nils-herrmann/clean_scirepeval huggingface dataset:
Building Detection Using Enhanced HOG–LBP Features and Region Refinement Processes

Building detection from two-dimensional high-resolution satellite images is a computer vision, photogrammetry, and remote sensing task that has arisen in the last decades with the advances in sensors technology and can be utilized in several applications that require the creation of urban maps or the study of urban changes. However, the variety of irrelevant objects that appear in an urban environment and resemble buildings, and the significant variations in the shape and generally the appearance of buildings render building detection a quite demanding task. As a result, automated methods that can robustly detect buildings in satellite images are necessary. To this end, we propose a building detection method that consists of two modules. The first module is a feature detector that extracts histograms of oriented gradients (HOG) and local binary patterns (LBP) from image regions. Using a novel approach, a support vector machine classifier is trained with the introduction of a special denoising distance measure for the computation of distances between HOG–LBP descriptors before their classification to the building or nonbuilding class. The second module consists of a set of region refinement processes that employs the output of the HOG–LBP detector in the form of detected rectangular image regions. Image segmentation is performed and a novel building recognition methodology is proposed to accurately identify building regions, while simultaneously discard false detections of the first module of the proposed method. We demonstrate that the proposed methodology can robustly detect buildings from satellite images and outperforms state-of-the-art building detection methods.
3-D human body tracking from depth images using analysis by synthesis

A general method to estimate MPEG-4 body animation parameters (BAPs) from depth images by using an analysis-by-synthesis technique is presented. A generic body model is first adapted to the specific person geometry. Then, the mean square error between the synthetic depth image, produced by model rendering, and the original depth image is minimized using the downhill simplex minimization method. Use of this error norm is seen to yield improved results, when compared to two alternative error norms, which were also evaluated. Results are presented for the specific application, where six animation parameters of the arm and the palm are estimated. In this application, an initial estimate is obtained by applying an expectation-maximization (EM) algorithm, which identifies three arm parts and two joint positions (elbow, wrist). A significant advantage of this procedure is that the resulting information can be exploited for reducing the search space and for automatic scale adaptation of each body part.
Safe airport operation based on innovative magnetic detector system

A novel magnetic sensing technology that forms the basis of an innovative system to monitor ground vehicle movements at airports is presented. The operating principle of this system is the detection of interaction of aircraft or ground vehicles with the earth's magnetic field using sensitive magnetic field detectors. After development and laboratory testing of the detectors, test sites have been set up at three European airports. Potential applications of the detectors were designed and demonstrated. Tests have shown that the system can be applied for ground movement surveillance. The approach can be used as a complementary surveillance system for existing and future advanced surface movement guidance and control systems (A-SMGCS) at large airports or as a cost-effective stand-alone solution for monitoring critical areas at medium and small airports. Furthermore, this system can be applied as well in road traffic and car park occupancy monitoring. Unaffected by weather conditions, interference and shadowing effects, the system provides reliable vehicle position, velocity and direction information without requiring any equipment in aircraft or ground vehicles and thus it increases airport operational safety.
Classification of Multidimensional Time-Evolving Data Using Histograms of Grassmannian Points

In this paper, we address the problem of classifying multidimensional time-evolving data in dynamic scenes. To take advantage of the correlation between the different channels of data, we introduce a generalized form of a stabilized higher order linear dynamical system (sh-LDS) and we represent the multidimensional signal as a third-order tensor. In addition, we show that the parameters of the proposed model lie on a Grassmann manifold and we attempt to address the classification problem through study of the geometric properties of the sh-LDS’s space. Moreover, to tackle the problem of nonlinearity of the observation data, we represent each multidimensional signal as a cloud of points on the Grassmann manifold and we create a codebook by identifying the most representative points. Finally, each multidimensional signal is classified by applying a bag-of-systems approach having first modeled the variation of the class of each codeword on its tangent space instead of the sh-LDS’s space. The proposed methodology is evaluated in three different application domains, namely, video-based surveillance systems, dynamic texture categorization, and human action recognition, showing its great potential.
Disparity and occlusion estimation in multiocular systems and their coding for the communication of multiview image sequences

An efficient disparity estimation and occlusion detection algorithm for multiocular systems is presented. A dynamic programming algorithm, using a multiview matching cost as well as pure geometrical constraints, is used to estimate disparity and to identify the occluded areas in the extreme left and right views. A significant advantage of the proposed approach is that the exact number of views in which each point appears (is not occluded) can be determined. The disparity and occlusion information obtained may then be used to create virtual images from intermediate viewpoints. Furthermore, techniques are developed for the coding of occlusion and disparity information, which is needed at the receiver for the reproduction of a multiview sequence using the two encoded extreme views. Experimental results illustrate the performance of the proposed techniques.
3D content generation for autostereoscopic displays

Content generation is one of the most critical issues for the growth of 3DTV services in future. New autostereoscopic displays, such as the Philips Wow® 3D display have significant advantages, including improved 3D viewing experience, wider viewing angles, multiple viewers and no need for special glasses. However, due to their content formatting requirements (2D + depth), live-action content is much more difficult to create. In this paper a new approach for 3D content generation is proposed, by integrating an existing state-of-the-art MRF-based disparity estimation method with additional pre- and post-processing steps. The proposed method uses rectification and colour segmentation to solve significant problems in disparity estimation as well as decomposition of the scene in foreground and background depth maps. Tests with different stereo sequences have already produced very promising results.
Video and Signal Based Surveillance for Airport Applications

In this paper a comparative study of three ground traffic surveillance systems for airports based on different sensing technologies is presented. The first two solutions are based on video-based surveillance, while the third one is based on signal-based surveillance. More specifically, the first solution employs a network of smart cameras, whereas the second utilizes multiple autonomous tracking units to capture and process images from one or more pre-calibrated cameras. The third solution is based on a novel magnetic sensing technology, which detects ferromagnetic objects such as vehicle motors, aircraft engines and landing gears through their deformation of the earth’s magnetic field. Evaluation and analysis of the performance of these systems, based on real test results in different European airports are presented. The proposed systems are flexible, scalable and suitable for a broad field of monitoring and surveillance applications.
Video sensor network for real-time traffic monitoring and surveillance

Sensor networks and associated infrastructures become ever more important to the traffic monitoring and control because of the increasing traffic demands in terms of congestion and safety. These systems allow authorities not only to monitor the traffic state at the detection sites, but also to obtain real-time related information (e.g. traffic loads). This study presents a real-time vision system for automatic traffic monitoring based on a network of autonomous tracking units (ATUs) that capture and process images from one or more pre-calibrated cameras. The proposed system is flexible, scalable and suitable for a broad field of applications, including traffic monitoring of tunnels at highways and aircraft parking areas at airports. Another objective of this work is to test and evaluate different image processing and data fusion techniques in order to be incorporated to the final system. The output of the image processing unit is a set of information for each moving object in the scene, such as target ID, position, velocity and classification, which are transmitted to a remote traffic control centre, with remarkably low bandwidth requirements. This information is analysed and used to provide real-time output (e.g. alerts, electronic road signs, ramp meters etc.) as well as to extract useful statistical information (traffic loads, lane changes, average velocity etc.).
Interactive mobile application, web technologies and fire simulations in the service of forest fire volunteers

In this paper we present a novel interactive platform for the operational support and training of the forest fire volunteer groups that are accredited through the mechanisms of Civil Protection Services. The proposed system is based on mobile and web technologies, fire simulation techniques and shortest-path routing calculation. The system consists of a mobile application (client) allowing the volunteers to report fire incidents and vegetation types of an area to a Control Center (server). The Control Center is also capable of organizing Training Scenarios, based on fire simulations and detailed safe routes, which are sent to the volunteers' mobile devices. The system was tested by Greek and Bulgarian volunteer firefighters in the context of the `OUTLAND' project. The architecture and the functionalities of the proposed system are presented accompanied by figures and indicative screenshots.
Designing Serious Games for ICH education

The paper presents the experience matured in the i-Treasures project, in which 3D game-like applications were designed and developed with the aim of providing innovative tools for Intangible Cultural Heritage (ICH) education. The decision to develop Serious Games grounded on the specific learning needs linked to the nature of the ICH considered (dances, craftsmanship, singing expressions, etc.) and to the benefits that the research in Games Based Learning has pointed out regarding the use of games with educational purposes. In the paper, the i-Treasures game-like applications are described, with particular attention to the design and developmental process that was carried out with the involvement of a multidisciplinary team. In the game development, a huge effort was put into making the most of the benefits of games; these aspects will be discussed and highlighted. The difficulties met will be discussed as well, so as to provide a clear idea about the process and the issues encountered.
Experimental study of skeleton tracking abilities from microsoft kinect non-frontal views

Human body motion tracking is important for a large variety of applications e.g., entertainment, human computer interaction, creative industry etc. Marker-based motion capture systems offer accurate motion tracking, however, they cannot be widely used due to their high cost and operational complexity. To this end, emerging depth sensing technologies, such as Microsoft Kinect, can be employed in many cases as an alternative low cost marker-less solution. Such sensors are mainly used to track the frontal view of the user, since it provides high tracking quality. To assess the potential tracking abilities of Kinect nonfrontal views, we compare the tracking results from four views (front, left side, right side, rear) with those from an off-the-self optical motion capture system. The experimental results show that the skeleton tracking accuracy of the rear view is close to the accuracy of the frontal view, although Kinect sensor cannot distinguish between the front and back sides of the user. In addition, the left and right side views can produce valuable tracking results for the body parts closer to each sensor.
Sprite generation and coding in multiview image sequences

A novel algorithm for the generation of background sprite images from multiview image sequences is presented. A dynamic programming algorithm, first proposed by Grammalidis and Strintzis (see  1998) using a multiview matching cost, as well as pure geometrical constraints, is used to provide an estimate of the disparity field and to identify occluded areas. By combining motion, disparity, and occlusion information, a sprite image corresponding to the first (main) view at the first time instant is generated. Image pixels from other views that are occluded in the main view are also added to the sprite. Finally, the sprite coding method defined by MPEG-4 is extended for multiview image sequences based on the generated sprite. Experimental results are presented, demonstrating the performance of the proposed technique and comparing it with standard MPEG-4 coding methods applied independently to each view.
Spatio-Temporal Flame Modeling and Dynamic Texture Analysis for Automatic Video-Based Fire Detection

Every year, a large number of wildfires all over the world burn forested lands, causing adverse ecological, economic, and social impacts. Beyond taking precautionary measures, early warning and immediate response are the only ways to avoid great losses. To this end, in this paper we propose a computer vision approach for fire-flame detection to be used by an early-warning fire monitoring system. Initially, candidate fire regions in a frame are defined using background subtraction and color analysis based on a nonparametric model. Subsequently, the fire behavior is modeled by employing various spatio-temporal features, such as color probability, flickering, spatial, and spatio-temporal energy, while dynamic texture analysis is applied in each candidate region using linear dynamical systems and a bag-of-systems approach. To increase the robustness of the algorithm, the spatio-temporal consistency energy of each candidate fire region is estimated by exploiting prior knowledge about the possible existence of fire in neighboring blocks from the current and previous video frames. As a final step, a two-class support vector machine classifier is used to classify the candidate regions. Experimental results have shown that the proposed method outperforms existing state-of-the-art algorithms.
ISMAEL - Reliable Eyes for Air Traffic Controllers at Airports

Against the background of aggravated capacity constraints at airports due to increasing air traffic, airports are in need of innovative systems enabling air traffic controllers to improve situational awareness of the position of vehicles and aircraft moving around the airport, even under reduced visibility conditions. While this demand is addressed by the development and introduction of advanced surface movement guidance and control systems (A-SMGCS), technologies currently applied to A-SMGCS feature some weak points regarding coverage, interference and climate conditions. ISMAEL targets these weak points by developing an innovative detection solution based on magnetic sensing technology. Thus, ISMAEL represents a valuable and cost-efficient complementary contribution to existing and planned A-SMGCS at small and large airports
Detection of centroblasts in H&E stained images of follicular lymphoma

This paper presents a complete framework for automatic detection of malignant cells in microscopic images acquired from tissue biopsies of follicular lymphoma. After pre-processing to remove noise and suppress small details, images are segmented by using intensity thresholding, in order to detect the cell nuclei. Subsequently, touching cells are being separated using Expectation Maximization algorithm. Candidate centroblasts are then selected for classification by using size, shape and intensity histogram criteria. Finally, candidates are classified by using a Linear Discriminant Analysis classifier. The application of the methodology in a generated dataset of microscopic images, stained with Hematoxylin and Eosin, showed promising results by detecting in average 82.58% of the annotated malignant cells.
Generation of 3-D head models from multiple images using ellipsoid approximation for the rear part

A system for building a three dimensional (3-D) human head model from two camera views is proposed. A generic 3-D face model is adapted to a human face of which the frontal and profile views are given. Then, a 3-D ellipsoid approximation technique is used to fit an ellipsoid to the 3-D wireframe model. This ellipsoid is then used to approximate the back of the head, while the the initial 3-D face model provides information for the high-detail front part of the head. The part of the ellipsoid model that corresponds to the back of the head is then identified and an algorithm to merge the front (face) and the back part is proposed. A cylindrical texture map is finally built covering the entire area of the head by exploiting the inherent face symmetry. The final result is a complete, textured model of a specific person's head.
Real time video fire detection using spatio-temporal consistency energy

In this paper a new algorithm is proposed for detecting fire from video data in real time based on a combination of features, including a new spatio-temporal consistency feature. A significant challenge in flame detection systems is to discriminate between actual fire and false alarms caused by fire colored objects. Towards this aim, we propose an algorithm that has significant advantages: a) it is fast since rectangular non-overlapping blocks are used as basic elements (instead of arbitrary-shaped regions) b) a spatio-temporal consistency feature is used in addition to color probability, spatial energy, flickering and spatio-temporal energy features and c) an improved rule-based classification approach is proposed, after evaluating seven different classification approaches. Experimental results are presented that confirm the efficiency of the proposed approach.
Coupling eye-motion and ego-motion features for first-person activity recognition

We focus on the use of first-person eye movement and ego-motion as a means of understanding and recognizing indoor activities from an “inside-out” camera system. We show that when eye movement captured by an inside looking camera is used in tandem with ego-motion features extracted from an outside looking camera, the classification accuracy of first-person actions can be improved. We also present a dataset of over two hours of realistic indoor desktop actions, including both eye tracking information and a high quality outside camera video. We run experiments and show that our joint feature is effective and robust over multiple users.
Sensing, predicting, and utilizing human visual attention

In this talk, I will describe our recent works on human visual attention from three aspects: sensing, predicting, and utilizing visual attention. Over the last two decades, the concept of visual saliency and its computational models have attracted a lot of interest, inspired by the seminal work by Koch and Ullman on a computational model of visual saliency. Visual saliency models predict our eye fixations driven by our vision system's bottom-up control triggered by visual stimuli, and it has been shown experimentally that a visual saliency map computed by a visual saliency model is highly correlated with an actual distribution of our fixation points. Based on this observation, we introduce a method for estimating gaze directions using visual saliency maps without explicit personal calibration. The key idea is to use the saliency maps of the video frames that a person is looking at as the probability distributions of the gaze points so that we can avoid cumbersome calibration procedures asking a user to fixate calibration targets. I will explain the details of our method and experimental results. I will also talk about our recent attempt to develop a new computational model of visual saliency for better accuracy in predicting gaze fixation points. Unlike the existing visual saliency models, our model elaborates the fact that the characteristics of human eyes vary significantly within the visual field, e.g., fovea and peripheral vision. Experiments using human fixation data in a wide field of view setting demonstrate that our visual saliency model achieves higher accuracy than a current state-of-the-art model. Lastly, we briefly talk about several applications of eye movements for activity recognition and image analysis.
An Ego-Vision System for Hand Grasp Analysis

This paper presents an egocentric vision (ego-vision) system for hand grasp analysis in unstructured environments. Our goal is to automatically recognize hand grasp types and to discover the visual structures of hand grasps using a wearable camera. In the proposed system, free hand–object interactions are recorded from a first-person viewing perspective. State-of-the-art computer vision techniques are used to detect hands and extract hand-based features. A new feature representation that incorporates hand tracking information is also proposed. Then, grasp classifiers are trained to discriminate among different grasp types from a predefined grasp taxonomy. Based on the trained grasp classifiers, visual structures of hand grasps are learned using an iterative grasp clustering method. In experiments, grasp recognition performance in both laboratory and real-world scenarios is evaluated. The best classification accuracy our system achieves is <inline-formula><tex-math notation="LaTeX">$\text{92}\%$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$\text{59}\%$</tex-math></inline-formula>, respectively. System generality to different tasks and users is also verified by the experiments. Analysis in a real-world scenario shows that it is possible to automatically learn intuitive visual grasp structures that are consistent with expert-designed grasp taxonomies.