nils-herrmann/clean_scirepeval huggingface dataset:
Nonlinear Fingerprint Orientation Smoothing by Median Filter

The fingerprint local ridge orientation is a kind of very important intrinsic property in fingerprint image processing and recognition. Getting a reliable orientation field is still a great challenge in poor quality fingerprint caused by scars and ridge breaks. This paper employs a median filter to attenuate impulsive-like noise in the orientation field such as the scars and ridge breaks, etc. In addition, a reliability measure of estimated orientation by the median filter is proposed and an adaptive orientation smoothing algorithm is developed to compute a reliable orientation field based on varying smoothing windows. The experimental results of some synthetic images and real poor quality fingerprints demonstrate that our proposed approach works well to attenuate noise especially the impulsive-like noise gracefully while maintaining the orientation localization of the high-curvature area
Supervised trace lasso for robust face recognition

In this paper, we address the robust face recognition problem. Recently, trace lasso was introduced as an adaptive norm based on the training data. It uses the correlation among the training samples to tackle the instability problem of sparse representation coding. Trace lasso naturally clusters the highly correlated data together. However, the face images with similar variations, such as illumination or expression, often have higher correlation than those from the same class. In this case, the result of trace lasso is contradictory to the goal of recognition, which is to cluster the samples according to their identities. Therefore, trace lasso is not a good choice for face recognition task. In this work, we propose a supervised trace lasso (STL) framework by employing the class label information. To represent the query sample, the proposed STL approach seeks the sparsity of the number of classes instead of the number of training samples. This directly coincides with the objective of the classification. Furthermore, an efficient algorithm to solve the optimization problem of proposed method is given. The extensive experimental results have demonstrated the effectiveness of the proposed framework.
Fingerprint image quality analysis

Fingerprint image quality analysis is crucial in eliminating poor fingerprint images, which will affect the performance of the automatic fingerprint identification system. In this article, two types of new quality measures will be introduced: ridge and valley clarity and global orientation flow to calculate the overall image quality score that can be used to quantitatively determine the quality of the fingerprint image. In order to evaluate the performance of the proposed algorithm, the quality measure is used to rank the performance of a fingerprint recognition system and the ranking is compared with the quality measure rated manually. The result shows that the proposed scheme will return a score that ensures its reliability to indicate the quality of a given fingerprint image.
Learning binarized pixel-difference pattern for scene recognition

Local binary pattern (LBP) and its variants have been used in scene recognition. However, most existing approaches rely on a pre-defined LBP structure to extract features. Those pre-defined structures can be generalized as the patterns constructed from the binarized pixel differences in a local neighborhood. Instead of using a handcraft structure, we propose to learn binarized pixel-difference patterns (BPP). We cast the problem as a feature selection problem and solve it by an incremental search via the criterion of minimum-redundancy-maximum-relevance. Then, BPP features are extracted based on the structures derived. On two challenging scene recognition databases, the proposed approach significantly outperforms the state of the arts.
Classwise Sparse and Collaborative Patch Representation for Face Recognition

Sparse representation has shown its merits in solving some classification problems and delivered some impressive results in face recognition. However, the unsupervised optimization of the sparse representation may result in undesired classification outcome if the variations of the data population are not well represented by the training samples. In this paper, a method of class-wise sparse representation (CSR) is proposed to tackle the problems of the conventional sample-wise sparse representation and applied to face recognition. It seeks an optimum representation of the query image by minimizing the class-wise sparsity of the training data. To tackle the problem of the uncontrolled training data, this paper further proposes a collaborative patch (CP) framework, together with the proposed CSR, named CSR-CP. Different from the conventional patch-based methods that optimize each patch representation separately, the CSR-CP approach optimizes all patches together to seek a CP groupwise sparse representation by putting all patches of an image into a group. It alleviates the problem of losing discriminative information in the training data caused by the partition of the image into patches. Extensive experiments on several benchmark face databases demonstrate that the proposed CSR-CP significantly outperforms the sparse representation-related holistic and patch-based approaches.
A fast and accurate cascade subspace face/eye detector on mobile devices

Mobile vision has attracted increasing research attention recently. A fast and accurate face/eye detector can enable manymobile vision applications. Most ofmobile face detections are close-range face detection. In such a scenario only a limited number of scanning windows are required. Thus the speed is less demanding but the detection accuracy is of higher importance. We further discover that for close-range face detection the covariance matrices of the face/eye class are less reliable compared to the covariance matrices of the non-face/non-eye class. Therefore, a larger weight should be assigned to the face/eye class when building the covariance mixture matrix to remove the unreliable dimensions. The proposed cascade subspace face/eye detector utilizes the focus-attention strategy in all aspects and detects eyes at precise locations at an acceptable speed. The average distance from the detected eyes to the marked eyes is 1.03 pixels only. The detected eyes are almost as accurate as the marked eyes. It takes 0.26 seconds to accurately detect both eyes on O2 XDA Flame.
On orientation and anisotropy estimation for online fingerprint authentication

Local dominant orientation estimation is one of the most important operations in almost all automatic fingerprint authentication systems. Robust orientation and anisotropy estimation improves the system's reliability in handling low-quality fingerprints, which is crucial for the system's massive application such as securing multimedia. This paper analyzes the robustness of the orientation and anisotropy estimation methods and the effect of the modulus normalization on the estimation performance. A two-stage averaging framework with block-wise modulus handling is introduced to inherit the merits of the both linear and normalized averaging methods. We further propose to set the modulus of an orientation vector to be its anisotropy estimate instead of unity so that the orientation inconsistency of gradients is included in the second stage of averaging. These two measures improve the robustness of the fingerprint local dominant orientation estimation and lead to an anisotropy estimate that reflects the characteristics of fingerprint more effectively. In addition, the proposed approach is computationally efficient for online fingerprint authentication. Extensive experiments using both synthetic images and real fingerprints verify the feasibility of the proposed approach and demonstrate its robustness to noise and low-quality fingerprints.
A novel LBP-based Color descriptor for face recognition

LBP-based color features have shown excellent performance for color face recognition tasks, such as Color LBP, CLBP and LCVBP. However, existing methods encode the inter-channel information on pairs of color channels by applying the same spatial structure as that used in the intra-channel encoding. This results in a very high dimensional feature vector yet ineffective in encoding inter-channel information. Moreover, the difference of pixel values across color channels may not be a proper measure if they are not quantitatively comparable. To tackle these problems of existing methods, we propose a novel LBP-based color feature, Ternary-Color LBP (TCLBP), to encode the inter-channel information more effectively and efficiently. Extensive experiments on 4 public face databases, Color FERET, Georgia Tech, FRGC and LFW, are conducted to verify the effectiveness of the proposed TCLBP color feature for face recognition. Results show that the proposed TCLBP leads to visibly better face recognition performance than Color LBP, CLBP and LCVBP consistently over the 4 databases.
Effective and efficient fingerprint image postprocessing

Minutiae extraction is a crucial step in an automatic fingerprint identification system. However, the presence of noise in poor-quality images causes a large number of extraction errors, including the dropping of true minutiae and production of false minutiae. A study on these errors reveals that postprocessing is effective in removing false minutiae while keeping true ones. Furthermore, the overall processing efficiency could be improved because of the reduction in total minutia number. In this paper, we present a novel fingerprint image postprocessing algorithm. It is developed based on several rules, which are generalized through a study on the errors that commonly occur in minutiae extraction and their effects on the overall verification performance. Thorough experimental tests demonstrate the proposed postprocessing algorithm to be both effective and efficient.
LBP Encoding Schemes Jointly Utilizing the Information of Current Bit and Other LBP Bits

Local binary pattern (LBP) is sensitive to image noise. Noise-resistant LBP (NRLBP) improves the robustness to noise by incorporating the prior knowledge of images and information of other LBP bits into encoding process. However, it encodes the small pixel difference in such a way that its sign and magnitude are ignored. Although the small pixel difference may be easily distorted by noise, some of its information is still useful for LBP encoding. In this letter, we propose two enhanced NRLBPs that jointly utilize the sign and the magnitude of the current pixel difference, and also the information of other LBP bits. The proposed approaches are validated on two benchmark databases and demonstrate a superior performance compared with NRLBP and other LBP variants. The performance gain is significant when the noise level is high.
Database Clustering Based on Multi-Prototype Representation of Cluster

Clustering is a useful technique to provide the organization of multimedia database. Using single prototype to represent each cluster may not adequately model the different types of clusters and hence limits the clustering performance on the complex data structure. This paper proposes a clustering algorithm based on multi-prototype representation of cluster. The square-error clustering is used to produce a number of prototypes to locate the regions of high density. The prototypes are organized into a given number of clusters in agglomerative method based on a proposed separation measure. New prototypes are iteratively added to improve the poor cluster boundaries. As a result, the proposed algorithm can discover the clusters of complex structure. Experimental results demonstrate the effectiveness of the proposed clustering algorithm.
Enhancing retinal vessel segmentation by color fusion

Accurate segmentation of retinal vessel plays an important role in the computer-aided diagnosis of eye diseases. Existing supervised methods extract features only from green channel due to its much higher contrast between vessel and background than in red and blue channels. However, red and blue channels also contain useful information for distinguishing vessel from background. This work investigates various ways of combining information in all 3 color channels to enhance the segmentation performance, based on which an effective color fusion scheme is proposed in this paper. Its performance is evaluated on two publicly available databases DRIVE and STARE. Results demonstrate that the proposed feature fusion with dimensionality reduction by asymmetric PCA visibly enhances the segmentation performance consistently on both databases, rendering better performance than state-of-the-art methods in dealing with healthy and pathological retinal images.
Fingerprint minutiae matching based on the local and global structures

Proposes a fingerprint minutia matching technique, which matches the fingerprint minutiae by using both the local and global structures of minutiae. The local structure of a minutia describes a rotation and translation invariant feature of the minutia in its neighborhood. It is used to find the correspondence of two minutiae sets and increase the reliability of the global matching. The global structure of minutiae reliably determines the uniqueness of fingerprint. Therefore, the local and global structures of minutiae together provide a solid basis for reliable and robust minutiae matching. The proposed minutiae matching scheme is suitable for an online processing due to its high processing speed. Experimental results show the performance of the proposed technique.
Online Fingerprint Template Improvement

This work proposes a technique that improves fingerprint templates by merging and averaging minutiae of multiple fingerprints. The weighted averaging scheme enables the template to change gradually with time in line with changes of the skin and imaging conditions. The recursive nature of the algorithm greatly reduces the storage and computation requirements of this technique. As a result, the proposed template improvement procedure can be performed online during the fingerprint verification process. Extensive experimental studies demonstrate the feasibility of the proposed algorithm.
Evaluation of multiparameter micrograph analysis with synthetical benchmark images

To analyze multiparametric images of biological systems in vivo, advanced image processing, visualization and data mining tools are under development. To evaluate the algorithms, fit the parameters and assess the algorithm's accuracy, benchmark data sets supplied with a ground truth label are required. Because manual evaluation results, done by expensive experts are hard to achieve, new innovative strategies for evaluation are needed to allow the computer scientists to evaluate their algorithms. We propose an algorithm for the generation of synthetical multiparameter images of cell bodies. The algorithm generates a stack of intensity images, with different grey value characteristics from a reasonable set of parameters. Each image is composition of single synthesized grey value images. Tuning the parameters allow an individual incorporation of expert knowledge and supplies the developer with ground truth labels. We describe and motivate the algorithm and present three applications of evaluation tools to synthetical micrographs. The results show the approach's high relevance to application oriented image analysis in biomedicine.
Polyp Activity Estimation and Monitoring for Cold Water Corals with a Deep Learning Approach

Fixed underwater observatories (FUOs) equipped with a variety of sensors including cameras, allow long-term monitoring with a high temporal resolution of a limited area of interest. FUOs equipped with HD cameras enable in situ monitoring of biological activity, such as live cold-water corals on a level of detail down to individual polyps. We present a workflow which allows monitoring the activity of cold water coral polyps automatically from photos recorded at the FUO LoVe (Lofoten - Vesterålen). The workflow consists of three steps: First the manual polyp activity-level identification, carried out by three observers on a region of interest in 13 images to generate a gold standard. Second, the training of a convolutional neural network (CNN) on the gold standard to automate the polyp activity classification. Third, the computational activity classification is integrated into an algorithmic estimation of polyp activity in a region of interest. We present results obtained for an image series from April to November 2015 that shows interesting temporal behavior patterns correlating with other posterior measurements.
BIIGLE Tools – A Web 2.0 Approach for Visual Bioimage Database Mining

In this paper we want to discuss the usage of web 2.0 techniques to realize information visualization based exploration and annotation of huge volume, semi-structured data, and in particular high throughput bioimage series. To this end, we developed a toolbox for a graphical representation of different displays in a browser context which can be used for image database exploration in a link &amp; brush fashion. The web based approach proved to be capable of information visualization tasks and supports collaboration of several users at arbitrary locations.
Reagent-free automatic cell viability determination using neural networks based machine vision and dark-field microscopy in Saccharomyces cerevisiae

Fermentation industries require in-situ real-time monitoring of cell viability during fermentation processes. For this purpose, reagent-free approaches are desired because they can be used for in situ analysis and reduce the system's complexity. We have developed an automatic way of determining cell viability via analysis of time-lapse image sequences taken by dark field microscopy without the aid of any additional reagents. The image processing is based on neural networks based machine vision, involving principal component analysis (PCA) to investigate the dynamic information of intracellular movements. In consequence, the essential features as the vital sign of the target cells are discovered. Viability predictions using the support vector machine (SVM) classifier have been done successfully on the datasets with different qualities. Accuracy up to above 90% has been obtained on the basis of image enhancement. Robustness of the system is proved by the results of the tests. The model organism we have used is Saccharomyces cerevisiae, however, this technique can promisingly be applied for the identification of cell viability of other organisms as well
An adaptive tissue characterization network for model-free visualization of dynamic contrast-enhanced magnetic resonance image data

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has become an important source of information to aid cancer diagnosis. Nevertheless, due to the multi-temporal nature of the three-dimensional volume data obtained from DCE-MRI, evaluation of the image data is a challenging task and tools are required to support the human expert. We investigate an approach for automatic localization and characterization of suspicious lesions in DCE-MRI data. It applies an artificial neural network (ANN) architecture which combines unsupervised and supervised techniques for voxel-by-voxel classification of temporal kinetic signals. The algorithm is easy to implement, allows for fast training and application even for huge data sets and can be directly used to augment the display of DCE-MRI data. To demonstrate that the system provides a reasonable assessment of kinetic signals, the outcome is compared with the results obtained from the model-based three-time-points (3TP) technique which represents a clinical standard protocol for analysing breast cancer lesions. The evaluation based on the DCE-MRI data of 12 cases indicates that, although the ANN is trained with imprecisely labeled data, the approach leads to an outcome conforming with 3TP without presupposing an explicit model of the underlying physiological process.
Biigle - Web 2.0 enabled labelling and exploring of images from the Arctic deep-sea observatory HAUSGARTEN

Deep-sea research relies strongly on the use of high-resolution cameras which generate large quantities of footage. The material can currently, however, often not be used to its full potential as the analysis is time- and labour-intensive and requires the input of many different taxonomic experts. Here, we present a system which enables the collaboration of experts from various places and the application of machine-vision tools on footage from the Arctic deep-sea observatory,  Biigle (Bielefeld Image Graphical Labeller and Explorer) is a Web 2.0 based platform containing easily uploaded images that can be accessed by collaborating scientists. Since Biigle is realised as a rich internet application, there is no need for the local installation of complex software packages. The scientists can use a standard web browser to access the image database and immediately explore or label images. They have instant access to the data submitted by other scientists and are directly involved in the emerging community. Biigle also offers an application interface for machine-vision components aiming at the automated analysis of seafloor images. As a first module, a laser point detection allows for an automated calibration of the area covered by the camera which is vital to derive faunal density estimates. The laser points were detected in all but eight of 1883 images tested in total. The combination of human expert labels and machine-vision results can be exported into spreadsheets offering a well-established standard for further data analyses. Biigle can be accessed at http://www.biigle.de with the username and the password “test” for testing purposes.
Ranking Color Correction Algorithms Using Cluster Indices

In the field of underwater imaging it is often necessary to pre-process images to homogenize image quality across a whole image transect and to compensate variations in imaging conditions. A variety of pre-processing methods have been developed in recent years to overcome different problems occurring in underwater imaging, hence performing different on different image sets. Protocols for an objective comparison and scoring of those methods are needed. Here we show how to use cluster indices to rank the differently methods regarding their performances on different image sets. Our results show different rankings for four preprocessing methods for two chosen sets of benthic images from the deep seafloor.
Rapid image processing and classification in underwater exploration using advanced high performance computing

Computational underwater image analysis is developing into a mature field of research, with an increasing number of companies, academic groups and researchers showing interest in it. While on the one hand, the basic question is addressed by many groups, how algorithms can be applied to automatically detect and classify objects of interest (OOI) in underwater image footage, on the other hand the questions for efficiency and performance, i.e. the time a computer (or a compute cluster) needs to perform this task, has received much attention yet. In this paper we will show, how nowadays methods for high performance computing like parallelization and GPU computing via CUDA (Compute Unified Device Architecture) can be used to achieve both, image enhancement and segmentation in less than 0.2 sec per image (4224 × 2376 pixel) on average, which paves the way to real time online applications.