nils-herrmann/clean_scirepeval huggingface dataset:
Shape from Texture Using Local Spectral Moments

Presents a non-feature-based solution to the problem of computing the shape of curved surfaces from texture information. First, the use of local spatial-frequency spectra and their moments to describe texture is discussed and motivated. A new, more accurate method for measuring the local spatial-frequency moments of an image texture using Gabor elementary functions and their derivatives is presented. Also described is a technique for separating shading from texture information, which makes the shape-from-texture algorithm robust to the shading effects found in real imagery. Second, a detailed model for the projection of local spectra and spectral moments of any surface reflectance patterns (not just textures) is developed. Third, the conditions under which the projection model can be solved for the orientation of the surface at each point are explored. Unlike earlier non-feature-based, curved surface shape-from-texture approaches, the assumption that the surface texture is isotropic is not required; surface texture homogeneity can be assumed instead. The algorithm's ability to operate on anisotropic and nondeterministic textures, and on both smooth- and rough-textured surfaces, is demonstrated.
Snakules for automatic classification of candidate spiculated mass locations on mammography

In this paper, we describe a novel approach for the automatic classification of candidate spiculated mass locations on mammography. Our approach is based on “Snakules” — an evidence-based active contour algorithm that we have recently developed for the annotation of spicules on mammography. We use snakules to extract features characteristic of spicules and spiculated masses, and use these features to classify whether a region of a mammogram contains a spiculated mass or not. The results from our initial classification experiment demonstrate the strong potential of snakules as an image analysis technique to extract features specific to spicules and spiculated masses, which can subsequently be used to distinguish true spiculated mass locations from non-lesion locations on a mammogram and improve the specificity of computer-aided detection (CADe) algorithms.
Multi-component AM-FM image models and wavelet-based demodulation with component tracking

In this paper we utilize multi-component AM-FM functions to model multi-partite nonstationary images that are locally coherent, yet globally wideband. We also detail an approach for simultaneously estimating the modulating functions associated with each of the multiple components. Components are isolated by a multiband bank of Gabor wavelets, and estimates of the modulating functions are derived from each channel. We use simple one-dimensional Kalman filters to track each identified component across the channels.<<ETX>>
Segmentation and Fuzzy-Logic Classification of M-FISH Chromosome Images

Multicolor fluorescence in-situ hybridization (m-fish) technique provides color karyotyping that allows simultaneous analysis of numerical and structural abnormalities of whole human chromosomes. Currently available m-fish systems exhibit misclassifications of multiple pixel regions that are often larger than the actual chromosomal rearrangement. This paper presents a novel unsupervised classification method based on fuzzy logic classification and a prior adjusted reclassification method. Utilizing the chromosome boundaries, the initial classification results improved significantly after the prior adjusted reclassification while keeping the translocations intact. This paper also presents a new segmentation method that combines both spectral and edge information. Ten m-fish images from a publicly available database were used to test our methods. The segmentation accuracy was more than 98% on average.
Efficient Stereoscopic Ranging via Stochastic Sampling of Match Quality

We present an efficient method that computes dense stereo correspondences by stochastically sampling match quality values. Nonexhaustive sampling facilitates the use of quality metrics that take unique values at noninteger disparities. Depth estimates are iteratively refined with a stochastic cooperative search by perturbing the estimates, sampling match quality, and reweighting and aggregating the perturbations. The approach gains significant efficiencies when applied to video, where initial estimates are seeded using information from the previous pair in a novel application of the Z-buffering algorithm. This significantly reduces the number of search iterations required. We present a quantitative accuracy evaluation wherein the proposed method outperforms a microcanonical annealing approach by Barnard and a cooperative approach by Zitnick and Kanade , while using fewer match quality evaluations than either. The approach is shown to have more attractive memory usage and scaling than alternatives based on exhaustive sampling.
Fast lossless image compression

In this paper, we present a simple algorithm for fast lossless compression of grayscale images. It consists of differential pulse code modulation (DPCM) followed by Huffman coding of the most-likely residual magnitudes. Our tests show that it attains higher compression with less computation than the lossless JPEG method. For the large dynamic ranges possible with pixel sizes greater than 8-bits, it requires significantly less computation than any compression scheme known to the authors.
Numerical analysis of visual patterns

and other low-level cooperative image analysis tasks. Visual pattern analysis proceeds analogously via estimation of emergent 2D image frequencies. Unlike shape-from-X or optical flow paradigms, constraints are derived from the responses of multiple oriented spatial frequency channels rather than directly from the image irradiance measurements. By selecting channel filters that are sufficiently concentrated in both space and frequency, highly accurate spatial frequency estimates are computed on a local basis. Two related methods are proposed. In the first, a constrained estimate of the emergent image frequencies is obtained by resolving the responses of multiple channel filters in a process similar to photometric stereo. The second approach formulates the estimation of frequencies as an extremum problem regularized by a smoothing term. An iterative constraint propagation algorithm is developed analogous to those used in variational/relaxational approaches to shape-from-X (shading, texture) and optical flow. Examples illustrate both approaches using synthetic and natural images.<<ETX>>
Stereoscopic Phase-Differencing: Multiscale Synthesis

Exploiting the quasi-linear relationship between local phase and disparity, phase-differencing registration algorithms provide a fast, powerful means for disparity estimation. Unfortunately, phase-differencing techniques suffer from a significant impediment: the neglect of multi- scale information. In this work, we introduce a novel registration algorithm that combines strategies of both phase- differencing and local correlation. This hybrid approach retains the advantageous properties of phase-differencing while incorporating the multiscale aspects of local correlation.
Unifying analysis of full reference image quality assessment

This paper studies two increasingly popular paradigms for image quality assessment - Structural SIMilarity (SSIM) metrics and Information Fidelity metrics. The relation of the SSIM metric to Mean Squared Error and Human Visual System (HVS) based models of quality assessment are studied. The SSIM model is shown to be equivalent to models of contrast gain control of the HVS. We study the information theoretic metrics and show that the Information Fidelity Criterion (IFC) is a monotonic function of the structure term of the SSIM index applied in the sub-band filtered domain. Our analysis of the Visual Information Fidelity (VIF) criterion shows that improvements in VIF include incorporation of a contrast comparison, in addition to the structure comparison in IFC. Our analysis attempts to unify quality metrics derived from different first principles and characterize the relative performance of different QA systems.
3D Visual Activity Assessment Based on Natural Scene Statistics

One of the most challenging ongoing issues in the field of 3D visual research is how to perceptually quantify object and surface visualizations that are displayed within a virtual 3D space between a human eye and 3D display. To seek an effective method of quantification, it is necessary to measure various elements related to the perception of 3D objects at different depths. We propose a new framework for quantifying 3D visual information that we call 3D visual activity (3DVA), which utilizes natural scene statistics measured over 3D visual coordinates. We account for important aspects of 3D perception by carrying out a 3D coordinate transform reflecting the nonuniform sampling resolution of the eye and the process of stereoscopic fusion. The 3DVA utilizes the empirical distortions of wavelet coefficients to a parametric generalized Gaussian probability distribution model and a set of 3D perceptual weights. We conducted a series of simulations that demonstrate the effectiveness of the 3DVA for quantifying the statistical dynamics of visual 3D space with respect to disparity, motion, texture, and color. A successful example application is also provided, whereby 3DVA is applied to the problem of predicting visual fatigue experienced when viewing 3D displays.
Joint Source-Channel Distortion Modelling for MPEG-4 Video

Joint source-channel coding is becoming more important for wireless multimedia transmission due to high bandwidth requirements of these multimedia sources. Design of all joint source-channel coding schemes require an estimate of distortion at different source coding rates and under different channel conditions. In this paper, we present one such distortion model for estimating distortion due to quantization and channel errors in a joint manner for MPEG-4 compressed video streams. This model takes into account important aspects of video compression such as transform coding, motion compensation, and variable length coding. Results show that our model estimates distortion within 1 dB of actual simulation values in terms of peak-signal-to-noise-ratio.
Computational stereo vision using color

An investigation of the efficiency of color or chromatic information as an aid in solving the stereo correspondence problem is described. Two very simple stereo algorithms differing only in the use of chromatic information are applied to images containing various chromatic characteristics. The intensity-matching algorithm matches the zero crossings in a Laplacian-of-Gaussian filtered image, while the color-matching algorithm includes chromatic gradients that characterize the intensity zero crossings. The algorithms have been defined with a very simple matching structure in an effort to isolate the effects of including chromatic information in early stereo processing. The results indicate that the use of chromatic information can significantly reduce the ambiguity between potential matches while increasing the accuracy of the resulting matches.<<ETX>>
SSIM-optimal linear image restoration

In this paper, we present an algorithm for designing a linear equalizer that is optimal with respect to the structural similarity (SSIM) index. The optimization problem is shown to be non-convex, thereby making it non-trivial. The non-convex problem is first converted to a quasi-convex problem and then solved using a combination of first order necessary conditions and bisection search. To demonstrate the usefulness of this solution, it is applied to image denoising and image restoration examples. We show using these examples that optimizing equalizers for the SSIM index does indeed result in higher perceptual image quality compared to equalizers optimized for the ubiquitous mean squared error (MSE).
Delivery quality score model for Internet video

The vast majority of today's internet video services are consumed over-the-top (OTT) via reliable streaming (HTTP via TCP), where the primary noticeable delivery-related impairments are startup delay and stalling. In this paper we introduce an objective model called the delivery quality score (DQS) model, to predict user's QoE in the presence of such impairments. We describe a large subjective study that we carried out to tune and validate this model. Our experiments demonstrate that the DQS model correlates highly with the subjective data and that it outperforms other emerging models.
Fast algorithms for foveated video processing

This paper explores the problem of communicating high-quality, foveated video streams in real time. Foveated video exploits the nonuniform resolution of the human visual system by preferentially allocating bits according to the proximity to assumed visual fixation points, thus delivering perceptually high quality at greatly reduced bandwidths. Foveated video streams possess specific data density properties that can be exploited to enhance the efficiency of subsequent video processing. Here, we exploit these properties to construct several efficient foveated video processing algorithms: foveation filtering (local bandwidth reduction), motion estimation, motion compensation, video rate control, and video postprocessing. Our approach leads to enhanced computational efficiency by interpreting nonuniform-density foveated images on the uniform domain and by using a foveation protocol between the encoder and the decoder.
Three Dimensional Face Recognition using Wavelet Decomposition of Range Images

Interest in face recognition systems has increased significantly due to the emergence of significant commercial opportunities in surveillance and security applications. In this paper we propose a novel technique to extract features from 3D face representations. In this technique, first the nose tip is automatically located on the range image, then the range data from a hexagonal region of interest around this landmark is decomposed using Barycentric wavelet kernels. The dimensionality of the extracted coefficients at each resolution level is reduced using principal component analysis (PCA). These new features are tested on 206 range images, and a high classification accuracy is achieved using a small number of features. The obtained accuracy is competitive to that of other techniques in literature.
Depth estimation from monocular color images using natural scene statistics models

We consider the problem of estimating a dense depth map from a single monocular image. Inspired by psychophysical evidence of visual processing in human vision systems (HVS) and natural scene statistics (NSS) models of image and range, we propose a Bayesian framework to recover detailed 3D scene structure by exploiting the statistical relationships between local image features and depth variations inherent in natural images. By observing that similar depth structures may exist in different types of luminance/chrominance textured regions in natural scenes, we build a dictionary of canonical range patterns as the prior, and fit a multivariate Gaussian mixture (MGM) model to associate local image features to different range patterns as the likelihood. Compared with the state-of-the-art depth estimation method, we achieve similar performance in terms of pixel-wise estimated range error, but superior capability of recovering relative distant relationships between different parts of the image.
BUCKET: Scheduling of Solar-Powered Sensor Networks via Cross-Layer Optimization

Renewable solar energy harvesting systems have received considerable attention as a possible substitute for conventional chemical batteries in sensor networks. However, it is difficult to optimize the use of solar energy based only on empirical power acquisition patterns in sensor networks. We apply acquisition patterns from actual solar energy harvesting systems and build a framework to maximize the utilization of solar energy in general sensor networks. To achieve this goal, we develop a cross-layer optimization-based scheduling scheme called binding optimization of duty cycling and networking through energy tracking (BUCKET), which is formulated in four-stages: 1) prediction of energy harvesting and arriving traffic; 2) internode optimization at the transport and network layers; 3) intranode optimization at the medium access control layer; and 4) flow control of generated communication task sets using a token-bucket algorithm. Monitoring of the structural health of bridges is shown to be a potential application of an energy-harvesting sensor network. The example network deploys five sensor types: 1) temperature; 2) strain gauge; 3) accelerometer; 4) pressure; and 5) humidity. In the simulations, the BUCKET algorithm displays performance enhancements of ~12-15% over those of conventional methods in terms of the average service rate.
Texture segmentation using a class of narrowband filters

A class of 2D filters is proposed for segmenting visible images into regions of uniform texture. The filters used, known as Gabor filters, are optimal in several senses: they have tunable orientation bandwidths, they can be defined to operate over a range of spatial frequency channels, and they obey the uncertainty principle in two dimensions. The filters are interpreted as transforming the image into a modulated narrowband signal whose envelope coincides with the textured region to which the filter is tuned. Moreover, the receptive fields of neurons in the visual cortex are known to have shapes that approximate 2D Gabor filters, whose purpose has been uncertain. We suggest that they may play an important role in texture segmentation/surface perception. The technique is demonstrated using a variety of natural and synthetic textures.
The Multilinear ICA Decompositionwith Applications to NSS Modeling

We refine the classical independent component analysis (ICA) decomposition using a multilinear expansion of the probability density function of the source statistics. In particular, to model the source statistics of natural image textures, we introduce a specific non-linear system that allows us to elegantly capture the statistical dependences between the responses of the multilinear ICA (MICA) filters. The resulting multilinear probability density is analytically tractable and does not require Monte Carlo simulations to estimate the model parameters. We demonstrate the success of the MICA model on natural textures and discuss applications to non-stationarity detection and natural scene statistics (NSS) modeling.
Automatic prediction of saliency on JPEG distorted images

We propose an algorithm to detect salient regions for JPEG distorted images for two tasks: quality assessment and free viewing. The algorithm extracts low-level features such as contrast, luminance, quality and so on and uses a machine-learning framework to predict salient regions in JPEG distorted images. We demonstrate that the automatically predicted regions-of-interest highly correlate with those from (human) ground truth saliency maps. Further, we evaluate the relevance of extracted low-level features for saliency prediction and analyze how incorporation of quality as a feature improves prediction performance as a function of the distortion severity. Applications of such a saliency prediction framework include developing novel pooling strategies for image quality assessment.
The multilinear compound Gaussian distribution

We introduce a novel generalization of the compound Gaussian (CG) (or Gaussian Scale Mixture ) distribution which extends the Gaussian component of the CG model to a multilinear distribution. The resulting model, which we call the Multilinear Compound Gaussian (MCG) distribution, subsumes both GSM [1] and the previously developed MICA [3-4] distributions as complementary special cases; thereby allowing us to model a richer class of stochastic phenomena. First we derive the structural characterization of the MCG distribution and develop some of its important theoretical properties. Thereafter we describe a parameter estimation algorithm for learning this model from sample data, and then deploy this for modeling textures, including natural (i.e. optical) and SAR images. Our simulation results demonstrate how, for each case, we obtain improved performance over the CG model; thus indicating the versatility of the MCG model in accurately modeling various natural phenomena of interest.
No-reference quality assessment using natural scene statistics: JPEG2000

Measurement of image or video quality is crucial for many image-processing algorithms, such as acquisition, compression, restoration, enhancement, and reproduction. Traditionally, image quality assessment (QA) algorithms interpret image quality as similarity with a "reference" or "perfect" image. The obvious limitation of this approach is that the reference image or video may not be available to the QA algorithm. The field of blind, or no-reference, QA, in which image quality is predicted without the reference image or video, has been largely unexplored, with algorithms focussing mostly on measuring the blocking artifacts. Emerging image and video compression technologies can avoid the dreaded blocking artifact by using various mechanisms, but they introduce other types of distortions, specifically blurring and ringing. In this paper, we propose to use natural scene statistics (NSS) to blindly measure the quality of images compressed by JPEG2000 (or any other wavelet based) image coder. We claim that natural scenes contain nonlinear dependencies that are disturbed by the compression process, and that this disturbance can be quantified and related to human perceptions of quality. We train and test our algorithm with data from human subjects, and show that reasonably comprehensive NSS models can help us in making blind, but accurate, predictions of quality. Our algorithm performs close to the limit imposed on useful prediction by the variability between human subjects.
A stereo VPIC system

A binocular stereo system for images coded by visual pattern image coding (VPIC) is presented and evaluated. The use of VPIC for stereo vision applications is motivated by the capability of its coding primitives to reflect meaningful physical properties of projected real-scene surfaces: high-information edge regions and uniform regions. An algorithm for spatial matching of VPIC primitives is proposed. It involves the solution of a complex optimization problem satisfying VPIC-related constraints. A dense disparity map is obtained and then used, together with one of the VPIC coded images of the stereo pair, to predict the second stereo image. The proposed stereo system allows a very high compression of the overall stereo information. Experimental results are presented to illustrate the performance of both stereo VPIC matching and predicting algorithms in natural images.
Vergence control using a hierarchical image structure

This paper presents a fast and reliable vergence control method using a hierarchical image structure for active vision systems. In this method, the sign pyramid is generated from the Laplacian pyramid. The search for the common fixation point and the disparity value of that point is performed by sign-correlation matching and a coarse-to-fine search strategy. This method works well for a very large range of disparities and is not sensitive to calibration problems common to stereo images.<<ETX>>
Virtual top-view camera calibration for accurate object representation

Simple perspective transforms are used in Advanced Driver Assistance Systems (ADAS) in vehicles for converting captured images of lateral, front, and rear views into a synthesized top-view image, which displays the scene as viewed from above the vehicle. These transforms, however, are typically derived using a basic calibration procedure that is only capable of correctly mapping ground-plane points in captured images to their corresponding locations in the top-view image, and any off-the-ground points look distorted. We present a new method, which uses four orthogonally-placed fisheye cameras, for calibrating a top-view image, in which objects and off-the-ground points are accurately represented.