nils-herrmann/clean_scirepeval huggingface dataset:
3D Scene Reconstruction Based on Robust Camera Motion Estimation and Space Sweeping for a Cultural Heritage Virtual Tour System

An efficient 3D reconstruction technique based on robust camera motion estimation and an improved version of the space-sweeping stereo reconstruction approach is presented. The proposed approach is focused on generation of usable and fully automatic reconstruction of wide-area scenes with the computational resources of a conventional PC. The aim is to use this technique to capture such scenes in 3D utilizing off-the-shelf equipment. Color information is finally added to the derived 3D model of the scene and the result can be converted to common 3D scene modeling formats. The 3D models are integrated with GIS technologies within a web-based virtual tour system.
Aircraft detection and tracking using intelligent cameras

Systems that provide ground movement management at airports, maintaining ground safety and increasing air traffic capacity, are called A-SMGCS (advanced surface movement guidance and control systems). However, common A-SMGCS systems, based on surface movement radars (SMR), are affected by limitations in their coverage due to reflections or shadows from objects on the airport surface. Hence, the use of a complementary system is necessary to act as a "gap-filler", i.e. to provide accurate ground movement information for these problematic areas. This paper aims to present a novel cost-effective video-based system to act as a "gap-filler" for existing A-SMGCS systems. The proposed system consists of a network of intelligent digital cameras, which are able to detect the presence of an aircraft or vehicle in specific locations within their fields of view, and then provide this information to a multiple hypothesis tracking (MHT) algorithm to extract target tracks.
Gesture synthesis from sign language notation using MPEG-4 humanoid animation parameters and inverse kinematics

This paper presents a novel approach for generating VRML animation sequences from sign language notation, based on MPEG-4 face and body animation. Sign language notation, in the well-known sign writing system, is provided as input and is initially converted to SWML (Sign Writing Markup Language), an XML-based format that has recently been developed for the storage, indexing and processing of sign writing notation. Each basic sign, namely signbox, is then converted to a sequence of body animation parameters (BAPs) of the MPEG-4 standard, corresponding to the represented gesture. Inverse Kinematics are also employed for synthesizing complex animation sequences (e.g. contacts). In addition, if a sign contains facial expressions, these are converted to a sequence of MPEG-4 facial animation parameters (FAPs), while exact synchronization between facial and body movements is guaranteed. These sequences, which can also be coded and/or reproduced by MPEG-4 BAP and FAP players, are then used to animate H-anim compliant VRML avatars, reproducing the exact gestures represented in the sign language notation. Envisaged applications include interactive information systems for the persons with hearing disabilities (Web, E-mail, info-kiosks) and automatic translation of written texts to sign language (e.g. for TV newscasts)
Sprite generation and coding of multiview image sequences

An algorithm to generate background sprite images from multiview image sequences is presented. A dynamic programming algorithm, 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 added to the sprite. 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 methods using sprite generation from monoscopic sequences.
A face and gesture recognition system based on an active stereo sensor

The paper presents several novel 3D image analysis algorithms, applied towards the segmentation and modeling of faces and hands. These are subsequently used to build a face-based authentication system and a system for human-computer interaction based on static and dynamic gestures. The system relies on an active stereo sensor that uses a structured light approach to obtain 3D information. In this paper we demonstrate how the use of 3D information may significantly improve the efficiency of traditional face and gesture recognition techniques that use 2D images only.
Estimating body animation parameters from depth images using analysis by synthesis

We present a general method to estimate MPEG-4 body animation parameters (BAPs) from video depth images by using an analysis-by-synthesis technique. A generic body model is first adapted to the geometry of the specific person. 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. Using this depth reconstruction, error norm is seen to yield improved results when compared to two alternative choices for the 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 case, an initial estimate is obtained by applying an expectation-maximization (EM) algorithm, which identifies three arm parts and two joint positions (elbow, wrist). This information is also used for reducing the search space and for automatic scale adaptation of each body part.
3-D Time-Varying Scene Capture Technologies—A Survey

Advances in image sensors and evolution of digital computation is a strong stimulus for development and implementation of sophisticated methods for capturing, processing and analysis of 3D data from dynamic scenes. Research on perspective time-varying 3D scene capture technologies is important for the upcoming 3DTV displays. Methods such as shape-from-texture, shape-from-shading, shape-from-focus, and shape-from-motion extraction can restore 3D shape information from a single camera data. The existing techniques for 3D extraction from single-camera video sequences are especially useful for conversion of the already available vast mono-view content to the 3DTV systems. Scene-oriented single-camera methods such as human face reconstruction and facial motion analysis, body modeling and body motion tracking, and motion recognition solve efficiently a variety of tasks. 3D multicamera dynamic acquisition and reconstruction, their hardware specifics including calibration and synchronization and software demands form another area of intensive research. Different classes of multiview stereo algorithms such as those based on cost function computing and optimization, fusing of multiple views, and feature-point reconstruction are possible candidates for dynamic 3D reconstruction. High-resolution digital holography and pattern projection techniques such as coded light or fringe projection for real-time extraction of 3D object positions and color information could manifest themselves as an alternative to traditional camera-based methods. Apart from all of these approaches, there also are some active imaging devices capable of 3D extraction such as the 3D time-of-flight camera, which provides 3D image data of its environment by means of a modulated infrared light source.
Multiview sprite generation and coding

An algorithm to generate background sprite images from multiview image sequences is presented. A dynamic-programming algorithm, first proposed in Grammalidis and Strinzis (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. The sprite coding method defined by MPEG-4 is extended for multiview image sequences, based on the generated sprite. Experimental results demonstrating the performance of the proposed technique and comparing it with methods using sprite generation from monoscopic sequences are presented.
Higher Order Linear Dynamical Systems for Smoke Detection in Video Surveillance Applications

In this paper, we consider the problem of multi-dimensional dynamic texture analysis, and we introduce a new higher order linear dynamical system (h-LDS) descriptor. The proposed h-LDS descriptor is based on the higher order decomposition of the multidimensional image data and enables the analysis of dynamic textures by using information from various image elements. In addition, we propose a methodology for its application to video-based early warning systems that focus on smoke identification. More specifically, the proposed methodology enables the representation of video subsequences as histograms of h-LDS descriptors produced by the smoke candidate image patches in each subsequence. Finally, to further improve the classification accuracy, we propose the combination of multidimensional dynamic texture analysis with the spatiotemporal modeling of smoke by using a particle swarm optimization approach. The ability of the h-LDS to analyze the dynamic texture information is evaluated through a multivariate comparison against the standard LDS descriptor. The experimental results that use two video datasets have shown the great potential of the proposed smoke detection method.
FAP extraction using three-dimensional motion estimation

An integral part of the MPEG-4 standard is the definition of face animation parameters (FAPs). This paper presents a method for the determination of FAPs by using three dimensional (3-D) rigid and nonrigid motion of human facial features found from two-dimensional (2-D) image sequences. The proposed method assumes that a 3-D model has been fitted to the first frame of the sequence, tracks the motion of characteristic facial features, calculates the 3-D rigid and nonrigid motion of facial features, and through this, estimates the FAPs as defined by the MPEG-4 coding standard. The 2-D tracking process is based on a novel enhanced version of the algorithm proposed by Kanade, Lucas, and Tomasi (1994, 1991). The nonrigid motion estimation is achieved using the same tracking mechanism guided by the facial motion model implied by the MPEG-4 FAPs.
3D Forest Fire Propagation Simulation

The increase in the number of forest fires in the last few years dispatch governments to take precautions. Besides prevention, early intervention is also very important in fire fighting. If the firefighters know where the fire will be in some time, it would be easier for them to stop the fire. Therefore a big need for simulating the fire behavior exists. In this paper we are proposing a system which can simulate the propagation of fire in time. Also this system can visualize the propagation of fire in any 3D-GIS environment, that accepts KMZ as a file format. Besides, any user demanded data can be visualized on the map of the system. This gives the chance of fire planning to firefighters. The system can visualize its results on 3D screens in 3D. Therefore, a better understanding of the terrain can be obtained.
Traffic Monitoring using Multiple Cameras, Homographies and Multi-Hypothesis Tracking

This paper aims to present an intelligent system for tracking moving objects (such as vehicles, persons etc) based on a network of autonomous tracking units that capture and process images from one or more pre-calibrated visual sensors. The proposed system, which has been developed within the framework of TRAVIS (TRAffic VISual monitoring) project, is flexible, scalable and can be applied in a broad field of applications in the future. By the end of the project, two prototypes will be developed, each focusing on different traffic monitoring applications, such as the traffic control of aircraft parking areas at airports and tunnels at highways. First experimental results of the proposed system using two different data fusion techniques are also presented in the paper.
A novel neural network technique for the detection of human faces in visual scenes

This paper presents a user interactive system that manages, with minimum user interaction, to locate a set of characteristic feature points in the frontal view of a person's face. Our method is user adaptive and is implemented as a combination of a classification-based facial region extraction technique with a transition based facial feature extraction method. First, we allow the system to be trained to recognize image blocks belonging to the facial area by proper training of a feedforward neural network. Then, we initialize the feature detection algorithm with a blink of the user's eyes, which localizes the exact contours of the eyes. Finally, we locate the remaining facial features (face contour, eyebrows, mouth and nose) by adaptively thresholding the facial area and utilizing knowledge of the geometrical structure of the face.
Head detection and tracking by 2-D and 3-D ellipsoid fitting

A novel procedure for segmenting a set of scattered 3D data obtained from a head and shoulders multiview sequence is presented. The procedure consists of two steps. In the first step, two ellipses corresponding to the head and the body of the person are identified based on ellipse fitting of the outline of the person in each image. The fitting is based on a fast direct least squares method using the constraint that forces a general conic to be an ellipse. In order to achieve head/body segmentation, a K-means algorithm is used to minimise the fitting error between the points and the two ellipsoids. In the second step, a 3D ellipsoid model corresponding to the head of the person is identified using an extension of the above method. Robustness and outlier removal can be achieved if a 3D ellipsoid model estimation technique is used in conjunction with the Median of Least Squares (MedLS) technique, which minimises the median of the errors corresponding to each 3D point. An interesting application of the proposed method is the combination of the 3D ellipsoid model with a generic face model which is adapted to the face images to provide information only for the high-detail front part of the head while the 3D ellipsoid is used for the back of the head, which is usually not visible.
Morphological and textural analysis of centroblasts in low-thickness sliced tissue biopsies of follicular lymphoma

This paper presents a new method for discriminating centroblast (CB) from non-centroblast cells in microscopic images acquired from tissue biopsies of follicular lymphoma. In the proposed method tissue sections are sliced at a low thickness level, around 1-1.5um, which provides a more detailed depiction of the nuclei and other textural information of cells usually not distinguishable in thicker specimens, such as 4-5um, that have been used in the past by other researchers. To identify CBs, a morphological and textural analysis is applied in order to extract various features related to their nuclei, nucleoli and cytoplasm. The generated feature vector is then used as input in a two-class SVM classifier with ε-Support Vector Regression and radial basis kernel function. Experimental results with an annotated dataset consisting of 300 images of centroblasts and non-centroblasts, derived from high-power field images of follicular lymphoma stained with Hematoxylin and Eosin, have shown the great potential of the proposed method with an average detection rate of 97.44%.
Intelligent traffic monitoring and surveillance with multiple cameras

The paper presents a real-time vision system for automatic traffic monitoring based on a network of autonomous tracking units that capture and process images from one or more pre-calibrated cameras. The system, which has been developed within the framework of TRAVIS (TRAffic VISual monitoring) project, is flexible, scalable and can be useable for a broad field of applications, including traffic monitoring of tunnels at highways and aircraft parking areas at airports. Different image processing and data fusion techniques were tested and evaluated in order to be incorporated to the system. The final output of the image processing is a set of information for each moving object in the scene, such as target ID, position, velocity and classification. This can be transmitted to a remote traffic control centre, as the network demands are remarkably low. This information is analyzed 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).
Generation of stereoscopic image sequences using structure and rigid motion estimation by extended Kalman filters

In this paper an object-based method to generate additional stereo views from monoscopic image sequences using rigid motion and structure estimation by extended Kalman filters is presented. First, rigid scene objects are segmented and feature points in each object are extracted and tracked throughout the video sequence. Then, motion, structure and focal length are estimated recursively for each object, using extended Kalman filters, as described in Azarbayejani and Pentland (1995). Furthermore, the feature points and depths in the stereo image are computed and interpolated using 2-D Delaunay triangulation. Finally, a stereo image generation algorithm is proposed that uses the camera and structure equations to project the 3-D points in a new virtual stereo view for each frame. The generation of stereoscopic scenes is possible even when multiple moving rigid objects exist in the scene. Experimental results show that a layered stereo object representation yields improved results.
Using intelligent digital cameras to monitor aerodrome surface traffic

An aircraft is most at risk for an accident when it's still on the ground - when taxiing before takeoff or after landing. This is because traffic throughput on the ground is limited by inadequate airport infrastructures and is often incapacitated during conditions of poor visibility. The INTERVUSE project, funded by the European Commission, aims to address these problems by developing a cost-effective artificial intelligence system based on a network of intelligent digital cameras. The system uses image-processing techniques to detect traffic and correlates and fuses data to generate a synthetic ground-situation display.
3D forest-fire spread simulation system

In the last few years, due to the global warming and draught related to it, there is an increase in the number of forest fires. Forest fire detection is mainly done by people but there exists some automated systems in this field too. Besides the detection of the forest fires, effective fire extinguishing has an important role in fire fighting. If the spread of the fire can be predicted from the starting, early intervene can be achieved and fire can be extinguished swiftly. Using the fire propagation simulator explained here it is aimed, to predict the fire development beforehand and to visulalize this predictions on a 3D-GIS environment.
Improved 3D video synthesis combining graph cuts and chroma key technology

Depth information is essential for 3DTV applications, as it is used for rendering in many modern 3D displays. In this paper, we propose a new approach for 3DTV content synthesis, by integrating an existing state-of-the-art MRF-based disparity estimation method with additional pre- and post-processing steps. The key of the proposed method is the decomposition of the scene in foreground and background. To this end, chroma key technology is used to facilitate the detection of foreground objects and to significantly improve depth estimation for these objects. Then, we can combine the foreground objects with any background, for both color images and depth maps, to generate a new representation of the scene. Experimental results have shown that the proposed approach can produce satisfactory depth maps with improved silhouettes for foreground objects and a stable background for all the frames of the sequence.
Object-based coding of stereo image sequences using joint 3-D motion/disparity compensation

An object-based coding scheme is proposed for the coding of a stereoscopic image sequence using motion and disparity information. A hierarchical block-based motion estimation approach is used for initialization, while disparity estimation is performed using a pixel-based hierarchical dynamic programming algorithm. A split-and-merge segmentation procedure based on three-dimensional (3-D) motion modeling is then used to determine regions with similar motion parameters. The segmentation part of the algorithm is interleaved with the estimation part in order to optimize the coding performance of the procedure. Furthermore, a technique is examined for propagating the segmentation information with time. A 3-D motion-compensated prediction technique is used for both intensity and depth image sequence coding. Error images and depth maps are encoded using discrete cosine transform (DCT) and Huffman methods. Alternately, an efficient wireframe depth modeling technique may be used to convey depth information to the receiver. Motion and wireframe model parameters are then quantized and transmitted to the decoder along with the segmentation information. As a straightforward application, the use of the depth map information for the generation of intermediate views at the receiver is also discussed. The performance of the proposed compression methods is evaluated experimentally and is compared to other stereoscopic image sequence coding schemes.
Using the expectation-maximization algorithm for depth estimation and segmentation of multi-view images

An algorithm for joint depth estimation and segmentation from multi-view images is presented. The distribution of the luminance of each image pixel is modeled as a random variable, which is approximated by a "mixture of Gaussians model". After recovering 3D motion, a reference image is segmented into a fixed number of regions, each characterized by a distinct affine depth model with three parameters. The estimated depth parameters and segmentation masks are iteratively estimated using an expectation-maximization algorithm, similar to that proposed in Sawhney et al. (1996). In addition, the proposed algorithm is extended for cases where more than two images are available.