image registration and the recognition of multiple objects in a 3-D scene.
Direct method for shape recovery from polarization and shading

Polarization imaging can give information about surface shape, and roughness. Polarization has been used for shape recovery, but with convex/concave reconstruction ambiguity. In this paper, we present a direct method to shape recovery using both polarization and shading that resolves this ambiguity, without the need for nonlinear optimization routines. Several experiments on synthetic and real datasets are reported to evaluate the proposed method. The method consistently outperforms some well-known methods based on polarization information alone.
Gaussian curvature flowmodel for colonic polyp detection in CT colonography

In this paper, we propose a novel anisotropic 3D surface evolution model for detecting protrusion shape based colonic polyp on the curved surface. The important feature of the proposed model is that it can detect protrusions with both convex and concave shapes. Protrusion shapes are defined as the extension beyond the usual limits or above a plane surface. Based on Gaussian and mean curvature flows, the approach works by locally deforming the convex or concave surface until the second principal curvature goes to zero. The diffusion directions are changed to prevent convex surfaces from converting into concave shapes, and vice versa. The deformation field quantitatively measures the amount of protrudeness. The proposed method has been evaluated by using synthetic phantoms and real colon datasets.
Supervised fuzzy and Bayesian classification of high dimensional data: a comparative study

This paper presents a supervised fuzzy c-mean (SFCM) classifier for the classification of high dimensional data. The proposed SFCM classifier can be iterative or non iterative to reduce the computational time. Comparison with the conventional FCM clustering technique and the Bayesian classification technique is also presented. Performance results of the three algorithms are presented on simulated and real remote sensing multispectral data, which show improvement in the classification accuracy using the SFCM technique.
Occlusal surface reconstruction of human teeth from a single image based on object and sensor physical characteristics

Image formation involves understanding sensor characteristics and object reflectance. In dentistry, an accurate 3-D representation of the human jaw may be used for diagnostic and treatment purposes. Photogrammetry can offer a flexible, cost effective solution for accurate 3-D representation of the human teeth, which can be used for diagnostic and treatment purposes. Nonetheless there are several challenges, such as the non-friendly image acquisition environment inside the human mouth, problems with lighting and errors due to the data acquisition sensors. In this paper, we focus on the 3D surface reconstruction aspect for human teeth based on a single image. We introduce a more realistic formulation of the shape-from-shading (SFS) problem by considering the image formation components; the camera, the light source, and the surface reflectance. We propose a non-Lambertian SFS algorithm under perspective projection which benefits from camera calibration parameters. We take into account the attenuation of illumination due to near-field imaging. The surface reflectance is modeled using Oren-Nayar-Wolff model which accounts for the retro-reflection case. Our experiments provide promising quantitative metric results for the proposed approach.
A modified FCM algorithm for adaptive segmentation, bias field elimination, and partial volume estimation in MRI data

We present the application of a novel algorithm for adaptive fuzzy segmentation of MRI data, estimation of intensity inhomogeneities in MRI volumes, and partial volume estimation using fuzzy logic. MRI intensity inhomogeneities can be attributed to imperfections in the RF coils or some problems associated with the acquisition sequences. The result is a slowly-varying shading artifact over the image that can produce errors with conventional intensity-based classification. Our algorithm is formulated by modifying the objective function of the standard fuzzy c-means (FCM) algorithm to compensate for such inhomogeneities and to allow the labeling of a pixel (voxel) to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution towards piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by salt and pepper noise. The new algorithm also solves the partial volume (PV) effect. Due to the partial volume effect, boundary transitions from one tissue to another can occupy more than one voxel. In most researches this has been treated as an artifact that affect the classification of PV voxels into the right cluster. However, this effect can not be neglected when accurate volumetric measurements are brought to focus. The Modified FCM (MFCM) algorithm is shown to have the ability to overcome the noise sensitivity problem in segmentation without changing the voxel values thus keeping the whole information at the edges for accurate PV estimation.
Shape modeling of the corpus callosum

A novel approach for shape modeling of the corpus callosum (cc) is introduced where the contours of the cc are extracted by image/volume segmentation, and a Bezier curve is used to connect the vertices of the sampled contours, generating a parametric polynomial representation. These polynomials are shown to maintain the characteristics of the original cc, thus are suitable for classification of populations. The Bernstein polynomials are used in fitting the Bezier curves. The coefficients of the Bernstein polynomials are shown to capture the geometric features of the cc, and are able to describe deformations. We use these coefficients, in conjunction with the Fourier Descriptors and other features, to discriminate between autistic and normal brains. The approach is tested on T1-weighted MRI scans of 16 normal and 22 autistic subjects and shows its ability to provide perfect classification, suggesting that the approach is worth investigating on a larger population with the hope of providing early identification and intervention of autism using neuroimaging.
Long term facial parts tracking in thermal imaging for uncooperative emotion recognition

This paper proposes a tracking approach for regions of interest (ROI) in thermal image videos, where vital signs can be measured for emotion recognition. The proposed tracking framework overcomes a number of problems associated with this goal; mainly size of the ROI, appearance variations in the ROI with physiological changes, and the duration of tracking in a practical setting. The proposed framework consists of three modules: An adaptive particle filter tracker, an online detector, and finally a module to integrate the outputs of the two previous modules for learning as well as the final decision. The template of the adaptive particle filter tracker is updated based on the learning decision module to avoid drifting. In the detector module, a randomized classifier is used to detect the ROI. Then the output of this classifier is enhanced by removing false positives using a proposed geometrical constraint. The proposed framework is tested and compared to the state of art approaches on 32 human subjects with different physiological changes. Experimental results show that proposed method outperforms the others.
Robust robotic path planning using level sets

In this paper, we propose a general and robust robotic path planning framework for both planar and terrain environments using level set methods. The framework is general in the sense that it can be used for both 2D and 3D environments. It generates a collision-free optimum paths for the entire or a portion of the configuration space. The optimum planned path can be controlled to follow the safest, shortest, or hybrid path. We have demonstrated the robustness of the proposed framework by correctly extracting all planned paths of complex maps with several obstacles.