nils-herrmann/clean_scirepeval huggingface dataset:
Critically sampled graph-based wavelet transforms for image coding

In this paper, we propose a new approach for image compression using graph-based biorthogonal wavelet filterbanks (referred to as graphBior filterbanks). These filterbanks, proposed in our previous work, operate on the graph representations of images, which are formed by linking nearby pixels with each other. The connectivity and the link weights are chosen so as to reflect the geometrical structure of the image. The filtering operations on these edge-aware image graphs avoid filtering across the image discontinuities, thus resulting in a significant reduction in the amount of energy in the high frequency bands. This reduces the bit-rate requirements for the wavelet coefficients, but at the cost of sending extra edge-information bits to the decoder. We discuss efficient ways of representing and encoding this edge information. Our experimental results, based on the SPIHT codec, demonstrate that the proposed approach achieves better R-D performance than the standard CDF9/7 filter on piecewise smooth images such as depth maps.
Stereo image coding using hierarchical MRF model and selective overlapped block disparity compensation

In this paper, we propose a novel hierarchical disparity estimation/compensation (DE/DC) algorithm for stereo image coding. One way to limit the well-known drawbacks of block matching (e.g., inaccurate disparity and blocking artifacts in the decoded image) is to resort to variable size block matching (VSBM). However, VSMB may result in an inconsistent disparity estimation (i.e., such that the estimated disparity field does not correspond to the true disparity) especially as the subblock becomes small, thus leading to high frequency energy at block boundaries in the residue image. To address these problems, in this paper we propose a hybrid quadtree-based DE/DC scheme, where a Markov Random Field (MRF) model is used in combination with VSBM and selective overlapped disparity compensation to improve the disparity field consistency and lead to higher coding efficiency. Our experimental results demonstrate that the proposed block segmentation scheme achieves a higher PSNR and a more consistent disparity field, as compared to conventional VSBM schemes.
Optimization of wireless networks via graph interpolation

A novel framework for the analysis and optimization of wireless networks is presented. The framework is based on the representation as a multi-dimensional graph of the Finite State Machine determining the temporal evolution of the network's state. Wireless protocols generate graphs with regular multi-scale connectivity structure. Additionally, cost functions measuring network performance metrics present strong regularity on the multi-dimensional state space of the FSM. The framework proposed herein uses the regularity of the graph and cost functions to achieve accurate recovery of the functions measuring the long-term cost incurred by the network from a small number of state observations. The approach takes inspiration from signal processing techniques where the partially know long-term cost is formulated as a downsampled-upsampled signal on a graph. In the graph domain, a low-pass filtering is applied to generate a smooth approximation of the value function from the known samples. The framework finds applications in distributed optimization and online learning.
Fault detection by means of wavelet transform in a PMSMW under demagnetization

This paper presents a study of a permanent magnet synchronous machine (PMSM) running under demagnetization. The demagnetization fault is analyzed through stator currents analysis and processing tools at different speeds. Advanced signal analysis by means of wavelet transforms is presented. Simulations were carried out by using two dimensional (2-D) finite element analysis (FEA), and result were compared with experimental ones, showing the effectiveness of the proposed method for magnetization fault detection and identification.
VBR video: tradeoffs and potentials

The authors examine the transport and storage of video compressed with a variable bit rate (VBR). They focus primarily on networked video, although they also briefly consider other applications of VBR video, including satellite transmission (channel sharing), playback of stored video, and wireless transport. Packet video research requires careful integration between the network and the video systems; however, a major stumbling block has resulted because commonly used terms are often interpreted differently by the video and networking communities. The paper then, has two main goals: (i) to clarify the definitions of terms that are often used with different meaning by networking and video-coding researchers and (ii) to explore the tradeoffs entailed by each of the various modalities of VBR transmission (unconstrained, shaped, constrained, and feedback). In particular, they evaluate the tradeoff among the advantages (better video quality, less delay, and more calls) that were identified by early proponents of VBR video transmission. An underlying theme of this paper is that increased interaction between the video and network design has potential for improving overall decoded video quality without changing the network capacity.
Three dimensional spot detection by multiscale analysis

We present a method to detect and count spots in three dimensional fluorescence images coming from biological immunomicroscopy experiments. It is based on the multiscale product of subband images resulting from a undecimated three dimensional wavelet transform decomposition of the original 3D image, after thresholding of non-significant coefficients. The multiscale product of the filtered wavelet coefficients, which allows the multiscale peaks due to spots to be enhanced while reducing noise, combines information coming from different levels of resolution and gives a clear and distinctive characterization of the spots. The effectiveness of the method is illustrated on microscopy images of yeast cells.
Basal cell carcinoma detection in full field OCT images using convolutional neural networks

In this paper we introduce a new application that exploits the emerging imaging modality of full field optical coherence tomography (FFOCT) as a means of optical biopsy. The objective is to build a computer-aided diagnosis (CAD) tool that can speed up the detection of tumoral areas in skin excisions resulting from Mohs surgery. Since there is little prior knowledge about the appearance of cancer cell morphology in this type of imagery, deep learning techniques are applied. Using convolutional neural networks (CNN), we train a feature extractor able to find representative characteristics for FFOCT data and a classifier that learns a generalized distribution of the data. With a dataset of 40 high-resolution images, we obtained a classification accuracy of 95.93%.
Alternating Direction Graph Matching

In this paper, we introduce a graph matching method that can account for constraints of arbitrary order, with arbitrary potential functions. Unlike previous decomposition approaches that rely on the graph structures, we introduce a decomposition of the matching constraints. Graph matching is then reformulated as a non-convex non-separable optimization problem that can be split into smaller and much-easier-to-solve subproblems, by means of the alternating direction method of multipliers. The proposed framework is modular, scalable, and can be instantiated into different variants. Two instantiations are studied exploring pairwise and higher-order constraints. Experimental results on widely adopted benchmarks involving synthetic and real examples demonstrate that the proposed solutions outperform existing pairwise graph matching methods, and competitive with the state of the art in higher-order settings.
Landmark-based segmentation of lungs while handling partial correspondences using sparse graph-based priors

In this paper, we propose a new segmentation algorithm that combines a graph-based shape model with image cues based on boosted features. The landmark-based shape model encodes prior constraints through the normalized Euclidean distances between pairs of control points, alleviating the need of a large database for the training. Moreover, the graph topology is deduced from the dataset using manifold learning and unsupervised clustering. In a graph-matching-like manner, we formulate the segmentation task as a labeling problem where we seek to match the model landmarks to image points that are extracted using the boosted classifiers. We also propose to overcome the limitation of missing correspondences by incorporating an additional label to account for outliers. Then, we repair the outlier positions to complete the segmentation. State-of-the-art discrete optimization techniques are used to provide our experimental results for the segmentation of the right lung in 2D chest radiographs, demonstrating the potentials of our method.
Wavelet-driven knowledge-based MRI calf muscle segmentation

We propose a novel representation of shape variation using diffusion wavelets, and a search paradigm based on local features. The representation can reflect arbitrary and continuous interdependencies in the training data. In contrast to state-of-the-art methods our approach during the learning stage optimizes the coefficients as well as the number and the position of landmarks using geometric constraints. During the learning stage the approach obtains a landmark shape model, based on diffusion maps. For the model search we apply an approach related to active feature models; the location of landmarks is updated iteratively, using local features, and the canonical correlation analysis. The resulting search is independent from the topology of the anatomical structure, and can represent complex geometric and photometric dependencies of the structure of interest. We report promising results on challenging medical data sets of T1 MRI full calf muscles.
Modeling the structure of multivariate manifolds: Shape maps

We propose a shape population metric that reflects the interdependencies between points observed in a set of examples. It provides a notion of topology for shape and appearance models that represents the behavior of individual observations in a metric space, in which distances between points correspond to their joint modeling properties. A Markov chain is learnt using the description lengths of models that describe sub sets of the entire data. The according diffusion map or shape map provides for the metric that reflects the behavior of the training population. With this metric functional clustering, deformation- or motion segmentation, sparse sampling and the treatment of outliers can be dealt with in a unified and transparent manner. We report experimental results on synthetic and real world data and compare the framework with existing specialized approaches.
Graph-Based Registration, Change Detection, and Classification in Very High Resolution Multitemporal Remote Sensing Data

In this paper, we propose a modular, scalable, metric-free, single-shot change detection/registration method. The developed framework exploits the relation between the registration and change detection problems, while under a fruitful synergy, the coupling energy term constrains adequately both tasks. In particular, through a decomposed interconnected graphical model, the registration similarity constraints are relaxed in the presence of change detection. Moreover, the deformation space is discretized, while efficient linear programming and duality principles are used to optimize a joint solution space where local consistency is imposed on the deformation and the detection space as well. The proposed formulation is able to operate in a fully unsupervised manner addressing binary change detection problems, i.e., change or no-change with respect to different similarity metrics. Furthermore, the framework has been formulated to address automatically the detection of from-to change trajectories under a supervised setting. Promising results on large scale experiments demonstrate the extreme potentials of our method.
Segmentation of building facades using procedural shape priors

In this paper we propose a novel approach to the perceptual interpretation of building facades that combines shape grammars, supervised classification and random walks. Procedural modeling is used to model the geometric and the photometric variation of buildings. This is fused with visual classification techniques (randomized forests) that provide a crude probabilistic interpretation of the observation space in order to measure the appropriateness of a procedural generation with respect to the image. A random exploration of the grammar space is used to optimize the sequence of derivation rules towards a semantico-geometric interpretation of the observations. Experiments conducted on complex architecture facades with ground truth validate the approach.
Recognition-Driven Two-Dimensional Competing Priors Toward Automatic and Accurate Building Detection

In this paper, a novel recognition-driven variational framework, toward multiple building extraction from aerial and satellite images, is introduced. To this end, competing shape priors are considered, and building extraction is addressed through an image segmentation approach that involves the use of a data-driven term constrained from the prior models. The proposed framework extends previous approaches toward the integration of multiple shape priors into the level-set segmentation. In particular, it estimates the number of buildings as well as their pose from the observed data. Therefore, it can address multiple building extraction from a single optical image, a highly demanding task of fundamental importance in various geoscience and remote-sensing applications. Furthermore, it can be easily extended to deal with other remote-sensing data through a simple modification of the image term. Very promising experimental results and the performed qualitative and quantitative evaluation demonstrate the potential of our approach.
Large-Scale Building Reconstruction Through Information Fusion and 3-D Priors

In this paper, a novel variational framework is introduced toward automatic 3-D building reconstruction from remote-sensing data. We consider a subset of building models that involve the footprint, their elevation, and the roof type. These models, under a certain hierarchical representation, describe the space of solutions and, under a fruitful synergy with an inferential procedure, recover the observed scene's geometry. Such an integrated approach is defined in a variational context, solves segmentation both in optical images and digital elevation maps, and allows multiple competing priors to determine their pose and 3-D geometry from the observed data. The very promising experimental results and the performed quantitative evaluation demonstrate the potentials of our approach.
Data fusion through cross-modality metric learning using similarity-sensitive hashing

Visual understanding is often based on measuring similarity between observations. Learning similarities specific to a certain perception task from a set of examples has been shown advantageous in various computer vision and pattern recognition problems. In many important applications, the data that one needs to compare come from different representations or modalities, and the similarity between such data operates on objects that may have different and often incommensurable structure and dimensionality. In this paper, we propose a framework for supervised similarity learning based on embedding the input data from two arbitrary spaces into the Hamming space. The mapping is expressed as a binary classification problem with positive and negative examples, and can be efficiently learned using boosting algorithms. The utility and efficiency of such a generic approach is demonstrated on several challenging applications including cross-representation shape retrieval and alignment of multi-modal medical images.
Time-Varying Linear Autoregressive Models for Segmentation

Tracking highly deforming structures in space and time arises in numerous applications in computer vision. Static Models are often referred to as linear combinations of a mean model and modes of variation learned from training examples. In Dynamic Modeling, the shape is represented as a function of shapes at previous time steps. In this paper, we introduce a novel technique that uses the spatial and the temporal information on the object deformation. We reformulate tracking as a high order time series prediction mechanism that adapts itself on-line to the newest results. Samples (toward dimensionality reduction) are represented in an orthogonal basis, and are introduced in an auto-regressive model that is determined through an optimization process in appropriate metric spaces. Toward capturing evolving deformations as well as cases that have not been part of the learning stage, a process that updates on-line both the orthogonal basis decomposition and the parameters of the autoregressive model is proposed. Experimental results with a nonstationary dynamic system prove adaptive AR models give better results than both stationary models and models learned over the whole sequence.
Illumination estimation and cast shadow detection through a higher-order graphical model

In this paper, we propose a novel framework to jointly recover the illumination environment and an estimate of the cast shadows in a scene from a single image, given coarse 3D geometry. We describe a higher-order Markov Random Field (MRF) illumination model, which combines low-level shadow evidence with high-level prior knowledge for the joint estimation of cast shadows and the illumination environment. First, a rough illumination estimate and the structure of the graphical model in the illumination space is determined through a voting procedure. Then, a higher order approach is considered where illumination sources are coupled with the observed image and the latent variables corresponding to the shadow detection. We examine two inference methods in order to effectively minimize the MRF energy of our model. Experimental evaluation shows that our approach is robust to rough knowledge of geometry and reflectance and inaccurate initial shadow estimates. We demonstrate the power of our MRF illumination model on various datasets and show that we can estimate the illumination in images of objects belonging to the same class using the same coarse 3D model to represent all instances of the class.
MRF Energy Minimization and Beyond via Dual Decomposition

This paper introduces a new rigorous theoretical framework to address discrete MRF-based optimization in computer vision. Such a framework exploits the powerful technique of Dual Decomposition. It is based on a projected subgradient scheme that attempts to solve an MRF optimization problem by first decomposing it into a set of appropriately chosen subproblems, and then combining their solutions in a principled way. In order to determine the limits of this method, we analyze the conditions that these subproblems have to satisfy and demonstrate the extreme generality and flexibility of such an approach. We thus show that by appropriately choosing what subproblems to use, one can design novel and very powerful MRF optimization algorithms. For instance, in this manner we are able to derive algorithms that: 1) generalize and extend state-of-the-art message-passing methods, 2) optimize very tight LP-relaxations to MRF optimization, and 3) take full advantage of the special structure that may exist in particular MRFs, allowing the use of efficient inference techniques such as, e.g., graph-cut-based methods. Theoretical analysis on the bounds related with the different algorithms derived from our framework and experimental results/comparisons using synthetic and real data for a variety of tasks in computer vision demonstrate the extreme potentials of our approach.
MULTI-VIEW STEREO RECONSTRUCTION OF TOTAL KNEE REPLACEMENT FROM X-RAYS

In total knee replacement surgery, also known as total knee arthroplasty, prosthetics are implanted in the knee joint as treatment for progressive diseases such as arthritis or trauma. In this paper, we aim to recover the 3-D shape of bones and prosthetic devices in patients who have undergone total knee replacement. Such an objective is addressed using a collection of 2-D X-rays acquired from multiple viewpoints. Then, both bone and prosthetic shape recovery are formulated as a multi-view reconstruction process through the minimization of an objective function. The objective function is designed to recover 3-D surfaces, which once projected to 2-D images, minimize edge-based and region-based criteria. The calculus of variations and gradient descent are used to obtain a surface with minimum cost through a 3-D level set implementation. Promising results are given for real X-ray data.
Image transport regression using mixture of experts and discrete Markov Random Fields

The registration of multi-modal images is the process of finding a transformation which maps one image to the other according to a given similarity metric. In this paper, we introduce a novel approach for metric learning, aiming to address highly non functional correspondences through the integration of statistical regression and multi-label classification. We developed a position-invariant method that models the variations of intensities through the use of linear combinations of kernels that are able to handle intensity shifts. Such transport functions are considered as the singleton potentials of a Markov Random Field (MRF) where pair-wise connections encode smoothness as well as prior knowledge through a local neighborhood system. We use recent advances in the field of discrete optimization towards recovering the lowest potential of the designed cost function. Promising results on real data demonstrate the potentials of our approach.
Integrating edge/boundary priors with classification scores for building detection in very high resolution data

Automatic and accurate detection of man-made objects, such as buildings, is one of the main problems that the remote sensing community has been focusing on for the last decades. In this paper, we propose a Conditional Random Field (CRF) formulation which is using edge/boundary localization priors towards accurate building detection. These edge priors have been integrated/fused with the classification scores from a deep learning Convolutional Neural Network (CNN) architecture under a single energy formulation. The validation of the developed methodology had been performed on the recently published SpaceNet dataset. Experimental results and quantitative evaluation, based on different accuracy statistics, indicate the great potential of the proposed approach.
Variable Bandwidth Image Denoising Using Image-based Noise Models

This paper introduces a variational formulation for image denoising based on a quadratic function over kernels of variable bandwidth. These kernels are scale adaptive and reflect spatial and photometric similarities between pixels. The bandwidth of the kernels is observation-dependent towards improving the accuracy of the reconstruction process and is constrained to be locally smooth. We analyze the evolution of the noise model form the RAW space to the RGB one, by propagating it over the image formation process. The experimental results demonstrate that the use of a variable bandwidth approach and an image intensity dependent noise variance ensures better restoration quality.