nils-herrmann/clean_scirepeval huggingface dataset:
"Universal" transform image coding based on joint adaptation of filter banks, tree structures and quantizers

Transform coding has become the de facto standard for image and video compression. The design of adaptive signal transforms for image coding usually follows one of the two approaches: adaptive tree/quantizer design with fixed subband filter banks and adaptive subband filter bank design with fixed quantizers and tree topology. The main objective of our work is to integrate these two paradigms in an image coder in which subband filter banks, tree structures and quantizers are all adapted. We design a codebook for the filters, tree and quantizers. The codebook design algorithm uses a training set made of images that are assumed to be representative of the broad class of images of interest. We first design the filters and then the quantizers. In the filter design phase, we visit nodes in a top-down fashion and design a filter codebook for each tree node. The optimal filter codebook for each node is designed so as to minimize the theoretical coding gain-based rate. The design of the quantizers and the weights for the splitting decisions is done jointly using a greedy iterative algorithm based on the single tree algorithm of Ramchandran et al. (1993). The actual coding algorithm finds, based on the codebook design, the optimized filter banks, tree structure, and quantizer choices for each node of the tree. In our experimental setup we used a training set of 20 images representative of four image classes.
Spatio-temporal Context Modeling for BoW-Based Video Classification

We propose an autocorrelation Cox process that extends the traditional bag-of-words representation to model the spatio-temporal context within a video sequence. Bag-of-words models are effective tools for representing a video by a histogram of visual words that describe local appearance and motion. A major limitation of this model is its inability to encode the spatio-temporal structure of visual words pertaining to the context of the video. Several works have proposed to remedy this by learning the pair wise correlations between words. However, pair wise analysis leads to a quadratic increase in the number of features, making the models prone to over fitting and challenging to learn from data. The proposed autocorrelation Cox process model encodes, in a compact way, the contextual information within a video sequence, leading to improved classification performance. Spatio-temporal autocorrelations of visual words estimated from the Cox process are coupled with the information gain feature selection to discern the essential structure for the classification task. Experiments on crowd activity and human action dataset illustrate that the proposed model achieves state-of-the-art performance while providing intuitive spatio-temporal descriptors of the video context.
Multimodal tracking and classification of audio-visual features

The surge of interest in multimedia and multimodal interfaces has prompted the need for novel estimation and classification techniques for data from different but coupled modalities. Unimodal techniques ported to this domain have only exhibited limited success. We propose a new framework for feature prediction and classification based on multimodal knowledge-constrained hidden Markov models (HMMs). The classical role of HMMs as statistical classifiers is enhanced by their new role as multimodal feature predictors. Moreover, by fusing the multimodal formulation with higher level knowledge we allow the influence of such knowledge to be reflected in feature prediction as well as in feature classification.
Boosted learning in dynamic Bayesian networks for multimodal detection

Bayesian networks are an attractive modeling tool for human sensing, as they combine an intuitive graphical representation with efficient algorithms for inference and learning. Temporal fusion of multiple sensors can be efficiently formulated using dynamic Bayesian networks (DBNs) which allow the power of statistical inference and learning to be combined with contextual knowledge of the problem. Unfortunately, simple learning methods can cause such appealing models to fail when the data exhibits complex behavior We first demonstrate how boosted parameter learning could be used to improve the performance of Bayesian network classifiers for complex multimodal inference problems. As an example we apply the framework to the problem of audiovisual speaker detection in an interactive environment using "off-the-shelf" visual and audio sensors (face, skin, texture, mouth motion, and silence detectors). We then introduce a boosted structure learning algorithm. Given labeled data, our algorithm modifies both the network structure and parameters so as to improve classification accuracy. We compare its performance to both standard structure learning and boosted parameter learning. We present results for speaker detection and for datasets from the UCI repository.
A Profile Hidden Markov Model Framework for Modeling and Analysis of Shape

In this paper we propose a new framework for modeling 2D shapes. A shape is first described by a sequence of local features (e.g., curvature) of the shape boundary. The resulting description is then used to build a profile hidden Markov model (PHMM) representation of the shape. PHMMs are a particular type of hidden Markov models (HMMs) with special states and architecture that can tolerate considerable shape contour perturbations, including rigid and non-rigid deformations, occlusions and missing contour parts. Different from traditional HMM-based shape models, the sparseness of the PHMM structure allows efficient inference and learning algorithms for shape modeling and analysis. The new framework can be applied to a wide range of problems, from shape matching and classification to shape segmentation. Our experimental results show the effectiveness and robustness of this new approach in the three application domains.
Learning a restricted Bayesian network for object detection

Many classes of images have the characteristics of sparse structuring of statistical dependency and the presence of conditional independencies among various groups of variables. Such characteristics make it possible to construct a powerful classifier by only representing the stronger direct dependencies among the variables. In particular, a Bayesian network compactly represents such structuring. However, learning the structure of a Bayesian network is known to be NP complete. The high dimensionality of images makes structure learning especially challenging. This paper describes an algorithm that searches for the structure of a Bayesian network based classifier in this large space of possible structures. The algorithm seeks to optimize two cost functions: a localized error in the log-likelihood ratio function to restrict the structure and a global classification error to choose the final structure of the network. The final network structure is restricted such that the search can take advantage of pre-computed estimates and evaluations. We use this method to automatically train detectors of frontal faces, eyes, and the iris of the human eye. In particular, the frontal face detector achieves state-of-the-art performance on the MIT-CMU test set for face detection.
A Generative Model for Depth-Based Robust 3D Facial Pose Tracking

We consider the problem of depth-based robust 3D facial pose tracking under unconstrained scenarios with heavy occlusions and arbitrary facial expression variations. Unlike the previous depth-based discriminative or data-driven methods that require sophisticated training or manual intervention, we propose a generative framework that unifies pose tracking and face model adaptation on-the-fly. Particularly, we propose a statistical 3D face model that owns the flexibility to generate and predict the distribution and uncertainty underlying the face model. Moreover, unlike prior arts employing the ICP-based facial pose estimation, we propose a ray visibility constraint that regularizes the pose based on the face models visibility against the input point cloud, which augments the robustness against the occlusions. The experimental results on Biwi and ICT-3DHP datasets reveal that the proposed framework is effective and outperforms the state-of-the-art depth-based methods.
Bayesian networks as ensemble of classifiers

Classification of real-world data poses a number of challenging problems. Mismatch between classifier models and true data distributions on the one hand and the use of approximate inference methods on the other hand all contribute to inaccurate classification. Recent work on boosting by Schapire et al. and additive probabilistic models by Hastie et al. have shown that improved classification can be achieved by linearly combining a number of simple classifiers. Building upon this spirit, we present a Bayesian network-based framework for mixing multiple classifiers. We also analyze the bound on the generalization error for this combined classifier. We give results on some standard datasets and demonstrate their usefulness in a real-world task of multimodal speaker detection where we improve upon performance of a more complex Bayesian network model. Improved results indicate the significant potential of the Bayesian network of classifiers approach.
Sparse Logistic Classifiers for Interpretable Protien Homology Detection

Computational classification of proteins using methods such as string kernels and Fisher-SVM has demonstrated great success. However, the resulting models do not offer an immediate interpretation of the underlying biological mechanisms. In particular, some recent studies have postulated the existence of a small subset of positions and residues in protein sequences may be sufficient to discriminate among different protein classes. In this work, we propose a hybrid setting for the classification task. A generative model is trained as a feature extractor, followed by a sparse classifier in the extracted feature space to determine the membership of the sequence, while discovering features relevant for classification. The set of sparse biologically motivated features together with the discriminative method offer the desired biological interpretability. We apply the proposed method to a widely used dataset and show that the performance of our models is comparable to that of the state-of-the-art methods. The resulting models use fewer than 10% of the original features. At the same time, the sets of critical features discovered by the model appear to be consistent with confirmed biological findings
PUnDA: Probabilistic Unsupervised Domain Adaptation for Knowledge Transfer Across Visual Categories

This paper introduces a probabilistic latent variable model to address unsupervised domain adaptation problems. Specifically, we tackle the task of categorization of visual input from different domains by learning projections from each domain to a latent (shared) space jointly with the classifier in the latent space, which simultaneously minimizes the domain disparity while maximizing the classifier's discriminative power. Furthermore, the non-parametric nature of our adaptation model makes it possible to infer the latent space dimension automatically from data. We also develop a novel regularized Variational Bayes (VB) algorithm for efficient estimation of the model parameters. We compare the proposed model with the state-of-the-art methods for the tasks of visual domain adaptation using both handcrafted and deep-net features. Our experiments show that even with a simple softmax classifier, our model outperforms several state-of-the-art methods that take advantage of more sophisticated classification schemes.
Conditional State Space Models for Discriminative Motion Estimation

We consider the problem of predicting a sequence of real-valued multivariate states from a given measurement sequence. Its typical application in computer vision is the task of motion estimation. State Space Models are widely used generative probabilistic models for the problem. Instead of jointly modeling states and measurements, we propose a novel discriminative undirected graphical model which conditions the states on the measurements while exploiting the sequential structure of the problem. The major benefits of this approach are: (1) It focuses on the ultimate prediction task while avoiding probably unnecessary effort in modeling the measurement density, (2) It relaxes generative models' assumption that the measurements are independent given the states, and (3) The proposed inference algorithm takes linear time in the measurement dimension as opposed to the cubic time for Kalman filtering, which allows us to incorporate large numbers of measurement features. We show that the parameter learning can be cast as an instance of convex optimization. We also provide efficient convex optimization methods based on theorems from linear algebra. The performance of the proposed model is evaluated on both synthetic data and the human body pose estimation from silhouette videos.
A New Adaptive Segmental Matching Measure for Human Activity Recognition

The problem of human activity recognition is a central problem in many real-world applications. In this paper we propose a fast and effective segmental alignment-based method that is able to classify activities and interactions in complex environments. We empirically show that such model is able to recover the alignment that leads to improved similarity measures within sequence classes and hence, raises the classification performance. We also apply a bounding technique on the histogram distances to reduce the computation of the otherwise exhaustive search.
Discriminative Learning for Dynamic State Prediction

We consider the problem of predicting a sequence of real-valued multivariate states that are correlated by some unknown dynamics, from a given measurement sequence. Although dynamic systems such as the State-Space Models are popular probabilistic models for the problem, their joint modeling of states and observations, as well as the traditional generative learning by maximizing a joint likelihood may not be optimal for the ultimate prediction goal. In this paper, we suggest two novel discriminative approaches to the dynamic state prediction: 1) learning generative state-space models with discriminative objectives and 2) developing an undirected conditional model. These approaches are motivated by the success of recent discriminative approaches to the structured output classification in discrete-state domains, namely, discriminative training of Hidden Markov Models and Conditional Random Fields (CRFs). Extending CRFs to real multivariate state domains generally entails imposing density integrability constraints on the CRF parameter space, which can make the parameter learning difficult. We introduce an efficient convex learning algorithm to handle this task. Experiments on several problem domains, including human motion and robot-arm state estimation, indicate that the proposed approaches yield high prediction accuracy comparable to or better than state-of-the-art methods.
Speech/Gesture Interface to a Visual-Computing Environment

We developed a speech/gesture interface that uses visual hand-gesture analysis and speech recognition to control a 3D display in VMD, a virtual environment for structural biology. The reason we used a particular virtual environment context was to set the necessary constraints to make our analysis robust and to develop a command language that optimally combines speech and gesture inputs. Our interface uses: automatic speech recognition (ASR), aided by a microphone, to recognize voice commands; two strategically positioned cameras to detect hand gestures; and automatic gesture recognition (AGR), a set of computer vision techniques to interpret those hand gestures. The computer vision algorithms can extract the user's hand from the background, detect different finger positions, and distinguish meaningful gestures from unintentional hand movements. Our main goal was to simplify model manipulation and rendering to make biomolecular modeling more playful. Researchers can explore variations of their model and concentrate on biomolecular aspects of their task without undue distraction by computational aspects. They can view simulations of molecular dynamics, play with different combinations of molecular structures, and better understand the molecules' important properties. A potential benefit, for example, might be reducing the time to discover new compounds for new drugs.
Ancient Coin Recognition Based on Spatial Coding

Roman coins play an important role to understand the Roman empire because they convey rich information about key historical events of the time. Moreover, as large amounts of coins are daily traded over the Internet, it becomes necessary to develop automatic coin recognition systems to prevent illegal trades. In this paper, we propose an automatic recognition method for ancient Roman coins. The proposed method exploits the structure of the coin by using a spatially local coding method. Results show that the proposed method outperforms traditional rigid spatial structure models such as the spatial pyramid.
Hybrid On-Line 3D Face and Facial Actions Tracking in RGBD Video Sequences

In this paper, we propose a hybrid model-based tracker for simultaneous tracking of 3D head pose and facial actions in sequences of texture and depth frames. Our tracker utilizes a generic wireframe model, the Candide-3, to represent facial deformations. This wireframe model is initially fit into the first frame by an Iterative Closest Point algorithm. Given the result after the first frame, our tracking algorithm combines both Iterative Closest Point technique and Appearance Model for head pose and facial actions tracking. The tracker is capable of adapting on-line to the changes in appearance of the target and thus the prior training process is avoided. Furthermore, the tracking system works automatically without any intervention from human operators.
A graphical model framework for coupling MRFs and deformable models

This paper proposes a new framework for image segmentation based on the integration of MRFs and deformable models using graphical models. We first construct a graphical model to represent the relationship of the observed image pixels, the true region labels and the underlying object contour. We then formulate the problem of image segmentation as the one of joint regioncontour inference and learning in the graphical model. The graphical model representation allows us to use an approximate structured variational inference technique to solve this otherwise intractable joint inference problem. Using this technique, the MAP solution to the original model is obtained by finding the MAP solutions of two simpler models, an extended MRF model and a probabilistic deformable model, iteratively and incrementally. In the extended MRF model, the true region labels are estimated using the BP algorithm in a band area around the estimated contour from the probabilistic deformable model, and the result in turn guides the probabilistic deformable model to an improved estimation of the contour. Experimental results show that our new hybrid method outperforms both the MRF-based and the deformable model-based methods.
Dimensionality reduction using covariance operator inverse regression

We consider the task of dimensionality reduction for regression (DRR) whose goal is to find a low dimensional representation of input covariates, while preserving the statistical correlation with output targets. DRR is particularly suited for visualization of high dimensional data as well as the efficient regressor design with a reduced input dimension. In this paper we propose a novel nonlinear method for DRR that exploits the kernel Gram matrices of input and output. While most existing DRR techniques rely on the inverse regression, our approach removes the need for explicit slicing of the output space using covariance operators in RKHS. This unique property make DRR applicable to problem domains with high dimensional output data with potentially significant amounts of noise. Although recent kernel dimensionality reduction algorithms make use of RKHS covariance operators to quantify conditional dependency between the input and the targets via the dimension-reduced input, they are either limited to a transduction setting or linear input subspaces and restricted to non-closed-form solutions. In contrast, our approach provides a closed-form solution to the nonlinear basis functions on which any new input point can be easily projected. We demonstrate the benefits of the proposed method in a comprehensive set of evaluations on several important regression problems that arise in computer vision.
Unsupervised domain adaptation with copula models

We study the task of unsupervised domain adaptation, where no labeled data from the target domain is provided during training time. To deal with the potential discrepancy between the source and target distributions, both in features and labels, we exploit a copula-based regression framework. The benefits of this approach are two-fold: (a) it allows us to model a broader range of conditional predictive densities beyond the common exponential family; (b) we show how to leverage Sklar's theorem, the essence of the copula formulation relating the joint density to the copula dependency functions, to find effective feature mappings that mitigate the domain mismatch. By transforming the data to a copula domain, we show on a number of benchmark datasets (including human emotion estimation), and using different regression models for prediction, that we can achieve a more robust and accurate estimation of target labels, compared to recently proposed feature transformation (adaptation) methods.
Robotics-based micro-reeling of magnetic microfibers to fabricate helical structure for smooth muscle cells culture

Helical structure assembled by hydrogel microfibers is significant for culture of smooth muscle cells. However, the helical structure is only fabricated at the macroscale, while the fabrication of helical microstructure is still a challenge due to the lack of assembly method. In this paper, we propose a robotics-based assembly method to handle such challenge. An electromagnetic needle (EMN) is employed as end-effector to magnetically reel the microfiber encapsulating magnetic nanoparticles around a micropillar, and a dual-ring structure is designed to keep the microfiber being attracted at the EMN tip. For enhancing the stability of tip attraction, the manipulation mode of anticlockwise pushing microfiber is established. Moreover, the interaction mechanism between EMN tip and microfiber is analyzed by developing a static force model, and then the key condition of stably reeling microfiber is concluded. Furthermore, a robotics-based motion trajectory of EMN tip is planned to achieve a smooth reeling process. Based on such planning, the size of dual-ring structure is further optimized to improve the success rate of reeling. Finally, the helical microstructure with there-turn coils is successfully fabricated.
Real-time distance measurement using a modified camera

In this paper we propose a real-time method which can measure distance using a modified camera. The camera's image sensor is inclined by a certain angle. Thus, the image projected on the sensor is defocused differently at different areas. The area where the image is focused best is just at projection plane. The position of the projection plane can be obtained after finding out this area. The distance between the projection plane and the lens of the camera is image distance. Object distance can be obtained by applying image distance to lens formula. This method is proved to be effective and a single distance measurement can be performed within 3.21ms.