nils-herrmann/clean_scirepeval huggingface dataset:
Finding faces in photographs

Two new schemes are presented for finding human faces in a photograph. The first scheme approximates the unknown distributions of the face and the face-like manifolds wing higher order statistics (HOS). An HOS-based data clustering algorithm is also proposed. In the second scheme, the face to non-face and non-face to face transitions are learnt using a hidden Markov model (HMM). The HMM parameters are estimated corresponding to a given photograph and the faces are located by examining the optimal state sequence of the HMM. Experimental results are presented on the performance of both the schemes.
A Novel Technique Based on Deep Learning and a Synthetic Target Database for Classification of Urban Areas in PolSAR Data

The classification of urban areas in polarimetric synthetic aperture radar (PolSAR) data is a challenging task. Moreover, urban structures oriented away from the radar line of sight pose an additional complexity in the classification process. The characterization of such areas is important for disaster relief and urban sprawl monitoring applications. In this paper, a novel technique based on deep learning is proposed, which leverages a synthetic target database for data augmentation. The PolSAR dataset is rotated by uniform steps and collated to form a reference database. A stacked autoencoder network is used to transform the information in the augmented dataset into a compact representation. This significantly improves the generalization capabilities of the network. Finally, the classification is performed by a multilayer perceptron network. The modular architecture allows for easy optimization of the hyperparameters. The synthetic target database is created and the classification performance is evaluated on an L-band airborne UAVSAR dataset and L-band space-borne ALOS-2 dataset acquired over San Francisco, USA. The proposed technique shows an overall accuracy of $91.3{\%}$ . An improvement over state-of-the-art techniques is achieved, especially in urban areas rotated away from the radar line of sight.
Robust detection of skew in document images

We describe a robust yet fast algorithm for skew detection in binary document images. The method is based on interline cross-correlation in the scanned image. Instead of finding the correlation for the entire image, it is calculated over small regions selected randomly. The proposed method does not require prior segmentation of the document into text and graphics regions. The maximum median of cross-correlation is used as the criterion to obtain the skew, and a Monte Carlo sampling technique is chosen to determine the number of regions over which the correlations have to be calculated. Experimental results on detecting skews in various types of documents containing different linguistic scripts are presented here.
Recovery of relative depth from a single observation using an uncalibrated (real-aperture) camera

In this paper we investigate the challenging problem of recovering the depth layers in a scene from a single defocused observation. The problem is definitely solvable if there are multiple observations. In this paper we show that one can perceive the depth in the scene even from a single observation. We use the inhomogeneous reverse heat equation to obtain an estimate of the blur, thereby preserving the depth information characterized by the defocus. However, the reverse heat equation, due to its parabolic nature, is divergent. We stabilize the reverse heat equation by considering the gradient degeneration as an effective stopping criterion. The amount of (inverse) diffusion is actually a measure of relative depth. Because of ill-posedness we propose a graph-cuts based method for inferring the depth in the scene using the amount of diffusion as a data likelihood and a smoothness condition on the depth in the scene. The method is verified experimentally on a varied set of test cases.
Zoom based super-resolution through SAR model fitting

We propose a technique for super-resolution imaging of a scene from observations at different camera zooms. Given a sequence of images with different zoom factors of a static scene, we obtain a picture of the entire scene at a resolution corresponding to the most zoomed observation. We model the high resolution image as a simultaneous autoregressive (SAR) model, the parameters of which are learnt from the most zoomed observation. Assuming that the entire scene can be described by a homogeneous SAR model, the learnt parameters are then used in a suitable regularization technique to estimate the high resolution field.
A perceptually organized method for image interpolation

Perception of an image depends on its visual representation. In this paper we present a perceptually organized scheme for image expansion or scattered data interpolation. This is done by decomposing the image (or data) into appropriate perceptual groups, carrying out the interpolation in individual groups as per the perceptual necessity and subsequently transforming the interpolated values back to the image domain. Various perceptual properties of the image, such as the 3D shape of an object, textural homogeneity, local variations in scene reflectivity, visual transparency can be better preserved during the interpolation process.
On analyzing the performance of least squares methods in motion estimation

An algorithm is presented here to obtain the total least squares (TLS) estimates of the motion parameters of an object from range data or perspective views. TLS estimates are suitable when data in both frames are corrupted by noise. We analyze the robustness of different least squares methods for the estimation of motion parameters against the possible mismatches in establishing the feature point correspondence. As the errors in point correspondence increase, the performance of ordinary least squares (LS) estimator was found to deteriorate much faster than that of TLS estimator.
A Novel Domain Adaptation Bayesian Classifier for Updating Land-Cover Maps With Class Differences in Source and Target Domains

This paper addresses the problem of land-cover map updating by classification of multitemporal remote-sensing images in the context of domain adaptation (DA). The basic assumptions behind the proposed approach are twofold. The first one is that training data (ground reference information) are available for one of the considered multitemporal acquisitions (source domain) whereas they are not for the other (target domain). The second one is that multitemporal acquisitions (i.e., target and source domains) may be characterized by different sets of classes. Unlike other approaches available in the literature, the proposed DA Bayesian classifier based on maximum a posteriori decision rule (DA-MAP) automatically identifies whether there exist differences between the set of classes in the target and source domains and properly handles these differences in the updating process. The proposed method was tested in different scenarios of increasing complexity related to multitemporal image classification. Experimental results on medium-resolution and very high resolution multitemporal remote-sensing data sets confirm the effectiveness and the reliability of the proposed DA-MAP classifier.
An MRF Model-Based Approach to Simultaneous Recovery of Depth and Restoration from Defocused Images

In this paper, we propose a MAP-Markov random field (MRF) based scheme for recovering the depth and the focused image of a scene from two defocused images. The space-variant blur parameter and the focused image of the scene are both modeled as MRFs and their MAP estimates are obtained using simulated annealing. The scheme is amenable to the incorporation of smoothness constraints on the spatial variations of the blur parameter as well as the scene intensity. It also allows for inclusion of line fields to preserve discontinuities. The performance of the proposed scheme is tested on synthetic as well as real data and the estimates of the depth are found to be better than that of the existing window-based depth from defocus technique. The quality of the space-variant restored image of the scene is quite good even under severe space-varying blurring conditions.
Opportunistic adaptive haptic sampling on forward channel in telehaptic communication

We propose a network-based opportunistic improvisation to adaptive sampling for the forward channel telehaptic data stream on a time-varying network. The algorithm explores real-time tuning of the perceptual deadband parameter to minimize network underutilization, and consequently improves the quality of telehaptic communication. We describe in detail the rationale behind the design choices of the proposed sampling scheme. We perform both real-time telehaptic experiments and simulations to test the proof of concept. The reconstructed haptic signals reveal a substantial improvement in average SNR of 3.57 dB, suggesting that the proposed method outperforms the conventional adaptive sampling technique to a large extent. In addition to satisfying the telehaptic Quality of Service (QoS) requirements, we also demonstrate that our method does not overwhelm the network or penalize the concurrent traffic streams.
Volume preserving haptic pottery

This paper proposes a realistic deformation model for pottery in which the user can interact with a rotating clay volume using a haptic tool. The deformation algorithm preserves the volume of clay to model the incompressible nature of semi-solid clay used in pottery. The interactive clay volume consists of an array of cylindrical elements stacked up vertically, providing simple and efficient collision detection and response. As a part of collision response, the force feedback consisting of both normal spring deformation force and friction force is rendered. Volume preservation is achieved by distributing the removed clay due to interactions, to the entire clay volume using a Rayleigh density function. To depict the real life pottery experience, the mechanical stability of the rotating clay volume is also included.
Self-induced color correction for skin tracking under varying illumination

An important challenge of any skin color based tracking system is to accommodate varying illumination conditions. We present a method for automatic transformation of the color planes to match the skin color model learnt for a fixed illumination. The first couple of initial frames are used to automatically extract the palm region which in turn serves as an observed skin color palette which needs to be color-transformed to match a similar palette under a known illumination. A neural network implementing back-propagation learning rule then performs the color correction for the entire sequence. We use a condensation algorithm for tracking after the color correction for improved performance.
Hierarchical Subspace Learning Based Unsupervised Domain Adaptation for Cross-Domain Classification of Remote Sensing Images

We address the problem of automatic updating of land-cover maps by using remote sensing images under the notion of domain adaptation (DA) in this paper. Essentially, unsupervised DA techniques aim at adapting a classifier modeled on the source domain by considering the available ground truth and evaluate the same on a related yet diverse target domain consisting only of test samples. Traditional subspace learning based strategies in this respect inherently assume the existence of a single subspace spanning the data from both the domains. However, such a constraint becomes rigid in many scenarios considering the diversity in the statistical properties of the underlying semantic classes and problem due to data overlapping in the feature space. As a remedy, we propose an automated binary-tree based hierarchical organization of the semantic classes and subsequently introduce the notion of node-specific subspace learning from the learned tree. We validate the method on hyperspectral, medium-resolution, and very high resolution datasets, which exhibits a consistently improved performance in comparison to standard single subspace learning based strategies as well as other representative techniques from the literature.
Scene Recognition From Optical Remote Sensing Images Using Mid-Level Deep Feature Mining

We solve the problem of scene recognition from very high-resolution optical satellite remote sensing (RS) images by exploring the notion of mid-level feature mining. The existing mid-level feature extraction techniques are based on applying feature encodings over a set of discriminatively selected localized feature descriptors from the images. Such techniques inherently suffer from two shortcomings: 1) the local descriptors are not enough discriminative, since they are mostly based on scale invariant feature transform (SIFT) like ad hoc features and 2) the definition of a robust ranking function to select discriminative local features is nontrivial. As a remedy, we propose a pattern mining-based approach for an efficient discovery of mid-level visual elements, which considers convolutional neural network features of the category-independent region proposals extracted from the images as the local descriptors. While the region proposals depict better semantic information than the SIFT like features, the proposed pattern mining strategy can efficiently highlight the correlations between such local descriptors and the class labels. Experimental results suggest that the proposed technique outperforms a number of existing mid-level feature descriptors for the standard optical RS data sets.
Simultaneous depth recovery and image restoration from defocused images

We propose a method for simultaneous recovery of depth and restoration of scene intensity, given two defocused images of a scene. The space-variant blur parameter and the focused image of the scene are modeled as Markov random fields (MRFs). Line fields are included to preserve discontinuities. The joint posterior distribution of the blur parameter and the intensity process is examined for locality property and we derive an important result that the posterior is again Markov. The result enables us to obtain the maximum a posterior (MAP) estimates of the blur parameter and the focused image, within reasonable computational limits. The estimates of depth and the quality of the restored image are found to be quite good, even in the presence of discontinuities.
Tensorization of Multifrequency PolSAR Data for Classification Using an Autoencoder Network

A novel tensorization framework is proposed, which utilizes the Kronecker product to combine multifrequency polarimetric synthetic aperture radar data in conjunction with an artificial neural network (ANN) for classification. The ANN comprises of two stages, where an unsupervised stochastic sampling autoencoder learns an efficient representation and a supervised feed forward network performs classification. The proposed framework is demonstrated using multifrequency (C-, L-, and P-bands) data sets collected by the AIRSAR system. The classification performance of single tensor product of dual- and triple-band combinations is evaluated. It is observed that the classification accuracy of the tensor products outperforms single, as well as, the simple augmentation of the frequency bands.
Fragment Based Tracking for Scale and Orientation Adaptation

In this work, we propose a simple yet highly effective algorithm for tracking a target through significant scale and orientation change. We divide the target into a number of fragments and tracking of the whole target is achieved by coordinated tracking of the individual fragments. We use the mean shift algorithm to move the individual fragments to the nearest minima, though any other method like integral histograms could also be used. In contrast to the other fragment based approaches, which fix the relative positions of fragments within the target, we permit the fragments to move freely within certain bounds. Furthermore, we use a constant velocity Kalman filter for two purposes. Firstly, Kalman filter achieves robust tracking because of usage of a motion model. Secondly, to maintain coherence amongst the fragments, we use a coupled state transition model for the Kalman filter. Using the proposed tracking algorithm, we have experimented on several videos consisting of several hundred frames length each and obtained excellent results.
Optimal recovery of depth from defocused images using an MRF model

A MAP-MRF based scheme is proposed for simultaneous recovery of the depth and the focused image of a scene from two defocused images. The space-variant blur parameter and the focused image of the scene are both modeled as MRFs and their MAP estimates are obtained using simulated annealing. The performance of the proposed scheme is tested on synthetic as well as real data and the estimates of the depth are found to be better than that of existing window-based techniques.
Design of a haptic environment to study the effect of loading on resting and action tremors

We propose a novel design of haptic virtual environments for the quantification of both resting and action tremor. To quantify the resting tremor, a haptic virtual environment simulating the movement of hand is subjected to a variable resistance. The subject is asked to rest his/her hand in this environment and the velocity of hand motion (tremor) is captured by the haptic device. The variation of tremor parameters with virtual loading of the hand is estimated. To quantify action tremor, a dynamic virtual haptic environment is designed where a subject is asked to move a rigid body from a starting point to an end point along a specified path. During this manipulation, velocity of hand motion is captured by the haptic device to extract tremor using appropriate signal processing tools. The mass of the rigid body is increased to study the effect of loading on tremor.
Detection of blood vessels in retinal images using two-dimensional matched filters.

Blood vessels usually have poor local contrast, and the application of existing edge detection algorithms yield results which are not satisfactory. An operator for feature extraction based on the optical and spatial properties of objects to be recognized is introduced. The gray-level profile of the cross section of a blood vessel is approximated by a Gaussian-shaped curve. The concept of matched filter detection of signals is used to detect piecewise linear segments of blood vessels in these images. Twelve different templates that are used to search for vessel segments along all possible directions are constructed. Various issues related to the implementation of these matched filters are discussed. The results are compared to those obtained with other methods.
Low bit-rate compression of video and light-field data using coded snapshots and learned dictionaries

The method of coded snapshots has been proposed recently for compressive acquisition of video data to overcome the space-time trade-of inherent in video acquisition. The method involves modulation of the light entering the video camera at different time instants during the exposure period by means of a different and randomly generated code pattern at each of those time instants, followed by integration across time, leading to a single coded snapshot image. Given this image and knowledge of the random codes, it is possible to reconstruct the underlying video frames - by means of sparse coding on a suitably learned dictionary. In this paper, we apply a modified version of this idea, proposed formerly in the compressive sensing literature, to the task of compression of videos and light-field data. At low bit rates, we demonstrate markedly better reconstruction fidelity for the same storage costs, in comparison to JPEG2000 and MPEG-4 (H.264) on light-field and video data respectively. Our technique can cope with overlapping blocks of image data, thereby leading to suppression of block artifacts.
Automatic robot programming by visual demonstration of task execution

We propose a novel approach to program a robot by demonstrating the task multiple number of times in front of a vision system. The system analyzes the visual data captured during the demonstration of the task by a human operator and produces manipulator level commands, so that the robot can replicate the task. Here we integrate human dexterity with sensory data using computer vision techniques in a single platform. This paper describes a fast and efficient algorithm for the vision system to process a binocular video sequence to obtain the trajectories of each component of the end-effector. The concept of trajectory bundle is introduced to avoid singularity and to obtain an optimal path.
Regularized depth from defocus

In the area of depth estimation from images an interesting approach has been structure recovery from defocus cue. Towards this end, there have been a number of approaches . Here we propose a technique to estimate the regularized depth from defocus using diffusion. The coefficient of the diffusion equation is modeled using a pair-wise Markov random field (MRF) ensuring spatial regularization to enhance the robustness of the depth estimated. This framework is solved efficiently using a graph-cuts based techniques. The MRF representation is enhanced by incorporating a smoothness prior that is obtained from a graph based segmentation of the input images. The method is demonstrated on a number of data sets and its performance is compared with state of the art techniques.
Hand tremor analysis using deformable object manipulation in a haptic environment

We have developed a system for analysing hand tremors using a deformable object manipulation in a haptic environment. The subject performs some specific manipulative tasks, like moving a soft object from one location to another, in a virtual 3D environment using a haptic device. While the task is being performed, the velocity of hand movement is sampled by the haptic device and this is later processed for tremor analysis. To validate the efficacy of this system and to compare it with standard methods of tremor analysis using accelerometers, we have simultaneously collected velocity data through accelerometers and compared our results with those obtained from the accelerometer based method. We have designed the necessary haptic rendering system as well as the signal analysis schemes to build the complete system.
Perception-Based Compression of Haptic Data Streams Using Kalman Filters

In order to realize truly immersive and stable telepresence and teleaction over the Internet it is necessary to keep delay, data rate, and packet rate of haptic data streams as low as possible. In addition, the compression scheme for haptic data, which is necessary to achieve those goals, must be fast enough to work on a sample by sample basis to not add further delay. This paper presents an approach that reduces haptic data traffic in networked telepresence and teleaction systems to a small fraction of the original rate without impairing performance by using fast Kalman filters on the input signals combined with model based prediction of haptic signals. Our approach reduces the number of transmitted packets to 9.8% (velocity) and 6.2% (force) of the original rate without impairing immersiveness
Perception-Based Data Reduction and Transmission of Haptic Data in Telepresence and Teleaction Systems

We present a novel approach for the transmission of haptic data in telepresence and teleaction systems. The goal of this work is to reduce the packet rate between an operator and a teleoperator without impairing the immersiveness of the system. Our approach exploits the properties of human haptic perception and is, more specifically, based on the concept of just noticeable differences. In our scheme, updates of the haptic amplitude values are signaled across the network only if the change of a haptic stimulus is detectable by the human operator. We investigate haptic data communication for a 1 degree-of-freedom (DoF) and a 3 DoF teleaction system. Our experimental results show that the presented approach is able to reduce the packet rate between the operator and teleoperator by up to 90% of the original rate without affecting the performance of the system.