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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...
Phase Congruency (PC) can highlight small discontinuities in images with varying illumination and contrast using the congruency of phase in Fourier components. PC can not only detect the subtle variations in the image intensity but can also highlight the anomalous values to develop a deeper understanding of the images content and context. In this paper, we propose a new method based on PC for computational...
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 microand macro-features. Then, we build a scale space using Gaussian filters and calculate the histogram...
In this paper, we investigate the challenge of distortion map feature selection and spatiotemporal pooling in perceptual video quality assessment (PVQA). We analyze three distortion maps representing different visual features spatially and temporally: squared error, local pixel-level SSIM, and absolute difference of optical flow magnitudes. We examine the performance of each of these maps with different...
In this paper, we introduce a non-parametric texture similarity measure based on the singular value decomposition of the curvelet coefficients followed by a content-based truncation of the singular values. This measure focuses on images with repeating structures and directional content such as those found in natural texture images. Such textural content is critical for image perception and its similarity...
In this paper, we analyze the effect of boosting in image quality assessment through multi-method fusion. On the contrary of existing studies that propose a single quality estimator, 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 studies, we propose using neural networks in the boosting...
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...
Localizing seismic structures that can form traps for hydrocarbon reservoirs within large seismic volumes is a very challenging task. Due to the lack of accurately labeled data, we propose a weakly-supervised model for labeling seismic volumes using only a few labeled exemplars. Using six manually-labeled patches, we are able to extract patches that contain instances of similar geophysical structures...
This paper presents a full-reference image quality estimator based on SIFT descriptor matching over reliability-weighted feature maps. Reliability assignment includes a smoothing operation, a transformation to perceptual color domain, a local normalization stage, and a spectral residual computation with global normalization. The proposed method ReSIFT is tested on the LIVE and the LIVE Multiply Distorted...
This paper proposes a biologically-inspired low-level spatiochromatic-model-based similarity method (BLeSS) to assist full-reference image-quality estimators that originally oversimplify color perception processes. More specifically, the spatiochromatic model is based on spatial frequency, spatial orientation, and surround contrast effects. The assistant similarity method is used to complement image-quality...
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...
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...
We propose a method to delineate salt-dome structures by tracking manually labeled boundaries through seismic volumes. We first extract texture features from boundary regions using the tensor-based subspace learning method. Then, we utilize one seismic attribute, the gradient of texture (GoT), as a constraint on the tracking process. Using texture features and GoT maps, we can identify tracked points...
In this paper, we propose an algorithm denoted as HeartBEAT that tracks heart rate from wrist-type photo-plethysmography (PPG) signals and simultaneously recorded three-axis acceleration data. HeartBEAT contains three major parts: spectrum estimation of PPG signals and acceleration data, elimination of motion artifacts in PPG signals using recursive least Square (RLS) adaptive filters, and auxiliary...
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...
Automating the interpretation of post-migrated seismic data would considerably reduce the time and cost of the interpretation process. In this paper, we study the role of texture perception in automating the delineation of an important sub-surface structure: salt dome. First, we propose a new seismic attribute, the gradient of texture, based on which we develop a framework for detecting and delineating...
Over the last few years, industry and academia have been shifting towards computer aided models for seismic interpretation because manual labeling and salt dome delineation is becoming very time consuming and labor intensive as the volume of seismic data is in the order of tera-bytes. In this paper, we have proposed an edge-based active contour with an arc length penalty using level set implementation...
Video saliency detection is typically performed by combining temporal saliency and spatial saliency, which are detected separately. Among various available techniques, uncertainty-based combination is a unique and promising approach. In this paper, we study the uncertainty of each point within a map generated from video saliency detection. We develop an adaptive temporal correlation-based method for...
This paper presents a new unsupervised uncertainty estimation method for video saliency detection using spatial cues of the saliency map. The algorithm exploits the relationship between a pixel and its spatial neighbours in saliency maps to estimate the uncertainty of the saliency detected at the pixel location. Unlike supervised methods that fits uncertainty model to available training data, the...
We introduce a new curvelet-based distance measure for post-migration seismic data. The measure exploits the highly directional content of seismic images. It calculates the sum of the squared chord distances between the histograms of the curvelet coefficients of two images, over all orientations and scales. In a retrieval task consisting of 918 retrieval instances, the proposed method successfully...
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