nils-herrmann/clean_scirepeval huggingface dataset:
SalSi: A new seismic attribute for salt dome detection

In this paper, we propose a saliency-based attribute, SalSi, to detect salt dome bodies within seismic volumes. SalSi is based on the saliency theory and modeling of the human vision system (HVS). In this work, we aim to highlight the parts of the seismic volume that receive highest attention from the human interpreter, and based on the salient features of a seismic image, we detect the salt domes. Experimental results show the effectiveness of SalSi on the real seismic dataset acquired from the North Sea, F3 block. Subjectively, we have used the ground truth and the output of different salt dome delineation algorithms to validate the results of SalSi. For the objective evaluation of results, we have used the receiver operating characteristics (ROC) curves and area under the curves (AUC) to demonstrate SalSi is a promising and an effective attribute for seismic interpretation.
Interactive Fault Extraction in 3-D Seismic Data Using the Hough Transform and Tracking Vectors

The exploration of reservoir regions has a close relationship with the localization of faults. Although faults can be labeled in seismic volumes by experienced interpreters, such manual interpretation is inefficient when dealing with a dramatically growing amount of collected seismic data. To speed up the interpretation efficiency of faults, in this paper, we propose a method that semiautomatically detects fault surfaces by using the Hough transform as well as tracking vectors. In the proposed method, we classify seismic sections into reference and predicted ones by borrowing the concept of I- and B-frames in video-coding techniques. For these two types of seismic sections, we introduce different strategies to delineate faults. In reference sections, we first highlight likely fault regions from corresponding coherence maps and apply the Hough transform to extract the features of faults. After removing false features, we optimally connect remaining features under the constraints of coherence maps. Since the accuracy of fault detection in reference sections depends highly on several parameters, to avoid replicating the tweaking of parameters in all seismic sections, we propose tracking detected faults in reference sections through remaining predicted sections, in which faults are labeled based on estimated tracking vectors and geological constraints. To evaluate the performance of the proposed method, we introduce a fault similarity (FauSIM) index that describes the similarity between detected faults and manually picked faults. The FauSIM index based on the Fréchet distance focuses on both the local and global comparisons of faults. Experimental results show that the proposed method has the capability to accurately detect faults in seismic sections, and the tracking process improves interpretation efficiency by eliminating tweaked parameters. In addition, comparisons between faults delineated by various methods and faults manually picked show that the FauSIM index is highly correlated with interpreters’ subjective perception.
Characteristics of spatio-temporal signals acquired by optical motion tracking

The optical tracking is well studied in the field of computer vision, and the optical motion tracking is commonly achieved by a serial position tracking in time. In this paper, we look into the optical motion tracking at the perspective of spatio-temporal signal processing. We reveal possible sources that may affect the signal acquisition and estimation. We also introduce a general method for studying the characteristics of the spatio-temporal signals acquired from the optical tracking system, which is the first step that leads to better representation and acquisition of the spatio-temporal signal.
SPS Global Presence and Extinct Technologies [From the Editor]

This is my third and last editorial in IEEE Signal Processing Magazine (SPM) as the area editor for Columns and Forums (2009–2011). Our Editor-in-Chief (EIC) Li Deng gave each area editor the opportunity to write an editorial once a year. This privilege provides us with a window to directly communicate with you, our valuable readers. As I am finalizing the columns for the remaining issues in 2011, I wanted to talk about a number of subjects in this editorial. I would like to ask your help in getting our magazine and the entire IEEE Signal Processing Society (SPS) into new places. Also, I would like to discuss the issue of vanishing technologies and innovations, especially those related to signal processing.
Fault detection in seismic datasets using hough transform

In this paper, we present a semi-automatic algorithm to detect faults in seismic datasets using Hough transform. As a multistage approach, our method first highlights the likely fault points from the discontinuity map of one seismic section. Hough transform is then applied to detect faults features. Considering geological constraints of faults, false features are removed using a double-threshold method. Then, we get an initial fault line by connecting the remaining faults features. In the last stage, by incorporating the discontinuity information from step one, we tweak the initial fault line to obtain more accurate and reliable results. Our experimental results show that our method can delineate the fault lines in seismic sections more accurately than a state-of-the-art method.
Distributed Estimation in Energy-Constrained Wireless Sensor Networks

In this paper, we consider distributed estimation of a noise-corrupted deterministic parameter in energy-constrained wireless sensor networks from energy-distortion perspective. Given a total energy budget allowable to be used by all sensors, there exists a tradeoff between the subset of active sensors and the energy used by each active sensor in order to minimize the estimation MSE. To determine the optimal quantization bit rate and transmission energy of each sensor, a concept of equivalent unit-energy MSE function is introduced. Based on this concept, an optimal energy-constrained distributed estimation algorithm for homogeneous sensor networks and a quasi-optimal energy-constrained distributed estimation algorithm for heterogeneous sensor networks are proposed. Moreover, the theoretical energy-distortion performance bound for distributed estimation is addressed and it is shown that the proposed algorithm is quasi-optimal within a factor 2 of the theoretical lower bound. Simulation results also show that the proposed method can achieve a significant reduction in the estimation MSE when compared with other uniform schemes. Finally, the proposed algorithm is easy to implement in a distributed manner and it adapts well to the dynamic sensor environments.
BaTex3: Bit Allocation for Progressive Transmission of Textured 3-D Models

The efficient progressive transmission of a textured 3D model, which has a texture image mapped on a geometric mesh, requires careful organization of the bit stream to quickly present high-resolution visualization on the user's screen. Bit rates for the mesh and the texture need to be properly balanced so that the decoded model has the best visual fidelity during transmission. This problem is addressed in the paper, and a bit allocation framework is proposed. In particular, the relative importance of the mesh geometry and the texture image on visual fidelity is estimated using a fast quality measure (FQM). Optimal bit distribution between the mesh and the texture is then computed under the criterion of maximizing the quality measured by FQM. Both the quality measure and the bit allocation algorithm are designed with low computation complexity. Empirical studies show that not only the bit allocation framework maximizes the receiving quality of the textured 3D model, but also it does not require sending additional information to indicate bit boundaries between the mesh and the texture in the multiplexed bit stream, which makes the streaming application more robust to bit errors that may occur randomly during transmission.
Power of tempospatially unified spectral density for perceptual video quality assessment

We propose a perceptual video quality assessment (PVQA) metric for distorted videos by analyzing the power spectral density (PSD) of a group of pictures. This is an estimation approach that relies on the changes in video dynamic calculated in the frequency domain and are primarily caused by distortion. We obtain a feature map by processing a 3D PSD tensor obtained from a set of distorted frames. This is a full-reference tempospatial approach that considers both temporal and spatial PSD characteristics. This makes it ubiquitously suitable for videos with varying motion patterns and spatial contents. Our technique does not make any assumptions on the coding conditions, streaming conditions or distortion. This approach is also computationally inexpensive which makes it feasible for real-time and practical implementations. We validate our proposed metric by testing it on a variety of distorted sequences from PVQA databases. The results show that our metric estimates the perceptual quality at the sequence level accurately. We report the correlation coefficients with the differential mean opinion scores (DMOS) reported in the databases. The results show high and competitive correlations compared with the state of the art techniques.
Subsurface Structure Analysis Using Computational Interpretation and Learning: A Visual Signal Processing Perspective

Understanding Earth's subsurface structures has been and continues to be an essential component of various applications such as environmental monitoring, carbon sequestration, and oil and gas exploration. By viewing the seismic volumes that are generated through the processing of recorded seismic traces, researchers were able to learn from applying advanced image processing and computer vision algorithms to effectively analyze and understand Earth's subsurface structures. In this article, we first summarize the recent advances in this direction that relied heavily on the fields of image processing and computer vision. Second, we discuss the challenges in seismic interpretation and provide insights and some directions to address such challenges using emerging machine-learning algorithms.
Cooperative on-demand delivery for IPTV networks

In this paper, we investigate the use of cooperative transmission strategies to support timely and efficient delivery of on-demand content to end users. Within the proposed framework, a cooperative transmission strategy suggests that users who have access to the requested content cooperatively transmit to the targeted user(s) to minimize the servicing requirements at the server side and to improve the scalability performance in the network. Through extensive simulation-based studies, we show that significant performance improvements can be achieved with the proposed framework to enable efficient access to an ever growing on-demand content.
A low-complexity video encoder with decoder motion estimator

We investigate the use of video compression methods that require a simple encoder and a complex decoder for applications such as video surveillance, smart spaces, and sensor networks. In our proposed method, the encoder tries to identify the locations whose content cannot be predicted at the decoder, and codes such areas at higher fidelity. Typically, high-motion macro-blocks represent such significant state regions. A shape-adaptive (SA) SPIHT (set partitioning in hierarchical trees) encoder is then used to code these regions efficiently. On the decoder side, we perform motion extrapolation using the previously decoded frames to construct an estimate for the current frame. This estimate then serves as the side information at the decoder. Receiving the SA-SPIHT coded and the motion extrapolated frame, the decoder fuses the information in these two frames to produce a frame that is of higher quality than the component images. Experimental results show that the proposed codec outperforms H.264 intra mode by 3 dB.
6D motion gesture recognition using spatio-temporal features

Depending on the tracking technology in use, a 6D motion gesture can be tracked and represented explicitly by the position and orientation or implicitly by the acceleration and angular speed. In this work, we first present the reasoning for the definition and recognition of motion gestures. Five basic feature vectors are then derived from the 6D motion data. Our main contribution is to investigate the relative effectiveness of various feature dimensions for motion gesture recognition in both user dependent and user independent cases. We also propose a feature normalization procedure and prove its effectiveness in achieving “scale” invariance especially in the user independent case. Our study gives an insight into the attainable recognition rate with different tracking devices.
Hierarchical Hole-Filling(HHF): Depth image based rendering without depth map filtering for 3D-TV

In this paper we propose a new approach for disocclusion removal in depth image-based rendering (DIBR) for 3D-TV. The new approach, Hierarchical Hole-Filling (HHF), eliminates the need for any preprocessing of the depth map. HHF uses a pyramid like approach to estimate the hole pixels from lower resolution estimates of the 3D wrapped image. The lower resolution estimates involves a pseudo zero canceling plus Gaussian filtering of the wrapped image. Then starting backwards from the lowest resolution hole-free estimate in the pyramid, we interpolate and use the pixel values to fill in the hole in the higher up resolution image. The procedure is repeated until the estimated image is hole-free. Experimental results show that HHF yields virtual images that are free of any geometric distortions, which is not the case in other algorithms that preprocess the depth map. Experiments has also shown that unlike previous DIBR techniques, HHF is not sensitive to depth maps with high percentage of bad matching pixels.
Understanding spatial correlation in eye-fixation maps for visual attention in videos

In this paper, we present an analysis of recorded eye-fixation data from human subjects viewing video sequences. The purpose is to better understand visual attention for videos. Utilizing the eye-fixation data provided in the CRCNS (Collaborative Research in Computational Neuroscience) dataset, this paper focuses on the relation between the saliency of a pixel and that of its direct neighbors, without making any assumption about the structure of the eye-fixation maps. By employing some basic concepts from information theory, the analysis shows substantial correlation between the saliency of a pixel and the saliency of its neighborhood. The analysis also provides insights into the structure and dynamics of the eye-fixation maps, which can be very useful in understanding video saliency and its applications.
Saliency detection for seismic applications using multi-dimensional spectral projections and directional comparisons

In this paper, we propose a novel approach for saliency detection for seismic applications using 3D-FFT local spectra and multi-dimensional plane projections. We develop a projection scheme by dividing a 3D-FFT local spectrum of a data volume into three distinct components, each depicting changes along a different dimension of the data. The saliency detection results obtained using each projected component are then combined to yield a saliency map. To accommodate the directional nature of seismic data, in this work, we modify the center-surround model, proven to be biologically plausible for visual attention, to incorporate directional comparisons around each voxel in a 3D volume. Experimental results on real seismic dataset from the F3 block in Netherlands offshore in the North Sea prove that the proposed algorithm is effective, efficient, and scalable. Furthermore, a subjective comparison of the results shows that it outperforms the state-of-the-art methods for saliency detection.
Optimal weighted data gathering in multi-hop heterogeneous sensor networks

We consider the problem of gathering data from an energy-limited wireless sensor network consisting of a set of sensor nodes and a sink node. Based on the heterogeneous nature of sensor networks, where data generated at different sensor nodes are of different fidelity and importance, we formulate the data gathering problem as maximizing the weighted sum of data generated at all sensors and turn it to a linear programming problem subject to the limited-energy constraints. Furthermore, we show that the optimal multi-hop routing for the weighted data gathering problem is character-based routing, where only ldquobadrdquo sensors relay data for ldquogoodrdquo sensors. An example application and simulation results are given to justify the proposed concept and algorithm.
Joint Source and Channel Coding for 3-D Scene Databases Using Vector Quantization and Embedded Parity Objects

Three-dimensional graphic scenes contain various mesh objects in one geometric space where different objects have potentially unequal importance regarding display. This paper proposes an object-oriented system for efficiently coding and streaming 3-D scene databases in lossy and rate-constrained environments. Vector quantization (VQ) is exploited to code 3-D scene databases into multiresolution hierarchies. For the best distortion-rate performance, adaptive quantization precisions are allocated to different objects and different layers of each object based on a weighted distortion model. Upon transmission, scalably coded objects are delivered in respective packet sequences to preserve their manipulation independency. For packet loss resilience, a plurality of FEC codes are generated as "parity objects" parallel to graphic objects, which protect the graphic objects concurrently and also preferentially in regard to their unequal decoding importance. A rate-distortion optimization framework is then developed, which performs rate allocation between graphic objects and parity objects and generates the parity data properly. We show that, by treating graphic objects jointly and preferentially in source and channel coding while preserving their independencies in transport, the proposed system reduces the receiving distortion of the 3-D database significantly compared to conventional methods
MIQM: A Multicamera Image Quality Measure

Although several subjective and objective quality assessment methods have been proposed in the literature for images and videos from single cameras, no comparable effort has been devoted to the quality assessment of multicamera images. With the increasing popularity of multiview applications, quality assessment of multicamera images and videos is becoming fundamental to the development of these applications. Image quality is affected by several factors, such as camera configuration, number of cameras, and the calibration process. In order to develop an objective metric specifically designed for multicamera systems, we identified and quantified two types of visual distortions in multicamera images: photometric distortions and geometric distortions. The relative distortion between individual camera scenes is a major factor in determining the overall perceived quality. In this paper, we show that such distortions can be translated into luminance, contrast, spatial motion, and edge-based structure components. We propose three different indices that can quantify these components. We provide examples to demonstrate the correlation among these components and the corresponding indices. Then, we combine these indices into one multicamera image quality measure (MIQM). Results and comparisons with other measures, such as peak signal-to noise ratio, mean structural similarity, and visual information fidelity show that MIQM outperforms other measures in capturing the perceptual fidelity of multicamera images. Finally, we verify the results against subjective evaluation.
Motion estimation using block overlap minimization

This paper presents a block-overlap-based method for handling the ill-posed nature of motion estimation. The proposed method uses the volume of motion-compensated block overlap as an additional term in minimizing the overall energy. By reducing the amount of block overlap, the proposed method results in a significant improvement in the quality of the motion field. Experimental results show that the proposed method outperforms several existing methods in the literature in terms of motion vector (MV) and interpolation quality. In terms of interpolation quality, our algorithm outperforms all other block-based methods as well as several complex optical flow methods. In addition, it is the fastest non-GPU implementation at the time of this writing.
Misalignment correction for depth estimation using stereoscopic 3-D cameras

This paper presents a misalignment correction method for reducing depth errors that result from camera shift. The proposed method uses a real-time motion estimation approach to correct for alignment errors between stereo cameras. Unlike existing methods in the literature, the natural disparity between stereo views is incorporated into a constrained motion estimation framework. The proposed method is shown to accurately estimate synthetic misalignments due to translation, rotation, scaling, and perspective transformation. In addition, real images taken from a stereo camera rig confirm that the proposed method is capable of significantly reducing misalignments due to camera yaw, pitch, and roll.
3VQM: A vision-based quality measure for DIBR-based 3D videos

In this paper, we present a new method for objectively evaluating the quality of stereoscopic 3D videos generated by depth-image-based rendering (DIBR). First we show how to derive an ideal depth estimate at each pixel value that would constitute a distortion-free rendered video. The ideal depth estimate will then be used to derive three distortion measures to objectify the visual discomfort in the stereoscopic videos. The three measures are temporal outliers (TO), temporal inconsistencies (TI), and spatial outliers (SO). The combination of the three measures will constitute a vision-based quality measure for 3D DIBR-based videos, 3VQM. Finally, 3VQM will be presented and verified against a fully conducted subjective evaluation. The results show that our proposed measure is significantly accurate, coherent and consistent with the subjective scores.
Design of a Transmission Protocol for a CVE

Virtual reality is a useful tool to train individuals for various different situations or scenarios. A single person utilizing a virtual environment can greatly enhance their skill set, but the greatest gain in skill is obtained when multiple users realistically interact in the virtual environment. To accomplish this, a network protocol must be designed to efficiently communicate vital information to participants in the virtual environment. The purpose of this paper is to document the design of a transmission protocol for a collaborative virtual environment (CVE). In addition, the network protocol was tested with a network emulator to understand how well the protocol functioned under various network conditions. When the jitter level was below 50 ms the transmission protocol was able to reproduce the smooth avatar movement even at transmission intervals as high as 100 ms. However as the jitter increased the transmission interval had to be kept as low as 10 ms to reproduce realistic human movement.
Function-Based Network Lifetime for Estimation in Wireless Sensor Networks

Network lifetime is a critical concern in the design of wireless sensor networks. Though many different definitions of network lifetime have been used in the literature, we introduce a function-based network lifetime definition, which focuses on whether the network can perform a given task instead of whether any individual sensor is dead. Then we derive an upper bound of function-based network lifetime for estimation, and we maximize it by introducing a concept of equivalent unit-resource mean square error (MSE) function. The proposed algorithm is optimal and the simulation results show that a significant gain is achieved by the proposed algorithm compared with heuristic methods.