nils-herrmann/clean_scirepeval huggingface dataset:
Fast and robust active contours for image segmentation

Active models are widely used in applications like image segmentation and tracking. Region-based active models are known for robustness to weak edges and high computational complexity. We found previous region-based models can easily get stuck in local minimums if initialization is far from the true object boundary. This is caused by an inherent ambiguity in evolution direction of the level set function when minimizing the energy. To solve this problem, we propose an intensity re-weighting (IR) model to bias the evolution process in certain direction. IR model can effectively avoid local minimums and enable much faster convergence of the evolution process. The proposed method is applied to both real and synthetic images with promising results.
Classification-Based relay selection for video streaming over wireless multihop networks

Real-time streaming of audiovisual content over wireless networks is emerging as an important technology in multimedia communication. In this paper, we present a classification-based method to select appropriate relays in support of our prior work on Time-aware Opportunistic Relaying for video streaming over wireless mesh networks. The proposed scheme works as follows: each candidate relay runs a Support Vector Machine (SVM) to decide whether it should participate in forwarding packets for a source-destination pair or not. The SVM considers features that are sensitive to video quality and is trained with an aim to maximize user perceived quality. Our evaluation results show that the classificationbased approach outperforms the baseline scheme.
Toward efficient static analysis of finite-precision effects in DSP applications via affine arithmetic modeling

We introduce a static error analysis technique, based on smart interval methods from affine arithmetic, to help designers translate DSP codes from full-precision floating-point to smaller finite-precision formats. The technique gives results for numerical error estimation comparable to detailed simulation, but achieves speedups of three orders of magnitude by avoiding actual bit-level simulation. We show results for experiments mapping common DSP transform algorithms to implementations using small custom floating point formats.
Audio-to-visual conversion for multimedia communication

Although humans rely primarily on hearing to process speech, they can also extract a great deal of information with their eyes through lipreading. This skill becomes extremely important when the acoustic signal is degraded by noise. It would, therefore, be beneficial to find methods to reinforce acoustic speech with a synthesized visual signal for high noise environments. This paper addresses the interaction between acoustic speech and visible speech. Algorithms for converting audible speech into visible speech are examined, and applications which can utilize this conversion process are presented. Our results demonstrate that it is possible to animate a natural-looking talking head using acoustic speech as an input.
Understanding images of groups of people

In many social settings, images of groups of people are captured. The structure of this group provides meaningful context for reasoning about individuals in the group, and about the structure of the scene as a whole. For example, men are more likely to stand on the edge of an image than women. Instead of treating each face independently from all others, we introduce contextual features that encapsulate the group structure locally (for each person in the group) and globally (the overall structure of the group). This “social context” allows us to accomplish a variety of tasks, such as such as demographic recognition, calculating scene and camera parameters, and even event recognition. We perform human studies to show this context aids recognition of demographic information in images of strangers.
Vector space framework for unification of one- and multidimensional filter bank theory

A number of results in filter bank theory can be viewed using vector space notations. This simplifies the proofs of many important results. In this paper, we first introduce the framework of vector space, and then use this framework to derive some known and some new filter bank results as well. For example, the relation among the Hermitian image property, orthonormality, and the perfect reconstruction (PR) property is well-known for the case of one-dimensional (1-D) analysis/synthesis filter banks. We can prove the same result in a more general vector space setting. This vector space framework has the advantage that even the most general filter banks, namely, multidimensional nonuniform filter banks with rational decimation matrices, become a special case. Many results in 1-D filter bank theory are hence extended to the multidimensional case, with some algebraic manipulations of integer matrices. Some examples are: the equivalence of biorthonormality and the PR property, the interchangeability of analysis and synthesis filters, the connection between analysis/synthesis filter banks and synthesis/analysis transmultiplexers, etc. Furthermore, we obtain the subband convolution scheme by starting from the generalized Parseval's relation in vector space. Several theoretical results of wavelet transform can also be derived using this framework. In particular, we derive the wavelet convolution theorem.
Sparsity-based deconvolution of low-dose brain perfusion CT in subarachnoid hemorrhage patients

Functional imaging serves as an important supplement to anatomical imaging modalities such as MR and CT in modern health care. In perfusion CT (CTP), hemodynamic parameters are derived from the tracking of the first-pass of the contrast bolus entering a tissue region of interest. In practice, however, the post-processed parametric maps tend to be noisy, especially in low-dose CTP, in part due to the noisy contrast enhancement profile and oscillatory nature of results generated by current computational methods. In this paper, we propose a sparsity-based perfusion parameter deconvolution approach that consists of a non-linear processing based on sparsity prior in terms of residue function dictionaries. Our simulated results from numerical data and experiments in aneurysmal subarachnoid hemorrhage patients with clinical vasospasm show that the algorithm improves the quality and reduces the noise of the perfusion parametric maps in low-dose CTP, compared to state-of-the-art methods.
Unsupervised Image Categorization and Object Localization using Topic Models and Correspondences between Images

Topic models from the text understanding literature have shown promising results in unsupervised image categorization and object localization. Categories are treated as topics, and words are formed by vector quantizing local descriptors of image patches. Limitations of topic models include their weakness in localizing objects, and the requirement of a fairly large proportion of words coming from the object. We present a new approach that employs correspondences between images to provide information about object configuration, which in turn enhances the reliability of object localization and categorization. This approach is efficient, as it requires only a small number of correspondences. We show improved categorization and localization performance on real and synthetic data. Moreover, we can push the limits of topic models when the proportion of words coming from the object is very low.
Recovering depth of a dynamic scene using real world motion prior

Given a video of a dynamic scene captured using a dynamic camera, we present a method to recover a dense depth map of the scene with a focus on estimating the depth of the dynamic objects. We assume that the static portions of the scene help estimate the pose of the cameras. We recover a dense depth map of the scene via a plane sweep stereo approach. The relative motion of the dynamic object in the scene however, results in an inaccurate depth estimate. Estimating the accurate depth of the dynamic object is an ambiguous problem since both the depth and the real world speed of the object are unknown. In this work, we show that by using occlusions and putting constraints on the speed of the object we can bound the depth of the object. We can then incorporate this real world motion into the plane sweep stereo framework to obtain a more accurate depth for the dynamic object. We focus on videos with people walking in the scene and show the effectiveness of our approach through quantitative and qualitative results.
Aesthetic Visual Quality Assessment of Paintings

This paper aims to evaluate the aesthetic visual quality of a special type of visual media: digital images of paintings. Assessing the aesthetic visual quality of paintings can be considered a highly subjective task. However, to some extent, certain paintings are believed, by consensus, to have higher aesthetic quality than others. In this paper, we treat this challenge as a machine learning problem, in order to evaluate the aesthetic quality of paintings based on their visual content. We design a group of methods to extract features to represent both the global characteristics and local characteristics of a painting. Inspiration for these features comes from our prior knowledge in art and a questionnaire survey we conducted to study factors that affect human's judgments. We collect painting images and ask human subjects to score them. These paintings are then used for both training and testing in our experiments. Experimental results show that the proposed work can classify high-quality and low-quality paintings with performance comparable to humans. This work provides a machine learning scheme for the research of exploring the relationship between aesthetic perceptions of human and the computational visual features extracted from paintings.
3D augmented Markov random field for object recognition

In this paper, we propose the use of 3D information to augment theMarkov random field (MRF) model for object recognition. Conventional MRF for image-based object recognition usually uses appearance and 2D location as features in themodel. We estimate rough 3D information from stereo image pairs, and incorporate this information into node and edge potentialmodels in the conventionalMRF. Introducing 3D information into the node potential allows to leverage the distribution statistics of 3D location for different classes. We solve the object recognition problem by finding the globally optimal class assignment that minimizes an energy function defined in the augmentedMRF.We showthat the introduction of 3D distance in the edge potential can help distinguish “true” neighbors from “fake” neighbors in 2D. We demonstrate improved recognition results by using the proposed technique.
Personal authentication based on generalized symmetric max minimal distance in subspace

We introduce an improved classification algorithm based on the concept of symmetric maximized minimal distance in subspace (SMMS). Given the training data of authentic samples and imposter samples in the feature space, our previous approach, SMMS, tried to identify a subspace in which all the authentic samples were projected onto the origin and all the imposter samples were far away from the origin. The optimality of the subspace was determined by maximizing the minimal distance between the origin and the imposter samples in the subspace. The generalized SMMS relaxes the constraint of fitting all the authentic samples to the origin in the subspace to achieve the optimality and considers the optimal direction of the linear support-vector machines (SVM) as a feasible solution in our optimization procedure to guarantee that our result is no worse than the linear SVM. We present a procedure to achieve such optimality and to identify the subspace and the decision boundary. Once the subspace is trained, the verification procedure is simple since we only need to project the test sample onto the subspace and compare it against the decision boundary. Using face authentication as an example, we show that the proposed algorithm outperforms the linear classifier based on SMMS and SVM. The proposed algorithm also applies to multimodal feature spaces. The features can come from any modalities, such as face images, voices, fingerprints, etc.
Bringing diverse classifiers to common grounds: dtransform

Several classification scenarios employ multiple independently trained classifiers and the outputs of these classifiers need to be combined. However, since each of the trained classifiers exhibit different statistical characteristics, it is not appropriate to combine them using techniques that are blind to these differences. We propose a transform, dtransform, that transforms outputs of classifiers to approximate posterior probabilities, and caters to the statistical behavior of the classifier while doing so. The transformed outputs are now comparable, and can be combined using any of the classical combination rules. We show convincing results that demonstrate the effectiveness of the proposed transform in providing better estimates of the posterior probabilities as compared to standard transformations, as demonstrated by lower KL distance from the true distribution, higher classification accuracies and higher effectiveness of the standard classifier combination rules.
A distributed vision-based infrastructure for multi-robot navigation

With an accurate estimate of the robot pose, robots will be able to navigate in their environment autonomously with the aid of dynamic path planning. In this paper, an infrastructure of Distributed Vision-Based System (DVS) for multi-robot navigation is presented. The infrastructure is comprised of Localization and Motion Planning Process. The Localization Process receives images from cameras mounted in the environment and then utilizes ID tags to localize multiple robots. Motion Planning Process is designed to generate paths and steer the robots to target positions. Experiments with a network of small robot platforms and overhead cameras show that the proposed infrastructure could simultaneously localize and navigate multiple robots with localization errors within 0.1 meters.
Active learning for piecewise planar 3D reconstruction

This paper presents an active-learning algorithm for piecewise planar 3D reconstruction of a scene. While previous interactive algorithms require the user to provide tedious interactions to identify all the planes in the scene, we build on successful ideas from the automatic algorithms and introduce the idea of active learning, thereby improving the reconstructions while considerably reducing the effort. Our algorithm first attempts to obtain a piecewise planar reconstruction of the scene automatically through an energy minimization framework. The proposed active-learning algorithm then uses intuitive cues to quantify the uncertainty of the algorithm and suggest regions, querying the user to provide support for the uncertain regions via simple scribbles. These interactions are used to suitably update the algorithm, leading to better reconstructions. We show through machine experiments and a user study that the proposed approach can intelligently query users for interactions on informative regions, and users can achieve better reconstructions of the scene faster, especially for scenes with texture-less surfaces lacking cues like lines which automatic algorithms rely on.
Latent Layout Analysis for Discovering Objects in Images

Latent layout analysis (LLA) is a novel unsupervised learning technique to discover objects in unseen images using a set of un-annotated training images. LLA defines a generative model that associates latent aspects to local appearances. The dependency between aspects and position is captured by a spatial sensitive aspect model. This dependency distinguishes LLA from probabilistic latent semantic analysis (PLSA). The latent aspects together with the latent layout constitute a compact scene representation. We demonstrate that the proposed LLA significantly outperforms probabilistic latent semantic analysis in two tasks: object discovery (detection) and object localization
Consideration of Lombard effect for speechreading

We propose a method for integrating audio and visual information to enhance speech recognition in adverse environments. We train the audio hidden Markov model and the visual hidden Markov model separately, and then use a Viterbi algorithm to decode both channels in parallel. The decoding process is asynchronous between the two channels to capture the asynchronous nature of audio and visual speech. We test the proposed method using speech corrupted by various types of noise and speech with the Lombard effect.
Not Everybody's Special: Using Neighbors in Referring Expressions with Uncertain Attributes

Referring expression generation is widely considered a basic building block of any natural language generation system. Generating these phrases, which can point out a single object from a group of objects, has been studied extensively in that community. However, to build systems which can discuss images in an intelligent way, it is necessary to consider additional factors unique to the visual domain. In this paper we consider the use of neighbors as anchors to create a referring expression for a person in a group image. We describe a target person using the people around him, when we cannot find a reliable set of attributes to describe the target himself. We first present a method for including neighbors in a referring expression, and discuss several ways of presenting this data to a user. We show through experiments that using descriptions with neighbors can significantly improve the probability of conveying the correct information to a user.
Trademark retrieval using contour-skeleton stroke classification

In this paper we propose a method to retrieve trademarks using query by sketches. The trademark images are first filtered to remove noise. Then each filtered image is segmented into regions based on pixel connectivity. For each region, a decision is made about whether thinning or edge extraction should be applied. Afterwards stroke tracing is performed to extract the sketch of the trademark. The user can then provide a query sketch that will be compared with those extracted sketches from the database trademark images in order to retrieve similar trademarks. Query by sketch is useful in the trademark retrieval application since the user can search for similar trademarks by providing a rough sketch instead of keywords. Our approach saves a lot of labor because it does not require the trademark database to be manually annotated with keywords and the experimental result shows that our scheme outperforms an existing method in terms of retrieval performance.
Proposed FPGA Hardware Architecture for High Frame Rate (≫100 fps) Face Detection Using Feature Cascade Classifiers

Face detection is the first and most crucial step in any face recognition systems with applications to face tracking, recognition and automatic surveillance. AdaBoost based face detection training methods have been proposed for producing a rapidly fast detection implementation compared to other methods using the integral image for evaluating a series of weak classifiers very fast. Current software version implementations can achieve about 15-25 frames per second (fps) with a tunable compromise between detection accuracy and speed. In this paper, a novel hardware architecture design on FPGA based on AdaBoost face training and detection algorithm for detecting faces in high resolution images at high frame rates (>100 fps) is proposed. The proposed architecture can evaluate each sub-window in a single clock cycle, which is the fastest possible speed land is limited by the H/W clock running speed. Thus in this proposed approach, detection speed is independent from the number of weak classifiers implemented. The proposed architecture is verified on Xilinx Virtex-II Pro FPGA platform where experimental results show that the speed and memory outperform previous approaches in literature, achieving 143 fps for image size of 640 by 480 pixels using a single scan window. Parallelizing the scan window can lead to double/triple this speed and is dependent on the gate capacity of the FPGA.
Design of IFIR eigenfilters

The interpolated finite impulse response (IFIR) and eigenfilter approaches are combined. The eigenfilter approach to the design of FIR filters requires very simple computation and has the advantage that both time- and frequency-domain constraints can be incorporated. On the other hand, the IFIR filter approach can design FIR filters efficiently and implement them with significant savings in the number of arithmetic operations. The authors present a design example which gives a comparison of the eigenfilter approach and linear programming approach under the unit-step-response constraint.<<ETX>>
Face Recognition Challenge: Object Recognition Approaches for Human/Avatar Classification

Recently, a novel "completely automated public Turing test to tell computers and humans apart (CAPTCHA)'' system has been proposed, in which users are asked to separate natural faces of humans and artificial faces of virtual world avatars. The system is based on the assumption that computers cannot separate them while it is an easy task for humans. Conventional digital forensics approaches to distinguish natural images from computer graphics images are mostly based on statistical analysis of the images such as noise in CMOS image sensors or Bayer matrix estimation. On the other hand, this paper uses face recognition and object classification based approaches. The experiments show that our approaches work surprisingly well and yields more than 99% accuracy. Our object classification based approach can also tell us how likely the input images are regarded as human/avatar faces.
A learning-based framework for depth ordering

Depth ordering is instrumental for understanding the 3D geometry of an image. Humans are surprisingly good at depth ordering even with abstract 2D line drawings. In this paper we propose a learning-based framework for depth ordering inference. Boundary and junction characteristics are important clues for this task, and we have developed new features based on these attributes. Although each feature individually can produce reasonable depth ordering results, each still has limitations, and we can achieve better performance by combining them. In practice, local depth ordering inferences can be contradictory. Therefore, we propose a Markov Random Field model with terms that are more global than previous work, and use graph optimization to encourage a globally consistent ordering. In addition, to produce better object segmentation for the task of depth ordering, we propose to explicitly enforce closed loops and long edges for the occlusion boundary detection. We collect a new depth-order dataset for this problem, including more than a thousand human-labeled images with various daily objects and configurations. The proposed algorithm shows promising performance over conventional methods on both synthetic and real scenes.