nils-herrmann/clean_scirepeval huggingface dataset:
Learning and inference in parametric switching linear dynamic systems

We introduce parametric switching linear dynamic systems (P-SLDS) for learning and interpretation of parametrized motion, i.e., motion that exhibits systematic temporal and spatial variations. Our motivating example is the honeybee dance: bees communicate the orientation and distance to food sources through the dance angles and waggle lengths of their stylized dances. Switching linear dynamic systems (SLDS) are a compelling way to model such complex motions. However, SLDS does not provide a means to quantify systematic variations in the motion. Previously, Wilson & Bobick (1999) presented parametric HMMs, an extension to HMMs with which they successfully interpreted human gestures. Inspired by their work, we similarly extend the standard SLDS model to obtain parametric SLDS. We introduce additional global parameters that represent systematic variations in the motion, and present general expectation-maximization (EM) methods for learning and inference. In the learning phase, P-SLDS learns canonical SLDS model from data. In the inference phase, P-SLDS simultaneously quantifies the global parameters and labels the data. We apply these methods to the automatic interpretation of honey-bee dances, and present both qualitative and quantitative experimental results on actual bee-tracks collected from noisy video data
Video editing using figure tracking and image-based rendering

We describe a new approach to video editing based on the semi-automatic segmentation of video into multiple layers and the composition of layers using image-based rendering. Using figure tracking and background motion estimation, we can segment a moving figure and reconstruct the background. Using geometrically-correct pixel reprojection, layers can be composited on the basis of the geometry of the underlying scene and the position of a virtual camera. We have implemented a prototype editing system called SpliceWorld.
A Modular Approach to the Analysis and Evaluation of Particle Filters for Figure Tracking

This paper presents the first systematic empirical study of the particle filter (PF) algorithms for human figure tracking in video. Our analysis and evaluation follows a modular approach which is based upon the underlying statistical principles and computational concerns that govern the performance of PF algorithms. Based on our analysis, we propose a novel PF algorithm for figure tracking with superior performance called the Optimized Unscented PF. We examine the role of edge and template features, introduce computationally-equivalent sample sets, and describe a method for the automatic acquisition of reference data using standard motion capture hardware. The software and test data are made publicly-available on our project website.
Multi-scale perception and path planning on probabilistic obstacle maps

We present a path-planning algorithm that leverages a multi-scale representation of the environment. The algorithm works in n dimensions. The information of the environment is stored in a tree representing a recursive dyadic partitioning of the search space. The information used by the algorithm is the probability that a node of the tree corresponds to an obstacle in the search space. The complexity of the proposed algorithm is analyzed and its completeness is shown.
Terrain Synthesis from Digital Elevation Models

In this paper, we present an example-based system for terrain synthesis. In our approach, patches from a sample terrain (represented by a height field) are used to generate a new terrain. The synthesis is guided by a user-sketched feature map that specifies where terrain features occur in the resulting synthetic terrain. Our system emphasizes large-scale curvilinear features (ridges and valleys) because such features are the dominant visual elements in most terrains. Both the example height field and user's sketch map are analyzed using a technique from the field of geomorphology. The system finds patches from the example data that match the features found in the user's sketch. Patches are joined together using graph cuts and Poisson editing. The order in which patches are placed in the synthesized terrain is determined by breadth-first traversal of a feature tree and this generates improved results over standard raster-scan placement orders. Our technique supports user-controlled terrain synthesis in a wide variety of styles, based upon the visual richness of real-world terrain data.
Robust video segment proposals with painless occlusion handling

We propose a robust algorithm to generate video segment proposals. The proposals generated by our method can start from any frame in the video and are robust to complete occlusions. Our method does not assume specific motion models and even has a limited capability to generalize across videos. We build on our previous least squares tracking framework, where image segment proposals are generated and tracked using learned appearance models. The innovation in our new method lies in the use of two efficient moves, the merge move and free addition, to efficiently start segments from any frame and track them through complete occlusions, without much additional computation. Segment size interpolation is used for effectively detecting occlusions. We propose a new metric for evaluating video segment proposals on the challenging VSB-100 benchmark and present state-of-the-art results. Preliminary results are also shown for the potential use of our framework to track segments across different videos.
Learning contact locations for pushing and orienting unknown objects

We present a method by which a robot learns to predict effective contact locations for pushing as a function of object shape. The robot performs push experiments at many contact locations on multiple objects and records local and global shape features at each point of contact. Each trial attempts to either push the object in a straight line or to rotate the object to a new orientation. The robot observes the outcome trajectories of the manipulations and computes either a push-stability or rotate-push score for each trial. The robot then learns a regression function for each score in order to predict push effectiveness as a function of object shape. With this mapping, the robot can infer effective push locations for subsequent objects from their shapes, regardless of whether they belong to a previously encountered object class. These results are demonstrated on a mobile manipulator robot pushing a variety of household objects on a tabletop surface.
State management in Web services

In the paper, we identify a problem for certain applications wishing to use the Web service paradigm to enhance interoperability: rapid, robust state maintenance. While many features are available to support session data, special mechanisms for application state maintenance are less well developed. We discuss three different models to solve the problem and compare the advantages and disadvantages of each. Experimental results show that which model to use depends on application requirements. D-Stampede.NET is a platform supporting the development of applications that involve large time-sequenced data communication among heterogeneous clients. We describe our Web service implementation along with our state server solution to the application state management problem. A simple demonstration application is described and measured to validate performance.
Vision for a smart kiosk

We describe a novel computer vision application: vision-based human sensing for a Smart Kiosk interface. A Smart Kiosk is a free-standing information dispensing computer appliance capable of engaging in public interactions with multiple people. Vision sensing is a critical component of the kiosk interface, where it is used to determine the context for the interaction. We present a taxonomy of vision problems for a kiosk interface and describe a prototype kiosk which uses color stereo tracking and graphical output to interact with several users.
Temporal causality for the analysis of visual events

We present a novel approach to the causal temporal analysis of event data from video content. Our key observation is that the sequence of visual words produced by a space-time dictionary representation of a video sequence can be interpreted as a multivariate point-process. By using a spectral version of the pairwise test for Granger causality, we can identify patterns of interactions between words and group them into independent causal sets. We demonstrate qualitatively that this produces semantically-meaningful groupings, and we demonstrate quantitatively that these groupings lead to improved performance in retrieving and classifying social games from unstructured videos.
AID-ME: Automatic identification of dressing failures through monitoring of patients and activity Evaluation

Monitoring and evaluation of Activities of Daily Living in general, and dressing activity in particular, is an important indicator in the evaluation of the overall cognitive state of patients. In addition, the effectiveness of therapy in patients with motor impairments caused by a stroke, for example, can be measured through long-term monitoring of dressing activity. However, monitoring of dressing activity has not received significant attention. In this paper, we describe a system that can automatically monitor dressing activity and identify dressing failures exhibited by patients. The system uses a synergistic combination of RFID and computer vision in order to identify a number of common dressing failures exhibited by the patients.
First-Person Action Decomposition and Zero-Shot Learning

In this work, we decompose a first-person action into verb and noun. We then study how the coupling of an action's constituent verb and noun affects the learners' ability to learn them separately and to combine them to perform recognition. We compare different information fusion methods on conventional action recognition and zero-shot learning, of which the latter is a strong indication of the feature's ability to capture one concept (verb/noun) and not be confounded by the other. To achieve the decoupling of verb/noun concepts, we extract features that are specialized for each of them. Specifically, we use improved dense trajectories and convolutional neural network activations. We show that by constructing specialized features for the decomposed concepts, our method succeeds in zero-shot learning. More surprisingly, it also outperforms previous results in conventional action recognition when the performance gaps of different features on verb/noun concepts are significant.
Dermoscopic interest point detector and descriptor

Dermoscopy is an imaging technique dermatologists use to better visualize pigmented skin lesions (PSLs) and determine their malignancy. Dermoscopic features revealed by this technique have been shown to correlate with histopathology features, and are used as diagnosis indicators by many dermatologists. Hence, automated detection and classification of these features is the first step toward computer-aided diagnosis of melanoma in dermoscopy. In this paper, we present a novel scale- and rotation-invariant feature detector and descriptor specifically designed as a general visual vocabulary of dermoscopic features. We compare our feature detector and descriptor to the popular interest point detectors in the vision community, namely, SIFT, and a more recent fast variant, SURF. We demonstrate that our feature detector is more discriminative and reliable for dermoscopic features.
DigitEyes: vision-based hand tracking for human-computer interaction

Computer sensing of hand and limb motion is an important problem for applications in human-computer interaction (HCI), virtual reality, and athletic performance measurement. Commercially available sensors are invasive, and require the user to wear gloves or targets. We have developed a noninvasive vision-based hand tracking system, called DigitEyes. Employing a kinematic hand model, the DigitEyes system has demonstrated tracking performance at speeds of up to 10 Hz, using line and point features extracted from gray scale images of unadorned, unmarked hands. We describe an application of our sensor to a 3D mouse user-interface problem.<<ETX>>
Heuristic strategy for feature matching in parallel curve detection

Feature matching has been proved very efficient as an approach for curve matching. However, when there are lots of features detected in the two curves, the possible combinations of matching among these features are prohibitive computationally. In this paper, we propose a heuristic algorithm to select the most promising coupling schemes out of these possible patterns. For parallel curve detection, the rationales we employed include proximity measure and compatibility between tangent orientations at these feature points.
Simulating vivid 3D solid textures from 2D growable patterns

An efficient model-independent 3D texture synthesis algorithm based on texture growing and texture turbulence is presented to simulate vivid 3D solid textures from 2D growable texture patterns. Given a 2D texture pattern of some growable material, our algorithm is able to create a tileable anisotropic 3D texture pattern to simulate the natural property of the material. Target objects are directly dipped into the 3D texture pattern to generate creative, sculpture like models that can be presented with reasonable interactive frame rates. Additionally, our method is conceptually simple, computationally fast, and storage efficient. To the best of our knowledge, this is the first approach that transfers a given 2D texture naturally to point rendering systems
Automatic Semantic Annotation of Images using Spatial Hidden Markov Model

This paper presents a new spatial-HMM (SHMM)for automatically classifying and annotating natural images. Our model is a 2D generalization of the traditional HMM in the sense that both vertical and horizontal transitions between hidden states are taken into consideration. The three basic problems with HMM-liked model are also solved in our model. Given a sequence of visual features, our model automatically derives annotations from keywords associated with the most appropriate concept class, and with no need of a pre-defined length threshold. Our experiments showed that our model outperformed the previous 2D MHMM in recognition accuracy and also achieved a high annotation accuracy
Arbitrary facial views generation from two orthogonal facial images

A new scheme for generating arbitrary views of 3D individual face automatically from side view and front view of facial images is presented in this paper. In this scheme, a generic facial model is introduced to be fit to the front and side views of the person's head. It consists of four stages. (1) Preprocessing include facial images acquisition and feature points identification. (2) Feature extraction. (3) Computation of distortion vector field which will be used to adjust the generic facial model. (4) Synthesis of arbitrary side and front views of the individual 3D facial model and texture-mapping. The resulting individual 3D facial model will cater for various applications such as facial surgical planning and visual communication.
Spatial Markov Kernels for Image Categorization and Annotation

This paper presents a novel discriminative stochastic method for image categorization and annotation. We first divide the images into blocks on a regular grid and then generate visual keywords through quantizing the features of image blocks. The traditional Markov chain model is generalized to capture 2-D spatial dependence between visual keywords by defining the notion of “past” as what we have observed in a row-wise raster scan. The proposed spatial Markov chain model can be trained via maximum-likelihood estimation and then be used directly for image categorization. Since this is completely a generative method, we can further improve it through developing new discriminative learning. Hence, spatial dependence between visual keywords is incorporated into kernels in two different ways, for use with a support vector machine in a discriminative approach to the image categorization problem. Moreover, a kernel combination is used to handle rotation and multiscale issues. Experiments on several image databases demonstrate that our spatial Markov kernel method for image categorization can achieve promising results. When applied to image annotation, which can be considered as a multilabel image categorization process, our method also outperforms state-of-the-art techniques.
Histological image retrieval based on semantic content analysis

The demand for automatic recognition and retrieval of medical images for screening, reference, and management is increasing. We present an intelligent content-based image retrieval system called I-Browse, which integrates both iconic and semantic content for histological image analysis. The I-Browse system combines low-level image processing technology with high-level semantic analysis of medical image content through different processing modules in the proposed system architecture. Similarity measures are proposed and their performance is evaluated. Furthermore, as a byproduct of semantic analysis, I-Browse allows textual annotations to be generated for unknown images. As an image browser, apart from retrieving images by image example, it also supports query by natural language.
Fitting Ellipses to a Region with Application in Calligraphic Stroke Reconstruction

Given a region, it is a challenge to find a set of primitive shapes such as rectangles, circles or ellipses to cover it. This is in fact a set-covering problem, which is known to be NP-hard. The focus of this paper is on fitting a set of ellipses onto an image region. This problem was first formulated by identifying a number of criteria required for the ellipse fitting. A solution is then proposed for automatically determining the set of ellipses that best fits onto an image region. The proposed ellipse fitting algorithm has also been applied to strokes forming characters of Chinese calligraphic artwork. The results show that our proposed algorithm generates ellipses fitting onto stroke regions and capturing the characteristics of the strokes during turning, tilting and back-trace.
Hybrid Associative Retrieval of Three-Dimensional Models

In this paper, we propose a novel 3-D model retrieval framework, which is referred to as hybrid 3-D model associative retrieval. Unlike the conventional 3-D model similarity retrieval approach, the query model and the models obtained by 3-D model hybrid associative retrieval have the following properties: They belong to different model classes and have different shape characteristics in general but are semantically related and preassembled in a certain associative group. For instance, given a furniture associative group { desk, chair, bed}, we may probably like to use a desk as a query model to search for a list of matching models, which belong to the chair or bed class. We consider the following possibilities: 1) there can be more than two classes in an association group and 2) different association groups might have different numbers of classes. The hybrid associative retrieval is performed in two stages: 1) to establish the relationship between different 3-D model categories with semantic associations, we propose three approaches based on neural network learning and 2) to address the aforementioned two conditions, we use a cyclic-shift scheme to partition different associative groups into two-class pairwise associative groups and then adopt two different strategies to combine the final retrieval results. Experiments by using different data sets demonstrate the effectiveness and efficiency of our proposed framework on the new hybrid associative retrieval task.
Parallel evidence combination on a SB-tree architecture

The paper presents a specialised parallel architecture and algorithms for the computation of Dempster's rule in evidential reasoning based on the Dempster-Shafer theory. The architecture is scalable and the complexity of the associated parallel algorithms is linear with respect to the number of processors. The approach supports efficient dynamic load balancing which is essential for incremental reasoning. The technique has been simulated on a UNIX based machine and can easily be implemented on a multiprocessor symmetric machine.
Hand gesture animation from static postures using an anatomy-based model

Automatic interpretation and animation of human motion has become an important research topic among researchers in virtual reality and computer animation. One major problem encountered during hand motion analysis is the large amount of data that need to be captured and analyzed. Even for a human hand, although only a small part of the body is involved, there are about 30 motion parameters for each hand posture. The authors present an approach to hand motion animation using only static images of the set of target gestures. We achieve naturalistic hand motion animation by the use of an anatomy based hand model and a hand gesture coding system, which we called Hand Action Coding System (HACS). This allows complex sequences of hand gestures to be animated based only on the static image of the hand gestures to be animated. This approach greatly simplifies motion data acquisition and the process of motion analysis and synthesis.
Affine invariant retrieval of shapes based on hand-drawn sketches

Presents a compact and efficient shape signature that can be applied to shape retrieval from hand-drawn sketches. The two proposed invariant features that constitute our affine invariant signature are: (a) angle between spoke vectors, and (b) cumulated normalized area between consecutive spoke vectors. The signature can be computed efficiently from an input shape and supports fast similarity matching. The approach has also been extended for partial shape retrieval through a partial shape decomposition procedure.
Three-dimensional virtual-reality surgical planning and soft-tissue prediction for orthognathic surgery

Complex maxillofacial malformations continue to present challenges in analysis and correction beyond modern technology. The purpose of this paper is to present a virtual reality workbench for surgeons to perform virtual orthognathic surgical planning and soft-tissue prediction in three dimensions. A resulting surgical planning system, i.e., three-dimensional virtual reality surgical planning and soft-tissue prediction for orthognathic surgery, consists of four major stages: computed tomography (CT) data post-processing and reconstruction, three-dimensional (3-D) color facial soft-tissue model generation, virtual surgical planning and simulation, soft-tissue-change preoperative prediction. The surgical planning and simulation are based on a 3D CT reconstructed bone model, whereas the soft-tissue prediction is based on color texture-mapped and individualized facial soft-tissue model. Our approach is able to provide a quantitative osteotomy-simulated bone model and prediction of postoperative appearance with photorealistic quality. The prediction appearance can be visualized from any arbitrary viewing point using a low-cost personal computer-based system. This cost-effective solution can be easily adopted in any hospital for daily use.