nils-herrmann/clean_scirepeval huggingface dataset:
Non-Overlapping Multi-camera Detection and Tracking of Vehicles in Tunnel Surveillance

We propose a real-time multi-camera tracking approach to follow vehicles in a tunnel surveillance environment with multiple non-overlapping cameras. In such system, vehicles have to be tracked in each camera and passed correctly from one camera to another through the tunnel. This task becomes extremely difficult when intra-camera errors are accumulated. Most typical issues to solve in tunnel scenes are due to low image quality, poor illumination and lighting from the vehicles. Vehicle detection is performed using Adaboost detector, speeded up by separating different cascades for cars and trucks improving general accuracy of detection. A Kalman Filter with two observations, given by the vehicle detector and an averaged optical flow vector, is used for single-camera tracking. Information from collected tracks is used for feeding the inter-camera matching algorithm, which measures the correlation of Radon transform-like projections between the vehicle images. Our main contribution is a novel method to reduce the false positive rate induced by the detection stage. We impose recall over precision in the detection correctness, and identify false positives patterns which are then included subsequently in a high-level decision making step. Results are presented for the case of 3 cameras placed consecutively in an inter-city tunnel. We demonstrate the increased tracking performance of our method compared to existing Bayesian filtering techniques for vehicle tracking in tunnel surveillance.
A time-frequency analysis of the properties of orthogonal transforms

This paper deals with the problem of selecting an optimal base for representing a given signal. This problem, which is important in coding and filtering applications, has often been treated from a statistical point of view: the signal is assumed to be part of an ensemble with known statistics and the optimal base is the one which is best on average for this ensemble. This paper takes a different point of view: the signal of interest is assumed to be part of a signal space of locally band-limited functions. The paper shows that the optimal base for the signal space of locally band-limited signals consists of warped polynomials, which were introduced earlier by the author. It also shows that the local bandwidth point of view easily explains why, e.g., wavelet-approximation often outperforms approximation by cosines.
Improved Classification of VHR Images of Urban Areas Using Directional Morphological Profiles

Meter to submeter resolution satellite images have generated new interests in extracting man-made structures in the urban area. However, classification accuracies for such purposes are far from satisfactory. Spectral characteristics of urban land cover classes are so similar that they cannot be separated using only spectral information. As a result, there is an increased interest in incorporating geometrical information. One possible approach is the use of morphological profiles (MPs). In this paper, we introduce two improvements on the use of MPs. Current approaches use disk-shaped structuring elements (SEs) to derive an MP. This profile contains information about the minimum dimension of objects. In this paper, we extend this approach by using linear SEs. This results in a profile containing information about the maximum object dimension. We show that the addition of the line-based MP gives a substantial improvement of the classification result. A second improvement is achieved by using ldquopartial morphological reconstructionrdquo instead of the normal morphological reconstruction. Morphological reconstruction is commonly used to better preserve the shape of objects. However, we show that this leads to ldquoover-reconstructionrdquo in typical remote sensing images and a decreased classification performance. With ldquopartial reconstruction,rdquo we are able to overcome this problem and still preserve the shape of objects.
Scalable Semi-Automatic Annotation for Multi-Camera Person Tracking.

This paper proposes a generic methodology for semi-automatic generation of reliable position annotations for evaluating multi-camera people-trackers on large video datasets. Most of the annotation data is computed automatically, by estimating a consensus tracking result from multiple existing trackers and people detectors and classifying it as either reliable or not. A small subset of the data, composed of tracks with insufficient reliability is verified by a human using a simple binary decision task, a process faster than marking the correct person position. The proposed framework is generic and can handle additional trackers. We present results on a dataset of approximately 6 hours captured by 4 cameras, featuring a person in a holiday flat, performing activities such as walking, cooking, eating, cleaning, and watching TV. When aiming for a tracking accuracy of 60cm, 80% of all video frames are automatically annotated. The annotations for the remaining 20% of the frames were added after human verification of an automatically selected subset of data. This involved about 2.4 hours of manual labour. According to a subsequent comprehensive visual inspection to judge the annotation procedure, we found 99% of the automatically annotated frames to be correct. We provide guidelines on how to apply the proposed methodology to new datasets. We also provide an exploratory study for the multi-target case, applied on existing and new benchmark video sequences.
Urban area detection in SAR imagery using a new speckle reduction technique and Markov random field texture classification

The strength of synthetic aperture radar (SAR) as a land observation tool resides in S. Gautama et al. (1998) the sensitivity of radar backscatter to the moisture content of terrain media and to the geometrical parameters of the scatterers in the media (i.e., size, shape, roughness and orientation), and (2) an all-weather, day or night imaging capability. However, SAR images are degraded by multiplicative speckle noise due to interference between individual scatterers within one resolution cell. The authors first propose a new speckle reduction method, which preserves edges and doesn't need parameters to be adjusted, based on wavelet decomposition. Comparison with standard speckle filters shows that their filter removes speckle better, while preserving the same amount of detail. Next they use the filter technique in combination with the Markov random field (MRF) texture classification as stated in by S. Gautama et al. (1998) to detect urban areas in the images. The results show that classification results are better when using their proposed filter, compared to the classification results with images filtered with standard speckle filters.
Obstacle detection for pedestrians with a visual impairment based on 3D imaging

According to the World Health Organisation, 285 million people live with a visual impairment. Despite the fact that many efforts have been made recently, there is still no computer-guided system that is reliable, robust and practical enough to help these people to increase their mobility. Motivated by this shortcoming, we propose a novel obstacle detection system to assist the visually impaired. This work mainly focuses on indoor environments and performs classification of typical obstacles that emerge in these situations, using a 3D sensor. A total of four classes of obstacles are considered: walls, doors, stairs and a residual class (which covers loose obstacles and bumpy parts on the floor). The proposed system is very reliable in terms of the detection accuracy. In a realistic experiment, stairs are detected with 100% true positive rate and 8.6% false positive rate, while doors are detected with 86.4% true positive rate and 0% false positive rate.
Per Pixel Contextual Information for Classification of VHR Images of Urban Areas

Very high spatial resolution satellite images allow to identify individual man-made objects. However, automatic extraction of these objects is still very difficult, especially in urban areas. Spectral information is insufficient to separate the different man-made object classes. Therefore, there is an increased interest in incorporating shape and contextual information in the classification process. Object-based approaches provide a straightforward method to incorporate both shape and contextual information. However, these approaches require a segmentation of the image, which is a very difficult and sensitive task, especially in urban areas. Recently some attempts have been made to incorporate shape information on a pixel basis. In this paper we further develop this approach and propose a method to also derive contextual information on a pixel basis. The per-pixel features developed in this paper contain information about the distance from the object the pixel belongs to to the nearest shadow object. Clearly, this information can be helpful to identify buildings, which are generally accompanied by shadow. However, the proposed method can also be used to describe other contextual information.
Development of a realistic head model for EEG event-detection and source localization in newborn infants

In this work we present an integrated method for electroencephalography (EEG) source localization in newborn infants, based on a realistic head model. To build a realistic head model we propose an interactive hybrid segmentation method for T1 magnetic resonance images (MRI), consisting of active contours, fuzzy c-means (FCM) clustering and mathematical morphology. Subsequently, we solve the localization problem using a spike train detection algorithm and an algorithm that deals with the forward and inverse problem. The performance of this fused method indicates that our realistic head model is suitable for the accurate localization of the EEG activity. We will present both initial qualitative and quantitative results.
Object identification by using orthonormal circus functions from the trace transform

In this paper we present an efficient way to both compute and extract salient information from trace transform signatures to perform object identification tasks. We also present a feature selection analysis of the classical trace-transform functionals, which reveals that most of them retrieve redundant information causing misleading similarity measurements. In order to overcome this problem, we propose a set of functionals based on Laguerre polynomials that return orthonormal signatures between these functionals. In this way, each signature provides salient and non-correlated information that contributes to the description of an image object. The proposed functionals were tested considering a vehicle identification problem, outperforming the classical trace transform functionals in terms of computational complexity and identification rate.
Fully automatic and robust UAV camera calibration using chessboard patterns

Due to weight constraints, UAVs often carry cameras with lenses that create distortions in the image. For practical applications this distortion should be removed with a proper calibration procedure, without spending too much extra time. Existing methods require costly manual interaction when the grid is not fully visible, or when not all points can be extracted. In this paper we present an algorithm to perform the calibration without any user interaction whatsoever, which works under almost all possible conditions. The only inputs are a number of pictures of a checkerboard, taken with the camera. We extract the corners from the chessboard pictures, and set up a world coordinate grid that is robust to missing corner points, occlusion and deformations. We automatically omit the pictures that are too close to another picture, to avoid giving too much weight to often viewed areas. Finally we optimize the result by iteratively removing outlier pictures from the set.
On computing the FFT of digital images in quadtree format

This correspondence considers the computation of the two-dimensional fast Fourier transform (2-D-FFT) of images represented by quadtrees of maximum depth k<n, i.e., images that can be subdivided into squares of constant grey value, each consisting of M/spl times/M=2/sup n-k//spl times/2/sup n-k/ pixels. The correspondence shows that such 2-D-FFTs can be computed in terms of the 2-D-DFT of a subsampled version of the original image. As the subsampled image is only of size (N/M)/spl times/(N/M) the proposed procedure is faster than direct computation.
PhD forum: Dempster-Shafer based camera contribution evaluation for task assignment in vision networks

In a network of cameras, it is important that the right subset of cameras takes care of the right task. In this work, we describe a general framework to evaluate the contribution of subsets of cameras to a task. Each task is the observation of an event of interest and consists of assessing the validity of a set of hypotheses. All cameras gather evidence for those hypotheses. The evidence from different cameras is fused by using the Dempster-Shafer theory of evidence. After combining the evidence for a set of cameras, the remaining uncertainty about a set of hypotheses, allows us to identify how well a certain camera subset is suited for a certain task. Taking into account these subset contribution values, we can determine in an efficient way the set of subset-task assignments that yields the best overall task performance.
Parameter-unaware autocalibration for occupancy Mapping

People localization and occupancy mapping are common and important tasks for multi-camera systems. In this paper, we present a novel approach to overcome the hurdle of manual extrinsic calibration of the multi-camera system. Our approach is completely parameter unaware, meaning that the user does not need to know the focal length, position or viewing angle in advance, nor will these values be calibrated as such. The only requirement to the multi-camera setup is that the views overlap substantially and are mounted at approximately the same height, requirements that are satisfied in most typical multi-camera configurations. The proposed method uses the observed height of an object or person moving through the space to estimate the distance to the object or person. Using this distance to backproject the lowest point of each detected object, we obtain a rotated and anisotropically scaled view of the ground plane for each camera. An algorithm is presented to estimate the anisotropic scaling parameters and rotation for each camera, after which ground plane positions can be computed up to an isotropic scale factor. Lens distortion is not taken into account. The method is tested in simulation yielding average accuracies within 5cm, and in a real multi-camera environment with an accuracy within 15cm.
Segmentation of airways in lungs using projections in 3-D CT angiography images

In diagnosing lung diseases, the structure and shape of airways in lungs are of great importance. In this paper we propose a novel method for segmenting low-contrast 3-D CTA images of airways in lungs. Our approach is an edge-detecting slice-by-slice segmentation method, capable of segmenting low contrasted airway regions. Our segmentation using projections method shows robustness in images with high presence of noise.
Evaluating corner detectors for the extraction of man-made structures in urban areas

We analyze if the presence of corners in very high resolution (VHR) satellite images can give us an indication on the type of structure present in a scene (man-made versus natural structures). Two the corner detectors are validated in this respect: Harris and SUSAN. The detection performance is evaluated over a spectrum of spatial resolutions for current and future VHR systems (from 2 meters to 17 centimeters). The ground truth of this study consists of annotated image extracts containing different types of man-made structures, in which the relevant corners have been identified.
Principal view determination for camera selection in distributed smart camera networks

Within a camera network, the contribution of a camera to the observation of a scene depends on its viewpoint and on the scene configuration. This is a dynamic property, as the scene content is subject to change over time and the camera configuration might not be fixed, e.g. in a mobile network. In this work, we address the problem of effectively determining the principle viewpoint within a network, i.e. the view that contributes most to the desired observation of a scene. This selection is based on the information from each camerapsilas observations of persons in a scene, and only low data rate information is required to be sent over wireless channels since the image frames are first locally processed by each sensor node before transmission. The principal view, complemented with one or more helper views, constitutes a significantly more efficient scene representation than the totality of the available views. This is of great value for the reduction of the amount of image data that needs to be stored or transmitted over the network.
Computing contrast ratio in images using local content information

It is well know that a measure of contrast in images is not yet fully defined. The conventional measures of contrast consist of global computations and therefore they have a poor performance. At the same time image quality assessment is often based on quantifying the visibility between a structure of interest or foreground and its surrounding background, i.e., the contrast ratio. Then, a high quality image is the one in which structures of interest are well distinguishable from the background. Therefore, the computation of contrast ratio is important in automatic image quality assessment and it should be computed locally taking into account the local distribution of pixel values. We estimate the contrast ratio by using Weber contrast in local image patches. The main contribution of this work lies in the characterization of local distribution of pixel values which is used for computing the contrast ratio. Here, local image patches are characterized by bimodal histograms representing a set of pixels which are likely to be inside the foreground and another set likely to be in the background. The local contrast ratio is estimated using the ratio between mean intensity values of each mode of the histogram. Our experimental results over two public image databases show that the proposed method is able to accurately predict changes of quality due to contrast decrements (Pearson correlations higher than 90%).
Image interpolation using mathematical morphology

We present a new method for interpolating binary images that outperforms existing techniques. Bitmapped images have a specific horizontal and vertical resolution. When we magnify such an image, we want the resolution to be increased, allowing more details in the image. However, these extra details are not present in the original image. A blowup of the image using simple interpolation will introduce jagged edges, also called "jaggies". We present a new interpolation technique "mmINT", which avoids these errors. It is based on mathematical morphology, a theoretical framework to alter an image while preserving the image objects' geometry. The algorithm detects jaggies in the blown up image and removes them, making the edges smoother. This is done by replacing specific black pixels with white pixels, and vice versa. The results show that mmINT is a superior technique for the interpolation of binary images, like logos, diagrams, cartoons and maps
Cellular Wall Segmentation of A. Thaliana Epidermal Tissue in DIC Images

In this work, we discuss the problems of automatic segmentation of Arabidopsis thaliana epidermal cell patterning in images captured using a Differential Interference Contrast (DIC) microscope. These images are difficult to analyze due to the non-linear nature of DIC optics. The border of objects appear as a combination of bright and dark shadows with variable thickens and contrast levels. Our segmentation approach exploits prior knowledge on the optical properties of A. thaliana cell walls. A set of matching filters and a scale space line detector are used to generate an enhanced image that shows a single response at the location of cellular walls. To reduce the uncertainty in low contrast cellular walls several images are captured at different orientations. After image fusion, we obtain a single image that can be segmented using well established algorithms. Experiments on a manually annotated DIC image data set demonstrated the effectiveness of the proposed scheme.
A new embedded lossless/quasi-lossless image coder based on the Hadamard transform

This paper presents a lossless block-transform coder based on the Hadamard transform that achieves a compression ratio which is as good as that of the current lossless JPEG-standard and about 10 to 20% lower than the currently best state-of-the-art technique Calic. The main advantage of the proposed technique is that it produces an embedded data stream which may be truncated to achieve lossy compression. Preliminary results obtained on the "lena" image show that, at compression ratios up to 10, the proposed scheme (somewhat surprisingly) produces a better image quality (both visually and in terms of rms error) than lossy JPEG. This means that the proposed coder performs well over a wide range of compression ratios and is therefore a useful alternative for the lossy and lossless JPEG-standards in nearly-lossless coding or when lossless and lossy decoding must be supported simultaneously. Finally, the transform part of the proposed scheme requires fewer computations than lossy JPEG (and only slightly more than lossless JPEG).
Quality Assessment of D10 and DV25 Video Codecs for Broadcasting Purposes

In this study we investigate the image quality of two video codec standards (D10 and DV25) for broadcasting purposes. Different video sequences from the Flemish public broadcasting institute (VRT) archive were produced in Betacam format and subsequently compressed following the two codecs. Then, two instrumental measures being the Peak Signal to Noise Ratio (PSNR) and the Structural SImilarity Measure (SSIM) were used to assess the compressed video quality quantitatively. A psychovisual experiment based on the Multidimensional Scaling Framework (MDS) was set-up to assess the video quality qualitatively. Both approaches were compared and indicated unanonymously the preference for the D10 codec over the DV25 codec.
An overview of fuzzy filters for noise reduction

In this paper we give an overview of existing fuzzy filters for image noise reduction. The paper is a sequel to our overview of classical and fuzzy-classical filters (2001), and can be seen as a preparation to our comparative study of classical and fuzzy filters for image noise reduction. We discuss the ideas behind and the construction of the following fuzzy filters: the "fuzzy inference ruled by else-action" filters, the "fuzzy control based" filters, and the GOA filter. Our goal is to give a consistent overview of these fuzzy filters, and to clearly show the mutual differences between them.