nils-herrmann/clean_scirepeval huggingface dataset:
Detection of spiculated lesions in mammograms

In this paper, we present a new technique for the detection of spiculated masses in digitized mammograms. The techniques consist of two stages, enhancement of spiculations followed by the detection of the location where they converge. We describe a new algorithm for the enhancement and a new set of linear image filters which we have created for the detection stage. We have tested the algorithm on digitized mammograms obtained from the digital database for screening mammography (DDSM). Results of the detection algorithm are shown. Finally we show that the algorithm may be modified for the detection of architectural distortions.
Quality Assessment of Deblocked Images

We study the efficiency of deblocking algorithms for improving visual signals degraded by blocking artifacts from compression. Rather than using only the perceptually questionable PSNR, we instead propose a block-sensitive index, named PSNR-B, that produces objective judgments that accord with observations. The PSNR-B modifies PSNR by including a blocking effect factor. We also use the perceptually significant SSIM index, which produces results largely in agreement with PSNR-B. Simulation results show that the PSNR-B results in better performance for quality assessment of deblocked images than PSNR and a well-known blockiness-specific index.
Towards automated quality curation of video collections from a realistic perspective

We investigate the use of automated Video Quality Assessment (VQA) algorithms to evaluate digital video collections. These algorithms are driven by well-defined natural scene statistics (NSS), which capture the behavior of natural distortion-free videos. Because human vision has adapted to these real-world statistics over the course of evolution, quality predictions delivered by these NSS-based VQA algorithms correlate well with human opinions of quality. In particular, we expect these algorithms to accurately predict quality on sizable and diverse video collections. To test this hypothesis, we gathered a testbed of video clips that represent a larger video art collection. Next, we conducted a human study in which users scored the quality of the clips. Enabled by the human study, we trained three VQA algorithms (Video BLIINDS, BRISQUE, and VIIDEO) using our testbed collection to assess a real-world digital video art collection from our university museum. Two of the algorithms provided good automatic predictions of the quality of the videos. These same algorithms also highlighted limitations that arise when assessing artistic collections. We present current research progress and discuss future directions for testbed and algorithm improvement. Our ongoing effort furthers the field of Computational Archival Science by applying computational models of human perception to video appraisal and preservation tasks.
Visual Conspicuity Index: Spatial Dissimilarity, Distance, and Central Bias

We propose an image conspicuity index that combines three factors: spatial dissimilarity, spatial distance and central bias. The dissimilarity between image patches is evaluated in a reduced dimensional principal component space and is inversely weighted by the spatial separations between patches. An additional weighting mechanism is deployed that reflects the bias of human fixations towards the image center. The method is tested on three public image datasets and a video clip to evaluate its performance. The experimental results indicate highly competitive performance despite the simple definition of the proposed index. The conspicuity maps generated are more consistent with human fixations than prior state-of-the-art models when tested on color image datasets. This is demonstrated using both receiver operator characteristics (ROC) analysis and the Kullback-Leibler distance metric. The method should prove useful for such diverse image processing tasks as quality assessment, segmentation, search, or compression. The high performance and relative simplicity of the conspicuity index relative to other much more complex models suggests that it may find wide usage.
Automatic Channel Network Extraction From Remotely Sensed Images by Singularity Analysis

The quantitative analysis of channel networks plays an important role in river studies. To provide a quantitative representation of channel networks, we propose anew method that extracts channels from remotely sensed images and estimates their widths. Our fully automated method is based on a recently proposed multiscale singularity index that strongly responds to curvilinear structures but weakly responds to edges. The algorithm produces a channel map using a single image where water and nonwater pixels have contrast, such as a Landsat near-infrared band image or a water index defined on multiple bands. The proposed method provides a robust alternative to the procedures that are used in the remote sensing of fluvial geomorphology and makes the classification and analysis of channel networks easier. The source code of the algorithm is available at http://live.ece.utexas. edu/research/cne/.
Noise Analysis of a New Singularity Index

We analyze the noise sensitivity of a new singularity index that was designed to detect impulse singularities in signals of arbitrary dimensionality while rejecting step-like singularities see Muralidhar,  2013 and Muralidhar ,  2012. For example, the index responds strongly to curvilinear masses (ridges) in images, while weakly to jump discontinuities (edges). We analyze the detection power of the index in the presence of noise. Our analysis is geared towards answering the following questions: a) in the presence of noise only, what is the probability of falsely detecting an impulse given a threshold; b) given an impulse submerged in noise, what is the probability of detecting it given a threshold; and c) since the index is designed to be edge suppressing, what is the probability of incorrectly detecting an edge submerged in noise given a threshold. We compare the detection power of the index with that of a nominal impulse detector, the second derivative operator. Simulations and example applications in 1-D and 2-D reveal the efficacy of the new singularity index for correctly detecting impulses submerged in noise, while suppressing edges. A software version of the 2-D singularity index can be downloaded from: http://live.ece.utexas.edu/research/SingularityIndex/SingularityIndexCode.zip.
Nonlinearities in Stereoscopic Phase-Differencing

Exploiting the quasi-linear relationship between local phase and disparity, phase-differencing registration algorithms provide a fast, powerful means for disparity estimation. Unfortunately, these phase-differencing techniques suffer a significant impediment: phase nonlinearities. In regions of phase nonlinearity, the signals under consideration possess properties that invalidate the use of phase for disparity estimation. This paper uses the amenable properties of Gaussian white noise images to analytically quantify these properties. The improved understanding gained from this analysis enables us to better understand current methodologies for detecting regions of phase instability. Most importantly, we introduce a new, more effective means for identifying these regions based on the second derivative of phase.
A joint source-channel distortion model for JPEG compressed images

The need for efficient joint source-channel coding (JSCC) is growing as new multimedia services are introduced in commercial wireless communication systems. An important component of practical JSCC schemes is a distortion model that can predict the quality of compressed digital multimedia such as images and videos. The usual approach in the JSCC literature for quantifying the distortion due to quantization and channel errors is to estimate it for each image using the statistics of the image for a given signal-to-noise ratio (SNR). This is not an efficient approach in the design of real-time systems because of the computational complexity. A more useful and practical approach would be to design JSCC techniques that minimize average distortion for a large set of images based on some distortion model rather than carrying out per-image optimizations. However, models for estimating average distortion due to quantization and channel bit errors in a combined fashion for a large set of images are not available for practical image or video coding standards employing entropy coding and differential coding. This paper presents a statistical model for estimating the distortion introduced in progressive JPEG compressed images due to quantization and channel bit errors in a joint manner. Statistical modeling of important compression techniques such as Huffman coding, differential pulse-coding modulation, and run-length coding are included in the model. Examples show that the distortion in terms of peak signal-to-noise ratio (PSNR) can be predicted within a 2-dB maximum error over a variety of compression ratios and bit-error rates. To illustrate the utility of the proposed model, we present an unequal power allocation scheme as a simple application of our model. Results show that it gives a PSNR gain of around 6.5 dB at low SNRs, as compared to equal power allocation.
Statistical Modeling of 3-D Natural Scenes With Application to Bayesian Stereopsis

We studied the empirical distributions of luminance, range and disparity wavelet coefficients using a coregistered database of luminance and range images. The marginal distributions of range and disparity are observed to have high peaks and heavy tails, similar to the well-known properties of luminance wavelet coefficients. However, we found that the kurtosis of range and disparity coefficients is significantly larger than that of luminance coefficients. We used generalized Gaussian models to fit the empirical marginal distributions. We found that the marginal distribution of luminance coefficients have a shape parameter p between 0.6 and 0.8, while range and disparity coefficients have much smaller parameters p <; 0.32, corresponding to a much higher peak. We also examined the conditional distributions of luminance, range and disparity coefficients. The magnitudes of luminance and range (disparity) coefficients show a clear positive correlation, which means, at a location with larger luminance variation, there is a higher probability of a larger range (disparity) variation. We also used generalized Gaussians to model the conditional distributions of luminance and range (disparity) coefficients. The values of the two shape parameters (p,s) reflect the observed luminance-range (disparity) dependency. As an example of the usefulness of luminance statistics conditioned on range statistics, we modified a well-known Bayesian stereo ranging algorithm using our natural scene statistics models, which improved its performance.
Automatic Prediction of Perceptual Image and Video Quality

Finding ways to monitor and control the perceptual quality of digital visual media has become a pressing concern as the volume being transported and viewed continues to increase exponentially. This paper discusses the principles and methods of modern algorithms for automatically predicting the quality of visual signals. By casting the problem as analogous to assessing the efficacy of a visual communication system, it is possible to divide the quality assessment problem into understandable modeling subproblems. Along the way, we will visit models of natural images and videos, of visual perception, and a broad spectrum of applications.
Image restoration using order-constrained least-squares methods

In this paper we consider two new techniques for image restoration, using order-constrained least-squares methods. The first technique consists of a cross-shaped moving window, within which two operations are combined. The first operation consists of simple hypothesis tests for monotonicity in both the horizontal and vertical directions. The second step finds the best least-squares fit of the input variates in both directions, constrained by the results of the hypothesis tests. The second technique consists of a square moving window, again combining two operations. With the first operation, we introduce a new edge detector with specific edge height δ. Based on detection or non-detection of an edge, we either apply order-constrained least-squares methods to determine the output, or simply average. The techniques described are applied to an actual noise-corrupted image.
Indexes for Three-Class Classification Performance Assessment—An Empirical Comparison

Assessment of classifier performance is critical for fair comparison of methods, including considering alternative models or parameters during system design. The assessment must not only provide meaningful data on the classifier efficacy, but it must do so in a concise and clear manner. For two-class classification problems, receiver operating characteristic analysis provides a clear and concise assessment methodology for reporting performance and comparing competing systems. However, many other important biomedical questions cannot be posed as ldquotwo-classrdquo classification tasks and more than two classes are often necessary. While several methods have been proposed for assessing the performance of classifiers for such multiclass problems, none has been widely accepted. The purpose of this paper is to critically review methods that have been proposed for assessing multiclass classifiers. A number of these methods provide a classifier performance index called the volume under surface (VUS). Empirical comparisons are carried out using 4 three-class case studies, in which three popular classification techniques are evaluated with these methods. Since the same classifier was assessed using multiple performance indexes, it is possible to gain insight into the relative strengths and weakness of the measures. We conclude that: 1) the method proposed by Scurfield provides the most detailed description of classifier performance and insight about the sources of error in a given classification task and 2) the methods proposed by He and Nakas also have great practical utility as they provide both the VUS and an estimate of the variance of the VUS. These estimates can be used to statistically compare two classification algorithms.
Statistics of natural image distortions

Natural scene statistics (NSS) are an active area of research. Although there exist elegant models for NSS, the statistics of natural image distortions have received little attention. In this paper we study distorted image statistics (DIS) for natural scenes. We demonstrate that each distortion affects the statistics of natural images in a characteristic way and it is possible to parameterize this characteristic. We show that not only are DIS different for different distortions, but by such parametrization it is also possible to build a classifier that can classify a given image into a particular distortion category solely on the basis of DIS, with high accuracy. Applications of such categorization are of considerable scope and include DIS-based quality assessment and blind image distortion correction.
In-Capture Mobile Video Distortions: A Study of Subjective Behavior and Objective Algorithms

Digital videos often contain visual distortions that are introduced by the camera’s hardware or processing software during the capture process. These distortions often detract from a viewer’s quality of experience. Understanding how human observers perceive the visual quality of digital videos is of great importance to camera designers. Thus, the development of automatic objective methods that accurately quantify the impact of visual distortions on perception has greatly accelerated. Video quality algorithm design and verification require realistic databases of distorted videos and human judgments of them. However, most current publicly available video quality databases have been created under highly controlled conditions using graded, simulated, and post-capture distortions (such as jitter and compression artifacts) on high-quality videos. The commercial plethora of hand-held mobile video capture devices produces videos often afflicted by a variety of complex distortions generated during the capturing process. These in-capture distortions are not well-modeled by the synthetic, post-capture distortions found in existing VQA databases. Toward overcoming this limitation, we designed and created a new database that we call the LIVE-Qualcomm mobile in-capture video quality database, comprising a total of 208 videos, which model six common in-capture distortions. We also conducted a subjective quality assessment study using this database, in which each video was assessed by 39 unique subjects. Furthermore, we evaluated several top-performing no-reference IQA and VQA algorithms on the new database and studied how real-world in-capture distortions challenge both human viewers as well as automatic perceptual quality prediction models. The new database is freely available at: http://live.ece.utexas.edu/research/incaptureDatabase/index.html.
Video Quality Assessment by Reduced Reference Spatio-Temporal Entropic Differencing

We present a family of reduced reference video quality assessment (QA) models that utilize spatial and temporal entropic differences. We adopt a hybrid approach of combining statistical models and perceptual principles to design QA algorithms. A Gaussian scale mixture model for the wavelet coefficients of frames and frame differences is used to measure the amount of spatial and temporal information differences between the reference and distorted videos, respectively. The spatial and temporal information differences are combined to obtain the spatio-temporal-reduced reference entropic differences. The algorithms are flexible in terms of the amount of side information required from the reference that can range between a single scalar per frame and the entire reference information. The spatio-temporal entropic differences are shown to correlate quite well with human judgments of quality, as demonstrated by experiments on the LIVE video quality assessment database.
DCT-domain blind measurement of blocking artifacts in DCT-coded images

A method for DCT-domain blind measurement of blocking artifacts is proposed. By constituting a new block across any two adjacent blocks, the blocking artifact is modeled as a 2-D step function. A fast DCT-domain algorithm has been derived to constitute the new block and extract all parameters needed. Then an human visual system (HVS) based measurement of blocking artifacts is conducted. Experimental results have shown the effectiveness and stability of our method. The proposed technique can be used for online image/video quality monitoring and control in applications of DCT-domain image/video processing.
Algorithmic assessment of 3D quality of experience for images and videos

We propose a no-reference algorithm to assess the comfort associated with viewing stereo images and videos. The proposed measure of 3D quality of experience is shown to correlate well with human perception of quality on a publicly available dataset of 3D images/videos and human subjective scores. The proposed measure extracts statistical features from disparity and disparity gradient maps as well as indicators of spatial activity from images. For videos, the measure utilizes these spatial features along with motion compensated disparity differences to predict quality of experience. To the best of our knowledge the proposed approach is the first attempt in algorithmically assessing the subjective quality of experience on a publicly available dataset.
Natural motion statistics for no-reference video quality assessment

We model the motion statistics of video sequences, towards the development of no-reference video quality indices that take into account spatial as well as temporal characteristics of video signals. Here we explore the temporal characteristics of undistorted as well as distorted IP video sequences; (distorted by varying levels of packet loss rate) as extracted from optical flow vectors. We present an algorithm for extracting motion statistics by computing independent components (ICs) from the optical flow field. We then model the extracted ICs, and show that they are more closely Laplacian distributed than the entire nondecomposed features. We also observe that the lower the video quality, the higher the root-mean-square (RMS) error difference between the maximum-likelihood Laplacian fits of the two extracted ICs of the flow vectors.
Non-Stationarity Detection in Natural Images

We present a novel approach for non-stationarity detection in natural images by exploiting the prior knowledge of the independent component structure of scene statistics. Our proposed non-stationarity index is conceptually simple and is intertwined with the probabilistic structure of the image segment being analyzed. It shows consistently good results when applied to natural scenes and, we expect, will find useful applications in computer vision algorithms in as much as the detection of statistically non-stationary locations in images can be an important preliminary step toward the understanding of scene content and in the guiding of visual fixations.
Spectral analysis of order statistic filters

We analyze the effect of filter coefficient selection on the power spectral density of an order statistic filtered signal. Assuming that the input signal is a sequence of independent and identically distributed random variates, the autocovariance and the power spectrum of the output are computed. These PSDs are compared with those of the corresponding linear finite impulse response filters with identical coefficients. It is found that, in general, low frequency components predominate regardless of coefficient selection, suggesting an inherent smoothing in the ordering process.
Digital reconstruction of three-dimensional serially sectioned optical images

The nature of out-of-focus noise is analyzed and linear filtering approaches are demonstrated for minimizing its effect. The analysis is based on concepts of geometrical optics, and takes into account the passage of light through the object. The object is assumed to be nondiffractive and to have low contrast. These assumptions, although not usually met in practice, are not overly restrictive. Experience shows that even dark, diffractive objects show substantial improvement after filtering. Sample images of a pollen grain and a rat pancreas islet are used to demonstrate the efficacy of the technique.
Night-time dehazing by fusion

We introduce an effective technique to enhance night-time hazy scenes. Our technique builds on multi-scale fusion approach that use several inputs derived from the original image. Inspired by the dark-channel  we estimate night-time haze computing the airlight component on image patch and not on the entire image. We do this since under night-time conditions, the lighting generally arises from multiple artificial sources, and is thus intrinsically non-uniform. Selecting the size of the patches is non-trivial, since small patches are desirable to achieve fine spatial adaptation to the atmospheric light, this might also induce poor light estimates and reduced chance of capturing hazy pixels. For this reason, we deploy multiple patch sizes, each generating one input to a multiscale fusion process. Moreover, to reduce the glowing effect and emphasize the finest details, we derive a third input. For each input, a set of weight maps are derived so as to assign higher weights to regions of high contrast, high saliency and small saturation. Finally the derived inputs and the normalized weight maps are blended in a multi-scale fashion using a Laplacian pyramid decomposition. The experimental results demonstrate the effectiveness of our approach compared with recent techniques both in terms of computational efficiency and quality of the outputs.