nils-herrmann/clean_scirepeval huggingface dataset:
Progressive image watermarking

Progressive transmission of images is very useful in many applications, especially in image transmission over the Internet. To view an image, people would want to see part of the image while the image transmission is in progress, rather than having to wait until the end of the image transmission. On the other hand, the ease of transmission and copying of images creates the need to use digital watermarking to embed the copyright information seamlessly into the media. We propose a progressive image watermarking scheme. In this scheme, the watermark is embedded in such a way that we can retrieve part of it even when the watermarked image is still being transmitted. As transmission progresses, the retrieved watermark has a decreasing bit error rate. Our proposed methods not only transmit the watermarked image progressively, but also intelligently select watermark embedding locations robust to various attacks.
User-independent retrieval of free-form hand-drawn sketches

In this paper we propose a method to retrieve free-form hand-drawn sketches stored in the form of multiple strokes, by extracting the shape information for each stroke and by considering the geometric relationship between the strokes. To extract the shape information, a number of shape estimators are applied to each stroke to provide a soft decision about how similar it is to a particular shape type. Then, two strokes are matched according to a specific set of features for each shape type. The proximity of the corresponding strokes is used to account for the geometric relationship between multiple strokes during the matching stage. Our approach is robust to different drawing styles, thus making our retrieval system user-independent. Sketch retrieval is useful in applications where a user can easily search through a database of hand-draw sketches by inputting a sketch about what he/she is looking for, without the trouble of describing it using keywords.
A Bayesian hierarchical detection framework for parking space detection

In this paper, a 3-layer Bayesian hierarchical detection framework (BHDF) is proposed for robust parking space detection. In practice, the challenges of the parking space detection problem come from luminance variations, inter- occlusions among cars, and occlusions caused by environmental obstacles. Instead of determining the status of parking spaces one by one, the proposed BHDF framework models the inter-occluded patterns as semantic knowledge and couple local classifiers with adjacency constraints to determine the status of parking spaces in a row-by-row manner. By applying the BHDF to the parking space detection problem, the available parking spaces and the labeling of parked cars can be achieved in a robust and efficient manner. Furthermore, this BHDF framework is generic enough to be used for various kinds of detection and segmentation applications.
Creating a multiuser 3-D virtual environment

For many years online text chat software such as ICQ has been used as a means of multiuser interaction. As higher bandwidth is available, multipoint videoconferencing systems such as NetMeeting or CUseeMe that allow people from different geographical locations to see and talk to each other are becoming popular. However, these videoconferencing systems lack the sense of immersion because each participant sees other participants in separate windows, and it is often difficult to tell who is talking to whom. Based on user study, Argyle and Dean (1965) suggested that during face-to-face communication people vary physical proximity, eye contact, and other behaviors to optimize an overall level of intimacy. In addition, Tang and Isaacs (1993) found that gaze awareness is important because participants can use the visual cue to determine when another participant is paying attention to them. These studies suggest that a more immersive way of multiuser communication is to connect the users in a three-dimensional (3-D) virtual environment so that users feel that they are communicating face to face. Moving from a text-based chat room environment to a 3-D environment creates new challenges in several areas such as computer animation, signal processing, and computer vision. We introduce immersive interactive technologies used in multiuser 3-D virtual environments. We also survey existing systems.
Time-Aware Opportunistic Relay for Video Streaming Over WLANs

Robust video streaming over time-varying, error-prone wireless LANs (WLANs) poses many challenges. In this paper, we present a time-based opportunistic relay (TOR) scheme for high-quality video streaming over WLANs. The proposed scheme exploits path diversity in relaying packets with awareness of the time constraints of video data. Specifically, relay nodes continuously overhear and opportunistically forward packets to enhance end-to-end delivery rates. To relay a packet in a time-aware manner, a relay deadline is computed for each packet and used by relay nodes to determine whether they should relay or discard the packet. Simulation results show that TOR improves video quality by up to 9.76 dB.
Updating mixture of principal components for error concealment

We present a new statistical modeling technique called "updating mixture of principal components" (UMPC). UMPC specifically captures the non-stationary as well as the multi-modal characteristics of the data. Real-world data such as video data typically have these two characteristics. The video content changes over time and has a multi-modal probability distribution. We apply UMPC to perform error concealment for video data transmitted over networks with losses, and show that UMPC outperforms conventional error concealment methods.
Robust Low-Dose CT Perfusion Deconvolution via Tensor Total-Variation Regularization

Acute brain diseases such as acute strokes and transit ischemic attacks are the leading causes of mortality and morbidity worldwide, responsible for 9% of total death every year. “Time is brain” is a widely accepted concept in acute cerebrovascular disease treatment. Efficient and accurate computational framework for hemodynamic parameters estimation can save critical time for thrombolytic therapy. Meanwhile the high level of accumulated radiation dosage due to continuous image acquisition in CT perfusion (CTP) raised concerns on patient safety and public health. However, low-radiation leads to increased noise and artifacts which require more sophisticated and time-consuming algorithms for robust estimation. In this paper, we focus on developing a robust and efficient framework to accurately estimate the perfusion parameters at low radiation dosage. Specifically, we present a tensor total-variation (TTV) technique which fuses the spatial correlation of the vascular structure and the temporal continuation of the blood signal flow. An efficient algorithm is proposed to find the solution with fast convergence and reduced computational complexity. Extensive evaluations are carried out in terms of sensitivity to noise levels, estimation accuracy, contrast preservation, and performed on digital perfusion phantom estimation, as well as in vivo clinical subjects. Our framework reduces the necessary radiation dose to only 8% of the original level and outperforms the state-of-art algorithms with peak signal-to-noise ratio improved by 32%. It reduces the oscillation in the residue functions, corrects over-estimation of cerebral blood flow (CBF) and under-estimation of mean transit time (MTT), and maintains the distinction between the deficit and normal regions.
Revisiting Depth Layers from Occlusions

In this work, we consider images of a scene with a moving object captured by a static camera. As the object (human or otherwise) moves about the scene, it reveals pairwise depth-ordering or occlusion cues. The goal of this work is to use these sparse occlusion cues along with monocular depth occlusion cues to densely segment the scene into depth layers. We cast the problem of depth-layer segmentation as a discrete labeling problem on a spatio-temporal Markov Random Field (MRF) that uses the motion occlusion cues along with monocular cues and a smooth motion prior for the moving object. We quantitatively show that depth ordering produced by the proposed combination of the depth cues from object motion and monocular occlusion cues are superior to using either feature independently, and using a naive combination of the features.
Shot boundary detection using temporal statistics modeling

In multimedia information retrieval, shot boundary detection is a very active research topic. In order to perform shot boundary detection, we propose an algorithm for modeling temporal statistics using a novel eigenspace updating method. The feature extracted from the current frame is compared with a model trained from features in the previous frames. A shot boundary is detected if the new feature does not fit well to the existing model. The model is based on principal component analysis (PCA), or the eigenspace method, in which the eigenspace can be updated to capture the non-stationary statistics of the features. The experiment results show that the proposed algorithm outperforms the traditional direct differencing method.
Tree-metrics graph cuts for brain MRI segmentation with tree cutting

We tackle the problem of brain MRI image segmentation using the tree-metric graph cuts (TM) algorithm, a novel image segmentation algorithm, and introduce a “tree-cutting” method to interpret the labeling returned by the TM algorithm as tissue classification for the input brain MRI image. The approach has three steps: 1) pre-processing, which generates a tree of labels as input to the TM algorithm; 2) a sweep of the TM algorithm, which returns a globally optimal labeling with respect to the tree of labels; 3) post-processing, which involves running the “tree-cutting” method to generate a mapping from labels to tissue classes (GM, WM, CSF), producing a meaningful brain MRI segmentation. The TM algorithm produces a globally optimal labeling on tree metrics in one sweep, unlike conventional methods such as EMS and EM-style geo-cuts, which iterate the expectation maximization algorithm to find hidden patterns and produce only locally optimal labelings. When used with the “tree-cutting” method, the TM algorithm produces brain MRI segmentations that are as good as the Unified Segmentation algorithm used by SPM8, using a much weaker prior. Comparison with the current approaches shows that our method is faster and that our overall segmentation accuracy is better.
Audio-visual integration in multimodal communication

We review recent research that examines audio-visual integration in multimodal communication. The topics include bimodality in human speech, human and automated lip reading, facial animation, lip synchronization, joint audio-video coding, and bimodal speaker verification. We also study the enabling technologies for these research topics, including automatic facial-feature tracking and audio-to-visual mapping. Recent progress in audio-visual research shows that joint processing of audio and video provides advantages that are not available when the audio and video are processed independently.
CMUseum: A Location-aware Wireless Video Streaming System

Location-aware computing enables automatic tailoring of information and services based on the current location of a mobile user. We have designed and implemented CMUseum, a system that enables location-based video streaming applications, as well as other add-on services on top of the purposed infrastructure. Our novel design incorporates an 802.15.4 Zigbee sensor network for collecting location information as well as an 802.11 Wi-Fi network for streaming video contents. This framework is largely event-driven in order to support the real-time nature of the video streaming services it handles. We have demonstrated the viability of this system through implementing a working system in a museum tour guide scenario
Image authentication by detecting traces of demosaicing

With increasing technical advances, computer graphics are becoming more photorealistic. Therefore, it is important to develop methods for distinguishing between actual photographs from digital cameras and computer generated images. We describe a novel approach to this problem. Rather than focusing on the statistical differences between the image textures, we recognize that images from digital cameras contain traces of resampling as a result of using a color filter array with demosaicing algorithms. We recognize that estimation of the actual demosaicing parameters is not necessary; rather, detection of the presence of demosaicing is the key. The in-camera processing (rather than the image content) distinguishes the digital camera photographs from computer graphics. Our results show high reliability on a standard test set of JPEG compressed images from consumer digital cameras. Further, we show the application of these ideas for accurately localizing forged regions within digital camera images.
Learning boundaries with color and depth

To enable high-level understanding of a scene, it is important to understand the occlusion and connected boundaries of objects in the image. In this paper, we propose a new framework for inferring boundaries from color and depth information. Even with depth information, it is not a trivial task to find and classify boundaries. Real-world depth images are noisy, especially at object boundaries, where our task is focused. Our approach uses features from both the color (which are sharp at object boundaries) and depth images (for providing geometric cues) to detect boundaries and classify them as occlusion or connected boundaries. We propose depth features based on surface fitting from sparse point clouds, and perform inference with a Conditional Random Field. One advantage of our approach is that occlusion and connected boundaries are identified with a single, common model. Experiments show that our mid-level color and depth features outperform using either depth or color alone, and our method surpasses the performance of baseline boundary detection methods.
On commutativity of multidimensional downsamplers and upsamplers

The commutativity of multidimensional downsamplers and upsamplers have been discussed very intensively for the past few years. This is due to its important applications in sampling structure conversion, e.g., the conversion between conventional television signals and high definition television (HDTV) signals. Among many other results, one useful test for such commutativity was found to be that the two matrices which define the multidimensional downsampling and upsampling should be commutative and coprime. However, the problem of finding multidimensional downsamplers and upsamplers that satisfy these conditions has remained open. In this paper, we develop a systematic procedure to solve this open problem.<<ETX>>
Structures for time-reversed inversion in filter banks

Anticausal inversion of IIR transfer functions has gained importance in recent years, in the efficient implementation of IIR digital filter banks. In this paper we first introduce the idea of a causal dual, as an intermediate step in the implementation of anticausal IIR inverses. With time reversal operators at the input and output of the causal dual, we get the anticausal inverse of the original structure. The causal dual eliminates the need for similarity transformations, during a key step called blockwise state transfer, in implementing anticausal inverses. In the paper we identify efficient structures for causal duals of standard structures like the direct-form, cascade-form, coupled form, and IIR lattice structures, including the tapped lattice.
Active view selection for object and pose recognition

In this paper we present an algorithm for multi-view object and pose recognition. In contrast to the existing work that focuses on modeling the object using the images only; we exploit the information on the image sequences and their relative 3D positions, because under many circumstances the movements between multi-views are accessible and can be controlled by the users. Thus we can calculate the next optimal place to take a picture based on previous behaviors, and perform the object/pose recognition based on these obtained images. The proposed method uses HOG (Histograms of Oriented Gradient) and SVM (Support Vector Machine) as the basic object/pose classifier. To learn the optimal action, this algorithm makes use of a boosting method to find the best sequence across the multi-views. Then it exploits the relation between the different view points using the Adaboost algorithm. The experiment shows that the learned sequence improves recognition performance in early steps compared to a randomly selected sequence, and the proposed algorithm can achieve a better recognition accuracy than the baseline method.
Beyond trees: MRF inference via outer-planar decomposition

Maximum a posteriori (MAP) inference in Markov Random Fields (MRFs) is an NP-hard problem, and thus research has focussed on either finding efficiently solvable subclasses (e.g. trees), or approximate algorithms (e.g. Loopy Belief Propagation (BP) and Tree-reweighted (TRW) methods). This paper presents a unifying perspective of these approximate techniques called "Decomposition Methods". These are methods that decompose the given problem over a graph into tractable subproblems over subgraphs and then employ message passing over these subgraphs to merge the solutions of the subproblems into a global solution. This provides a new way of thinking about BP and TRW as successive steps in a hierarchy of decomposition methods. Using this framework, we take a principled first step towards extending this hierarchy beyond trees. We leverage a new class of graphs amenable to exact inference, called outer-planar graphs, and propose an approximate inference algorithm called Outer-Planar Decomposition (OPD). OPD is a strict generalization of BP and TRW, and contains both of them as special cases. Our experiments show that this extension beyond trees is indeed very powerful -OPD outperforms current state-of-art inference methods on hard non-submodular synthetic problems and is competitive on real computer vision applications.
Reconstructing dense light field from a multi-focus images array

The work presents a novel method for synthesizing a novel view from two sets of differently focused images taken by a sparse camera array for a scene of two approximately constant depths. The proposed method consists of two steps. The first step is a view interpolation to reconstruct an all-focused dense light field of the scene. The second step is to synthesize a novel view by a light-field rendering technique from the reconstructed dense light field. The view interpolation can be achieved simply by linear filters that are designed to convert defocus effects to parallax effects without estimating the depth map of the scene. The proposed method can effectively create a dense array of pin-hole cameras (i.e., all-focused images), so that the final novel view is better than traditional method using a sparse array of cameras. Experimental results on real images from four aligned cameras are shown.
Direct filtering method for image based rendering

Image based rendering (IBR) is basically a light ray resampling method for generating a novel image without aliasing artifacts from a given set of sampled light rays. To avoid aliasing artifacts, conventional methods require an estimate of the scene geometry. In this paper, we present a novel IBR method by linear filtering without estimating the scene geometry, for the simplified case when generating a virtual image at the center of 2/spl times/2 sparse camera array for a two depth-layers scene. The reconstruction filter used in the proposed method can derive by integrating all the process in our previously proposed method into a one-shot process.
Second-generation error concealment for video transport over error prone channels

Video transport over error prone channels may result in loss or erroneous decoding of the video. Error concealment is an effective mechanism to reconstruct the video content. In this paper, we review different error concealment methods and introduce a new framework, which we refer to as second-generation error concealment. All the error concealment methods reconstruct the lost video content by making use of some a priori knowledge about the video content. First-generation error concealment builds such a priori in a heuristic manner. The proposed second-generation error concealment builds the a priori by modeling the statistics of the video content. Context-based models are trained with the correctly decoded video content, and then used to replenish the lost video content. Trained models capture the statistics of the video content and thus reconstruct the lost video content better than reconstruction by heuristics.
Fine-grained rate shaping for video streaming over wireless networks

Streaming of precoded video, which is both source and channel coded, over wireless networks faces challenges of time-varying packet loss rate and fluctuating bandwidth. Rate shaping has been proposed (Chen, T.P.-C. and Chen, T., ICASSP, 2002) to "shape" the precoded video to adapt to the real-time bandwidth requirement and the packet loss rate. We now propose a novel "fine-grained rate shaping" (FGRS) scheme to allow for bandwidth adaptation over a wide range of bandwidth and packet loss rate. The video is precoded with fine granularity scalability (FGS) followed by forward error correction (FEC) coding with erasure codes. Utilizing the fine granularity property of FGS and FEC, FGRS selectively drops part of the precoded video and still yields a decodable bitstream at the decoder. A new two-stage rate-distortion (R-D) optimization, with model-based hyper-plane and hill-climbing based refinement, is proposed to select which part of the precoded video to drop. Promising results for FGRS are shown.
Object Detection in Video with Graphical Models

In this paper, we propose a general object detection framework which combines the hidden Markov model with the discriminative random fields. Recent object detection algorithms have achieved impressive results by using graphical models, such as Markov random field. These models, however, have only been applied to two dimensional images. In many scenarios, video is the directly available source rather than images, hence an important information for detecting objects has been omitted - the temporal information. To demonstrate the importance of temporal information, we apply graphical models to the task of text detection in video and compare the result of with and without temporal information. We also show the superiority of the proposed models over simple heuristics such as median filter over time
Role of anticausal inverses in multirate filter-banks .I. System-theoretic fundamentals

In a maximally decimated filter bank with identical decimation ratios for all channels, the perfect reconstructibility property and the nature of reconstruction filters (causality, stability, FIR property, and so on) depend on the properties of the polyphase matrix. Various properties and capabilities of the filter bank depend on the properties of the polyphase matrix as well as the nature of its inverse. In this paper we undertake a study of the types of inverses and characterize them according to their system theoretic properties (i.e., properties of state-space descriptions, McMillan degree, degree of determinant, and so forth). We find in particular that causal polyphase matrices with anticausal inverses have an important role in filter bank theory. We study their properties both for the FIR and IIR cases. Techniques for implementing anticausal IIR inverses based on state space descriptions are outlined. It is found that causal FIR matrices with anticausal FIR inverses (cafacafi) have a key role in the characterization of FIR filter banks.