nils-herrmann/clean_scirepeval huggingface dataset:
Dictionary design for matching pursuit and application to motion-compensated video coding

We present a new algorithm for matching pursuit (MP) dictionary design. This technique uses existing vector-quantization design techniques and an inner product-based distortion measure to learn functions from a set of training patterns. While this scheme can be applied to many MP applications, we focus on motion-compensated video coding. Given a set of training sequences, data are extracted from the high-energy packets of the motion-compensated frames. Dictionaries with different regions of support are trained, pruned, and finally evaluated on MPEG test sequences. We find that for high bit-rate QCIF sequences we can achieve improvements of up to 0.66 dB with respect to conventional MP with separable Gabor functions.
On stability of reconstruction from Fourier transform modulus

The lower bound on the condition number of reconstruction of two classes of sequences from their Fourier transform magnitude (FTM) is derived. The lower bound for one class is shown to be 1, and for the other class to be 1/2 N/sup Q/2/, where Q is the dimensionality of the sequence and N is the number of nonzero elements in each dimension. Stability of reconstruction from the FTM and the space-domain phase is discussed. It is found that randomizing the space-domain phase improves reconstruction robustness. Experimental results are presented to verify theoretical predictions.
Reduced-complexity data acquisition system for image-based localization in indoor environments

Image-based localization has important commercial applications such as augmented reality and customer analytics. In prior work, we developed a three step pipeline for image-based localization of mobile devices in indoor environments. In the first step, we generate a 2.5D georeferenced image database using an ambulatory backpack-mounted system originally developed for 3D modeling of indoor environments. Specifically, we first create a dense 3D point cloud and polygonal model from the side laser scanner measurements of the backpack, and then use it to generate dense 2.5D database image depthmaps by raytracing the 3D model. In the second step, a query image is matched against the image database to retrieve the best-matching database image. In the final step, the pose of the query image is recovered with respect to the best-matching image. Since the pose recovery in step three only requires sparse depth information at certain SIFT feature keypoints in the database image, in this paper we improve upon our previous method by only calculating depth values at these keypoints, thereby reducing the required number of sensors in our data acquisition system. To do so, we use a modified version of the classic multi-camera 3D scene reconstruction algorithm, thereby eliminating the need for expensive geometry laser range scanners. Our experimental results in a shopping mall indicate that the proposed reduced complexity sparse depthmap approach is nearly as accurate as our previous dense depth map method.
Multiple sender distributed video streaming

With the explosive growth of video applications over the Internet, many approaches have been proposed to stream video effectively over packet switched, best-effort networks. We propose a receiver-driven protocol for simultaneous video streaming from multiple senders to a single receiver in order to achieve higher throughput, and to increase tolerance to packet loss and delay due to network congestion. Our receiver-driven protocol employs a novel rate allocation algorithm (RAA) and a packet partition algorithm (PPA). The RAA, run at the receiver, determines the sending rate for each sender by taking into account available network bandwidth, channel characteristics, and a prespecified, fixed level of forward error correction, in such a way as to minimize the probability of packet loss. The PPA, run at the senders based on a set of parameters estimated by the receiver, ensures that every packet is sent by one and only one sender, and at the same time, minimizes the startup delay. Using both simulations and Internet experiments, we demonstrate the effectiveness of our protocol in reducing packet loss.
AtomMap: A probabilistic amorphous 3D map representation for robotics and surface reconstruction

We present a new 3D probabilistic occupancy map representation for robotics applications by relaxing the commonly-assumed constraint that space must be perfectly tessellated. We replace the regular structure of 3D grids with an unstructured collection of non-overlapping, equally-sized spheres, which we call “atoms”. Abandoning the grid structure allows a more accurate representation of space directly tangent to surfaces, which facilitates a number of applications such as high fidelity surface reconstruction and surface-guided path planning. Maps composed of atoms can distinguish between free, occupied, and unknown space, support computationally efficient insertions and collision queries, provide free space planning guarantees, and achieve state-of-the-art memory efficiency over large volumes. This is achieved while simultaneously reducing quantization effects in the vicinity of surfaces and defining a useful implicit surface representation.
Automatic registration of aerial imagery with untextured 3D LiDAR models

A fast 3D model reconstruction methodology is desirable in many applications such as urban planning, training, and simulations. In this paper, we develop an automated algorithm for texture mapping oblique aerial images onto a 3D model generated from airborne light detection and ranging (LiDAR) data. Our proposed system consists of two steps. In the first step, we combine vanishing points and global positioning system aided inertial system readings to roughly estimate the extrinsic parameters of a calibrated camera. In the second step, we refine the coarse estimate of the first step by applying a series of processing steps. Specifically, We extract 2D corners corresponding to orthogonal 3D structural corners as features from both images and the untextured 3D LiDAR model. The correspondence between an image and the 3D model is then performed using Hough transform and generalized M-estimator sample consensus. The resulting 2D corner matches are used in Lowepsilas algorithm to refine camera parameters obtained earlier. Our system achieves 91% correct pose recovery rate for 90 images over the downtown Berkeley area, and overall 61% accuracy rate for 358 images over the residential, downtown and campus portions of the city of Berkeley.
Indoor localization and visualization using a human-operated backpack system

Automated 3D modeling of building interiors is useful in applications such as virtual reality and entertainment. Using a human-operated backpack system equipped with 2D laser scanners and inertial measurement units (IMU), we develop scan matching based algorithms to localize the backpack in complex indoor environments such as a T-shaped corridor intersection, a staircase, and two indoor hallways from two separate floors connected by a staircase. When building 3D textured models, we find that the localization resulting from scan matching is not pixel accurate, resulting in misalignment between successive images used for texturing. To address this, we propose an image based pose estimation algorithm to refine the results from our scan matching based localization. Finally, we use the localization results within an image based renderer to enable virtual walkthroughs of indoor environments using imagery from cameras on the same backpack. Our renderer uses a three-step process to determine which image to display, and a RANSAC framework to determine homographies to mosaic neighboring images with common SIFT features. In addition, our renderer uses plane-fitted models of the 3D point cloud resulting from the laser scans to detect occlusions. We characterize the performance of our image based renderer on an unstructured set of 2709 images obtained during a five minute backpack data acquisition for a T-shaped corridor intersection.
Rate control for layered video compression using matching pursuits

The use of matching pursuits (MP) to compress motion compensated residual video signals and its extension to layered coding have recently been demonstrated. In this paper we describe a multi-pass rate control scheme for SNR scalable encoding based on MP. The rate control algorithm enforces constant quality on each layer, while keeping the bit budget for each layer at a pre-specified target level. We formulate this as a zero finding problem, and solve it using Newton's method. Experimental results on 14 video Sequences are included, showing that layered video can be encoded at constant quality in about 5 encoding iterations per layer, while satisfying bit budget constraints with 1.5% tolerance.
Rate control for streaming video over wireless

Rate control is an important issue in video streaming applications for both wired and wireless networks. A widely accepted rate control method in wired networks is equation based rate control (Sally Floyd et al., Aug. 2000), in which the TCP friendly rate is determined as a function of packet loss rate, round trip time and packet size. This approach, also known as TFRC, assumes that packet loss in wired networks is primarily due to congestion, and as such is not applicable to wireless networks in which the bulk of packet loss is due to error at the physical layer. We propose multiple TFRC connections as an end-to-end rate control solution for wireless video streaming. We show that this approach not only avoids modifications to the network infrastructure or network protocol, hut also results in full utilization of the wireless channel. NS-2 simulations and experiments over 1/spl times/RTT CDMA wireless data network are carried out to validate, and characterize the performance of our proposed approach.
Contention window adaptation using the busy-idle signal in 802.11 WLANs

In a wireless local area network (LAN), packets can be lost for a variety of reasons, including collisions due to high traffic and channel errors due to poor channel conditions. In practice, however, nodes cannot easily differentiate between these types of loss. As a result, adaptations based on packet loss alone can result in significantly degraded performance. In 802.11 networks, wireless nodes avoid collisions via the Binary Exponential Backoff (BEB) protocol. This performs well for moderate numbers of nodes and low channel error rates, but is inefficient for large numbers of nodes, high channel error rates, or in the presence of hidden terminals. In this paper, we propose a contention window adaptation scheme in which nodes use information shared by the AP to optimize contention window sizes in a distributed fashion to improve network utility. We show via NS-2 simulations that our method can improve throughput by as much as 24% in the high node count scenario, 35% in the high channel error scenario, and 350% in the presence of hidden terminals.
Image Based Localization in Indoor Environments

Image based localization is an important problem with many applications. In our previous work, we presented a two step pipeline for performing image based localization of mobile devices in outdoor environments. In the first step, a query image is matched against a georeferenced 3D image database to retrieve the "closest" image. In the second step, the pose of the query image is recovered with respect to the "closest" image using cell phone sensors. As such, a key ingredient of our outdoor image based localization is a 3D georeferenced image database. In this paper, we extend this approach to indoors by utilizing a 3D locally referenced image database generated by an ambulatory depth acquisition backpack that is originally developed for 3D modeling of indoor environments. We demonstrate retrieval rate of 94% over a set of 83 query images taken in an indoor shopping center and characterize pose recovery accuracy of the same set.
Packet Length Adaptation in WLANs with Hidden Nodes and Time-Varying Channels

In a wireless local area network (LAN), packets can be lost due to a multitude of reasons. It is possible to reduce the probability of occurrence of some of these loss mechanisms by reducing packet length at the medium access control (MAC) layer. However, there is an inherent tradeoff in that shorter packets decrease efficiency with respect to overhead. In current packet length adaptation literature, simplified or incomplete packet loss models are used, neglecting channel fading or collisions due to hidden nodes. In this paper, we apply a more complete packet loss model and propose a local packet length adaptation algorithm whereby each node dynamically adjusts its packet length based on estimates of the probabilities of each significant type of packet loss. In our technique, the access point periodically broadcasts channel occupancy information which each node uses in conjunction with its own local observations in order to estimate current network conditions. These are used to estimate the derivative of throughput with respect to packet length at each node under the current network conditions and to adapt the packet lengths accordingly. We demonstrate throughput gains of up to 20% via NS-2 simulations.
Optimal sampling and reconstruction of MRI signals resulting from sinusoidal gradients

The optimum continuous time filter and its various discrete time implementations for free induction delay signals resulting from sinusoidal gradients of magnetic resonance imaging (MRI) are formulated. It is found that the estimation error associated with implementation based on linear temporal sampling, or, equivalently, nonlinear spatial frequency sampling, is smaller than that of nonlinear temporal sampling. It is shown that the optimal maximum likelihood estimator for sinusoidal gradients has higher error variance than that of constant gradients. Experimental results verifying the theoretical predictions are presented.
A multivalued representation for view synthesis

We propose a new depth-based representation of 3-D scenes primarily for the problem of arbitrary view synthesis. The information contained in a given image sequence is compacted into a single multivalued array, where points are organized by occlusions rather than by coherent affine motions. This grouping facilitates an automatic process to determine the number of layers and helps to reduce the artifacts caused by occlusions in the scene. In addition, an iterative multiframe dynamic programming algorithm is described to produce piecewise smooth depth maps. A novel multiframe segmentation, tracking, and plane fitting algorithm is also proposed to handle the traditionally difficult low-contrast regions. Reconstructed views as well as arbitrary flyarounds of real scenes are presented to demonstrate the effectiveness of the approach.
Neural net-based continuous phase modulation receivers

The authors propose feedforward neural networks (NNs) as receivers for partial-response continuous-phase-modulation (CPM) systems. Their approach is to replace the entire receiver structure, excluding timing recovery, with a neural net unit whose inputs are time samples of the incoming baseband signals, and whose outputs are the decoded symbols. Simulation results for coherent and incoherent NN-based receivers are presented, and their performance is compared with that of the optimum maximum-likelihood receiver. The performance of NN-based receivers at large SNR is analyzed.
Fast similarity search on video signatures

Video signatures are compact representations of video sequences designed for efficient similarity measurement. In this paper, we propose a feature extraction technique to support fast similarity search on large databases of video signatures. Our proposed technique transforms the high dimensional video signatures into low dimensional vectors where similarity search can be efficiently performed. We exploit both the upper and lower bounds of the triangle inequalities in approximating the high-dimensional metric, and combine this approximation with the classical PCA to achieve the target dimension. Experimental results on a large set of Web video sequences show that our technique outperforms fastmap, Haar wavelet, PCA, and triangle-inequality pruning.
Internet video using error resilient scalable compression and cooperative transport protocol

We introduce a point to point video transmission scheme over the Internet combining a low-delay TCP-friendly transport protocol in conjunction with a novel, real time, error-resilient layered compression method. Compressed video is packetized into individually decodable packets that are of equal expected visual importance. As a result, relatively constant video quality can be achieved at the receiver under lossy conditions. The packets can be truncated to meet the time varying bandwidth imposed by the transport protocol. Actual Internet experiments together with simulations are used to evaluate the performance of the combined scheme.
Reconstruction of oversampled band-limited signals from Sigma Delta encoded binary sequences

The application of Sigma Delta modulators to analog-to-digital conversion is examined, with attention given to the case of bandlimited input signals. The novel nonlinear decoding scheme considered here is based on the theory of projections onto convex sets (POCS), and alternates between a bandlimiting operation and a time-domain operation to find a signal invariant under both. For general bandlimited inputs, the band limitation can be based on singular value decomposition of a certain matrix. The time-domain operation results in a quadratic programming problem. The authors show simulation results for the SNR (signal-to-noise ratio) performance of a POCS-based decoder for the single-loop Sigma Delta modulator and a specific fourth-order interpolative modulator, and present comparisons with linear decoding. Improvements in SNR of up to 20-30 dB can be achieved for the single-loop modulator, and up to 10-15 dB for the fourth-order modulator.<<ETX>>
Throughput Improvement in 802.11 WLANs Using Collision Probability Estimates in Link Adaptation

The 802.11 standard includes several modulation rates, each of which is optimal for a different channel condition. However, there are no simple and reliable methods for nodes to determine their current channel conditions. Existing link adaptation techniques use packet losses as an indication of poor channel conditions; however, when there is a significant probability of collision, this assumption fails, leading to degraded throughput. In this paper, we show that an estimate of the probability of collision can be used to improve link adaptation in 802.11 networks \tmphl{with hidden terminals}, and significantly increase throughput by up to a factor of five. We demonstrate this through NS-2 simulations of a few link adaptation techniques including a new algorithm, called SNRg.
The double loop sigma delta modulator with unstable filter dynamics: stability analysis and tone behavior

Conventional /spl Sigma//spl Delta/ modulators suffer from idle tones. In order to alleviate the tone problem, /spl Sigma//spl Delta/ modulators with unstable filter dynamics have been proposed. In this paper we present an analysis of the saturation characteristics and tone behavior of the double loop /spl Sigma//spl Delta/ modulator with unstable filter dynamics. We begin by deriving stability boundaries for the double loop /spl Sigma//spl Delta/ modulator with unstable filter dynamics which yield bounds on maximum internal signal levels. We then show via simulations and steady state analysis that while tonal properties are improved by using unstable filter dynamics, idle tones are not completely removed. Specifically, we show that some unstable limit cycles have an attractor region in their neighborhood which results in tones in the spectrum corresponding to the fundamental or harmonics of these limit cycles.
Interactive shadow analysis for camera heading in outdoor images

Image geo-localization is an important problem with many applications such as augmented reality and navigation. The most common ways to geo-localize an image are to use its meta-data such as GPS or to match it against a geotagged database. When neither of those is available, it is still possible to apply shadow analysis to determine the camera heading for outdoor images. This could be useful pruning the search space in geo-localization applications, for example by removing roads with incompatible orientations from a database such as Open Street Map. In this paper, we develop a novel interactive method for deducing the global heading of a query image using the shadows in it. We start by constructing a model of the sun-earth system to determine all shadows possible at a given approximate latitude, and compare shadows within the query to those possible under the model to determine the range of possible headings. We demonstrate this on 54 query images with known ground truth, and show that in 52 cases the ground truth lies in the computed range.
Fast 3D model generation in urban environments

Describes an approach to fast generation of three dimensional (3D) models in urban environments. Rather than using a single, expensive 3D scanner, the 3D scene is captured by a combination of two fast, inexpensive 2D laser scanners. Additionally, the data acquisition system consists of a digital camera, a velocimeter and a heading sensor, all mounted on top of a truck which is driven in city streets under normal traffic conditions. One of the laser scanners is used for relative position estimation via a scan matching algorithm, while the other measures the shape of building facades for point cloud generation. The accuracy of the position estimates obtained from laser scans is further improved by taking into account the data from other sensors. Our approach is tested in a real city environment, and the resulting path estimates and 3D models are shown.
Image augmented laser scan matching for indoor dead reckoning

Most existing approaches to indoor localization focus on using either cameras or laser scanners as the primary sensor for pose estimation. In scan matching based localization, finding scan point correspondences across scans is challenging as individual scan points lack unique attributes. In camera based localization, one has to deal with images with few or no visual features as well as scale factor ambiguities to recover absolute distances. In this paper, we develop multimodal approaches for two indoor localization problems by fusing a camera and laser scanners in order to alleviate the drawbacks of each individual modality. For our first problem we recover 3 Degrees of Freedom (DoF) of a camera-laser rig on a rolling cart in a 2D plane, by using visual odometry to facilitate scan correspondence estimation. We demonstrate this approach to result in a 0.3% loop closure error for a 60m loop around the interior corridor of a building. In our second problem, we recover 6 DoF of a human operator carrying a backpack system mounted with sensors in 3D, by merging rotation estimates from scan matching and translation estimates from visual odometry, resulting in a 1% loop closure error.