nils-herrmann/clean_scirepeval huggingface dataset:
3D structure recovery and errors on the intrinsic parameters

This paper addresses the problem of computing the Euclidean 3D structure of an observed scene. Given at least 2 images with pixel correspondences, the 3D structure of the scene and the motion of the camera (translation and rotation) are calculated simultaneously. We study here the effect of inaccurate intrinsic parameters on the quality of the recovered reconstruction. Classical methods based on the essential matrix computation have proven to be very unstable when the intrinsic parameters of the cameras are not known exactly. To overcome such unstability, we used a method where a particular choice of a 3D Euclidean coordinate system with a different parameterization of the motion/structure problem allowed us to reduce significantly the total number of unknowns. In addition, the simultaneous calculation of the camera motion and the 3D structure has made the computation of the motion and structure less sensitive to the errors in the values of the intrinsic parameters of the camera. Experiments with real images validated our method and experiments with simulated data showed how the errors on the intrinsic parameters affect the accuracy of the reconstruction.
Three-dimensional reconstruction using the perpendicularity constraint

This paper proposes a new method for calculating the Euclidean 3D structure of a scene using only two uncalibrated images and the basic Euclidean constraint resulting from the 3D perpendicularity. Instead of using the nonlinear self-calibration methods to estimate the intrinsic parameters, a linear search for the best values of these parameters is carried out. The criteria measuring the goodness of the intrinsic parameters' values is based on the Euclidean quality of the calculated 3D reconstruction using the 3D perpendicularity constraint. Experimental results on both simulated and real data have validated our method and have shown that the proposed method could be a better alternative to calibration-based reconstruction.
An Efficient and Fast Active Contour Model for Salient Object Detection

In this paper, we investigate the polarity information toimprove the active contour model proposed by Chunming etal. . Unlike the traditional level set formulations, thevariational level set formulation proposed by  forcesthe level set function to be close to a signed distance function,and therefore completely eliminates the need of the reinitializationprocedure and speeds up the curve evolution.However, like the majority of classical active contour models,the model proposed by  relies on a gradient basedstopping function, depending on the image gradient, to stopthe curve evolution. Consequently, using gradient informationfor noisy and textured images, the evolving curve maypass through or stop far from the salient object boundaries.Moreover, in this case, the isotropic smoothing Gaussianhas to be strong, which will smooth the edges too. For thesereasons, we propose the use of a polarity based stoppingfunction. In fact, comparatively to the gradient information,the polarity information accurately distinguishes theboundaries or edges of the salient objects. Hence, Combiningthe polarity information with the active contour modelof  we obtain a fast and efficient active contour modelfor salient object detection. Experiments are performed onseveral images to show the advantage of the polarity basedactive contour.
Camera self-calibration from two views

In this paper, an original and simplified formulation of the Kruppa's equations is presented. This formulation is particularly of interest when only two images are available. Indeed, given two images taken with a camera with zero skew and known principal point, we show that there are only three possible solutions for the horizontal and vertical scale factors instead of four as widely believed so far. In addition, when the aspect ratio is unity, the horizontal and vertical scale factors are equal and the solution is unique and straightforward. No additional knowledge on the scene is required. The issues of refining the solution and the influence of the principal point are addressed and discussed in our experiments.
Multibaseline stereo using a single-lens camera

In stereopsis, correspondence is a central problem that is known to suffer from several inherent difficulties. Single-lens stereo systems have brought a geometrical solution to some of the problems of stereopsis. The plenoptic camera is one of those systems that we found to simplify the process of depth recovery, but whose main limitation lies in its small baseline. For this particular reason, matches in the plenoptic camera are obtained more easily, however, this trades off against the accuracy with which they are recovered. In this paper, we show how its lack of accuracy due to a small baseline can be made up for to some extent, by using a multibaseline estimate in lieu of the commonly used equal-baseline estimate.
Robust registration of virtual objects for industrial applications

Vision-based registration techniques for augmented reality systems have been the subject of intensive research recently due to their potential to accurately align virtual objects with the real world. The registration process can be done in three steps: 1) Positioning: put the virtual object at a desired position. 2) Rendering: get the 2D image of the positioned 3D virtual object 3) The rendering of the virtual object will then be merged with the image of real environment to compose an augmented image. Rendering the virtual object is not trivial work, since it not only depends on its 3D location and orientation, but also depends on the camera's location and orientation. When the camera undergoes a transformation, the parameters for rendering will also be accordingly changed. Therefore, for a realtime augmented reality system, the camera pose must be dynamically tracked at frame rate. In order to simulate the real camera, we have to know its intrinsic and extrinsic parameters. The intrinsic parameters describe camera's optical, geometric and digital characteristics. The extrinsic parameters describe position and orientation of the camera. Both intrinsic and extrinsic parameters can be estimated through a camera calibration process (for example Zonglei and Boufama's method). However, calibrating the camera in real-time, for applications similar to industry, is not necessary (for example Kutulakos & vallino's method). In this paper we present our framework based on Zonglei & Boufama's approach and we describe Kutulakos & vallino's approach (we haven't yet results).
Accuracy and stability of novel view synthesis in the presence of noise

The problem of synthesizing novel views consists of generating new views of a scene, at arbitrary camera positions and orientations, using at least two reference views. This paper presents and compares three methods for novel view synthesis. For a given virtual location and orientation of the camera, the corresponding virtual image is syntheshed using two reference images. View synthesis was achieved through trilinear tensor, epipolar geometry and, SD-reconstruction respectively. We have considered the case where the intrinsic parameters are not precisely known. In particular, we have investigated how errors on the intrinsic parameters deet the syntheshed views. Keywordsnovel view synthesis, SD reconstruction, trifocal tensor, epipolar geometry, three-view geometry.
Affine camera calibration from homographies of parallel planes

This paper deals with the problem of retrieving the affine structure of a scene from two or more images of parallel planes. We propose a new approach that is solely based on plane homographies, calculated from point correspondences, and that does not require the recovery of the 3D structure of the scene. Neither vanishing points nor lines need to be extracted from the images. The case of a moving camera with constant intrinsic parameters and the one of cameras with possibly different parameters are both addressed. Extensive experiments with both synthetic and real images have validated our approach.
Motion and trajectory recovery for tracking multiple objects undergoing a planar motion

This paper describes a tracking system for multiple moving objects undergoing a planar motion in a scene observed by a still camera. The objects are tracked through a sequence of frames. The direction and velocity of each object are calculated between each pair of frames and are used to predict the position of the object in the next frame. The calibration procedure and the problems due to the presence of noise and shadows in the images are also addressed and the adopted strategies are detailed. Our system has successfully been tested on a fish tracking application and is currently used to study the behavior of the fish in response to changes in environmental conditions.
Camera self-calibration: a new approach for solving the modulus constraint

In this paper, the modulus constraint is used to retrieve the scale factors that are responsible for the nonlinearity of Kruppa's equations. In the case of constant intrinsic parameters, each pair of images identifies a pair of 3D points at infinity whose coordinates are expressed in terms of the scale factors we are looking for. By enforcing the coplanarity constraint on these points, a set of quintic bivariate equations is obtained for each triplet of images. Once the scale factors are calculated, the problems of retrieving the plane at infinity and solving Kruppa's equations become straightforward and linear.
Automatic license plate recognition: A comparative study

Automatic license plate recognition (ALPR) is the process of locating and extracting vehicles plate information from images or videos. The extracted information is essential for several everyday applications, ranging from automated payment services (e.g. parking and toll roads payment collection) to more critical applications, like border crossing security and traffic surveillance systems. Various solutions have been proposed for the ALPR problem, with many available commercial packages. However, amid plate variations from place to place, ALPR systems tend to be region-specific. There is no general solution that works effectively everywhere for every province/state or country. In this paper, we have reviewed a set of state-of-the-art ALPR methods and, compared their respective performances by testing them on a rich database of vehicles from Ontario (Canada).
Trajectory-based human activity recognition from videos

Sparse representation is widely used by different human activity recognition methods. Although many sparse feature extraction algorithms have been proposed in the literature, most of them focused on low-level features. This paper proposes a new method using trajectories, as mid-level features, for human activity recognition. Even though the use of trajectories is not new in this field, their potential is yet to be fully attained. In this paper, inspired by previous works, we have proposed new trajectory extraction methods, which are very flexible. Then we have emphasized the difference between trajectories and traditional descriptors, and have shown the advantages of using trajectories for human activity recognition. The pros and cons of trajectories are demonstrated through proposed trajectory-based methods. We have used a simple shape descriptor and the standard bag of word algorithm for human activity classification. The results of these different algorithms have been compared. We have also compared our results with other popular existing methods based on low level extracted features. In particular, we have shown that using proposed sparse trajectories can produce similar or better results than using dense trajectories. Furthermore, the computational time has been reduced as we are dealing with fewer data.
A novel eigenspace-based method for human action recognition

This paper describes a new robust appearance-based method for representing and recognizing human behaviours using the eigenspace technique. This method has three main advantages over the existing appearance-based methods. First, the centering of the human-body blob, in each background-subtracted video frame, together with the use of an incremental procedure for compression, have made the extraction of the motion features limited to the smallest possible area in the image. Second, a learning strategy based on the eigen-space technique is employed for dimensionality reduction using the Linear Discriminant Analysis algorithm (LDA), while providing maximum separability between classes. Third, data retrieving has been greatly enhanced by using a directed acyclic graph (DAG) structure based on the Euclidean distance between projected data. The system has been tested using a large number of training motion videos partitioned into 6 human behaviours (boxing, hand-clapping, hand-waving, jogging, running, and walking) captured for 25 different persons in 2 different scenarios (indoor and outdoor). The experimental results are very good, showing a high performance level of the proposed method.
A new combined KSVM and KFD model for classification and recognition

The Kernel Support Vector Machine (KSVM) is a powerful nonlinear classification methodology where, the Support Vectors (SVs) fully describe the decision surface by incorporating local information in the Kernel space. On the other hand, the Kernel Fisher Discriminant(KFD) is a non-linear classifier which has proven to be powerful and competitive to several state-of-the-art classifiers. This paper proposes a novel KSVM + KFD model which combines these two methods. This model can be viewed as an extension to the KSVM by incorporating 'global' characteristics of the data to estimate the decision boundary in the Kernel space. On the other hand, this new model could also be considered as an improvement to the KFD by incorporating the Support Vectors (local margin concept) into the KFD formulation. The KSVM + KFD model can be reduced to the classical KSVM model so that existing KSVM software can be used for easy implementation. An extensive comparison of the KSVM + KFD to the KFD, KSVM, Linear Discriminant Analysis (LDA), Linear Support Vector Machine (LSVM) and the combined LSVM and LDA, performed on real data sets, has shown the advantages of our proposed model. In particular, the experiments on face recognition have clearly shown the superiority of the KSVM + KFD over other methods.
Homography-based plane identification and matching

In this paper, we propose a new approach for extracting major planes of the scene from uncalibrated pairs of images. In contrast to existing methods, our method does not make any assumption on the images or co-planarity of points. The proposed method takes two uncalibrated images as input, extracts and matches interest points, and then performs plane identification and matching defined by sets of three points. For each set of three points, a plane homography is then calculated. Once all possible planes have been identified, a merging stage is carried out to improve the robustness and to make sure that same planes are associated with a single homography. Furthermore, the method is capable to distinguish between physical and virtual planes. Experiments on a variety of real images demonstrate the validity of the proposed approach.
Achieving efficient dense matching for uncalibrated images

This paper presents a new method to achieve fast dense matching in a pair of uncalibrated images. Classical area-based dense matching methods suffer from the high computational time resulting from intensive correlation calculations during the search/selection process. In contrast to conventional methods that are based on similarity and correlation techniques, this method is based on enforcing known geometric constraints and uses correlations only on a very small number of points. In particular, this paper proposes a hybrid matching technique that segment the image into two sets: the edge and the nonedge regions. For the edge regions, where discontinuities usually occur, the correlation-based classical matching method is used whereas, for nonedge regions, a segment mapping is used to achieve a correlation-free pixel matching. This segment mapping implicitly enforces all the four well known constraints in stereo matching: epipolar, continuity, uniqueness and, order constraints. The experiments on real images validated our method and showed drastic CPU-time reduction compared to classical methods.
A semi-automatic camera calibration method for augmented reality

Camera calibration is a fundamental problem in computer vision. In a camera calibration process, the correspondences between 3D feature points and their 2D projection points must be obtained. We developed a semi-automatic calibration method for our augmented reality system, which uses corner detection to extract pixel coordinates of the projection points and uses homography to build the correspondences. Experiments show that the method is very easy to use and has good practical accuracy.
Euclidean constraints for uncalibrated reconstruction

It is possible to recover the three-dimensional structure of a scene using images taken with uncalibrated cameras and pixel correspondences betweeen these images. But such reconstruction can only be performed up to a projective transformation of the 3-D space. Therefore, constraints have to be put on the reconstructed data to get the reconstruction in the Euclidean space. Such constraints arise from knowledge of the scene, such as the location of points, geometrical constraints on lines, etc. The kind of constraints that have to be added are discussed, and it is shown how they can be fed in a general framework. Experimental results on real data prove the feasibility, and experiments on simulated data address the accuracy of the results.<<ETX>>
Reconstruction-Free Parallel Planes Identification from Uncalibrated Images

This paper proposes a new method for identifying parallel planes in a scene from three or more uncalibrated images. By using the fact that parallel planes intersect at infinity, we were able to devise a linear relationship between the inter-image homographies of the parallel planes and the plane at infinity. This relationship is combined with the so-called modulus constraint for identifying pairs of parallel planes solely from point correspondences. Experiments with both synthetic and real images have validated our method.
Object Inter-camera Tracking with Non-overlapping Views: A New Dynamic Approach

Disjoint inter-camera object tracking is the task of tracking objects across video-surveillance cameras that have non-overlapping views. Unlike the closely related task of single-camera tracking, disjoint inter-camera tracking is difficult due to the gaps in observation as an object moves between camera views. To overcome this problem, appearance profiles of the objects seen in each camera are built and used for matching/tracking across different cameras. This paper proposes a new method that uses multiple features that are dynamically weighed for matching moving objects (people in our case) across cameras. In particular, the Zernike moment shape descriptor has been used together with blob histogram and other features to describe a moving object. Weighting emphasis is given to the better features, based on their stability, reliability and their time in the system (how recent they are). This weighting is used both during appearance aggregation and object comparison. Our experiments with real videos have shown the success of our proposed method even in difficult situations where the cameras used are different in terms of brand, quality and resolution.
Parallel Planes Identification Using Uncalibrated Zooming Cameras

This paper proposes a new method for the automatic identification of parallel planes form uncalibrated images of a scene. First, we demonstrate that, given a priori knowledge about a single pair of parallel planes, it is possible to identify all other parallel planes from uncalibrated images of a scene. Then, we show that a pair of parallel virtual planes can be made available when the scene is imaged by stationary non-rotating zooming cameras leading to a fully automatic identification method.Experiments with indoor and outdoor real images have validated our method.
Independent motion segmentation and collision prediction for road vehicles

This paper presents a method for doing motion segmentation for autonomous vehicles which drive on planar surfaces. There are two distinct types of independent motion that may occur within an image sequence taken from a moving vehicle. The first generic type of independent motion is when the projected motion of points on the independent object violate the epipolar constraint. The second case is where the epipolar constraint is not violated. This paper demonstrates that it is possible to detect this second type of independent motion by looking for progressive dis-occlusion of the road. A novel collision prediction method is also given. The method predicts the projection of a corridor down which the AGV will travel. This prediction may be used for time to contact collision prediction and the corridor width embodies an estimate of the vehicles size.<<ETX>>
The use of homographies for view synthesis

The problem of synthesizing novel views consists of generating new views of a scene using at least two reference views. Although novel views might be rendered after the explicit 3D reconstruction of the scene, image based view synthesis is currently the most attractive approach. Image based view synthesis may be achieved through either epipolar geometry or trilinear tensors. We present a different approach based on plane homographies for image based view synthesis. Two cases are investigated here. In the first case, we assume that two physical planes in the scene can be identified and we show that a minimum of six matched points across three images is sufficient for view synthesis. In the second case however, we do not make any assumption and we show that we can calculate two plane homographies and use them for view synthesis. Experimental results on both real and simulated images show the feasibility and accuracy of our approach.
Region segmentation and matching in stereo images

We propose a new method to simultaneously achieve segmentation and dense matching in a pair of stereo images. In constrast to conventional methods that are based on similarity or correlation techniques, this method is based on geometry and uses correlations only on a limited number of key points. Stemming from the observation that our environment is abundant in planes, this method focuses on segmentation and matching of planes in an observed scene. Neither prior knowledge about the scene nor camera calibration are needed. Using two uncalibrated images as inputs, the method starts with a rough identification of a potential plane, defined by three points only. Based on these three points, a plane homography is then calculated and used for validation. Starting from a seed region defined by the original three points, the method grows the current region by successive move/confirmation steps until occlusions and/or surface discontinuity occur. In this case, the homography-based mapping of points between the two images will fail. This failure is detected by the correlation, used in the confirmation process. In particular this method grows a region even across different colors as long as the region is planar. Experiments on real images validated our method and showed its capability and performance.