nils-herrmann/clean_scirepeval huggingface dataset:
Dynamic Bayesian Networks for Vehicle Classification in Video

Vehicle classification has evolved into a significant subject of study due to its importance in autonomous navigation, traffic analysis, surveillance and security systems, and transportation management. While numerous approaches have been introduced for this purpose, no specific study has been conducted to provide a robust and complete video-based vehicle classification system based on the rear-side view where the camera's field of view is directly behind the vehicle. In this paper, we present a stochastic multiclass vehicle classification system which classifies a vehicle (given its direct rear-side view) into one of four classes: sedan, pickup truck, SUV/minivan, and unknown. A feature set of tail light and vehicle dimensions is extracted which feeds a feature selection algorithm to define a low-dimensional feature vector. The feature vector is then processed by a hybrid dynamic Bayesian network to classify each vehicle. Results are shown on a database of 169 videos for four classes.
Dynamic Low-Level Context for the Detection of Mild Traumatic Brain Injury

Mild traumatic brain injury (mTBI) appears as low contrast lesions in magnetic resonance (MR) imaging. Standard automated detection approaches cannot detect the subtle changes caused by the lesions. The use of context has become integral for the detection of low contrast objects in images. Context is any information that can be used for object detection but is not directly due to the physical appearance of an object in an image. In this paper, new low-level static and dynamic context features are proposed and integrated into a discriminative voxel-level classifier to improve the detection of mTBI lesions. Visual features, including multiple texture measures, are used to give an initial estimate of a lesion. From the initial estimate novel proximity and directional distance, contextual features are calculated and used as features for another classifier. This feature takes advantage of spatial information given by the initial lesion estimate using only the visual features. Dynamic context is captured by the proposed posterior marginal edge distance context feature, which measures the distance from a hard estimate of the lesion at a previous time point. The approach is validated on a temporal mTBI rat model dataset and shown to have improved dice score and convergence compared to other state-of-the-art approaches. Analysis of feature importance and versatility of the approach on other datasets are also provided.
Representative reference-set and betweenness centrality for scene image categorization

Reference-based image classification approach introduces a reference-set for both image representation and dictionary learning. It significantly reduces the dimensionality of represented images and shows outstanding performance even with randomly selected reference images and simple distance measure. In this paper, we improve upon existing work with two major contributions. First, we show that a more representative reference-set contributes to better classification accuracy. To this end, we carefully adapt the K-means clustering algorithm in the feature space to select a distinguished reference-set. Second, in the image classification process, we propose to represent each image by measuring its betweenness centrality in a social network composed of the representative reference-set in each class, leading to a more coherent distance measure that considers the overall connectivity between the probe image and the reference-set. Extensive experiment results demonstrate that our proposed scheme achieves better performance than existing methods.
Quasi-invariants for recognition of articulated and non-standard objects in SAR images

Using SAR scattering center locations and magnitudes as features, invariances with articulation (i.e., turret rotation for the ZSU 23/4 gun and T72 tank), with configuration variants (e.g. fuel barrels, searchlights, etc.) and with a depression angle change are shown for real SAR images obtained from the MSTAR public data. This location and magnitude quasi-invariance forms a basis for an innovative SAR recognition engine that successfully identifies real articulated and non-standard configuration vehicles based on non-articulated, standard recognition models. Identification performance results are given as confusion matrices and ROC curves for articulated objects, for configuration variants, and for a small change in depression angle.
Novel representation for driver emotion recognition in motor vehicle videos

A novel feature representation of human facial expressions for emotion recognition is developed. The representation leveraged the background texture removal ability of Anisotropic Inhibited Gabor Filtering (AIGF) with the compact representation of spatiotemporal local binary patterns. The emotion recognition system incorporated face detection and registration followed by the proposed feature representation: Local Anisotropic Inhibited Binary Patterns in Three Orthogonal Planes (LAIBP-TOP) and classification. The system is evaluated on videos from Motor Trend Magazine's Best Driver Car of the Year 2014–2016. The results showed improved performance compared to other state-of-the-art feature representations.
Tracking Humans using Multi-modal Fusion

Human motion detection plays an important role in automated surveillance systems. However, it is challenging to detect non-rigid moving objects (e.g. human) robustly in a cluttered environment. In this paper, we compare two approaches for detecting walking humans using multi-modal measurements- video and audio sequences. The first approach is based on the Time-Delay Neural Network (TDNN), which fuses the audio and visual data at the feature level to detect the walking human. The second approach employs the Bayesian Network (BN) for jointly modeling the video and audio signals. Parameter estimation of the graphical models is executed using the Expectation-Maximization (EM) algorithm. And the location of the target is tracked by the Bayes inference. Experiments are performed in several indoor and outdoor scenarios: in the lab, more than one person walking, occlusion by bushes etc. The comparison of performance and efficiency of the two approaches are also presented.
A system for obstacle detection during rotorcraft low-altitude flight

Airborne vehicles such as rotorcraft must avoid obstacles such as antennas, towers, poles, fences, tree branches, and wires strung across the flight path. The paper analyzes the requirements of an obstacle detection system for rotorcrafts in low-altitude Nap-of-the-Earth flight based on various rotorcraft motion constraints. It argues that an automated obstacle detection system for the rotorcraft scenario should include both passive and active sensors. Consequently, it introduces a maximally passive system which involves the use of passive sensors (TV, FLIR) as well as the selective use of an active (laser) sensor. The passive component is concerned with estimating range using optical flow-based motion analysis and binocular stereo in conjunction with inertial navigation system information. Experimental results obtained using land vehicle data illustrate the particular approach to motion analysis.<<ETX>>
CAOS: A hierarchical robot control system

Control systems which enable robots to behave intelligently is a major issue in todays process of automating factories. This paper presents a hierarchical robot control system, termed CAOS for Control using Action Oriented Schemas, with ideas taken from the neurosciences. We are using action oriented schemas (called neuroschemas) as the basic building blocks in a hierarchical control structure which is being implemented on a BBN Butterfly Parallel Processor. Serial versions in C and LISP are presented with examples showing how CAOS achieves the goals of recognizing three-dimensional polyhedral objects. We also describe a simulation on how it manipulates objects. Moreover, the ongoing implementation of a parallel version of the system is discussed.
Automatic Target Recognition: State of the Art Survey

In this paper a review of the techniques used to solve the automatic target recognition (ATR) problem is given. Emphasis is placed on algorithmic and implementation approaches. ATR algorithms such as target detection, segmentation, feature computation, classification, etc. are evaluated and several new quantitative criteria are presented. Evaluation approaches are discussed and various problems encountered in the evaluation of algorithms are addressed. Strategies used in the data base design are outlined. New techniques such as the use of contextual cues, semantic and structural information, hierarchical reasoning in the classification and incorporation of multisensors in ATR systems are also presented.
Logical templates for feature extraction in fingerprint images

We present an approach for extraction of minutiae features from fingerprint images. The proposed approach is based on the use of logical templates for minutiae extraction in the presence of data distortion. A logical template is an expression that is applied to the binary ridge (valley) image at selected potential locations to detect the presence of minutiae at these locations. It is adapted to local ridge orientation and frequency. We discuss the proposed technique in detail, and present experimental results on low-resolution images of various qualities.
CAGD based 3-D vision

This paper presents the initial results of our work in using the Computer Aided Geometric Design (CAGD) representations and models as a basis for the visual recognition of 3-D objects for robotic applications. We describe some techniques and algorithms which allow the generation of computer representations and geometric models of complicated realizable 3-D objects in a systematic manner. These representations and models are obtained using (a) available CAGD techniques, and (b) data acquired from various range finding techniques. As compared to previous work in machine vision, multiple hierarchical representations of an object obtained from geometric models can be used for finding orientation and position information.
Latent Fingerprint Image Segmentation Using Fractal Dimension Features and Weighted Extreme Learning Machine Ensemble

Latent fingerprints are fingerprints unintentionally left at a crime scene. Due to the poor quality and often complex image background and overlapping patterns characteristic of latent fingerprint images, separating the fingerprint region-of-interest from complex image background and overlapping patterns is a very challenging problem. In this paper, we propose a latent fingerprint segmentation algorithm based on fractal dimension features and weighted extreme learning machine. We build feature vectors from the local fractal dimension features and use them as input to a weighted extreme learning machine ensemble classifier. The patches are classified into fingerprint and nonfingerprint classes. We evaluated the proposed segmentation algorithm by comparing the results with the published results from the state of the art latent fingerprint segmentation algorithms. The experimental results of our proposed approach show significant improvement in both the false detection rate (FDR) and overall segmentation accuracy compared to the existing approaches.
Model-based segmentation of FLIR images

The use of gray-scale intensities together with the edge information present in a forward-looking infrared (FLIR) image to obtain a precise and accurate segmentation of a target is presented. A model of FLIR images based on gray-scale and edge information is incorporated in a gradient relaxation technique which explicitly maximizes a criterion function based on the inconsistency and ambiguity of classification of pixels with respect to their neighbors. Four variations of the basic technique which provide automatic selection of thresholds to segment FLIR images are considered. These methods are compared, and several examples of segmentation of ship images are given.
Inertial navigation sensor integrated motion analysis for obstacle detection

A maximally passive approach to obstacle detection is described, and the details of an inertial sensor integrated optical flow analysis technique are discussed. The optical flow algorithm has been used to generate range samples using both synthetic data and real data (imagery and inertial navigation system information) obtained from a moving vehicle. The conditions under which the data were created/collected are described, and images illustrating the results of the major steps in the optical flow algorithm are provided.<<ETX>>
MFSC: A new shape descriptor with robustness to deformations

In this paper, we propose a new shape descriptor, Multi-scale Fuzzy Shape Context (MFSC), highlighted by its robustness to deformations. A novel multi-scale fuzzy model is presented and applied on the widely used shape descriptor Shape Context to generate MFSC. The multi-scale fuzzy model can handle shape deformations of different scales, which makes MFSC robust to various deformations. Experiments on an articulated shape dataset demonstrate performance improvement gained by MFSC over existing methods. We also applied MFSC on a real-world application, Content-Based Product Image Retrieval, and the experimental results further validate its effectiveness. We make our code and experimental data publicly available for future reference.
Continuously evolvable Bayesian Nets for human action analysis in videos

This paper proposes a novel data driven continuously evolvable Bayesian Net (BN) framework to analyze human actions in video. In unpredictable video streams, only a few generic causal relations and their interrelations together with the dynamic changes of these interrelations are used to probabilistically estimate relatively complex human activities. Based on the available evidences in streaming videos, the proposed BN can dynamically change the number of nodes in every frame and different relations for the same nodes in different frames. The performance of the proposed BN framework is shown for complex movie clips where actions like hand on head or waist, standing close, and holding hands take place among multiple individuals under changing pose conditions. The proposed BN can represent and recognize the human activities in a scalable manner
Visual and Contextual Modeling for the Detection of Repeated Mild Traumatic Brain Injury

Currently, there is a lack of computational methods for the evaluation of mild traumatic brain injury (mTBI) from magnetic resonance imaging (MRI). Further, the development of automated analyses has been hindered by the subtle nature of mTBI abnormalities, which appear as low contrast MR regions. This paper proposes an approach that is able to detect mTBI lesions by combining both the high-level context and low-level visual information. The contextual model estimates the progression of the disease using subject information, such as the time since injury and the knowledge about the location of mTBI. The visual model utilizes texture features in MRI along with a probabilistic support vector machine to maximize the discrimination in unimodal MR images. These two models are fused to obtain a final estimate of the locations of the mTBI lesion. The models are tested using a novel rodent model of repeated mTBI dataset. The experimental results demonstrate that the fusion of both contextual and visual textural features outperforms other state-of-the-art approaches. Clinically, our approach has the potential to benefit both clinicians by speeding diagnosis and patients by improving clinical care.
Semantic Concept Co-Occurrence Patterns for Image Annotation and Retrieval

Describing visual image contents by semantic concepts is an effective and straightforward way to facilitate various high level applications. Inferring semantic concepts from low-level pictorial feature analysis is challenging due to the semantic gap problem, while manually labeling concepts is unwise because of a large number of images in both online and offline collections. In this paper, we present a novel approach to automatically generate intermediate image descriptors by exploiting concept co-occurrence patterns in the pre-labeled training set that renders it possible to depict complex scene images semantically. Our work is motivated by the fact that multiple concepts that frequently co-occur across images form patterns which could provide contextual cues for individual concept inference. We discover the co-occurrence patterns as hierarchical communities by graph modularity maximization in a network with nodes and edges representing concepts and co-occurrence relationships separately. A random walk process working on the inferred concept probabilities with the discovered co-occurrence patterns is applied to acquire the refined concept signature representation. Through experiments in automatic image annotation and semantic image retrieval on several challenging datasets, we demonstrate the effectiveness of the proposed concept co-occurrence patterns as well as the concept signature representation in comparison with state-of-the-art approaches.
Visual learning by coevolutionary feature synthesis

In this paper, a novel genetically inspired visual learning method is proposed. Given the training raster images, this general approach induces a sophisticated feature-based recognition system. It employs the paradigm of cooperative coevolution to handle the computational difficulty of this task. To represent the feature extraction agents, the linear genetic programming is used. The paper describes the learning algorithm and provides a firm rationale for its design. Different architectures of recognition systems are considered that employ the proposed feature synthesis method. An extensive experimental evaluation on the demanding real-world task of object recognition in synthetic aperture radar (SAR) imagery shows the ability of the proposed approach to attain high recognition performance in different operating conditions.
Exploitation of meta knowledge for learning visual concepts

The paper proposes a content-based image retrieval system which can learn visual concepts and refine them incrementally with increased retrieval experiences captured over time. The approach consists of using fuzzy clustering for learning concepts in conjunction with statistical learning for computing "relevance" weights of features used to represent images in the database. As the clusters become relatively stable and correspond to human concept distribution, the system can yield fast retrievals with higher precision. The paper presents a discussion on problems such as the system mistakenly indentifying a concept, a large number of trials to achieve clustering, etc. Experiments on synthetic data and real image database demonstrate the efficacy of this approach.
Utility-based dynamic camera assignment and hand-off in a video network

In this paper we propose an approach for multi-camera multi-person seamless tracking that allows camera assignment and hand-off based on a set of user-supplied criteria. The approach is based on the application of game theory to camera assignment problem. Bargaining mechanisms are considered for collaborations as well as for resolving conflicts among the available cameras. Camera utilities and person utilities are computed based on a set of criteria. They are used in the process of developing the bargaining mechanisms. Experiments for multi-camera multi-person tracking are provided. Several different criteria and their combination of them are carried out and compared with each other to corroborate the proposed approach.
Real-time robot learning

This paper presents the design, implementation and testing of a real-time system using computer vision and machine learning techniques to demonstrate learning behavior in a miniature mobile robot. The miniature robot, through environmental sensing, learns to navigate a maze choosing the optimum route. Several reinforcement learning based algorithms, such as the Q-learning, Q(/spl lambda/)-learning, fast online Q(/spl lambda/)-learning and DYNA structure, are considered. Experimental results based on simulation and an integrated real-time system are presented for varying density of obstacles in a 15/spl times/15 maze.
Computation of two-dimensional complex cepstrum

A technique based on fitting splines to the phase derivative curve is presented for the efficient and reliable computation of the two-dimensional complex cepstrum. The technique is an adaptive numerical integration scheme and makes use of several computational strategies within the Tribolet's phase unwrapping algorithm. An application of the complex cepstrum in testing the stability of two-dimensional recursive digital filters is considered. Susceptibility of the computation of complex cepstrum to slight changes in the coefficients of a two-dimensional array is studied. Several examples of stable and unstable two-dimensional quarter-plane and non-symmetric half-plane recursive digital filters are presented.
Human Activity Recognition in Thermal Infrared Imagery

In this paper, we investigate human repetitive activity properties from thermal infrared imagery, where human motion can be easily detected from the background regardless of lighting conditions and colors of the human surfaces and backgrounds. We employ an efficient spatiotemporal representation for human repetitive activity recognition, which represents human motion sequence in a single image while preserving some temporal information. A statistical approach is used to extract features for activity recognition. Experimental results show that the proposed approach achieves good performance for human repetitive activity recognition.
Incremental Unsupervised Three-Dimensional Vehicle Model Learning From Video

In this paper, we present a new generic model-based approach for building 3-D models of vehicles from color video from a single uncalibrated traffic-surveillance camera. We propose a novel directional template method that uses trigonometric relations of the 2-D features and geometric relations of a single 3-D generic vehicle model to map 2-D features to 3-D in the face of projection and foreshortening effects. We use novel hierarchical structural similarity measures to evaluate these single-frame-based 3-D estimates with respect to the generic vehicle model. Using these similarities, we adopt a weighted clustering technique to build a 3-D model of the vehicle for the current frame. The 3-D features are then adaptively clustered again over the frame sequence to generate an incremental 3-D model of the vehicle. Results are shown for several simulated and real traffic videos in an uncontrolled setup. Finally, the results are evaluated by the same structural performance measure, underscoring the usefulness of incremental learning. The performance of the proposed method for several types of vehicles in two considerably different traffic spots is very promising to encourage its applicability in 3-D reconstruction of other rigid objects in video.
MDL-based Genetic Programming for Object Detection

In this paper, genetic programming (GP) is applied to synthesize composite operators from primitive operators and primitive features for object detection. To improve the efficiency of GP, smart crossover, smart mutation and a public library are proposed to identify and keep the effective components of composite operators. To prevent code bloat and avoid severe restriction on the GP search, a MDL-based fitness function is designed to incorporate the size of composite operator into the fitness evaluation process. The experiments with real synthetic aperture radar (SAR) images show that compared to normal GP, GP algorithm proposed here finds effective composite operators more quickly.