nils-herrmann/clean_scirepeval huggingface dataset:
A passive stereo system for 3D human face reconstruction and recognition at a distance

In this paper, we propose a front-end framework for 3D human face reconstruction and recognition at a distance. A stereo acquisition system is built and deployed to capture stereo pairs of subjects at different distances. Three main issues are addressed to achieve accurate face reconstruction, which leads to good recognition; Different illumination conditions between the stereo pair due to larger baseline and further distances, where a fast similarity measure based on normalized cross correlation is shown to tackle such problem. Due to the non-convexity nature of a human face, concave regions introduce occluded regions where cubic Splines are used to estimate the disparity. Disparity discontinuities are introduced due to the sparse nature of stereo reconstruction, where surface fitting is performed at prominent facial points. We present our database of 99 subjects at different ranges where reconstruction and recognition results are presented.
Vertebral body segmentation using a probabilistic and universal shape model

Osteoporosis is a bone disease characterised by a reduction in bone mass, resulting in an increased risk of fractures. Doctors need the bone mineral density (BMD) measurements of vertebral bodies in order to diagnose and treat osteoporosis. The authors' objective is to segment the VBs as accurately as possible and hence to increase the accuracy of the BMD measurements and fracture analysis. Three pieces of information (intensity, spatial interaction and shape) are modelled to optimise a probabilistic energy functional. A universal shape prior, which is modelled using the cervical, thoracic and lumbar spinal regions, is proposed. Volumetric computed tomography data sets with various challenges are used in this study. The authors classify data sets based on some features related to the anatomy, imaging modality and level of the bone health. The proposed framework is one of only a few reported in the literature tested on the data obtained from different imaging devices. The experimental results reveal that the proposed method is robust under various noise levels, less variant to the initialisation and faster than existing vertebrae segmentation reports in the literature.
A NEW GLOBAL REGISTRATION APPROACH OF MEDICAL IMAGING USING VECTOR MAPS

In this paper, we introduce a novel global image registration approach using vector distance functions (VDF's). Edges of the source and target objects are used to represent their images. The VDF's of these edges are calculated as an implicit representation of these boundaries. An energy is formulated to measure the differences of these vectors. A variational formulation is considered to estimate the transformation parameters. Promising results in 2D and 3D are demonstrated to show the efficiency of the proposed algorithm.
Graph Cut Based Segmentatioln Of Multimodal Images

This work proposes a new semi-unsupervised Maximum- A-Posteriori (MAP) based segmentation framework of multimodal images. In this work a joint Markov Gibbs random field (MGRF) model is used to describe the image. However, the main focus here is a more accurate model identification. We propose a new analytical approach to estimate spatial interaction potentials for the MGRF model. For a known number of classes in the given image, the empirical distributions of this image signals are precisely approximated by a linear combination of Gaussian (LCG) distributions with positive and negative components. The proposed framework consists of three stages. The first stage is the image signal modelling, and initial labeling stage. In the second stage the new analytically estimated potential is used to identify the spatial interaction between the neighboring pixels. Finally, an energy function using the previous models is formulated, and is globally minimized using graph cuts. Experimental results show that the developed technique gives promising accurate results compared to other known algorithms.
3D objects coding and recognition using surface signatures

This paper presents a new concept for 3D coding of free-form surfaces. The proposed coding technique uses the surface signature representation scheme. This representation scheme captures the 3D curvature information of many free-form surface and encodes it into a 2D image corresponding to a certain point on the surface. This image is unique for this point and is independent of the the object translation or orientation in space. For this reason this image is called "surface point signature" (SPS). Using SPS in 3D coding has many applications, such as in 3D compression, 3D pose estimation and 3D object recognition.
Using active contours and Fourier descriptors for motion tracking with applications in MRI

To estimate motion, the edges of a moving object can be detected using active contours. After detecting the edges throughout the duration of the motion, the contours are registered together in order to obtain the motion model parameters. This idea can be applied to motion artifacts in Magnetic Resonance Images (MRI) which are caused by patient motion during the acquisition of an MRI. Thus, if the patient motion can be determined, it will be possible to correct for the motion in the images. The proposed method eliminates motion artifacts by dividing the acquired MRI data into several subbands. These subbands create invariant tissue feature maps called subband images. Using active contours, the edges of each subband image are detected and the relative motion is analyzed across the different subband images, thus determining the motion parameters. From these motion parameters it is possible to correct the subband images, thus correcting the k-space yielding clear, noise-free MR images.
A fast level set algorithm for shape-based segmentation with multiple selective priors

This paper addresses the shape-based segmentation problem using level sets. In particular, we propose a fast algorithm to solve the piece-wise constant Chan-Vese segmentation model with shape priors and labeling functions. Instead of directly solving the underlying PDE, we calculate the energy and check how it changes when we move image points from inside the region enclosed by the evolving interface to the outside region and vice-vera. This algorithm is then extended to the case of multi-phase Chan-Vese model, with multiple selective shape priors and a corresponding labeling function for each prior. This makes our algorithm different from that in  and other similar works in different aspects. On one hand, our algorithm is not restricted to two regions, but allows segmentation into several regions. On the other hand, more than one shape prior can be taken into account in our implementation. In addition, the proposed algorithm improves dramatically the computational speed. Experimental results, on both synthetic and real images, demonstrate the performance of our algorithm and the computational improvements it offers.
A novel, fast, and complete 3D segmentation of vertebral bones

Bone mineral density (BMD) measurements and fracture analysis of the spine bones are restricted to the Vertebral bodies (VBs), especially the trabecular bones (TBs). In this paper, we propose a novel, fast, and robust 3D framework to segment VBs and trabecular bones in clinical computed tomography (CT) images without any user intervention. The Matched filter is employed to detect the VB region automatically. To segment the whole VB, the graph cuts method which integrates a linear combination of Gaussians (LCG) and Markov Gibbs Random Field (MGRF) is used. Then, the cortical and trabecular bones are segmented using local volume growing methods. Validity was analyzed using ground truths of data sets (expert segmentation) and the European Spine Phantom (ESP) as a known reference. Experiments on the data sets show that the proposed segmentation approach is more accurate than other known alternatives.
Locked, unlocked and semi-locked network weights for four different camera calibration problems

Backpropagation training algorithms typically view network weights as a single vector of isotropic parameters to be minimized. In contrast, we present a neural network in which each network weight has its own physical meaning and its different role during network training. The network is used to solve four different types of calibration problems found in computer vision applications. A network weight may be unlocked, locked or semi-locked during training according to the available information about the problem. Experiments show the network trained with the available backpropagation-based algorithms can provide superior results to some other widely-used calibration techniques.
Modeling Lambertian Surfaces Under Unknown Distant Illumination Using Hemispherical Harmonics

A surface reflectance function represents the process of turning irradiance signals into outgoing radiance. Irradiance signals can be represented using low-order basis functions due to their low-frequency nature. Spherical harmonics (SH) have been used to provide such basis. However the incident light at any surface point is defined on the upper hemisphere, full spherical representation is not needed. We propose the use of hemispherical harmonics (HSH) to model images of convex Lambertian objects under distant illumination. We formulate and prove the addition theorem for HSH in order to provide an analytical expression of the reflectance function in the HSH domain. We prove that the Lambertian kernel has a more compact harmonic expansion in the HSH domain when compared to its SH counterpart. We present the approximation of the illumination cone in the HSH domain where the non-negative lighting constraint is forced. Our experiments illustrate that the 1st order HSH outperforms 1st and 2nd order SH in the process of image reconstruction as the number of light sources grows. In addition, we provide empirical justification of the nonnecessity of enforcing the non-negativity constraint in the HSH domain, allowing us to use unconstrained least squares which is faster than the constrained one, while maintaining the same quality of the reconstructed images.
Face reconstruction and recognition using a statistical model combining shape and spherical harmonics

We present a face reconstruction framework based on a statistical model that merges shape (2D and height maps), appearance (albedo), and spherical harmonics projection (SHP) information. The framework takes a 2D frontal face image under arbitrary illumination as input and outputs the estimated 3D shape and appearance. Face identification is performed using the shape and albedo coefficients of the fitting process, as well as the actual shape/texture reconstructions. Results show decent face reconstructions and a perfect identification rate for the frontal images of the Extended Yale Database B.
Robust virtual forces-based camera positioning using a fusion of image content and intrinsic parameters

In this paper, we present a novel and robust model for camera planning in smart vision systems. The proposed approach uses virtual forces to adjust camera parameters (pan and tilt) to the most proper values with respect to the application. The proposed model employs the information in the acquired image and some of the intrinsic camera parameters to estimate pan and tilt displacements required to bring a target object into a specific location of interest in the image. Our model is a general framework and any vision system can be easily modeled to use it. This approach has several advantages over previous work in camera planning. It is portable, expandable, robust, and flexible. Also, there is no need for complicated calibration for the cameras or their pan-tilt heads. The results show that our approach is efficient even with poor system initialization and it is robust against possible weakness in the auxiliary algorithms used.
Multimodality image registration and fusion using neural network

Multi-modality image registration and fusion are essential steps in building 3D models from remote sensing data. In this paper, we present a neural network technique for the registration and fusion of multi-modality remote sensing data for the reconstruction of 3D models of terrain regions. A feedforward neural network is used to fuse the intensity data sets with the spatial data set after learning its geometry. Results on real data are presented. Human performance evaluation is assessed on several perceptual tests in order to evaluate the fusion results.
3D face recovery from intensities of general and unknown lighting using Partial Least Squares

We discuss a statistical shape-from-shading framework for images of general and unknown illumination. To overcome arbitrary illumination, the framework makes use of the fact that general lighting can be expressed using low-order spherical harmonics for convex Lambertian objects. We cast the classical shape-from-shading equation as a Partial Least Squares (PLS) regression problem, which allows for the rapid computation of the solution. Results show accurate shape recovery with respect to ground truth data.
A novel stability quantification of detected interest points in scale-space

In local invariant features, the detected interest points using scale-space representations are considered the most robust to many variations in the imaging conditions. The existing approaches extract interest points at scale-space differential singularities. In these approaches, all detected interest points are considered equally robust without taking in consideration the variable sensitivities of these interest points with respect to noise effects. In this paper, we analyze the robustness of detected interest points at scale-space singularities against noise effects. Also, we propose a novel quantitative stability measure of these interest points. The evaluation results show the effectiveness of the proposed stability measure in estimating the robustness of the detected interest points to noise effects.
A variational framework for 3D colonic polyp visualization in virtual colonoscopy

Colorectal cancer includes cancer of the colon, rectum, anus and appendix. Since it is largely preventable, it is extremely important to detect and treat the colorectal cancer in the earliest stage. Virtual colonoscopy is an emerging screening technique for colon cancer. One component of virtual colonoscopy, image pre-processing, is important for colonic polyp detection/ diagnosis, feature extraction and classification. This paper introduces a general variational approach based framework for a computer aided diagnosis system for colorectal cancer. It includes techniques for 3D colon segmentation, 3D colon object reconstruction by iso-surface generation, and 3D centerline extraction. The proposed framework has been validated on 22 real CT Colonography datasets.
Model-based 3D shape recovery from single images of unknown pose and illumination using a small number of feature points

This paper proposes a model-based approach for 3D facial shape recovery using a small set of feature points from an input image of unknown pose and illumination. Previous model-based approaches usually require both texture (shading) and shape information from the input image in order to perform 3D facial shape recovery. However, the methods discussed here need only the 2D feature points from a single input image to reconstruct the 3D shape. Experimental results show acceptable reconstructed shapes when compared to the ground truth and previous approaches. This work has potential value in applications such face recognition at-a-distance (FRAD), where the classical shape-from-X (e.g., stereo, motion and shading) algorithms are not feasible due to input image quality.
A system for human jaw modeling using intra-oral images

A novel integrated system is developed to obtain a record of the patient's occlusion using computer vision. Data acquisition is obtained using intra-oral video camera. A modified Shape from Shading (SFS) technique using perspective projection and camera calibration is then used to extract accurate 3D information from a sequence of 2D images of the jaw. A novel technique for 3D data registration using Grid Closest Point (GCP) transform and genetic algorithms (GA) is used to register the output of the SFS stage. Triangulization is then performed, and a solid 3D model is obtained via a rapid prototype machine. The overall purpose of this research is to develop a model-based vision system for orthodontics that will replace traditional approaches and can be used in diagnosis, treatment planning, surgical simulation and implant purposes.
Precise change detection in multi-spectral remote sensing imagery using SIFT-based registration

In this paper we propose a robust method for geometric co-registration, and an accurate change detection technique based on statistical method for multi-temporal high-resolution satellite imagery. The proposed algorithm is as the following: scale-invariant feature transform (SIFT) is used to extract a set of correspondence points in a pair, or multiple pairs, of images that are taken at different times and under different circumstances, then Random Sample Consensus (RANSAC) is used to remove the outlier set. the resulting inliers matched points is an accurate correspondences which used to register the given images. changes in registered images are identified using statistical analysis of image differences. Finally, Markov-Gibbs Random Field (MGRF) is used to model the spatial-contextual information contained in the resulting change mask. Experiments with generated synthetic multiband images, and LANDSAT5 Images, approved the accuracy the proposed algorithm.
A facial features detector integrating holistic facial information and part-based model

We propose a facial landmarks detector, in which a part-based model is incorporated with holistic face information. In the part-based model, the face is modeled by the appearance of different face parts and their geometric relation. The appearance is described by pixel normalized difference descriptor. This descriptor is the lowest computational complexity as compared with existing state-of-the-art while it has a similar accuracy. On the other hand, to model the geometric relation between the face parts, the complex Bingham distribution is adapted. This is because the complex Bingham distribution has a symmetric property so it is invariant to rotation, scale, and translation. After that the global information is incorporated with the local part-based model using a regression model. The regression model estimates the displacement to the final face shape model. The the proposed detector is evaluated on two datasets. Experimental results show that it outperforms the state-of-the-art approaches in detecting facial landmarks accurately.
Rigid neuroendoscope navigation system for minimally invasive surgery

This paper describes a high precision navigation system that helps neurosurgeons identify more easily the neuroendoscope's tip location in the patient's brain that can significantly reduce the length and increase the accuracy of minimally invasive procedures. It is an image-guided neurosurgery system that provides integrated information of preoperative 3-D medical volumes, intra-operative neuroendoscope 2-D images, position information of the endoscope and surgical tools. The system's components include data acquisition, segmentation, multi-modal registration, 3-D surface and volume reconstruction, 3-D visualization, surgical simulation and integration of tracking systems. An articulated arm (digitizer) is used for automated registration of the 3-D model to the patient, then it is integrated with the system to dynamically localize surgical instruments and the patient's head in the patient's coordinate system. The visualization subsystem shows the position and orientation of the neuroendoscope's tip in the 3-D model of preoperative Magnetic Resonance (MR) volume and in the three orthogonal (sagittal, cronoal and axial) MR slices. The system's components and clinical applications are described.
Face detection at a distance using saliency maps

Face detection has been one of the most studied topics in the biometrics literature. A critical factor in the face detection process is the complexity of the setup used to acquire the images. The face detection might be considered a solved problem in easy setups but for outdoor setups with unconstrained illumination and background conditions the problem is still hard. In this paper we propose using saliency maps as a clue for false positive rejection in the applications that require face detection at a distance in complex unconstrained outdoor backgrounds. The saliency maps will be used in a robust score fusion approach combined with other aspects like facial features and skin color. The advantage in using saliency maps is that it tackles the problem from another perspective that is not affected by pose, illumination and expression problems. Experimental results show that the proposed method is promising.
Face recognition at-a-distance based on sparse-stereo reconstruction

We describe a framework for face recognition at a distance based on sparse-stereo reconstruction. We develop a 3D acquisition system that consists of two CCD stereo cameras mounted on pan-tilt units with adjustable baseline. We first detect the facial region and extract its landmark points, which are used to initialize an AAM mesh fitting algorithm. The fitted mesh vertices provide point correspondences between the left and right images of a stereo pair; stereo-based reconstruction is then used to infer the 3D information of the mesh vertices. We perform experiments regarding the use of different features extracted from these vertices for face recognition. The cumulative rank curves (CMC), which are generated using the proposed framework, confirms the feasibility of the proposed work for long distance recognition of human faces with respect to the state-of-the-art.
Image retrieval based on content and image compression

Content-based image retrieval systems have become a reliable tool for many image database applications. There are several advantages of the image retrieval techniques compared to other simple retrieval approaches such as text-based retrieval techniques. This paper proposes an image retrieval technique that can be used for retrieving color images. In this paper, we propose two variations of an image abstraction technique based on signature bit-strings and an appropriate similarity metric. The technique provides a compact representation of an image based on its color content and yields better retrieval effectiveness than classical techniques based on the images' global color histograms (GCHs). The proposed technique has many applications. These applications are described in details in this paper.
Revamped fly-over for accurate colon visualisation in virtual colonoscopy

This study revisits a visualisation technique for virtual colonoscopy, known as virtual fly-over (FO). The method views the entire colon anatomy from above the centreline. It assigns two cameras located on opposite sides of the centreline, each of which is responsible for viewing one half of the colon. This approach has several advantages over related colon visualisation methods with regards to visibility coverage and polyp detection rate. However, the traditional FO implementation created a few drawbacks that hinder complete visualisation. For example, it could overlook polyps located on the line between the two halves, and it has no data-specific initialisation for cutting planes. The authors enhance the FO method in a number of respects in this study: resolve the cutting issue and improve the virtual camera setup in order to better visualise both the colon surface's hidden structures and polyps that are difficult to locate. Quantitative validation of the revamped FO on 30 actual clinical datasets with complicated shapes and large volumes demonstrated that the average surface visualisation rate is equal to 99.5 ± 0.2%. Also, true and synthetic polyps with various shapes and sizes were used to clinically validate the proposed method. Detection rate is up to 100% on the tested sets.