nils-herrmann/clean_scirepeval huggingface dataset:
Segmentation of Images Having Unimodal Distributions

A gradient relaxation method based on maximizing a criterion function is studied and compared to the nonlinear probabilistic relaxation method for the purpose of segmentation of images having unimodal distributions. Although both methods provide comparable segmentation results, the gradient method has the additional advantage of providing control over the relaxation process by choosing three parameters which can be tuned to obtain the desired segmentation results at a faster rate. Examples are given on two different types of scenes.
Integrated Model for Understanding Pollen Tube Growth in Video

Pollen tube growth is an essential part of the sexual reproductive process in plants. It is the result of a complex interaction of cytoplasmic contents (proteins, ions, cellular structures, etc.). Existing pollen tube models use differential equations to represent these complex intra-cellular interactions that lead to growth. As a result of this complex nature, these models are not used to verify the shape and growth behavior observed in living cells. We present a method of analyzing the growth behavior of pollen tubes in experimental videos through affine transformations on the detected cell tip. The method relies on underlying biological knowledge about the growth process and leverages these processes to determine tip morphology. Experimental results on videos of growing pollen tube cells show that our method is superior to the current method of treating cell tip morphology as well as adaptive active appearance models.
Understanding Dynamic Social Grouping Behaviors of Pedestrians

There have been many studies in the literature on social group recognition of crowds of pedestrians. However, most of these studies have approached the problem from a static point of view. A study on the dynamic property of social groups among people over time can provide significant insight into human behaviors and events. Inspired by sociological models of human collective behavior, in this work, we present a framework for characterizing hierarchical social groups based on evolving tracklet interaction network (ETIN) where the tracklets of pedestrians are represented as nodes and the their grouping behaviors are captured by the edges with associated weights. We use non-overlapping snapshots of the interaction network and develop the framework for a unified dynamic group identification and tracklet association. The approach is evaluated quantitatively and qualitatively on videos of pedestrian scenes where manually labeled ground-truth is given. The results of our approach are consistent to human-perceived dynamic social groups of the crowd. The performance analysis of our method shows that the approach is scalable and it provides situational awareness in a real-world scenarios.
Individual recognition by kinematic-based gait analysis

Current gait recognition approaches only consider individuals walking frontoparallel to the image plane. This makes them inapplicable for recognizing individuals walking from different angles with respect to the image plane. In this paper, we propose a kinematic-based approach to recognize individuals by gait. The proposed approach eastimates 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. A genetic algorithm is used for feature selection from the estimated parameters, and the individuals are then recognized from the feature vectors using a nearest neighbor method. Experimental results show that the proposed approach achieves good performance in recognizing individuals walking from different angles with respect to the image plane.
Fingerprint verification using genetic algorithms

Fingerprint matching is still a challenging problem for reliable person authentication because of the complex distortions involved in two impressions of the same finger. In this paper, we propose a fingerprint matching approach based on Genetic Algorithms (GA), which finds the optimal global transformation between two different fingerprints. In order to deal with low quality fingerprint images, which introduce significant occlusion and clutter of minutiae features, we design the fitness function based on the local properties of each triplet of minutiae. The experimental results on National Institute of Standards and Technology fingerprint database, NIST-4, not only show that the proposed approach can achieve good performance even when a large portion of fingerprints in the database are of poor quality, but also show that the proposed approach is better than another approach, which is based on mean-squared error estimation.
Target recognition for articulated and occluded objects in synthetic aperture radar imagery

Recognition of articulated occluded real-world man-made objects in synthetic aperture radar (SAR) imagery has not been addressed in the field of image processing and computer vision. The traditional approach to object recognition in SAR imagery (at one foot or worse resolution) typically involves template matching methods, which are not suited for these cases because articulation or occlusion changes global features like the object outline and major axis. In this paper the performance of a model-based automatic target recognition (ATR) engine with articulated and occluded objects in SAR imagery is characterized based on invariant properties of the objects. Although the approach is related to geometric hashing, it is a novel approach for recognizing objects in SAR images. The novelty and power of the approach come from a combination of a SAR specific method for recognition, taking into account azimuthal variation, articulation invariants and sensor resolution.
Closed-Loop Object Recognition Using Reinforcement Learning

Current computer vision systems whose basic methodology is open-loop or filter type typically use image segmentation followed by object recognition algorithms. These systems are not robust for most real-world applications. In contrast, the system presented here achieves robust performance by using reinforcement learning to induce a mapping from input images to corresponding segmentation parameters. This is accomplished by using the confidence level of model matching as a reinforcement signal for a team of learning automata to search for segmentation parameters during training. The use of the recognition algorithm as part of the evaluation function for image segmentation gives rise to significant improvement of the system performance by automatic generation of recognition strategies. The system is verified through experiments on sequences of indoor and outdoor color images with varying external conditions.
Adaptive integrated image segmentation and object recognition

The paper presents a general approach to image segmentation and object recognition that can adapt the image segmentation algorithm parameters to the changing environmental conditions. Segmentation parameters are represented by a team of generalized stochastic learning automata and learned using connectionist reinforcement learning techniques. The edge-border coincidence measure is first used as reinforcement for segmentation evaluation to reduce computational expenses associated with model matching during the early stage of adaptation. This measure alone, however, cannot reliably predict the outcome of object recognition. Therefore, it is used in conjunction with model matching where the matching confidence is used as a reinforcement signal to provide optimal segmentation evaluation in a closed-loop object recognition system. The adaptation alternates between global and local segmentation processes in order to achieve optimal recognition performance. Results are presented for both indoor and outdoor color images where the performance improvement over time is shown for both image segmentation and object recognition.
Integrating Social Grouping for Multitarget Tracking Across Cameras in a CRF Model

Tracking multiple targets across nonoverlapping cameras aims at estimating the trajectories of all targets, and maintaining their identity labels consistent while they move from one camera to another. Matching targets from different cameras can be very challenging, as there might be significant appearance variation and the blind area between cameras makes the target’s motion less predictable. Unlike most of the existing methods that only focus on modeling the appearance and spatiotemporal cues for inter-camera tracking, this paper presents a novel online learning approach that considers integrating high-level contextual information into the tracking system. The tracking problem is formulated using an online learned conditional random field (CRF) model that minimizes a global energy cost. Besides low-level information, social grouping behavior is explored in order to maintain targets’ identities as they move across cameras. In the proposed method, pairwise grouping behavior of targets is first learned within each camera. During inter-camera tracking, track associations that maintain single camera grouping consistencies are preferred. In addition, we introduce an iterative algorithm to find a good solution for the CRF model. Comparison experiments on several challenging real-world multicamera video sequences show that the proposed method is effective and outperforms the state-of-the-art approaches.
Image understanding research for automatic target recognition

Automatic target recognition (ATR) is an important capability for defense applications. Many aspects of image understanding (IU) research are traditionally used to solve ATR problems. The authors discuss ATR applications and problems in developing real-world ATR systems and present the status of technology for these systems. They identify several IU problems that need to be resolved in order to enhance the effectiveness of ATR-based weapon systems. They conclude that technological gains in developing robust ATR systems will lead to significant advances in many other areas of applications of image understanding.<<ETX>>
Discovering operators and features for object detection

In this paper, we learn to discover composite operators and features that are evolved from combinations of primitive image processing operations to extract regions-of-interest (ROIs) in images. Our approach is based on genetic programming (GP). The motivation for using GP is that there are a great many ways of combining these primitive operations and the human expert, limited by experience, knowledge and time, can only try a very small number of conventional ways of combination. Genetic programming, on the other hand, attempts many unconventional ways of combination that may never be imagined by human experts. In some cases, these unconventional combinations yield exceptionally good results. Our experimental results show that GP can find good composite operators to effectively extract the regions of interest in an image and the learned composite operators can be applied to extract ROIs in other similar images.
Object recognition results using MSTAR synthetic aperture radar data

This paper outlines an approach and experimental results for synthetic aperture radar (SAR) object recognition using the MSTAR data. With SAR scattering center locations and magnitudes as features, the invariance of these features is shown with object articulation (e.g., rotation of a tank turret) and with external configuration variants. This scatterer location and magnitude quasi-invariance is used as a basis for development of a SAR recognition system that successfully identifies articulated and non-standard configuration vehicles based on non-articulated, standard recognition models. The forced recognition results and pose accuracy are given. The effect of different confusers on the receiver operating characteristic (ROC) curves are illustrated along with ROC curves for configuration variants, articulations and small changes in depression angle. Results are given that show that integrating the results of multiple recognizers can lead to significantly improved performance over the single best recognizer.
Performance prediction and validation for object recognition

This paper addresses the problem of predicting fundamental performance of vote-based object recognition using 2-D point features. It presents a method for predicting a tight lower bound on performance. Unlike previous approaches, the proposed method considers data-distortion factors, namely uncertainty, occlusion, and clutter, in addition to model similarity, simultaneously. The similarity between every pair of model objects is captured by comparing their structures as a function of the relative transformation between them. This information is used along with statistical models of the data-distortion factors to determine an upper bound on the probability of recognition error. This bound is directly used to determine a lower bound on the probability of correct recognition. The validity of the method is experimentally demonstrated using synthetic aperture radar (SAR) data obtained under different depression angles and target configurations.
Estimating 3D Egomotion from Perspective Image Sequence

The computation of sensor motion from sets of displacement vectors obtained from consecutive pairs of images is discussed. The problem is investigated with emphasis on its application to autonomous robots and land vehicles. The effects of 3D camera rotation and translation upon the observed image are discussed, particularly the concept of the focus of expansion (FOE). It is shown that locating the FOE precisely is difficult when displacement vectors are corrupted by noise and errors. A more robust performance can be achieved by computing a 2D region of possible FOE locations (termed the fuzzy FOE) instead of looking for a single-point FOE. The shape of this FOE region is an explicit indicator of the accuracy of the result. It has been shown elsewhere that given the fuzzy FOE, a number of powerful inferences about the 3D sense structure and motion become possible. Aspects of computing the fuzzy FOE are emphasized, and the performance of a particular algorithm on real motion sequences taken from a moving autonomous land vehicle is shown.
Shape Model-Based 3D Ear Detection from Side Face Range Images

Ear detection is an important part of an ear recognition system. In this paper we propose a shape model-based technique for locating human ears in side face range images. The ear shape model is represented by a set of discrete 3D vertices corresponding to ear helix and anti-helix parts. Given side face range images, step edges are extracted considering the fact that there are strong step edges around the ear helix part. Then the edge segments are dilated, thinned and grouped into different clusters which are potential regions containing ears. For each cluster, we register the ear shape model with the edges. The region with the minimum mean registration error is declared as the detected ear region; the ear helix and anti-helix parts are meanwhile identified. Experiments are performed with a large number of real face range images to demonstrate the effectiveness of our approach. The contributions of this paper are: (a) a ear shape model for locating 3D ears in side face range images, (b) an effective approach to detect human ears from side face range images, (c) experimental results on a large number of ear images.
Fingerprint classification based on learned features

In this paper, we present a fingerprint classification approach based on a novel feature-learning algorithm. Unlike current research for fingerprint classification that generally uses well defined meaningful features, our approach is based on Genetic Programming (GP), which learns to discover composite operators and features that are evolved from combinations of primitive image processing operations. Our experimental results show that our approach can find good composite operators to effectively extract useful features. Using a Bayesian classifier, without rejecting any fingerprints from the NIST-4 database, the correct rates for 4- and 5-class classification are 93.3% and 91.6%, respectively, which compare favorably with other published research and are one of the best results published to date.
3D Filtering for Injury Detection in Brain MRI

This paper introduces a brain injury detection approach, using 3D filtering technique, for the images acquired by the magnetic resonance imaging (MRI) technique. The proposed method uses the symmetry property of brain MRI on both 2D images and 3D volumetric information of the MRI sequences. The approach consists of two key steps: (1) each slice of a brain image is segmented into different parts using a region growing algorithm, and a symmetry affinity matrix is computed, (2) non-symmetric regions are extracted, and they are further used to detect brain injury. The Kalman filter is explicitly used in step (2) to filter out the non-injury regions in 3D. Experiments are carried out to indicate the high efficiency of the method to detect the brain injuries.
Fingerprint Indexing Based on Novel Features of Minutiae Triplets

We are concerned with accurate and efficient indexing of fingerprint images. We present a model-based approach, which efficiently retrieves correct hypotheses using novel features of triangles formed by the triplets of minutiae as the basic representation unit. The triangle features that we use are its angles, handedness, type, direction, and maximum side. Geometric constraints based on other characteristics of minutiae are used to eliminate false correspondences. Experimental results on live-scan fingerprint images of varying quality and NIST special database 4 (NIST-4) show that our indexing approach efficiently narrows down the number of candidate hypotheses in the presence of translation, rotation, scale, shear, occlusion, and clutter. We also perform scientific experiments to compare the performance of our approach with another prominent indexing approach and show that the performance of our approach is better for both the live scan database and the ink based database NIST-4.
Super-Resolution of Facial Images in Video with Expression Changes

Super-resolution (SR) of facial images from video suffers from facial expression changes. Most of the existing SR algorithms for facial images make an unrealistic assumption that the ¿perfect¿ registration has been done prior to the SR process. However, the registration is a challenging task for SR with expression changes. This paper proposes a new method for enhancing the resolution of low-resolution (LR) facial image by handling the facial image in a non-rigid manner. It consists of global tracking, local alignment for precise registration and SR algorithms. A B-spline based resolution aware incremental free form deformation (RAIFFD) model is used to recover a dense local non-rigid flow field. In this scheme, low-resolution image model is explicitly embedded in the optimization function formulation to simulate the formation of low resolution image. The results achieved by the proposed approach are significantly better as compared to the SR approaches applied on the whole face image without considering local deformations. The results are also compared with two state-of-the-art SR algorithms to show the effectiveness of the approach in super-resolving facial images with local expression changes.
Modeling and Classifying Tip Dynamics of Growing Cells in Video

Plant biologists study pollen tubes to discover the functions of many proteins/ions and map the complex network of pathways that lead to an observable growth behavior. Many growth models have been proposed that address parts of the growth process: internal dynamics and cell wall dynamics, but they do not distinguish between the two types of growth segments: straight versus turning behavior. We propose a method of classifying segments of experimental videos by extracting features from the growth process during each interval. We use a stress-strain relationship to measure the extensibility in the tip region. A biologically relevant three-component Gaussian is used to model spatial distribution of tip extensibility and a second-order damping system is used to explain the temporal dynamics. Feature-based classification shows that the location of maximum tip extensibility is the most distinguishing feature between straight versus turning behavior.
Person Reidentification With Reference Descriptor

Person identification across nonoverlapping cameras, also known as person reidentification, aims to match people at different times and locations. Reidentifying people is of great importance in crucial applications such as wide-area surveillance and visual tracking. Due to the appearance variations in pose, illumination, and occlusion in different camera views, person reidentification is inherently difficult. To address these challenges, a reference-based method is proposed for person reidentification across different cameras. Instead of directly matching people by their appearance, the matching is conducted in a reference space where the descriptor for a person is translated from the original color or texture descriptors to similarity measures between this person and the exemplars in the reference set. A subspace is first learned in which the correlations of the reference data from different cameras are maximized using regularized canonical correlation analysis (RCCA). For reidentification, the gallery data and the probe data are projected onto this RCCA subspace and the reference descriptors (RDs) of the gallery and probe are generated by computing the similarity between them and the reference data. The identity of a probe is determined by comparing the RD of the probe and the RDs of the gallery. A reranking step is added to further improve the results using a saliency-based matching scheme. Experiments on publicly available datasets show that the proposed method outperforms most of the state-of-the-art approaches.
Error model for scene reconstruction from motion and stereo

Scene reconstruction from motion and stereo are the two most popular methods for 3D range reconstruction. Because both techniques are affected by different types of errors, combining these two approaches to carry out 3D reconstruction seems natural. In this work, we present the initial results of our work towards the goal of analytically describing the performance of such a synergistic system. We consider the case in which points are viewed from two vantage points, and analytically model the uncertainty in the reconstruction of their 3D position. The uncertainty is expressed as a function of the points' location in the scene, as well as of the viewpoint change between the two images. The analysis is complemented by numerical results, which shed light on several interesting properties of the estimation accuracy.
Fingerprint identification: classification vs. indexing

We present a comparison of two key approaches for fingerprint identification. These approaches are based on (a) classification followed by verification, and (b) indexing followed by verification. The fingerprint classification approach is based on a novel feature-learning algorithm. It learns to discover composite operators and features that are evolved from combinations of primitive image processing operations. These features are then used for classification of fingerprints into five classes. The indexing approach is based on novel triplets of minutiae. The verification algorithm, based on least square minimization over each of the possible minutiae triplet pairs, is used for identification in both cases. On the NIST-4 fingerprint database, the comparison shows that, although correct classification rate can be as high as 92.8% for 5-class problems, the indexing approach performs better, based on the size of the search space and identification results.