nils-herrmann/clean_scirepeval huggingface dataset:
Speckle Removal Using Diffusion Potential for Optical Coherence Tomography Images

We propose a fast and accurate solution to speckle reduction targeted specifically at optical coherence tomography images. The proposed speckle removing filter is designed using a novel potential function based on the gradient of the local variance of intensity. After filtering, the spatially neighboring pixels with close values of intensities converge to uniform gray values, while the edges remain intact. This filtering process results in removal of speckle without destroying the edges of the desired object. The proposed filter also prevents the generation of any false edges. Detailed experimental analysis shows at least 1-dB improvement in the peak signal-to-noise ratio for spectral domain optical coherence tomography images. The method also shows superior edge preservation, contrast, and speed compared to the state of the art in speckle removing filters.
GraDED: A graph-based parametric dictionary learning algorithm for event detection

Short-time event detection from videos obtained using handheld or car-mounted cameras is an overarching challenge in surveillance. The problem demands simultaneous spatiotemporal localization of the event along with removal of a dynamic background. Existing state-of-the-art techniques are sensitive to non-uniform jitter, changing background, and clutter. In this paper, we propose graph Laplacian assisted parametric dictionary learning, GraDED to account for the aforementioned variations. The temporal occurrence and duration of the event is determined from weights learned using a dynamic graph, while the spatial localization is performed by graph based dictionary learning. We demonstrate the efficacy of our approach by comparing with three state-of-the-art methods and achieve on average an overall increase of 0.08 in specificity and 0.6 in sensitivity for event detection.
Registering sequences of in vivo microscopy images for cell tracking using dynamic programming and minimum spanning trees

Registration of in vivo microscopy image sequences is important for tracking of cells. Registering a long sequence of in vivo microscopy images is particularly challenging for several reasons, which include motion artifacts created by the cardiac cycle and breathing movements of the living subject, occasional defocussing, illumination change, and noise in image acquisition. To accommodate these variations, we sample time points redundantly during microscopic image acquisition. Second, we use dynamic programming to select image frames with tolerable motion and eliminate those with large motion. Third, we employ a novel method based on the minimum spanning tree algorithm to register the selected image frames. Testing on actual in vivo image sequences reveals that our approach excels over three existing registration methods in terms of structural image similarity of the registered images.
Automated segmentation of surface soil moisture from Landsat TM data

This study demonstrates a method for satellite remote sensing of surface soil moisture and the automated segmentation of the acquired imagery. The remote sensing method exploits the relationship between surface radiant temperature, vegetation cover, and surface soil moisture. The segmentation process employs a watershed algorithm applied within a morphological image pyramid. This multi-resolution approach compares favorably to fixed-resolution techniques both in computational cost and feature scalability. Applications of both the remote sensing method and image segmentation technique are demonstrated for a Landsat TM image of southwestern Oklahoma.
A PDE technique for generating locally monotonic images

Local monotonicity provides a meaningful descriptor of smoothness for digital signals. Locally monotonic (LOMO) signals are scaleable by feature size, include both step and ramp edges, and are devoid of outliers due to noise. This paper contributes a partial differential equation (PDE) method for producing locally monotonic (LOMO) signals. Three different approaches for extending the LOMO diffusion technique to images are explored. Results demonstrate that the 2-D LOMO diffusion procedure affords effective image enhancement and denoising. In contrast to other anisotropic diffusion schemes, LOMO diffusion converges to a non-trivial signal, does not require ad hoc thresholds, and does not utilize additional filtering to remove impulses.
Image segmentation by level set analysis

This paper describes an automated image segmentation technique that subdivides regions of homogeneous texture. The method utilizes a level set analysis of scaled Gabor filter responses. Scaling is achieved via an area morphological process. Each scaled, filtered image is examined to locate important connected components based on minimal total internal variance and maximal edge localization. The candidate segments are selected using a granulometry of the gradient magnitude evaluated at the level lines of the connected components. The level set analysis avoids the high computational cost associated with conventional level set approaches by sampling only the significant level sets for processing. The target application for this segmentation technique is content based image retrieval.
Intravital leukocyte detection using the gradient inverse coefficient of variation

The problem of identifying and counting rolling leukocytes within intravital microscopy is of both theoretical and practical interest. Currently, methods exist for tracking rolling leukocytes in vivo, but these methods rely on manual detection of the cells. In this paper we propose a technique for accurately detecting rolling leukocytes based on Bayesian classification. The classification depends on a feature score, the gradient inverse coefficient of variation (GICOV), which serves to discriminate rolling leukocytes from a cluttered environment. The leukocyte detection process consists of three sequential steps: the first step utilizes an ellipse matching algorithm to coarsely identify the leukocytes by finding the ellipses with a locally maximal GICOV. In the second step, starting from each of the ellipses found in the first step, a B-spline snake is evolved to refine the leukocytes boundaries by maximizing the associated GICOV score. The third and final step retains only the extracted contours that have a GICOV score above the analytically determined threshold. Experimental results using 327 rolling leukocytes were compared to those of human experts and currently used methods. The proposed GICOV method achieves 78.6% leukocyte detection accuracy with 13.1% false alarm rate.
Robust fingerprint identification

Due to the complex distortions involved in two impressions of the same finger, fingerprint identification is still a challenging problem for person authentication. In this paper, we propose a fingerprint identification approach based on the triplets of minutiae. The features that we use to find the potential corresponding triangles include angles, triangle orientation, triangle direction, maximum side, minutiae density and ridge counts. False corresponding triangles are eliminated by applying constraints to the transformation between two potential corresponding triangles. The experimental results on National Institute of Standards and Technology special fingerprint database 4, NIST-4, show that, as compared to the linear search, the proposed approach provides a reduction by a factor of 200 for the number of the hypotheses that need to be considered and it can achieve good performance even when a large portion of fingerprints in the database are of poor quality.
Evolving Bayesian Graph for Three-Dimensional Vehicle Model Building From Video

Traffic videos often capture slowly changing views of moving vehicles. These different and incrementally related views provide visual cues for 3-D perception of the vehicles from 2-D videos. This paper focuses on 3-D model building of multiple vehicles with different shapes from a single generic 3-D vehicle model by incrementally accumulating evidences in streaming traffic videos collected from a single static uncalibrated camera. When we do not know a priori the class of the following vehicle to be seen (which is true in a real traffic scenario), a flexible and evolvable Bayesian graphical model (BGM) is required, where the number of nodes, the structure of links between them, and the associated conditional probability distributions can change on the fly. Current BGMs fail to provide such online flexibility. We propose a novel BGM, which is called structure-modifiable adaptive reason-building temporal Bayesian graph (SmartBG), that self-modifies in a data-driven way to model uncertainty propagation in 3-D vehicle model building from 2-D video features, where only a subset of the 2-D vehicle features is visible at any time point, e.g., out of field-of-view (entry/exit) and self-occlusion. Uncertainties are used as relative weights to fuse evidences and to compute the overall reliability of the generated models. Results for different vehicles from several traffic videos and two different viewpoints demonstrate the performance of the proposed method.
Bayesian-based performance prediction for gait recognition

Existing gait recognition approaches do not give their theoretical or experiential performance predictions. Therefore, the discriminating power of gait as a feature for human recognition cannot be evaluated. We first propose a kinematic-based approach to recognize humans by gait. The proposed. approach estimates 3D human walking parameters by performing a least squares fit of the 3D kinematic model to the 2D silhouette extracted from a monocular image sequence. Next, a Bayesian based statistical analysis is performed to evaluate the discriminating power of the extracted features. Through probabilistic simulation, we not only predict the probability of correct recognition (PCR) with regard to different within-class feature variance, but also obtain the upper bound on PCR with regard to different human silhouette resolutions. In addition, the maximum number of people in a database is obtained given the allowable error rate. This is extremely important for gait recognition in large databases.
On the fundamental performance for fingerprint matching

Fingerprints have long been used for person authentication. However, there is not enough scientific research to explain the probability that two fingerprints, which are impressions of different fingers, may be taken as the same one. In this paper, we propose a formal framework to estimate the fundamental algorithm independent error rate of fingerprint matching. Unlike a previous work, which assumes that there is no overlap between any two minutiae uncertainty areas and only measures minutiae's positions and orientations. In our model, we do not make this assumption and measure the relations, i.e., ridge counts between different minutiae as well as minutiae's positions and orientations. The error rates of fingerprint matching obtained by our approach is significantly lower than that of previously published research. Results are shown using NIST-4 fingerprint database. These results contribute toward making fingerprint matching a science and settling the legal challenges to fingerprints.
Reference-Based Scheme Combined With K-SVD for Scene Image Categorization

A reference-based algorithm for scene image categorization is presented in this letter. In addition to using a reference-set for images representation, we also associate the reference-set with training data in sparse codes during the dictionary learning process. The reference-set is combined with the reconstruction error to form a unified objective function. The optimal solution is efficiently obtained using the K-SVD algorithm. After dictionaries are constructed, Locality-constrained Linear Coding (LLC) features of images are extracted. Then, we represent each image feature vector using the similarities between the image and the reference-set, leading to a significant reduction of the dimensionality in the feature space. Experimental results demonstrate that our method achieves outstanding performance.
Kinematic-based human motion analysis in infrared sequences

In an infrared (IR) image sequence of human walking, the human silhouette can be reliably extracted from the background regardless of lighting conditions and colors of the human surfaces and backgrounds in most cases. Moreover, some important regions containing skin, such as face and hands, can be accurately detected in IR image sequences. In this paper, we propose a kinematic-based approach for automatic human motion analysis from IR image sequences. The proposed approach estimates 3D human walking parameters by performing a modified least squares fit of the 3D kinematic model to the 2D silhouette extracted from a monocular IR image sequence, where continuity and symmetry of human walking and detected hand regions are also considered in the optimization function. Experimental results show that the proposed approach achieves good performance in gait analysis with different view angles With respect to the walking direction, and is promising for further gait recognition.
Optimizing crowd simulation based on real video data

Tracking of individuals and groups in video is an active topic of research in image processing and analyzing. This paper proposes an approach for the purpose of guiding a crowd simulation algorithm to mimic the trajectories of individuals in crowds as observed in real videos, which can be further used in image processing and computer vision research extensively. This is achieved by tuning the parameters used in the simulation automatically. It is required because the result of crowd simulation is very sensitive to the parameters. In our experiment, the simulation trajectories are generated by the RVO2 library and the real trajectories are extracted from the UCSD crowd video dataset. The Edit Distance on Real sequence (EDR) between the simulated and real trajectories are calculated. A genetic algorithm is applied to find the parameters that minimize the distances. The experimental results demonstrate that the trajectory distances between simulation and reality are significantly reduced after tuning the parameters of the simulator.
Multi-object tracking in non-stationary video using bacterial foraging swarms

One of the key problems in the field of image processing is object tracking in video. Multiple objects, occlusion, and non-stationary video are some of the challenges that one may face in developing an effective approach. A less-studied approach considers swarm intelligence. This paper presents a new and improved algorithm based on Bacterial Foraging Optimization in order to track multiple objects in real-time video exposed to full and partial occlusion, using video from a moving camera. A comparison with various algorithms is provided.
Fusion of multiple trackers in video networks

In this paper, we address the camera selection problem by fusing the performance of multiple trackers. Currently, all the camera selection/hand-off approaches largely depend on the performance of the tracker deployed to decide when to hand-off from one camera to another. However, a slight inaccuracy of the tracker may pass the wrong information to the system such that the wrong camera may be selected and error may be propagated. We present a novel approach to use multiple state-of-the-art trackers based on different features and principles to generate multiple hypotheses and fuse the performance of multiple trackers for camera selection. The proposed approach has very low computational overhead and can achieve real-time performance. We perform experiments with different numbers of cameras and persons on different datasets to show the superior results of the proposed approach. We also compare results with a single tracker to show the merits of integrating results from multiple trackers.
Face recognition in video with closed-loop super-resolution

Video-based face recognition has received significant attention in the past few years. However, the facial images in a video sequence acquired from a distance are usually small in size and their visual quality is low. Enhancing low-resolution (LR) facial images from a video sequence is of importance for performing face recognition. Registration is a critical step in super-resolution (SR) of facial images from a video which requires precise pose alignment and illumination normalization. Unlike traditional approaches that perform tracking for each frame before using a SR method, in this paper, we present an incremental super-resolution technique in which SR and tracking are linked together in a closed-loop system. An incoming video frame is first registered in pose and normalized for illumination, and then combined with the existing super-resolved texture. This super-resolved texture, in turn, is used to improve the estimate of illumination and motion parameters for the next frame. This process passes on the benefits of the SR result to the tracking module and allows the entire system to reach its potential. We show results on a low-resolution facial video. We demonstrate a significant improvement in face recognition rates with the super-resolved images over the images without super-resolution.
Hierarchical Gabor filters for object detection in infrared images

This paper presents a new representation called "hierarchical Gabor filters" and associated novel local measures which are used to detect potential objects of interest in images. The "first stage" of the approach uses a wavelet set of wide-bandwidth separable Gabor filters to extract local measures from an image. The "second stage" makes certain spatial groupings explicit by creating small-bandwidth, non-separable Gabor filters that are tuned to elongated contours or periodic patterns. The non-separable filter responses are obtained from a weighted combination of the separable basis filters, which preserves the computational efficiency of separable filters while providing the distinctiveness required to discriminate objects from clutter. This technique is demonstrated on images obtained from a forward looking infrared (FLIR) sensor.<<ETX>>
Dynamic scene understanding for autonomous mobile robots

An approach to the dynamic scene analysis is presented which departs from previous work by emphasizing a qualitative strategy of reasoning and modeling. Instead of refining a single quantitative description of the observed environment over time, multiple qualitative interpretations are maintained simultaneously. This offers superior robustness and flexibility over traditional numerical techniques which are often ill-conditioned and noise-sensitive. The main tasks of the authors' approach are: (1) detect and classify the motion of individual objects in the scene; (2) estimate the robot's egomotion; and (3) derive the 3-D structure of the stationary environment. These three tasks strongly depend on each other. First, the direction of heading (i.e. translation) and rotation of the robot are estimated with respect to stationary locations in the scene. The focus of expansion (FOE) is not determined as particular image location, but as a region of possible FOE-locations called the fuzzy FOE. From this information, a rule-based system constructs and maintains a qualitative scene model. Results of this approach for real and synthetic imagery are presented.<<ETX>>
Automated spatial analysis of ARK2: Putative link between microtubules and cell polarity

In leaves of A. thaliana, there exists an intricate network of epidermal surface layer cells responsible for anatomical stability and vigor of flexibility to the entire leaf. Rho GTPases direct this organization of cell polarity, but full understanding of the underlying mechanisms demands further inquiry. We conduct two experiments: (1) a novel procedure is proposed that could be used in other life and plant science studies to quantify microtubule orientation, and (2) shape analysis. We hypothesize ARK2 as a putative interactor in cell polarity maintenance through stabilization of microtubule ordering. We are the first to automate pavement cell phenotype analysis for cell polarity and microtubule orientation. Breakthroughs in the signaling network regulating leaf cell polarity and development will lead science into the frontier of genetically modifying leaves to dramatically increase Earth's plant biomass; impending food shortages in the 21st century will be well served by such research.
Auction protocol for camera active control

In this paper, we apply the auction-based theories in economics to camera networks. We develop a set of auction protocols to do camera active control (pan/tilt/zoom) intelligently. Unlike the economic auction, the bid price in our case is formulated to have a vector representation, such that when a camera is available to follow multiple objects, we consider the “willingness” of this camera to track a particular object. Most of the computation is decentralized by computing the bid price locally while the final decision is made by a virtual auctioneer based on all the available bids, which is analogous to a real auction in economics. Thus, we can take the advantages of distributed/centralized computation and avoid their pitfalls. The experimental results show that the proposed approach is effective and efficient for dynamically active control based on user defined performance metrics.
Discriminant features for model-based image databases

A challenging topic in content-based image retrieval is to determine the discriminant features that improve classification performance. An approach to learn concepts is by estimating mixture model for image databases using EM algorithm; however, this approach is impractical to be implemented for large databases due to the high dimensionality of the feature space. Based on the over-splitting nature of our EM algorithm and the Bayesian analysis of the multiple users' labelling information derived from their relevance feedbacks, we propose a probabilistic MDA to find the discriminating features, and integrate it with the EM framework. The experimental results on Corel images show the effectiveness of concept learning with the probabilistic MDA, and the improvement of the retrieval performance.
Comparison of texture features for human embryonic stem cells with bio-inspired multi-class support vector machine

Determining the meaningful texture features for human embryonic stem cells (hESC) is important in the development of online hESC classification system. This paper proposes the use of novel support vector machine with bio-inspired one-against-all (OAA) multi-class structural and statistical Gabor descriptors for hESC classification. It investigates the statistical histogram information at four different orientations and two different window sizes of the Gabor filter. It demonstrates that statistical Gabor features are more accurate and reliable than a conventional histogram based features.