nils-herrmann/clean_scirepeval huggingface dataset:
A block shift-variant blur model for recovering depth from defocused images

The recovery of depth from defocus involves calculating the depth of various points in a scene by modeling the effect that the focal parameters of the camera have on images acquired with a small depth of field. We propose a method that, instead of analyzing an image region in isolation, uses a block shift-variant interactive blur model to account for the interaction among neighboring subimages. Simulation results are presented on the performance of the model.
Region-Based Retrieval of Remote Sensing Images Using an Unsupervised Graph-Theoretic Approach

This letter introduces a novel unsupervised graph-theoretic approach in the framework of region-based retrieval of remote sensing (RS) images. The proposed approach is characterized by two main steps: (1) modeling each image by a graph, which provides region-based image representation combining both local information and related spatial organization, and (2) retrieving the images in the archive that are most similar to the query image by evaluating graph-based similarities. In the first step, each image is initially segmented into distinct regions and then modeled by an attributed relational graph, where nodes and edges represent region characteristics and their spatial relationships, respectively. In the second step, a novel inexact graph matching strategy, which jointly exploits a subgraph isomorphism algorithm and a spectral graph embedding technique, is applied to match corresponding graphs and to retrieve images in the order of graph similarity. Experiments carried out on an archive of aerial images point out that the proposed approach significantly improves the retrieval performance compared to the state-of-the-art unsupervised RS image retrieval methods.(RS) images.
Performance Analysis for Image Super-Resolution Using Blur as a Cue

A number of algorithms for image super-resolution  using multiple images, have been developed over the last two decades. On the other hand, a very less      amount of efforts  have been made to explore the issues regarding performance analysis of these methods. Since the  problem of super-resolution is often a   parameter estimation problem, the Cramer-Rao bound proves to be useful tool in analyzing the performance of the estimators.  We focus on the problem of      super-resolving with blur as a cue. In this paper  we look at the factors affecting the achievable bounds in super-resolution. We analyze the effects of the magnification factor, modeling noise and the spectrum of the signal to be super-resolved.
Model based data compression for 3D virtual haptic teleinteraction

This paper presents a model based prediction of haptic data signals for data compression purposes in virtual haptic teleinteraction environments. The proposed data reduction itself is based on psychophysical properties of human perception. This scheme can be used for Internet-based multimedia applications like haptics-supported games or virtual haptic explorations. Using our approach haptic data packet rate reduction of up to 95% without impairing immersiveness is achieved.
Haptic rendering of dense 3D point cloud data

This work is aimed at rendering an object described by a dense point cloud data without a pre-computed polygonal mesh and surface normal information. We use a proxy based rendering technique with proxy collocated with the HIP in free space. To avoid sinking of proxy into the object during collision a sphere of sufficiently large radius is selected as the proxy so as to avoid it going through the point cloud. Once collision is detected, we minimize a cost function depending on the current HIP and proxy positions and find a new goal position for the proxy corresponding to the local minimum of the cost function. Our rendering algorithm continuously evaluates a tangential vector for the proxy to move over the object surface during collision. The penetration depth is calculated from the proxy to the HIP and is used to calculate the reaction force for the haptic device. We used our technique to render several dense point cloud based models. We also use the surface normal evaluated at the point of contact to shade the object surface when shown visually.
A Fuzzy Based Strategy for Improved Search Coverage of an Airborne Seeker

Missions involving unmanned aerial vehicles require extensive searching for potential targets from above. Antennas fitted to such vehicles are mounted on two gimbals for allowing full movement about the azimuth and elevation plane with respect to the body axis. In real time there is a need to use the available searching time optimally. It is worthwhile to tune the azimuth search angle within available time and energy constraints. This work shows how tentative a priori knowledge about the search area along with navigational information may be utilized for developing better search strategy.
Shock filters based on implicit cluster separation

One of the classic problems in low level vision is image restoration. An important contribution toward this effort has been the development of shock filters by Osher and Rudin (1990). It performs image deblurring using hyperbolic partial differential equations. In this paper we relate the notion of cluster separation from the field of pattern recognition to the shock filter formulation. A kind of shock filter is proposed based on the idea of gradient based separation of clusters. The proposed formulation is general enough as it can allow various models of density functions in the cluster separation process. The efficacy of the method is demonstrated through various examples.
Depth estimation and image restoration using defocused stereo pairs

We propose a method for estimating depth from images captured with a real aperture camera by fusing defocus and stereo cues. The idea is to use stereo-based constraints in conjunction with defocusing to obtain improved estimates of depth over those of stereo or defocus alone. The depth map as well as the original image of the scene are modeled as Markov random fields with a smoothness prior, and their estimates are obtained by minimizing a suitable energy function using simulated annealing. The main advantage of the proposed method, despite being computationally less efficient than the standard stereo or DFD method, is simultaneous recovery of depth as well as space-variant restoration of the original focused image of the scene.
Robust estimation of 3-D motion parameters in presence of correspondence mismatches

It is noted that the accuracy of a motion analysis scheme based on feature point correspondence is quite poor when there are mismatches between points. A small amount of mismatch (i.e., outlier data) may degrade the performance of the least-squares estimator significantly. It is shown that the least median of squares (LMedS) estimator can provide the required robustness. This method works well even when almost half of the given data are outliers. A Monte Carlo sampling technique is used to reduce the computational load. Subsequent use of the total least-squares or the constrained least-squares method on the trimmed data set enhances efficiency. Simulation experiments are carried out to evaluate the performance of the proposed method. By virtue of having a very high breakdown point, the LMedS estimator provides the much desired robustness against the correspondence mismatches.<<ETX>>
Motion analysis of a deformable object using subset correspondence

A method is proposed to estimate generalized three-dimensional motion parameters for a deformable object using only subset correspondences between two successive time frames, eliminating a major preprocessing step. Only a small amount of information has to be processed, reducing the computation time significantly. The movement in three-dimensional space thus can be modeled by the deformation undergone by the object during the specified time instants, a rotation about a fixed axis passing through its centroid, and a translation. The goal is to estimate the generalized motion parameters (i.e. deformation, rotation, and translational components) with as little information (i.e. point/line correspondence) as possible from the data.<<ETX>>
Multilabel Remote Sensing Image Retrieval Using a Semisupervised Graph-Theoretic Method

Conventional supervised content-based remote sensing (RS) image retrieval systems require a large number of already annotated images to train a classifier for obtaining high retrieval accuracy. Most systems assume that each training image is annotated by a single label associated to the most significant semantic content of the image. However, this assumption does not fit well with the complexity of RS images, where an image might have multiple land-cover classes (i.e., multilabels). Moreover, annotating images with multilabels is costly and time consuming. To address these issues, in this paper, we introduce a semisupervised graph-theoretic method in the framework of multilabel RS image retrieval problems. The proposed method is based on four main steps. The first step segments each image in the archive and extracts the features of each region. The second step constructs an image neighborhood graph and uses a correlated label propagation algorithm to automatically assign a set of labels to each image in the archive by exploiting only a small number of training images annotated with multilabels. The third step associates class labels with image regions by a novel region labeling strategy, whereas the final step retrieves the images similar to a given query image by a subgraph matching strategy. Experiments carried out on an archive of aerial images show the effectiveness of the proposed method when compared with the state-of-the-art RS content-based image retrieval methods.
Transition Detection in Body Movement Activities for Wearable ECG

It has been shown by Pawar (2007) that the motion artifacts induced by body movement activity (BMA) in a single-lead wearable electrocardiogram (ECG) signal recorder, while monitoring an ambulatory patient, can be detected and removed by using a principal component analysis (PCA)-based classification technique. However, this requires the ECG signal to be temporally segmented so that each segment comprises of artifacts due to a single type of body movement activity. In this paper, we propose a simple, recursively updated PCA-based technique to detect transitions wherever the type of body movement is changed
A context-sensitive Bayesian technique for the partially supervised classification of multitemporal images

An advanced context-sensitive classification technique that exploits a temporal series of remote sensing images for a regular updating of land-cover maps is proposed. This technique extends the use of spatio-contextual information to the framework of partially supervised approaches (that are capable of addressing the updating problem under the realistic, though critical, constraint that no ground-truth information is available for some of the images to be classified). The proposed classifier is based on an iterative partially supervised algorithm that jointly estimates the class-conditional densities and the prior model for the class labels on the image to be classified by taking into account spatio-contextual information. Experimental results point out that the proposed technique is effective and that it significantly outperforms the context-insensitive partially supervised approaches presented in the literature.
Estimation of motion parameters for a deformable object from range data

If the correspondence between two sets of points representing the coordinates of different points of an object undergoing rotational motion and deformation is known, the parameters can be estimated using different least-squares estimators. The total-least-squares (TLS) method is very appropriate when the observation and the data matrices are both perturbed by random noise. For Gaussian-distributed noise, the TLS solution is equivalent to maximum-likelihood estimation. The mean-square error in TLS is always smaller than in an ordinary least-squares (LS) estimator. The scope is analyzed of TLS in estimating the generalized motion parameters, as is the feasibility of decomposing the generalized motion parameters in terms of rotation and deformation parameters. The performance of TLS is compared to that of the LS estimator.<<ETX>>
Estimation of resolvability of user response in kinesthetic perception of jump discontinuities

Perceptually adaptive sampling based on Weber's law has been used to reduce the haptic packet rate for efficient haptic data transmission over Internet. In a teleoperation, perceptually significant force samples are transmitted from a robot to a human operator. If the time spacing between two consecutive perceptually sampled kinesthetic force stimuli is less than the minimum time spacing (temporal resolution Tr) required in perceiving the jump discontinuity, then the second force stimulus will not be perceived even if it is well above the just noticeable difference. Hence, there is no need to transmit the second force sample to the operator. Thus, for the transmission in a teleoperation, the temporal resolution Tr needs also to be considered while effecting perceptually adaptive sampling. To the best of our knowledge, it has not been studied in the literature. In this work, we propose a statistical method to estimate the temporal resolution Tr. In order to achieve this, we design an appropriate experimental set up, and record the haptic responses for several users extensively. We also study the effect of perceptual fatigue on the temporal resolution Tr, and validate all results using the classical psychometric approach.
A fast method for global surface interpolation

A fast algorithm is presented for a globally smooth interpolation for a visual surface from scattered range data. This method is based on matching lower order spatial moments, where the reconstructed surface is given by a linear combination of Legendre polynomials. This method can handle data that do not lie on a regular grid. The value of the function at points where the grid is not defined, or where the original data is missing, can be approximated from the constituent polynomials. Some simulation experiments evaluate the performance of the proposed scheme. An arbitrarily shaped surface is shown on a 35*35 grid, and the depth information is available only at a few selected grid points. The result of the implementation of Grimson's (1981) algorithm where the surface is constrained to pass through the given data set is given. The proposed method runs much faster than Grimson's algorithm. The coefficients of the Legendre polynomials can be precomputed.<<ETX>>
Analysis of Ambulatory ECG Signal

Ambulatory electrocardiogram (ECG) recorders are increasingly in use by people suffering from cardiac abnormalities. However, the ECG signal acquired by the ambulatory recorder is influenced by motion artifacts induced by any body movement activity (BMA). The goal of the paper is to demonstrate that it is possible to determine the BMA from the motion artifacts in the ECG signal itself. The ECG signal during a specific BMA is presumed to be an additive mix of signals due to cardiac activities, motion artifacts induced due to the BMA and sensor noise. We propose to characterize and determine the BMA from the corresponding motion artifact data in the ECG signal itself. The proposed technique is useful for removal of motion artifacts from the ECG signals for ambulatory cardiac monitoring
Analysis of an Adaptive Sampler Based on Weber's Law

Weber's law suggests a logarithmic relationship between perceptual stimuli and human perception. The Weber sampler is an adaptive, nonuniform sampling mechanism that exploits Weber's law to sample the signal at a minimum rate without significant perceptual degradation. In this paper, we introduce and analyze a regularized Weber sampler for smooth deterministic signals as well as smooth random processes. While analysis for a fixed Weber constant δ is analytically intractable except for some special cases, we exploit the fact that the Weber constant is usually small. Under suitable assumptions, for deterministic signals, we provide analytical approximations to the number of samples in a unit time interval and the intersample times. For a random signal, we give analytical approximations to the expectation and probability distribution function of the respective quantities. Our class of deterministic as well as random signals is quite wide, and in particular covers bandlimited signals and signals satisfying second order ordinary differential equations. We also present a number of simulations to demonstrate that our approximations are good up to a Weber constant of 0.2, which is the regime of practical interest. Our results provide a source model for the Weber sampler, which can be used in the study of transmission of perceptual stimuli signals over communication links.
Multi-objective super resolution: concepts and examples

Described methods for simultaneously generating the super-resolved depth map and the image from LR observations. Structural information is embedded within the observations and, through the two formulations of DFD and SFS problems, we were able to generate the super-resolved images and the structures. The first method described here avoids correspondence and warping problems inherent in current SR techniques involving the motion cue in the LR observations and uses a more natural depth-related defocus as a natural cue in real aperture imaging. The second method, while again avoiding the correspondence problems, also demonstrates the usefulness of the generalized interpolation scheme leading to more flexibility in the final SR image, in the sense that the LR image can be viewed at SR with an arbitrary light source position. The quality of the super-resolved depth and intensity maps has been found to be quite good. The MAP-MRF framework that was used in both methods models both the surface normal and the intensity field as separate MRFs, and this helps in regularizing the solution.
Robust shape based two hand tracker

This paper presents a robust shape-based on-line tracker for simultaneously tracking the motion of both hands, that is robust to cases of background clutter, other moving objects, occlusions of one hand by the other and a wide range of illumination variations. The tracker is based on an online predictive eigentracking framework. This framework allows efficient tracking of articulate objects, which change in appearance across views. We show results of successful tracking across all possible cases of motion dynamics of both hands during occlusion and a wide range of illumination conditions.
Toward automatic robot programming: learning human skill from visual data

We propose a novel approach to program a robot by demonstrating the task multiple number of times in front of a binocular vision system. We track artificially-induced features appearing in the image plane due to nonimpedimental color stickers attached at different fingertips and wrist joint, in a simultaneous feature detection and tracking framework. A Kalman filter does the tracking by recursively predicting the tentative feature location and a higher order statistics (HOS)-based data clustering algorithm extracts the feature. A fast and efficient algorithm for the vision system thus developed processes a binocular video sequence to obtain the trajectories and the orientation information of the end effector from the images of a human hand. The concept of trajectory bundle is introduced to avoid singularities and to obtain an optimal path.
Generation of super-resolution images from blurred observations using Markov random fields

This paper presents a new technique for generating a high resolution image from a blurred image sequence; this is also referred to as super-resolution restoration of images. The image sequence consists of decimated, blurred and noisy versions of the high resolution image. The high resolution image is modeled as a Markov random field (MRF) and a maximum a posteriori (MAP) estimation technique is used. A simple gradient descent method is used to optimize the functional. Further, line fields are introduced in the cost function and optimization using Graduated Non-Convexity (GNC) is shown to yield improved results. Lastly, we present results of optimization using Simulated Annealing (SA).
Data-driven multi-frame 3D motion estimation

We investigate how the temporal evolution of image point correspondences over multiple frames can be exploits for 3D motion and structure estimation. In comparison to other multi-frame approaches we do not separate the establishment of feature point correspondences and the 3D motion parameter computation. As a result our approach does not suffer from the limitations of the traditional two step approaches where even small errors in point correspondence can lead to large errors in 3D motion parameters. Experimental results on synthetic and real video sequences show that the estimation error is reduced when processing more than two frames simultaneously.
Identification of shift-variant point spread function for a class of imaging systems

A maximum a posteriori-Markov random field (MAP-MRF) based scheme is proposed for identifying a class of shift-variant imaging systems whose point spread function can be parameterized by a single blur parameter. The space-variant (SV) blur parameter is modeled as an MRF and its MAP estimate is obtained using simulated annealing. The performance of the proposed scheme is tested on both synthetic as well as real data and the estimates of the SV blur are found to be better than that of the window-based blur identification technique.
Automatic generation of robot program code: learning from perceptual data

We propose a novel approach to program a robot by demonstrating the task multiple number of times in front of a vision system. Here we integrate human dexterity with sensory data using computer vision techniques in a single platform. A simultaneous feature detection and tracking framework is used to track various features (finger tips and the wrist joint). A Kalman filter does the tracking by predicting the tentative feature location and a HOS-based data clustering algorithm extracts the feature. Color information of the features are used for establishing correspondences. A fast, efficient and robust algorithm for the vision system thus developed process a binocular video sequence to obtain the trajectories and the orientation information of the end effector. The concept of a trajectory bundle is introduced to avoid singularities and to obtain an optimal path.
Depth from defocus in presence of partial self occlusion

Contrary to the normal belief we show that self occlusion is present in any real aperture image and we present a method on how we can take care of the occlusion while recovering the depth using the defocus as the cue. The space-variant blur is modeled as an MRF and the MAP estimates are obtained for both the depth map and the everywhere focused intensity image. The blur kernel is adjusted in the regions where occlusion is present, particularly at the regions of discontinuities in the scene. The performance of the proposed algorithm is tested over synthetic data and the estimates are found to be better than the earlier schemes where such subtle effects were ignored.