nils-herrmann/clean_scirepeval huggingface dataset:
Vacant Parking Space Detection Based on Plane-Based Bayesian Hierarchical Framework

In this paper, we propose a vacant parking space detection system that operates day and night. In the daytime, the major challenges of the system include dramatic lighting variations, shadow effect, inter-object occlusion, and perspective distortion. In the nighttime, the major challenges include insufficient illumination and complicated lighting conditions. To overcome these problems, we propose a plane-based method which adopts a structural 3-D parking lot model consisting of plentiful planar surfaces. The plane-based 3-D scene model plays a key part in handling inter-object occlusion and perspective distortion. On the other hand, to alleviate the interference of unpredictable lighting changes and shadows, we propose a plane-based classification process. Moreover, by introducing a Bayesian hierarchical framework to integrate the 3-D model with the plane-based classification process, we systematically infer the parking status. Last, to overcome the insufficient illumination in the nighttime, we also introduce a preprocessing step to enhance image quality. The experimental results show that the proposed framework can achieve robust detection of vacant parking spaces in both daytime and nighttime.
Shape-From-Focus Depth Reconstruction With a Spatial Consistency Model

This paper presents a maximum a posteriori (MAP) framework to incorporate a spatial consistency prior model for depth reconstruction in the shape-from-focus (SFF) process. Existing SFF techniques, which reconstruct a dense 3-D depth from multifocus image frames, usually have poor performance over low-contrast regions and usually need a large number of frames to achieve satisfactory results. To overcome these problems, a new depth reconstruction process is proposed to estimate the depth values by solving an MAP estimation problem with the inclusion of a spatial consistency model. This consistency model assumes that within a local region, the depth value of each pixel can be roughly predicted by an affine transformation of the image features at that pixel. A local learning process is proposed to construct the consistency model directly from the multifocus image sequence. By adopting this model, the depth values can be inferred in a more robust way, especially over low-contrast regions. In addition, to improve the computational efficiency, a cell-based version of the MAP framework is proposed. Experimental results demonstrate the effective improvement in accuracy and robustness as compared with existing approaches over real and synthesized image data. In addition, experimental results also demonstrate that the proposed method can achieve quite impressive performance, even with only the use of a few image frames.
A feature-based scalable codec for image compression

This paper presents a feature-based scheme for image compression. In the proposed method, the features in an image are recorded by three-dimensional verge points, which are defined as high curvature points on the image surface. The verge point representation can be further condensed using B-spline control point approximation. By encoding and transmitting these control points, the features in an image could be revealed at the decoder side. Furthermore, by selecting different coding modes, the proposed method can offer diversified scalabilities, including SNR scalability, spatial scalability, and shape scalability. In addition, this coding technique is suitable for region of interest selection and transmission. Experimental results are provided to present the scalability and interactivity of the proposed method.
The use of visible color difference in the quantitative evaluation of color image segmentation

In this paper, we propose the use of visible color difference in a new quantitative evaluation scheme for color image segmentation. With visible color difference, two measurements, named intra-region visual error and inter-region visual error, are defined to evaluate the quality of segmentation results. To fit human visual perception, segmentation results with an excessive amount of intra-region error are regarded as under-segmentation, while segmentation results with too many inter-region errors are regarded as over-segmentation. Based on these two measurements, a complete scheme for the evaluation of color image segmentation is proposed. The simulation results demonstrate that this new scheme may provide a reliable and efficient way to automatically select the parameter settings for a given segmentation algorithm and to compare the performance between various segmentation algorithms.
An efficient Bayesian framework for image enhancement with spatial consideration

In this paper, a Bayesian framework is proposed for image enhancement. We model the image enhancement problem as a maximum a posteriori (MAP) estimation problem and the posteriori distribution function is formulated based on the local structures and local gradients of the given image. By solving the MAP estimation problem, image contrast gets properly enhanced while image noise gets suppressed at the same time. Moreover, since directly solving an MAP estimation problem is impractical for real-time applications, we further simplify the process to generate an intensity mapping function that achieves comparable performance in image enhancement. Simulation results have demonstrated the applicability of the proposed method in providing a flexible and efficient way for image enhancement.
A coarse-to-fine approach for the generation and tracking of mesh objects from a natural image sequence

A new scheme, which automatically generates a hierarchical mesh from a real image, is proposed. First, three distinct meshes are generated according to a three-layer image pyramid. By eliminating the duplicated mesh nodes between these three meshes, a hierarchical mesh is formed. Because each hierarchy represents the image in different resolutions, this hierarchical structure can achieve progressive coding. Furthermore, to generate a dynamic mesh for an image sequence, this hierarchical structure can help to reduce the computation complexity through the tracking of mesh nodes in the sequence. A method is also proposed to predict the moving direction of mesh nodes and to facilitate the motion estimation.
A cell-based matting Laplacian for contrast enhancement

While various kinds of local tone mapping approaches have been proposed for contrast enhancement, how to automatically recovering overly bright and/or overly dark regions in an image without introducing extra artifacts still remains a challenging problem. Conventional approaches usually apply a translation-invariant filter to decompose the contrast information of an image into a global component and a detailed component and then adjust the contrast components according to some preselected criteria. However, the use of translation-invariant filter makes it hard to adaptively decompose the contrast information based on local image contents. To overcome this problem, we derive a global optimization approach with the use of cell-based matting Laplacian matrix to obtain more accurate decomposition. Besides, for the adjustment of contrast component, we present a spatially varying gamma adjustment approach that can locally recover poorly exposed regions and properly enhance contrast detail. This translation-varying approach can successfully suppress the halo artifacts without the loss of image details.
Visible colour difference-based quantitative evaluation of colour segmentation

The authors present the use of visible colour difference in a new quantitative evaluation scheme for colour segmentation. To avoid directly evaluating the subjectively perceived quality of colour segmentation, two objective visual quantities, the quantity of missing boundaries and the quantity of fake boundaries, are considered. To explore how missing boundaries and fake boundaries affect the perceived quality of colour segmentation, a few visual rating experiments are made. On the other hand, to fit for humans' visual perception on colour difference, the visible colour difference is defined. Based on the experiments and the visible colour difference, two measures, named intra-region visual error and inter-region visual error, are designed to estimate the degrees of missing boundaries and fake boundaries, respectively. With these two measures, a complete scheme for the evaluation of colour segmentation is proposed. The simulation results demonstrate that this new scheme may evaluate segmentation results without any ground truth, and could help the automatic selection of parameter settings for a given segmentation algorithm.
Efficient Vision-Based Calibration for Visual Surveillance Systems with Multiple PTZ Cameras

Surveillance systems with multiple cameras have become increasingly important. The space relationship between cameras offers useful information for surveillance applications, such as object tracking or 3- D positioning. In this paper, we propose a visionbased approach to infer the relative positioning and orientation among multiple PTZ cameras. The relationship between the tilt angle of a PTZ camera and the 3D-to-2D coordinate transformation is built first. Then, the tilt angles and the altitudes of PTZ cameras are estimated based on the observation of some simple objects lying on a horizontal plane, with known intrinsic parameters of these PTZ cameras. With the estimated tilt angles and altitudes, the calibration of multiple cameras can be accomplished by comparing the back-projected world coordinates of a common vector in the 3-D space. The whole procedure does not need a huge amount of data and the computational load is light. Experimental results over real images have demonstrated the efficiency and feasibility of this approach.
Two systolic architectures for multiplication in GF (2m)

Two new systolic architectures are presented for multiplications in the finite field GF(2/sup m/). These two architectures are based on the standard basis representation. In Architecture-I, the authors attempt to speed up the operation by using a new partitioning scheme for the basic cell in a straightforward systolic architecture to shorten the clock cycle period. In Architecture-II, they eliminate the one clock cycle gap between iterations by pairing off the cells of Architecture-I. They compare their architectures with previously proposed systolic architectures and a semisystolic architecture, and show that their Architecture-I offers the highest speed and Architecture-II the lowest hardware complexity.
Contrast based color segmentation with adaptive thresholds

In this paper, we propose a color segmentation algorithm based on contrast information and adaptive thresholds. Given a color image, instead of the commonly used achromatic difference and chromatic difference, we use achromatic contrast and chromatic contrast to represent the significance of boundary. To fit for human visual perception, adaptive thresholds are applied to suppress perceptually faint boundaries. A complete segmentation scheme is proposed and the simulation results demonstrate the superiority of this approach in providing reasonable and reliable color segmentation.
Image enhancement based on quadratic programming

In this paper, a simple but efficient image enhancement algorithm is proposed. We model the image enhancement problem as a quadratic programming problem, and the cost function of the optimization problem is formulated based on local gradients of an image. By adjusting the definitions of gain function and weighting function in the cost function, we can derive various kinds of intensity transfer functions to fit for different applications. Simulation results for contrast enhancement and shadow enhancement have demonstrated the feasibility of this approach in providing a flexible and efficient mechanism for image enhancement.
Automatic 3-D depth recovery from a single urban-scene image

In this paper, we focus on recovering a 3-D depth map from a single image via ground-vertical boundary analysis. First, we generate a ground map from the input image based on the spectral matting method, followed by a spatial geometric inference. After that, we derive the depth information for the ground-vertical boundaries. Unlike conventional approaches which generally use plane models to reconstruct a 3-D structure that fits the estimated boundaries, we infer a dense depth map by solving a Maximum-A-Posteriori (MAP) estimation problem. In this MAP problem, we use a generalized spatial-coherence prior model based on the Matting Laplacian (ML) matrix in order to provide a more robust solution for depth inference. We demonstrate that this approach can produce more pleasant depth maps for cluttered scenes.
Bayesian Structure-Preserving Image Contrast Enhancement and its Simplification

In this paper, an efficient Bayesian framework is proposed for image contrast enhancement. Based on the image acquisition pipeline, we model the image enhancement problem as a maximum a posteriori (MAP) estimation problem, where the posteriori probability is formulated based on the local information of the given image. In our framework, we express the likelihood model as a local image structure preserving constraint, where the overall effect of the shutter speed and camera response function is approximated as a linear transformation. On the other hand, we design the prior model based on the observed image and some statistical property of natural images. With the proposed framework, we can effectively enhance the contrast of the image in a natural-looking way, while with fewer artifacts at the same time. Moreover, in order to apply the proposed MAP formulation to typical enhancement problems, like image editing, we further convert the estimation process into an intensity mapping process, which can achieve comparable enhancement performance with a much lower computational complexity. Simulation results have demonstrated the feasibility of the proposed framework in providing flexible and effective contrast enhancement.
Packetized video transmission for OFDM wireless systems with dynamic ordered subcarrier selection algorithm

In this paper, we proposed a dynamic ordered subcarrier selection algorithm (DOSSA) for OFDM based video transmission system. The proposed scheme is shown to achieve lower bit error rate (BER) than the previously proposed OSSA by first selecting a fraction of the subcarriers with highest channel gain. The content information is then exploited in order to extend the OSSA to achieve unequal error protection (UEP) for packets of different importance. Simulation results show that system that utilizes the proposed scheme can achieve higher PSNR, especially at low SNR, compared to those that use the equal error protection (EEP) OSSA.
Generalized Histogram Equalization Based on Local Characteristics

Histogram equalization (HE) and its variations have been widely used in image enhancement. Even though these approaches may enhance image contrast in an effective and efficient way, they usually face some undesired drawbacks, like loss of image details, noise amplification and over-enhancement. In this paper, we propose a generalized histogram equalization technique based on localized image analysis. Starting from designing two measures f1 and f2 to measure local characteristics around each pixel, the global statistics of these two local measures are then recorded into an extended histogram. Based on this extended histogram, we develop a procedure to generate suitable intensity transfer functions for various applications, like contrast enhancement and shadow enhancement. Experimental results show that the proposed algorithm provides a flexible and efficient way for image enhancement.
Probabilistic Modeling of Dynamic Traffic Flow across Non-overlapping Camera Views

In this paper, we propose a probabilistic method to model the dynamic traffic flow across non-overlapping camera views. By assuming the transition time of object movement follows a certain global model, we may infer the time-varying traffic status in the unseen region without performing explicit object correspondence between camera views. In this paper, we model object correspondence and parameter estimation as a unified problem under the proposed Expectation-Maximization (EM) based framework. By treating object correspondence as a latent random variable, the proposed framework can iteratively search for the optimal model parameters with the implicit consideration of object correspondence.
Representing images using points on image surfaces

This paper presents a new approach to represent an image by "verge points," which are defined as high-curvature points on the image surface. This representation offers a compact and reversible way to preserve the essence of the original image. Various applications, such as compression, edge detection, image enhancement, and image editing, can be achieved based on this representation. In this paper, the whole procedure for verge point representation is presented. Based on these verge points, image reconstruction can be easily achieved via iterative linear interpolation. These extracted verge points with compatible properties are further linked into verge curves to offer more compact representation. Progressive representation is also developed based on a multiscale extraction scheme. Some potential applications are then presented to demonstrate the versatility of this representation.
A cascaded hierarchical framework for moving object detection and tracking

In this paper we propose a cascaded hierarchical framework for object detection and tracking. We claim that, by integrating both detection and tracking into a unified framework, the detection and tracking of multiple moving objects in a complicated environment become more robust. Under the proposed architecture, detection and tracking cooperate with each other. Based on the result of moving object detection, a dynamic model is adaptively maintained for object tracking. On the other hand, the updated dynamic model is used for both temporal prior propagation of object labels and the update of foreground/background models, which step further to help the detection of moving objects. The experiments show accurate results can be obtained under situations with foreground/background appearance ambiguity, camera shaking, and object occlusion.
An Efficient Approach for the Calibration of Multiple PTZ Cameras

In this paper, we propose an efficient approach to infer the relative positioning and orientation among multiple pan-tilt-zoom (PTZ) cameras. In this approach, the tilt angle and altitude of each PTZ camera are estimated first based on the observation of some simple objects lying on a horizontal plane. With the estimated tilt angles and altitudes, the calibration of multiple cameras can be easily accomplished by comparing the back-projected world coordinates of some common vectors in 3-D space. This calibration method does not require particular system setup or specific calibration patterns. The sensitivity analysis with respect to parameter fluctuations and measurement errors is also discussed. Experiment results over real images have demonstrated the efficiency and feasibility of this approach. Note to Practitioners-This paper was motivated by the problem that pan-tilt-zoom (PTZ) cameras may change their poses from time to time to achieve better monitoring results. Whenever there is a change, we need to recalibrate the extrinsic parameters again. In this paper, we demonstrate a new and efficient approach to calibrate multiple PTZ cameras. The concept of our approach originated from the observation that people could usually make a rough estimate about the tilt angle of the camera simply based on some clues revealed in the captured images. Based on our approach, we can simply use some A4 papers on a table to calibrate multiple PTZ cameras. In our approach, there is no need to calculate the commonly used homography matrix. For real cases, once a set of PTZ cameras is settled, we can simply place a few simple patterns on a horizontal plane. These patterns can be A4 papers, books, boxes, etc.; and the horizontal plane can be a tabletop or the ground plane. The whole procedure does not need specially designed calibration patterns and requires only a light computational load. In the near future, we will work on the extension of the proposed approach so that we will be able to perform dynamic calibration when the PTZ cameras are under movement
Moving targets labeling and correspondence over multi-camera surveillance system based on Markov network

In this paper, we propose an efficient way to simultaneously label and map targets over a multi-camera surveillance system. In the system, we first fuse the detection results from multiple cameras into a posterior distribution. This distribution indicates the likelihood of having some moving targets on the ground plane. Based on the distribution, isolated targets, together with their 3-D positions, are identified in a sample-based manner, which combines Markov Chain Monte Carlo (MCMC), and Mean-Shift clustering. The induced 3-D scene information is further inputted into a 3-layer Bayesian hierarchical framework (BHF), which adopts a Markov network to deal with the object labeling and correspondence problems. In principle, labeling and correspondence are regarded as a unified optimal problem subject to 3-D scene prior, image color similarity, and detection results. The experiments show that accurate results can be gotten even under situations with severe occlusion.
A Hierarchical Bayesian Generation Framework for Vacant Parking Space Detection

In this paper, from the viewpoint of scene under standing, a three-layer Bayesian hierarchical framework (BHF) is proposed for robust vacant parking space detection. In practice, the challenges of vacant parking space inference come from dramatic luminance variations, shadow effect, perspective distortion, and the inter-occlusion among vehicles. By using a hidden labeling layer between an observation layer and a scene layer, the BHF provides a systematic generative structure to model these variations. In the proposed BHF, the problem of luminance variations is treated as a color classification problem and is tack led via a classification process from the observation layer to the labeling layer, while the occlusion pattern, perspective distortion, and shadow effect are well modeled by the relationships between the scene layer and the labeling layer. With the BHF scheme, the detection of vacant parking spaces and the labeling of scene status are regarded as a unified Bayesian optimization problem subject to a shadow generation model, an occlusion generation model, and an object classification model. The system accuracy was evaluated by using outdoor parking lot videos captured from morning to evening. Experimental results showed that the proposed framework can systematically determine the vacant space number, efficiently label ground and car regions, precisely locate the shadowed regions, and effectively tackle the problem of luminance variations.
Single-image 3-D depth estimation for urban scenes

In this paper, we focus on recovering a 3-D depth map from a single image. Given an image of urban scene, we extract linear perspective information to establish the 3-D scene model. Unlike approaches which use only occlusion relationship between objects to estimate the relative depth of the image, we further combine the perspective geometry information with the occlusion relationship between objects. Besides, we propose the construction of depth gradient maps to represent the depth variation trend along the vertical and horizontal directions. The image is first partitioned into geometric components and initial depth gradient maps are generated based on the relative position between the vanishing point and the classified components. Incorporating main directions of vanishing lines and occlusion boundaries in the initial depth gradient maps, a refined depth map is obtained by using a CRF (conditional random field) model. We demonstrate that our approach can produce realistic relative depth maps for images of urban scenes.