nils-herrmann/clean_scirepeval huggingface dataset:
Video quality assessment using objective parameters based on image segmentation

This work presents a methodology for video quality assessment using objective parameters based on image segmentation. Natural scenes are segmented into plane, edge and texture regions, and a set of objective parameters are assigned to each of these contexts. A perception-based model that predicts subjective ratings is defined by computing the relationship between objective measures and results of subjective assessment tests, applied to a set of natural scenes and MPEG-2 video codecs. In this model, the relationship between each objective parameter and the subjective impairment level is approximated by a logistic curve, resulting in an estimated impairment level for each parameter. The final result is achieved through a linear combination of estimated impairment levels, where the weight of each impairment level is proportional to its statistical reliability. The results presented show that the use of region-based objective measurements provides more accurate predictions compared to predictions based on global parameters.
Disclaimer: "Relative fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation"

The notion of fuzzy connectedness captures the idea of "hanging-togetherness" of image elements in an object by assigning a strength of connectedness to every possible path between every possible pair of image elements. This concept leads to powerful image segmentation algorithms based on dynamic programming whose effectiveness has been demonstrated on 1,000s of images in a variety of applications. In the previous framework, a fuzzy connected object is defined with a threshold on the strength of connectedness. We introduce the notion of relative connectedness that overcomes the need for a threshold and that leads to more effective segmentations. The central idea is that an object gets defined in an image because of the presence of other co-objects. Each object is initialized by a seed element. An image element c is considered to belong to that object with respect to whose reference image element c has the highest strength of connectedness. In this fashion, objects compete among each other utilizing fuzzy connectedness to grab membership of image elements. We present a theoretical and algorithmic framework for defining objects via relative connectedness and demonstrate utilizing the theory that the objects defined are independent of reference elements chosen as long as they are not in the fuzzy boundary between objects. An iterative strategy is also introduced wherein the strongest relative connected core parts are first defined and iteratively relaxed to conservatively capture the more fuzzy parts subsequently. Examples from medical imaging are presented to illustrate visually the effectiveness of relative fuzzy connectedness. A quantitative mathematical phantom study involving 160 images is conducted to demonstrate objectively the effectiveness of relative fuzzy connectedness. Disclaimer A claim of priority in research and publication appeared on page 1486 in the paper "Relative Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation" by J.K. Udupa, P.K. Saha, and' R.A. Lotufo ( 2002) with respect to the paper "Multiseeded Segmentation Using Fuzzy Connectedness by G.T. Herman and B.M. Carvalho" ( May 2001). Furthermore, the wording of this claim suggests professional misconduct on the part of G.T. Herman and B.M. Carvalho. Responsibility for the content of published papers rests with the authors. The peer review process is intended to determine the overall significance of the technical contribution of a manuscript. The peer review process does not provide a way to validate every statement in a manuscript. In particular, the IEEE has not validated the claim referred to in the first paragraph above. The IEEE regrets publishing this unauthenticated statement and the pain that such publication may have caused to G.T. Herman and B.M. Carvalho.
Analysis of the Watershed Algorithms Based on the Breadth-First and Depth-First Exploring Methods

In this paper, fifteen watershed algorithms are reviewed. For clarity, first we expose two graph exploring methods modified to be guidelines for understanding the approaches taken by these algorithms: the breadth-first watershed and the depth-first watershed. Both paradigms rely on the visiting order applied by the algorithms. The breadth-first is more recognizable as a seed region growing or marker expansion process, grouping both methods based on flooding and hierarchical queue. The depth-first groups the algorithms based on the drop of water simulation, forming a simple path until a regional minimum is found. We analyze and classify fifteen algorithms, and two of them were better characterized. Along with this, some useful information (i.e. use of markers and line over pixel) is organized, in order to facilitate the choice of an algorithm.
Fast Euclidean distance transform using a graph-search algorithm

Two new Euclidean distance transform algorithms are described. The algorithms are designed using a shortest path graph-search framework. The distance transform can be seen as the solution of a shortest path forest problem. Previous works have dealt with the Euclidean distance transform (EDT) and with the shortest path forest problem, but none of them have presented an EDT using the graph-search approach. The proposed algorithms are very simple and yet belong to the class of one of the most efficient sequential algorithms. The algorithms easily extend to higher dimensions.
Common Carotid Artery Lumen Segmentation from Cardiac Cycle-Resolved Cine Fast Spin Echo Magnetic Resonance Imaging

Atherosclerosis is a disease responsible for millions of deaths each year, primarily due to heart attack and stroke. Magnetic resonance (MR) imaging is a non-invasive method that can be used to analyze the carotid artery and detect signs of atherosclerosis. Most MR methods acquire high contrast, static images. These methods, however, are sensitive to artifacts from cardiac motion, produce time-averaged images, and do not allow for carotid distensibility analysis. Carotid distensibility is an important, systematic measure of vascular health. Cine fast spin echo (FSE) is a new MR imaging that can obtain dynamic MR data (i.e., cardiac phase-resolved datasets). Dynamic imaging, however, comes at the cost of lower spatial resolution and signal-to-noise ratio, making these data potentially more difficult to segment. This paper introduces a semi-automated segmentation method that segments the common carotid artery (CCA) lumen across the cardiac cycle from dynamic MR images. To the best of our knowledge, this work is the first proposed technique for segmenting cardiac cycle-resolved cine FSE images. It combines a priori knowledge about the size and shape of the CCA, with the max-tree data structure, the tie-zone watershed transform (using identified internal and external markers) and supervised classification, to segment the carotid artery wall-lumen boundary. The user has to select only a seed point (centred in the carotid artery lumen). Technique performance was assessed using forty-five cine FSE data sets, each consisting of images reconstructed at sixteen temporal bins across the cardiac cycle. The automatic segmentation results were compared against the consensus of three different manual segmentation results. Our technique achieved an average Dice coefficient, sensitivity and false positive rate of 0.928±0.031 (mean ± standard deviation), 0.915 ± 0.037 and 0.056 ± 0.049, respectively. Our method achieved higher agreement versus the consensus of the three manual segmentations than the individual manual segmentations versus the consensus.
Hyperspectral Data Classification Using Extended Extinction Profiles

This letter proposes a new approach for the spectral-spatial classification of hyperspectral images, which is based on a novel extrema-oriented connected filtering technique, entitled as extended extinction profiles. The proposed approach progressively simplifies the first informative features extracted from hyperspectral data considering different attributes. Then, the classification approach is applied on two well-known hyperspectral data sets, i.e., Pavia University and Indian Pines, and compared with one of the most powerful filtering approaches in the literature, i.e., extended attribute profiles. Results indicate that the proposed approach is able to efficiently extract spatial information for the classification of hyperspectral images automatically and swiftly. In addition, an array-based node-oriented max-tree representation was carried out to efficiently implement the proposed approach.
Maximal Max-Tree Simplification

The Max-Tree is an efficient data structure that represents all connected components resulting from all possible image upper threshold values. Usually, most of its nodes represent irrelevant extrema, i.e. noise, or small variations of a connected component. This paper proposes the Maximal Max-Tree Simplification (MMS) filter with a normalized threshold criterion (MMS-T) and a Maximally Stable Extremal Regions (MSER) criterion (MMS-MSER) and a methodology to apply them using the Extinction filter We show that after applying our simplification methodology which sets the number of maxima in the image, the number of Max-Tree nodes is at most twice this number. Two applications of the proposed methodology are illustrated.
Watershed-Based Segmentation of the Midsagittal Section of the Corpus Callosum in Diffusion MRI

The corpus callosum (CC) is one of the most important white matter structures of the brain, interconnecting the two cerebral hemispheres. The corpus callosum is related to several neurodegenerative diseases and, as segmentation is usually the first step for studies in this structure, it is important to have a robust method for CC segmentation. We propose here a new approach for fully automatic segmentation of the CC in the magnetic resonance diffusion tensor images. The method uses the watershed transform and is performed on the fractional anisotropy (FA) map weighted by the projection of the principal eigenvector in the left-right direction. It first computes the section of the CC in the midsagittal slice and uses it as a seed for the 3D volume segmentation. Experiments with real diffusion MRI data showed that the proposed method is able to quickly segment the CC without any user intervention, with great results when compared to manual segmentation. Since it is simple, fast and does not require parameter settings, the proposed method is well suited for clinical applications.
Evaluating software-based fingerprint liveness detection using Convolutional Networks and Local Binary Patterns

With the growing use of biometric authentication systems in the past years, spoof fingerprint detection has become increasingly important. In this work, we implement and evaluate two different feature extraction techniques for software-based fingerprint liveness detection: Convolutional Networks with random weights and Local Binary Patterns. Both techniques were used in conjunction with a Support Vector Machine (SVM) classifier. Dataset Augmentation was used to increase classifier's performance and a variety of preprocessing operations were tested, such as frequency filtering, contrast equalization, and region of interest filtering. The experiments were made on the datasets used in The Liveness Detection Competition of years 2009, 2011 and 2013, which comprise almost 50,000 real and fake fingerprints' images. Our best method achieves an overall rate of 95.2% of correctly classified samples - an improvement of 35% in test error when compared with the best previously published results.
Divergence map from diffusion tensor imaging: concepts and application to corpus callosum.

This work proposes a novel approach to the analysis of Diffusion Tensor Imaging (DTI) by applying the mathematical concept of divergence, used in vector analysis. This is achieved by choosing an arbitrary direction of analysis and using this direction to transform the diffusion tensor field into an oriented vector field. The method was inspired by the idea of imposing a liquid flow inside the biological tissues, oriented in the direction of analysis, and watching the direction it would be expected to take as it flows through the paths created by the fibers. The experiments were conducted for the particular case of the analysis of the corpus callosum, using real DTI from several subjects. Results showed that the divergence map allows extraction of useful information about the spatial organization of the corpus callosum, providing a way to determine a reference plane that could be used, for example, in studies involving intersubject comparison.
Fast multidimensional parallel Euclidean distance transform based on mathematical morphology

The paper presents a novel Euclidean distance transform algorithm formulated under the mathematical morphology approach. The distance transform is an erosion by a structuring function dependent on the distance metric used. To achieve high speed performance, the squared Euclidean distance structuring function is decomposed into a family of four one-dimensional two-point structuring functions. The erosion algorithm is based on a propagation scheme which resulted in an overall Euclidean distance transform algorithm very simple to code and understand, yet with speed performance compared to the Chamfer 3-5-7 sequential raster and anti-raster algorithm.
Diffusion Tensor Imaging Segmentation by Watershed Transform on Tensorial Morphological Gradient

While scalar image segmentation has been studied extensively, diffusion tensor imaging (DTI) segmentation is a relatively new and challenging task. Either existent segmentation methods have to be adapted to deal with tensorial information or completely new segmentation methods have to be developed to accomplish this task. Alternatively, what this work proposes is the computation of a tensorial morphological gradient of DTI, and its segmentation by IFT-based watershed transform. The strength of the proposed segmentation method is its simplicity and robustness, consequences of the tensorial morphological gradient computation. It enables the use, not only of well known algorithms and tools from the mathematical morphology, but also of any other segmentation method to segment DTI, since the computation of the tensorial morphological gradient transforms tensorial images in scalar ones. In order to validate the proposed method, synthetic diffusion tensor fields were generated, and Gaussian noise was added to them. A set of real DTI was also used in the method validation. All segmentation results confirmed that the proposed method is capable to segment different diffusion tensor images, including noisy and real ones.
Improved FingerCode Matching Function

FingerCode is a fingerprint correlation matching scheme that relies on texture information. In this scheme, the oriented components are extracted from a fingerprint image using a bank of Gabor filters, and a directional texture feature vector is computed for each oriented component. The feature vectors from the input and template images are compared and a matching score is obtained. Here, we explore ways to improve the matching score for the FingerCode method by using more complex matching functions. The best results were obtained by applying a nonlinear function to the texture values and weighting the texture vectors based on the spatial distribution
On integrating iterative segmentation by watershed with tridimensional visualization of MRIs

This work presents two fast and iterative methods that integrate segmentation by watershed and three-dimensional visualization, while the classical approach is to separate these two processes. The user-aided segmentation is based on iterative watershed, efficiently implemented using the image foresting transform (IFT). The first proposed algorithm consists of extracting segmented structures' borders during the segmentation and updating them at each iteration. Structure visualization is achieved by border projection. The second algorithm updates the image directly from the changes of the segmented scene occurred in each step. The two methods were implemented in C and tested with various magnetic resonance images. The results are satisfactory as compared to the classical approaches. The incremental image algorithm is a little faster than the classical one while the incremental border algorithm, although a little slower in segmenting, allows a very fast rendering, very suitable for object manipulation. Thus, the proposed methods provide a good feedback to user.
Automatic method for assessment of telangiectasia degreeing by mathematical morphology

This paper presents an automatic method to assess telangiectasia degreeing, by estimating the reduction of the varicose area from pre to post-treatment using digital image analysis techniques. First both images are geometrically transformed and color space reduced, in order to make them suitable for processing. Next, the varicose veins are segmented via watershed transform and the varicose areas in both images are detected. Finally, the assessment degreeing is done. The method was applied to twenty patients and the results were very consistent, according to the analysis of six vascular specialists.
Adessowiki: Collaborative Scientific Programming Environment

Adessowiki is a collaborative environment for teaching and research in image processing. Adessowiki is composed of a collection of collaborative web pages in the form of a wiki. The articles of this wiki can embed programming code that will be executed on the server when the page is rendered, incorporating the results as figures, texts and tables on the document. The integrated collaborative environment of Adessowiki, containing documentation, programming code and execution results is able to create several possibilities of applications. This paper presents some of the applications where Adessowiki has been used, such as Scientific Writing and Virtual Learning Environment.
Interactive digital image processing course on the World Wide Web

Khorosware, which is the concept of using Khoros as an "authoring system" was first reported in ICIP-94. We present here the effort of integrating the Khoros 2 system, hypertext (HTML), and network browsers to produce an interactive digital image processing course. The success of the Khorosware lies on the the broad scope of the Khoros system which encourages and facilitates collaboration. This paper describes how the course is organized, how it can impact teaching, learning, and research processes, and gives examples of how it is currently being used in several settings. This new interactive toolbox/course is available free of charge via the Internet at http://www.Khoros. Edul dip course.
A Motion Vector Prediction Method For Multi-view Video Coding

This paper proposes a motion vector (MV) prediction algorithm for increasing the gain of multiview video coding (MVC). In the conventional MVC algorithms, the main feature that brings coding gain is the interview MV estimation and compensation in anchor frames. Although all the other frames (non-anchor frames) also consider both temporal and interview MVs, the interview MVs are usually outnumbered by the temporal MVs. Note that the H.264 schemes encode the MVs in DPCM style and thus the accuracy of MV prediction from neighboring ones is important. When an interview MV is being considered, the conventional method predicts it by neighboring MVs regardless of their being temporal or interview directions, which results in inaccurate prediction and thus rare use of interview MVs. Hence we propose a new MV prediction and encoding scheme especially for the interview MVs. The proposed method first estimates the interview MVs that are not sent to the decoder, from the temporal reference frames. The interview MVs are well estimated in most cases because the depth information does not change rapidly. By using the estimated interview MVs instead of temporal MVs, current interview MV being encoded is more accurately predicted and thus the chance of their use is increased. This results in the increase of coding gain about 0.2dB on average and 0.33dB at maximum.
Camera-based document digitization using multiple images

Recently, there have been some attempts to use a portable digital camera for the document digitization. But unlike the conventional flatbed scanners, the document images taken by digital cameras suffer from the perspective distortion and the geometric distortion. These problems deteriorate not only the subjective quality but also the character recognition rate. In this paper, a new document dewarping algorithm that utilizes two input images is presented. The proposed algorithm requires neither auxiliary hardwares such that measure the 3D shape nor the impractical assumptions on the pose of books and cameras. Instead, the 3D shape of the book (document) surface is reconstructed from the geometric correspondence of two images and some post-processings. Contrast to the conventional works, the proposed algorithm do not need to find textlines. So it can be applied to not only text regions but also pictures, tables and mathematical equations. That is, we can rectify distorted images irrespective of languages or contents without auxiliary hardwares.
A fast algorithm for the conversion of DCT coefficients to H.264 transform coefficients

This paper proposes a fast algorithm that converts DCT coefficients into integer transform coefficients, for the transform domain transcoding from MPEG-x to H.264. For the transcoding in the same resolution, the 8 /spl times/ 8 DCT coefficients are converted to four 4/spl times/ 4 integer transform coefficients by decomposing the conversion matrix into sparse ones. For the reduction of resolution by half, we also propose an algorithm that converts DCT coefficients in the lower band into 4 /spl times/ 4 integer transform coefficients. The sparse matrices derived in this paper require fewer computations than the direct and conventional conversion matrices, and thus the overall transcoding using the proposed algorithm requires less computational complexity.
Hierarchical Prediction and Context Adaptive Coding for Lossless Color Image Compression

This paper presents a new lossless color image compression algorithm, based on the hierarchical prediction and context-adaptive arithmetic coding. For the lossless compression of an RGB image, it is first decorrelated by a reversible color transform and then Y component is encoded by a conventional lossless grayscale image compression method. For encoding the chrominance images, we develop a hierarchical scheme that enables the use of upper, left, and lower pixels for the pixel prediction, whereas the conventional raster scan prediction methods use upper and left pixels. An appropriate context model for the prediction error is also defined and the arithmetic coding is applied to the error signal corresponding to each context. For several sets of images, it is shown that the proposed method further reduces the bit rates compared with JPEG2000 and JPEG-XR.