arXiv subjects

Ghassan AlRegib

Publications and source records attributed to Ghassan AlRegib.

At least 127 records · Page 7Linked to original sources

arXiv2019

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.

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arXiv2019

Unsupervised uncertainty estimation using spatiotemporal cues in video saliency detection

In this paper, we address the problem of quantifying reliability of computational saliency for videos, which can be used to improve saliency-based video processing and enable more reliable performance and risk assessment of such processing. Our approach is twofold. First, we explore spatial correlations in both saliency map and eye-fixation map. Then, we learn spatiotemporal correlations that define a reliable saliency map. We first study spatiotemporal eye-fixation data from a public dataset and investigate a common feature in human visual attention, which dictates correlation in saliency between a pixel and its direct neighbors. Based on the study, we then develop an algorithm that estimates a pixel-wise uncertainty map that reflects our confidence in the associated computational saliency map by relating a pixel's saliency to the saliency of its neighbors. To estimate such uncertainties, we measure the divergence of a pixel, in a saliency map, from its local neighborhood. Additionally, we propose a systematic procedure to evaluate the estimation performance by explicitly computing uncertainty ground truth as a function of a given saliency map and eye fixations of human subjects. In our experiments, we explore multiple definitions of locality and neighborhoods in spatiotemporal video signals. In addition, we examine the relationship between the parameters of our proposed algorithm and the content of the videos. The proposed algorithm is unsupervised, making it more suitable for generalization to most natural videos. Also, it is computationally efficient and flexible for customization to specific video content. Experiments using three publicly available video datasets show that the proposed algorithm outperforms state-of-the-art uncertainty estimation methods with improvement in accuracy up to 63% and offers efficiency and flexibility that make it more useful in practical situations.

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arXiv2018

The role of visual saliency in the automation of seismic interpretation

In this paper, we propose a workflow based on SalSi for the detection and delineation of geological structures such as salt domes. SalSi is a seismic attribute designed based on the modeling of human visual system that detects the salient features and captures the spatial correlation within seismic volumes for delineating seismic structures. Using SalSi, we can not only highlight the neighboring regions of salt domes to assist a seismic interpreter but also delineate such structures using a region growing method and post-processing. The proposed delineation workflow detects the salt-dome boundary with very good precision and accuracy. Experimental results show the effectiveness of the proposed workflow on a real seismic dataset acquired from the North Sea, F3 block. For the subjective evaluation of the results of different salt-dome delineation algorithms, we have used a reference salt-dome boundary interpreted by a geophysicist. For the objective evaluation of results, we have used five different metrics based on pixels, shape, and curvedness to establish the effectiveness of the proposed workflow. The proposed workflow is not only fast but also yields better results as compared to other salt-dome delineation algorithms and shows a promising potential in seismic interpretation.

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arXiv2018

Noise-robust detection and tracking of salt domes in postmigrated volumes using texture, tensors, and subspace learning

The identification of salt dome boundaries in migrated seismic data volumes is important for locating petroleum reservoirs. The presence of noise in the data makes computer-aided salt dome interpretation even more challenging. In this paper, we develop noise-robust algorithms that can label boundaries of salt domes both effectively and efficiently. Our research is twofold. First, we utilize a texture-based gradient to accomplish salt dome detection. We show that by employing a dissimilarity measure based on two-dimensional (2D) discrete Fourier transform (DFT), the algorithm is capable of efficiently detecting salt dome boundaries with accuracy. At the same time, our analysis shows that the proposed algorithm is robust to noise. Once the detection is performed for an initial 2D seismic section, we propose to track the initial boundaries through the data volume to accomplish an efficient labeling process by avoiding parameters tuning that would have been necessary if detection had been performed for every seismic section. The tracking process involves a tensor-based subspace learning process, in which we build texture tensors using patches from different seismic sections. To accommodate noise components with various levels in a texture tensor, we employ noise-adjusted principal component analysis (NA-PCA), so that principal components corresponding to greater signal-to-noise ratio values may be selected for tracking. We validate our detection and tracking algorithms through experiments using seismic datasets acquired from Netherland offshore F3 block in the North Sea with very encouraging results.

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arXiv2018

A comparative study of texture attributes for characterizing subsurface structures in seismic volumes

In this paper, we explore how to computationally characterize subsurface geological structures presented in seismic volumes using texture attributes. For this purpose, we conduct a comparative study of typical texture attributes presented in the image processing literature. We focus on spatial attributes in this study and examine them in a new application for seismic interpretation, i.e., seismic volume labeling. For this application, a data volume is automatically segmented into various structures, each assigned with its corresponding label. If the labels are assigned with reasonable accuracy, such volume labeling will help initiate an interpretation process in a more effective manner. Our investigation proves the feasibility of accomplishing this task using texture attributes. Through the study, we also identify advantages and disadvantages associated with each attribute.

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arXiv2018

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.

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arXiv2018

Scale Selective Extended Local Binary Pattern for Texture Classification

In this paper, we propose a new texture descriptor, scale selective extended local binary pattern (SSELBP), to characterize texture images with scale variations. We first utilize multi-scale extended local binary patterns (ELBP) with rotation-invariant and uniform mappings to capture robust local micro- and macro-features. Then, we build a scale space using Gaussian filters and calculate the histogram of multi-scale ELBPs for the image at each scale. Finally, we select the maximum values from the corresponding bins of multi-scale ELBP histograms at different scales as scale-invariant features. A comprehensive evaluation on public texture databases (KTH-TIPS and UMD) shows that the proposed SSELBP has high accuracy comparable to state-of-the-art texture descriptors on gray-scale-, rotation-, and scale-invariant texture classification but uses only one-third of the feature dimension.

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arXiv2018

Completed Local Derivative Pattern for Rotation Invariant Texture Classification

In this paper, we propose a new texture descriptor, completed local derivative pattern (CLDP). In contrast to completed local binary pattern (CLBP), which involves only local differences at each scale, CLDP encodes the directional variation of the local differences of two scales as a complementary component to local patterns in CLBP. The new component in CLDP, with regarded as the directional derivative pattern, reflects the directional smoothness of local textures without increasing computation complexity. Experimental results on the Outex database show that CLDP, as a uni-scale pattern, outperforms uni-scale state-of-the-art texture descriptors on texture classification and has comparable performance with multi-scale texture descriptors.

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arXiv2018

A High-Speed, Real-Time Vision System for Texture Tracking and Thread Counting

In garment manufacturing, an automatic sewing machine is desirable to reduce cost. To accomplish this, a high speed vision system is required to track fabric motions and recognize repetitive weave patterns with high accuracy, from a micro perspective near a sewing zone. In this paper, we present an innovative framework for real-time texture tracking and weave pattern recognition. Our framework includes a module for motion estimation using blob detection and feature matching. It also includes a module for lattice detection to facilitate the weave pattern recognition. Our lattice detection algorithm utilizes blob detection and template matching to assess pair-wise similarity in blobs' appearance. In addition, it extracts information of dominant orientations to obtain a global constraint in the topology. By incorporating both constraints in the appearance similarity and the global topology, the algorithm determines a lattice that characterizes the topological structure of the repetitive weave pattern, thus allowing for thread counting. In our experiments, the proposed thread-based texture tracking system is capable of tracking denim fabric with high accuracy (e.g., 0.03 degree rotation and 0.02 weave-thread' translation errors) and high speed (3 frames per second), demonstrating its high potential for automatic real-time textile manufacturing.

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arXiv2018

Image Quality Assessment and Color Difference

An average healthy person does not perceive the world in just black and white. Moreover, the perceived world is not composed of pixels and through vision humans perceive structures. However, the acquisition and display systems discretize the world. Therefore, we need to consider pixels, structures and colors to model the quality of experience. Quality assessment methods use the pixel-wise and structural metrics whereas color science approaches use the patch-based color differences. In this work, we combine these approaches by extending CIEDE2000 formula with perceptual color difference to assess image quality. We examine how perceptual color difference-based metric (PCDM) performs compared to PSNR, CIEDE2000, SSIM, MS-SSIM and CW-SSIM on the LIVE database. In terms of linear correlation, PCDM obtains compatible results under white noise (97.9%), Jpeg (95.9%) and Jp2k (95.6%) with an overall correlation of 92.7%. We also show that PCDM captures color-based artifacts that can not be captured by structure-based metrics.

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arXiv2018

Fault Detection Using Color Blending and Color Transformations

In the field of seismic interpretation, univariate databased maps are commonly used by interpreters, especially for fault detection. In these maps, contrast between target regions and the background is one of the main factors that affect the accuracy of the interpretation. Since univariate data-based maps are not capable of providing a high contrast representation, to overcome this issue, we turn these univariate data-based maps into multivariate data-based representations using color blending. We blend neighboring time sections, frames that are viewed in the time direction of migrated seismic volumes, as if they corresponded to the red, green, and blue channels of a color image. Furthermore, we apply color transformations to extract more reliable structural information. Experimental results show that the proposed method improves the accuracy of fault detection by limiting the average distance between detected fault lines and the ground truth into one pixel.

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arXiv2018

Coding of 3D Videos Based on Visual Discomfort

We propose a rate-distortion optimization method for 3D videos based on visual discomfort estimation. We calculate visual discomfort in the encoded depth maps using two indexes: temporal outliers (TO) and spatial outliers (SO). These two indexes are used to measure the difference between the processed depth map and the ground truth depth map. These indexes implicitly depend on the amount of edge information within a frame and on the amount of motion between frames. Moreover, we fuse these indexes considering the temporal and spatial complexities of the content. We test the proposed method on a number of videos and compare the results with the default rate-distortion algorithms in the H.264/AVC codec. We evaluate rate-distortion algorithms by comparing achieved bit-rates, visual degradations in the depth sequences and the fidelity of the depth videos measured by SSIM and PSNR.

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arXiv2018

A Comparative Study of Quality and Content-Based Spatial Pooling Strategies in Image Quality Assessment

The process of quantifying image quality consists of engineering the quality features and pooling these features to obtain a value or a map. There has been a significant research interest in designing the quality features but pooling is usually overlooked compared to feature design. In this work, we compare the state of the art quality and content-based spatial pooling strategies and show that although features are the key in any image quality assessment, pooling also matters. We also propose a quality-based spatial pooling strategy that is based on linearly weighted percentile pooling (WPP). Pooling strategies are analyzed for squared error, SSIM and PerSIM in LIVE, multiply distorted LIVE and TID2013 image databases.

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arXiv2018

Generating Adaptive and Robust Filter Sets Using an Unsupervised Learning Framework

In this paper, we introduce an adaptive unsupervised learning framework, which utilizes natural images to train filter sets. The applicability of these filter sets is demonstrated by evaluating their performance in two contrasting applications - image quality assessment and texture retrieval. While assessing image quality, the filters need to capture perceptual differences based on dissimilarities between a reference image and its distorted version. In texture retrieval, the filters need to assess similarity between texture images to retrieve closest matching textures. Based on experiments, we show that the filter responses span a set in which a monotonicity-based metric can measure both the perceptual dissimilarity of natural images and the similarity of texture images. In addition, we corrupt the images in the test set and demonstrate that the proposed method leads to robust and reliable retrieval performance compared to existing methods.

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arXiv2018

Effectiveness of 3VQM in Capturing Depth Inconsistencies

The 3D video quality metric (3VQM) was proposed to evaluate the temporal and spatial variation of the depth errors for the depth values that would lead to inconsistencies between left and right views, fast changing disparities, and geometric distortions. Previously, we evaluated 3VQM against subjective scores. In this paper, we show the effectiveness of 3VQM in capturing errors and inconsistencies that exist in the rendered depth-based 3D videos. We further investigate how 3VQM could measure excessive disparities, fast changing disparities, geometric distortions, temporal flickering and/or spatial noise in the form of depth cues inconsistency. Results show that 3VQM best captures the depth inconsistencies based on errors in the reference views. However, the metric is not sensitive to depth map mild errors such as those resulting from blur. We also performed a subjective quality test and showed that 3VQM performs better than PSNR, weighted PSNR and SSIM in terms of accuracy, coherency and consistency.

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arXiv2018

Boosting in Image Quality Assessment

In this paper, we analyze the effect of boosting in image quality assessment through multi-method fusion. Existing multi-method studies focus on proposing a single quality estimator. On the contrary, we investigate the generalizability of multi-method fusion as a framework. In addition to support vector machines that are commonly used in the multi-method fusion, we propose using neural networks in the boosting. To span different types of image quality assessment algorithms, we use quality estimators based on fidelity, perceptually-extended fidelity, structural similarity, spectral similarity, color, and learning. In the experiments, we perform k-fold cross validation using the LIVE, the multiply distorted LIVE, and the TID 2013 databases and the performance of image quality assessment algorithms are measured via accuracy-, linearity-, and ranking-based metrics. Based on the experiments, we show that boosting methods generally improve the performance of image quality assessment and the level of improvement depends on the type of the boosting algorithm. Our experimental results also indicate that boosting the worst performing quality estimator with two or more additional methods leads to statistically significant performance enhancements independent of the boosting technique and neural network-based boosting outperforms support vector machine-based boosting when two or more methods are fused.

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arXiv2018

MS-UNIQUE: Multi-model and Sharpness-weighted Unsupervised Image Quality Estimation

In this paper, we train independent linear decoder models to estimate the perceived quality of images. More specifically, we calculate the responses of individual non-overlapping image patches to each of the decoders and scale these responses based on the sharpness characteristics of filter set. We use multiple linear decoders to capture different abstraction levels of the image patches. Training each model is carried out on 100,000 image patches from the ImageNet database in an unsupervised fashion. Color space selection and ZCA Whitening are performed over these patches to enhance the descriptiveness of the data. The proposed quality estimator is tested on the LIVE and the TID 2013 image quality assessment databases. Performance of the proposed method is compared against eleven other state of the art methods in terms of accuracy, consistency, linearity, and monotonic behavior. Based on experimental results, the proposed method is generally among the top performing quality estimators in all categories.

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arXiv2018

A Comparative Study of Computational Aesthetics

Objective metrics model image quality by quantifying image degradations or estimating perceived image quality. However, image quality metrics do not model what makes an image more appealing or beautiful. In order to quantify the aesthetics of an image, we need to take it one step further and model the perception of aesthetics. In this paper, we examine computational aesthetics models that use hand-crafted, generic and hybrid descriptors. We show that generic descriptors can perform as well as state of the art hand-crafted aesthetics models that use global features. However, neither generic nor hand-crafted features is sufficient to model aesthetics when we only use global features without considering spatial composition or distribution. We also follow a visual dictionary approach similar to state of the art methods and show that it performs poorly without the spatial pyramid step.

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