nils-herrmann/clean_scirepeval huggingface dataset:
Segmentation of Pollen Tube Growth Videos Using Dynamic Bi-Modal Fusion and Seam Carving.

The growth of pollen tubes is of significant interest in plant cell biology, as it provides an understanding of internal cell dynamics that affect observable structural characteristics such as cell diameter, length, and growth rate. However, these parameters can only be measured in experimental videos if the complete shape of the cell is known. The challenge is to accurately obtain the cell boundary in noisy video images. Usually, these measurements are performed by a scientist who manually draws regions-of-interest on the images displayed on a computer screen. In this paper, a new automated technique is presented for boundary detection by fusing fluorescence and brightfield images, and a new efficient method of obtaining the final cell boundary through the process of Seam Carving is proposed. This approach takes advantage of the nature of the fusion process and also the shape of the pollen tube to efficiently search for the optimal cell boundary. In video segmentation, the first two frames are used to initialize the segmentation process by creating a search space based on a parametric model of the cell shape. Updates to the search space are performed based on the location of past segmentations and a prediction of the next segmentation.Experimental results show comparable accuracy to a previous method, but significant decrease in processing time. This has the potential for real time applications in pollen tube microscopy.
Prediction and validation of indexing performance for biometrics

The performance of a recognition system is usually experimentally determined. Therefore, one cannot predict the performance of a recognition system a priori for a new dataset. In this paper, a statistical model to predict the value of k in the rank-k identification rate for a given biometric system is presented. Thus, one needs to search only the topmost k match scores to locate the true match object. A geometrical probability distribution is used to model the number of non match scores present in the set of similarity scores. The model is tested in simulation and by using a public dataset. The model is also indirectly validated against the previously published results. The actual results obtained using publicly available database are very close to the predicted results which validates the proposed model.
Anomalous activity classification in the distributed camera network

Unlike existing methods that used the human actions or trajectories to analyze the human activity in overlapping field-of-views, this paper proposes the appearance and travel time-based human activity classification in the camera network of non-overlapping field-of-views. The mixture of Gaussian-based appearance similarity model incorporates the appearance variance between different cameras to address changes in varying lighting conditions. To address the problem of limited labeled training data, we propose the use of semi-supervised expectation-maximization algorithm for activity classification. The human activities observed in a simulated camera network with nine cameras and twenty-five nodes are classified into one normal and three anomalous classes. A similar camera network is built and tested in real-life experiments, in which the proposed approach achieves satisfactory performance.
Person Re-Identification by Robust Canonical Correlation Analysis

Person re-identification is the task to match people in surveillance cameras at different time and location. Due to significant view and pose change across non-overlapping cameras, directly matching data from different views is a challenging issue to solve. In this letter, we propose a robust canonical correlation analysis (ROCCA) to match people from different views in a coherent subspace. Given a small training set as in most re-identification problems, direct application of canonical correlation analysis (CCA) may lead to poor performance due to the inaccuracy in estimating the data covariance matrices. The proposed ROCCA with shrinkage estimation and smoothing technique is simple to implement and can robustly estimate the data covariance matrices with limited training samples. Experimental results on two publicly available datasets show that the proposed ROCCA outperforms regularized CCA (RCCA), and achieves state-of-the-art matching results for person re-identification as compared to the most recent methods.
Recognition of walking humans in 3D: Initial results

It has been challenging to recognize walking humans at arbitrary poses from a single or small number of video cameras. Attempts have been made mostly using a 2D image/silhouette-based representation and a limited use of 3D kinematic model-based approaches. In this paper, the problem of recognizing walking humans at arbitrary poses is addressed. Unlike all the previous work in computer vision and pattern recognition the models of walking humans are built using the sensed 3D range data at selected poses without any markers. An instance of a walking individual at a different pose is recognized using the 3D range data at that pose. Both modeling and recognition of an individual are done using the dense 3D range data. The proposed approach first measures 3D human body data that consists of the representative poses during a gait cycle. Next, a 3D human body model is fitted to the body data using an approach that overcomes the inherent gaps in the data and estimates the body pose with high accuracy. A gait sequence is synthesized by interpolation of joint positions and their movements from the fitted body models. Both dynamic and static gait features are obtained which are used to define a similarity measure for an individual recognition in the database. The experimental results show high recognition rates using our range based 3D gait database.
Physical models for moving shadow and object detection in video

Current moving object detection systems typically detect shadows cast by the moving object as part of the moving object. In this paper, the problem of separating moving cast shadows from the moving objects in an outdoor environment is addressed. Unlike previous work, we present an approach that does not rely on any geometrical assumptions such as camera location and ground surface/object geometry. The approach is based on a new spatio-temporal albedo test and dichromatic reflection model and accounts for both the sun and the sky illuminations. Results are presented for several video sequences representing a variety of ground materials when the shadows are cast on different surface types. These results show that our approach is robust to widely different background and foreground materials, and illuminations.
Shape Matching of Two-Dimensional Objects

In this paper we present results in the areas of shape matching of nonoccluded and occluded two-dimensional objects. Shape matching is viewed as a ``segment matching'' problem. Unlike the previous work, the technique is based on a stochastic labeling procedure which explicitly maximizes a criterion function based on the ambiguity and inconsistency of classification. To reduce the computation time, the technique is hierarchical and uses results obtained at low levels to speed up and improve the accuracy of results at higher levels. This basic technique has been extended to the situation where various objects partially occlude each other to form an apparent object and our interest is to find all the objects participating in the occlusion. In such a case several hierarchical processes are executed in parallel for every object participating in the occlusion and are coordinated in such a way that the same segment of the apparent object is not matched to the segments of different actual objects. These techniques have been applied to two-dimensional simple closed curves represented by polygons and the power of the techniques is demonstrated by the examples taken from synthetic, aerial, industrial and biological images where the matching is done after using the actual segmentation methods.
Improving person re-identification by soft biometrics based reranking

The problem of person re-identification is to recognize a target subject across non-overlapping distributed cameras at different times and locations. The applications of person re-identification include security, surveillance, multi-camera tracking, etc. In a real-world scenario, person re-identification is challenging due to the dramatic changes in a subject's appearance in terms of pose, illumination, background, and occlusion. Existing approaches either try to design robust features to identify a subject across different views or learn distance metrics to maximize the similarity between different views of the same person and minimize the similarity between different views of different persons. In this paper, we aim at improving the re-identification performance by reranking the returned results based on soft biometric attributes, such as gender, which can describe probe and gallery subjects at a higher level. During reranking, the soft biometric attributes are detected and attribute-based distance scores are calculated between pairs of images by using a regression model. These distance scores are used for reranking the initially returned matches. Experiments on a benchmark database with different baseline re-identification methods show that reranking improves the recognition accuracy by moving upwards the returned matches from gallery that share the same soft biometric attributes as the probe subject.
How current BNs fail to represent evolvable pattern recognition problems and a proposed solution

In the real world, systems/processes often evolve without fixed and predictable dynamic models. To represent such applications we need uncertainty models, like Bayesian nets (BN) that are formed online and in a self-evolving data-driven way. But current BN frameworks cannot handle simultaneous scalability in the model structure and causal relations. We show how current BNs fail in different applications from several fields, ranging from computer vision to database retrieval to medical diagnostics. We propose a novel structure modifiable adaptive reason-building temporal Bayesian networks (SmartBN) that has scalability for uncertainty in both, structures and causal relations. We evaluate its performance for a 3D model building application for vehicles in traffic video.
CAD-based robotics

We describe an approach which facilitates and makes explicit the organization of the knowledge necessary to map robotic system requirements onto an appropriate assembly of algorlthms, processors, sensors, and actuators. In order to achieve this mapping several kinds of knowledge are needed. In this paper, we describe a system under development which exploits the Computer Aided Design (CAD) database in order to synthesize. •recognition Code for vision systems (both 2-D and 3-D), •grasping sites for simple parallel grippers, and •manipulation strategies for dextrous manipulation. We use an object-based approach and give an example application of the system to CAD-based 2-D vision.
Image retrieval with feature selection and relevance feedback

This paper proposes a new content based image retrieval (CBIR) system combined with relevance feedback and the online feature selection procedures. A measure of inconsistency from relevance feedback is explicitly used as a new semantic criterion to guide the feature selection. By integrating the user feedback information, the feature selection is able to bridge the gap between low-level visual features and high-level semantic information, leading to the improved image retrieval accuracy. Experimental results show that the proposed method obtains higher retrieval accuracy than a commonly used approach.
Long-Term Cross-Session Relevance Feedback Using Virtual Features

Relevance feedback (RF) is an iterative process, which refines the retrievals by utilizing the user's feedback on previously retrieved results. Traditional RF techniques solely use the short-term learning experience and do not exploit the knowledge created during cross sessions with multiple users. In this paper, we propose a novel RF framework, which facilitates the combination of short-term and long-term learning processes by integrating the traditional methods with a new technique called the virtual feature. The feedback history with all the users is digested by the system and is represented in a very efficient form as a virtual feature of the images. As such, the dissimilarity measure can dynamically be adapted, depending on the estimate of the semantic relevance derived from the virtual features. In addition, with a dynamic database, the user's subject concepts may transit from one to another. By monitoring the changes in retrieval performance, the proposed system can automatically adapt the concepts according to the new subject concepts. The experiments are conducted on a real image database. The results manifest that the proposed framework outperforms the traditional within-session and log-based long-term RF techniques.
Evolutionary Feature Synthesis for Image Databases

The high dimensionality of visual features is one of the major challenges for content-based image retrieval (CBIR) systems, and a variety of dimensionality reduction approaches have been proposed to find the discriminant features. In this paper, we investigate the effectiveness of coevolutionary genetic programming (CGP) in synthesizing feature vectors for image databases from traditional features that are commonly used. The transformation for feature dimensionality reduction by CGP has two unique characteristics for image retrieval: 1) nonlinearlity: CGP does not assume any class distribution in the original visual feature space; 2) explicitness: unlike kernel trick, CGP yields explicit transformation for dimensionality reduction so that the images can be searched in the low-dimensional feature space. The experimental results on multiple databases show that (a) CGP approach has distinct advantage over the linear transformation approach of multiple discriminant analysis (MDA) in the sense of the discrimination ability of the low-dimensional features, and (b) the classification performance using the features synthesized by our CGP approach is comparable to or even superior to that of support vector machine (SVM) approach using the original visual features.
Improving object classification in far-field video

Object classification in far-field video sequences is a challenging problem because of low-resolution imagery and projective image distortion. Most existing far-field classification systems are trained to work well in a constrained set of scenes, but can fail dramatically when applied to new scenes, or even different views of the same scene. We identify discriminative object features for classifying vehicles and pedestrians and develop a scene-invariant classification system that is trained on a small number of labeled examples from a few scenes, but transfers well to a wide range of new scenes. Simultaneously, we demonstrate that use of scene-specific context features (such as image position and direction of motion of objects) can greatly improve classification in any given scene. To combine these ideas, we propose a new algorithm for adapting a scene-invariant classifier to scene-specific features by retraining with the help of unlabelled data in a novel scene. Experimental results demonstrate the effectiveness of our context features and scene-transfer/adaptation algorithm for multiple urban and highway scenes.
A system for model-based recognition of articulated objects

This paper presents a model-based matching technique for recognition of articulated objects (with two parts) and the poses of these parts in synthetic aperture radar (SAR) images. Using articulation invariants as features, the recognition system first hypothesizes the pose of the larger part and then the pose of the smaller part. Geometric reasoning is carried out to correct identification errors. The thresholds for the quality of match are determined dynamically by minimizing the probability of a random match. Results are presented using SAR images of three articulated objects. The system performance is evaluated with respect to identification performance, accuracy of estimates for the poses of the object parts and noise.
Global-to-Local Non-Rigid Shape Registration

Non-rigid shape registration is an important issue in computer vision. In this paper we propose a novel global-to-local procedure for aligning non-rigid shapes. The global similarity transformation is obtained based on the corresponding pairs found by matching shape context descriptors. The local deformation is performed within an optimization formulation, in which the bending energy of thin plate spline transformation is incorporated as a regularization term to keep the structure of the model shape preserved under the shape deformation. The optimization procedure drives the initial global registration towards the target shape that results in the one-to-one correspondence between the model and target shape. Experimental results demonstrate the effectiveness of the proposed approach
Individual recognition using gait energy image

In this paper, we propose a new spatio-temporal gait representation, called Gait Energy Image (GEI), to characterize human walking properties for individual recognition by gait. To address the problem of the lack of training templates, we also propose a novel approach for human recognition by combining statistical gait features from real and synthetic templates. We directly compute the real templates from training silhouette sequences, while we generate the synthetic templates from training sequences by simulating silhouette distortion. We use a statistical approach for learning effective features from real and synthetic templates. We compare the proposed GEI-based gait recognition approach with other gait recognition approaches on USF HumanID Database. Experimental results show that the proposed GEI is an effective and efficient gait representation for individual recognition, and the proposed approach achieves highly competitive performance with respect to the published gait recognition approaches
Human Activity Classification Based on Gait Energy Image and Coevolutionary Genetic Programming

In this paper, we present a novel approach based on gait energy image (GET) and co-evolutionary genetic programming (CGP) for human activity classification. Specifically, Hu's moment and normalized histogram bins are extracted from the original GEIs as input features. CGP is employed to reduce the feature dimensionality and learn the classifiers. The strategy of majority voting is applied to the CGP to improve the overall performance in consideration of the diversification of genetic programming. This learning-based approach improves the classification accuracy by approximately 7 percent in comparison to the traditional classifiers
Tracking pedestrians with bacterial foraging optimization swarms

Pedestrian tracking is an important problem with many practical applications in fields such as security, animation, and human computer interaction (HCI). In this paper, we introduce a previously-unexplored swarm intelligence approach to multi-object monocular tracking by using Bacterial Foraging Optimization (BFO) swarms to drive a novel part-based pedestrian appearance tracker. We show that tracking a pedestrian by segmenting the body into parts outperforms popular blob-based methods and that using BFO can improve performance over traditional Particle Swarm Optimization and Particle Filter methods.
Recognition of occluded targets using stochastic models

Recognition of occluded objects in synthetic aperture radar (SAR) images is a significant problem for automatic target recognition. In this paper, we present a hidden Markov modeling (HMM) based approach for recognizing objects in synthetic aperture radar (SAR) images. We identify the peculiar characteristics of SAR sensors and using these characteristics we develop feature based multiple models for a given SAR image of an object. The models exploiting the relative geometry of feature locations or the amplitude of SAR radar return are based on sequentialization of scattering centers extracted from SAR images. In order to improve performance we integrate these models synergistically using their probabalistic estimates for recognition of a particular target at a specific azimuth. Experimental results are presented using both synthetic and real SAR images.
Zapping Index:Using Smile to Measure Advertisement Zapping Likelihood

In marketing and advertising research, “zapping” is defined as the action when a viewer stops watching a commercial. Researchers analyze users' behavior in order to prevent zapping which helps advertisers to design effective commercials. Since emotions can be used to engage consumers, in this paper, we leverage automated facial expression analysis to understand consumers' zapping behavior. Firstly, we provide an accurate moment-to-moment smile detection algorithm. Secondly, we formulate a binary classification problem (zapping/non-zapping) based on real-world scenarios, and adopt smile response as the feature to predict zapping. Thirdly, to cope with the lack of a metric in advertising evaluation, we propose a new metric called Zapping Index (ZI). ZI is a moment-to-moment measurement of a user's zapping probability. It gauges not only the reaction of a user, but also the preference of a user to commercials. Finally, extensive experiments are performed to provide insights and we make recommendations that will be useful to both advertisers and advertisement publishers.
Predicting object recognition performance under data uncertainty, occlusion and clutter

We present a novel method for predicting the performance of an object recognition approach in the presence of data uncertainty, occlusion and clutter. The recognition approach uses a vote-based decision criterion, which selects the object/pose hypothesis that has the maximum number of consistent features (votes) with the scene data. The prediction method determines a fundamental, optimistic, limit on achievable performance by any vote-based recognition system. It captures the structural similarity between model objects, which is a fundamental factor in determining the recognition performance. Given a bound on data uncertainty, we determine the structural similarity between every pair of model objects. This is done by computing the number of consistent features between the two objects as a function of the relative transformation between them. Similarity information is then used, along with statistical models for data distortion, to estimate the probability of correct recognition (PCR) as a function of occlusion and clutter rates. The method is validated by comparing predicted PCR plots with ones that are obtained experimentally.
Automatic model construction for object recognition using ISAR images

This paper discusses model construction for object recognition using ISAR images. A learning-from-examples approach is used to construct recognition models of the objects from their ISAR data. Given a set of ISAR data of an object of interest, structural features are extracted from the images. Statistical analysis and geometrical reasoning are then used to analyze the features to find spatial and statistical invariance so that a structural model of the object suitable for object recognition can be constructed. Results of experiments using the automatically constructed model in object recognition are presented.