Browsing by Author "Wang, Sheng-Jyh"

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A Bayesian hierarchical detection framework for parking space detection
(2008)
Huang, Ching-Chun
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Wang, Sheng-Jyh
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Chang, Yao-Jen
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Chen, Tsuhan
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電子工程學系及電子研究所
hi this paper, a 3-layer Bayesian hierarchical detection framework (BHDF) is proposed for robust parking space detection. In practice, the challenges of the parking space detection problem come from luminance variations, inter-occlusions among cars, and occlusions caused by environmental obstacles. Instead of determining the status of parking spaces one by one, the proposed BHDF framework models the inter-occluded patterns as semantic knowledge and couple local classifiers with adjacency constraints to determine the status of parking spaces in a row-by-row manner. By applying the BHDF to the parking space detection problem, the available parking spaces and the labeling of parked cars can be achieved in a robust and efficient manner. Furthermore, this BHDF framework is generic enough to be used for various kinds of detection and segmentation applications.
A Bayesian Hierarchical Framework for Multitarget Labeling and Correspondence With Ghost Suppression Over Multicamera Surveillance System
(2012-01-01)
Huang, Ching-Chun
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Wang, Sheng-Jyh
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
In this paper, the main purpose is to locate, label, and correspond multiple targets with the capability of ghost suppression over a multicamera surveillance system. In practice, the challenges come from the unknown target number, the interocclusion among targets, and the ghost effect caused by geometric ambiguity. Instead of directly corresponding objects among different camera views, the proposed framework adopts a fusion-inference strategy. In the fusion stage, we formulate a posterior distribution to indicate the likelihood of having some moving targets at certain ground locations. Based on this distribution, a systematic approach is proposed to construct a rough scene model of the moving targets. In the inference stage, the scene model is inputted into a proposed Bayesian hierarchical detection framework, where the target labeling, target correspondence, and ghost removal are regarded as a unified optimization problem subject to 3-D scene priors, target priors, and foreground detection results. Moreover, some target priors, such as target height, target width, and the labeling results are iteratively refined based on an expectation-maximization (EM) mechanism to further boost system performance. Experiments over real videos verify that the proposed system can systematically determine the target number, efficiently label moving targets, precisely locate their 3-D locations, and effectively tackle the ghost problem. Note to Practitioners-As cooperative multicamera surveillance networks become more and more popular, the demand of multicamera information fusion and user-friendly representations becomes crucial. Motivated by the demand, this paper demonstrates a new system to efficiently integrate, summarize, and infer video messages from multiple client cameras. The ultimate goal is to provide a global view of the surveillance zone so that the managers in the control room may monitor the scene in an easier way. The main functions of the proposed system include the fusion of detection results from many client cameras, the summarization of consistent messages, and the inference of the target movement in the 3-D scene. In the near future, we will also take into consideration more information, like temporal clues, photometric clues, and object-level clues, in order to perform advanced scene analyses like abnormal behavior detection.
A CASCADED HIERARCHICAL FRAMEWORK FOR MOVING OBJECT DETECTION AND TRACKING
(2010)
Huang, Ching-Chun
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Wang, Sheng-Jyh
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
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.
A CELL-BASED MATTING LAPLACIAN FOR CONTRAST ENHANCEMENT
(2012)
Tseng, Chen-Yu
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Wang, Sheng-Jyh
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
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.
A Hierarchical Bayesian Generation Framework for Vacant Parking Space Detection
(2010-12-01)
Huang, Ching-Chun
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Wang, Sheng-Jyh
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
In this paper, from the viewpoint of scene understanding, 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 tackled 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.
A novel macroblock-layer rate-distortion model for rate control scheme of H.264/AVC
(2006)
Chiang, Tsung-Han
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Wang, Sheng-Jyh
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
The purpose of rate control is to adjust an encoder so that the amount of encoded bits can match the amount of desired bits. In this paper, we focus on the rate control of an H.264/AVC encoder. We first analyze the relation between the quantization parameter, MAD, and the coded bit number. We also analyze the encoding of header bits. Based on these analyses, we build up a new rate-distortion model. Moreover, we use motion vectors to predict the MAD value. Based on these modifications, we may better predict the quantization parameter and the amount of encoded bits. In experiments, our approach not only improves the stability of encoder buffer but also makes an obvious improvement of visual quality.
A PATCH-BASED CONTRACTION PROCESS FOR THE IMPROVEMENT OF IMAGE MATTING
(2017-01-01)
Syu, Jia-Hao
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Wang, Sheng-Jyh
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Wang, Li-Chun
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電子工程學系及電子研究所
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電信工程研究所
In this paper, we propose a patch-based contraction process for the improvement of image matting. Given an input image and a trimap, the proposed contraction process quickly refines the trimap by reclassifying the alpha values of the undetermined pixels into "foreground", "background", or "undetermined", based on the feature similarity of the undetermined pixels with respect to the foreground/background regions. The refinement of trimap causes the shrinkage of undetermined regions and may reveal more foreground/background information across isolated regions. Based on the refined trimap, the image matting algorithm can better estimate the alpha values of the image. Experimental results show that the proposed patch based process provides an effective and efficient way to obtain a refined trimap and the use of the trimap refinement improves the performance of image matting.
A Surface-based Vacant Space Detection for an Intelligent Parking Lot
(2012)
Huang, Ching-Chun
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Dai, Yu-Shu
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Wang, Sheng-Jyh
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
We proposed a surface-based vacant parking space detection system. Unlike many car-oriented or space-oriented methods, the proposed system is parking-lot-oriented. In the system, we treat the whole parking lot as a structure consisting of plentiful surfaces. A surface-based hierarchical framework is then proposed to integrate the 3-D scene information with the patch-based image observation for the inference of vacant space. To be robust, the feature vector of each image patch is extracted based on the Histogram of Oriented Gradients (HOG) approach. By incorporating these texture features into the proposed probabilistic models, we could systematically infer the optimal hypothesis of parking statuses while dealing with occlusion effect, shadow effect, perspective distortion, and fluctuation of lighting condition in both day time and night time.
A vision-based approach to extracting the tilt angle and altitude of a PTZ camera
(2006-01-01)
Chen, I-Hsien
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Wang, Sheng-Jyh
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
In this paper, the relationship between the tilt angle and altitude of a PTZ camera and the 3D-to-2D coordinate transformation is built. A vision-based approach is then proposed to accomplish the estimation of tilt angle and altitude based on the observation of some simple objects lying on a horizontal plane, with known intrinsic parameters of the PTZ camera. The patterns could be a circle, a corner with known angle, more than two corners with unknown but equal angles, or more than two line segments with unknown but equal lengths. Furthermore, if we are given a few line segments with known but not necessarily equal length, the estimation of tilt angle and altitude can also be achieved via an optimization procedure. The impacts of parameter fluctuations are analyzed via computer simulations. Experimental results on real images conform to our analysis and demonstrate the efficiency of this approach.
A vision-based approach to extracting the tilt angle and altitude of a PTZ camera - art. no. 606609
(2006)
Chen, I-Hsien
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Wang, Sheng-Jyh
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電子工程學系及電子研究所
;
Department of Electronics Engineering and Institute of Electronics
In this paper, the relationship between the tilt angle and altitude of a PTZ camera and the 3D-to-2D coordinate transformation is built. A vision-based approach is then proposed to accomplish the estimation of tilt angle and altitude based on the observation of some simple objects lying on a horizontal plane, with known intrinsic parameters of the PTZ camera. The patterns could be a circle, a corner with known angle, more than two corners with unknown but equal angles, or more than two line segments with unknown but equal lengths. Furthermore, if we are given a few line segments with known but not necessarily equal length, the estimation of tilt angle and altitude can also be achieved via an optimization procedure. The impacts of parameter fluctuations are analyzed via computer simulations. Experimental results on real images conform to our analysis and demonstrate the efficiency of this approach.
An Efficient Approach for Dynamic Calibration of Multiple Cameras
(2009-01-01)
Chen, I. -Hsien
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Wang, Sheng-Jyh
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
In this paper, we propose a new algorithm for dynamic calibration of multiple cameras. Based on the mapping between a horizontal plane in the 3-D space and the 2-D image plane on a panned and tilted camera, we utilize the displacement of feature points and the epipolar-plane constraint among multiple cameras to infer the changes of pan and tilt angles for each camera. This algorithm does not require a complicated correspondence of feature points. It can be applied to surveillance systems with wide-range coverage. It also allows the presence of moving objects in the captured scenes while performing dynamic calibration. The sensitivity analysis of our algorithm with respect to measurement errors and fluctuations in previous estimations is also discussed. The efficiency and feasibility of this approach has been demonstrated in some experiments over real scenery. Note to Practitioners-For a surveillance system with multiple cameras, the poses of cameras may be changed from time to time to acquire different views of the monitored scene. Whenever the poses of cameras are changed, the relative positioning and orientation among cameras need to be recalibrated. In this paper, we demonstrate a new and efficient approach to calibrate multiple cameras dynamically. The concept of our approach originated from the observation that people usually can identify the directions of the pan and tilt angles, and even make a rough estimate about the changes of pan and tilt angles, simply based on some clues revealed in the captured images. In our approach, a set of cameras are first calibrated based on a static calibration method. As cameras begin to pan or tilt, the images of these cameras change accordingly. We keep extracting and tracking a few feature points from these images. Based on the displacement of these feature points in consecutive images, the pan and tilt angle changes of the cameras can be automatically estimated via the proposed approach. There is no need to place calibration patterns or landmarks while performing dynamic calibration. In addition, there is no need to perform the complicated correspondence of feature points among cameras. The proposed approach is practical for a wide-range surveillance system with multiple cameras and is applicable for complicated environments. In the future, we will combine the proposed approach with an object tracking system to develop an efficient active surveillance system with multiple cameras.
An efficient approach for the calibration of multiple PTZ cameras
(2007-04-01)
Chen, I-Hsien
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Wang, Sheng-Jyh
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
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.
AN EFFICIENT BAYESIAN FRAMEWORK FOR IMAGE ENHANCEMENT WITH SPATIAL CONSIDERATION
(2010)
Jen, Tzu-Cheng
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Wang, Sheng-Jyh
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
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.
AUTOMATIC 3-D DEPTH RECOVERY FROM A SINGLE URBAN-SCENE IMAGE
(2012)
Tseng, Chen-yu
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Wang, Sheng-Jyh
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
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
(2012-06-01)
Jen, Tzu-Cheng
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Wang, Sheng-Jyh
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
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.
Calibration system for image capture apparatus and method thereof
(2007-08-23)
Chen, I-Hsien
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Wang, Sheng-Jyh
A calibration system and a method to be used in an image capture apparatus are disclosed. The calibration system includes a calibration appliance, a feature extraction unit and a processor. The calibration appliance has a plurality of mark points. The plurality of mark points is on the same plane to form a line segment with a known length. The image capture apparatus captures an image. Then based on the captured image, the feature extraction unit extracts at least three image feature points corresponding to the mark points. The processor calculates the inclination angle of the image capture apparatus, and the height between the image capture apparatus and the plane. Besides a line segment with a known length, the mark points corresponding to the image feature points may form a corner with a known angle, two corners with unknown but equal angles, or two line segments with unknown but equal lengths. Convenience for the calibration system is therefore improved.
Digital Image Restoration for Phase-Coded Imaging Systems
(2010)
Tseng, Chen-Yu
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Wang, Sheng-Jyh
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Chang, Chir-Weei
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Chen, Po-Chang
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Chang, Chuan-Chung
This paper proposes a digital image restoration algorithm for phase-coded imaging systems. In order to extend the depth-of-field (Dof), an imaging system equipped with a properly designed phase-coded lens can achieve an approximately constant point spread function (PSF) for a wide range of depths. In general, a phase-coded imaging system produces blurred intermediate images and requires subsequent restoration processing to generate clear images. For lowcomputational consumer applications, the kernel size of the restoration filter is a major concern. To fit for practical applications, a pyramid-based restoration algorithm is proposed in which we decompose the intermediate image into the form of Laplacian pyramid and perform restoration over each level individually. This approach provides the flexibility in filter design to maintain manufacturing specification. On the other hand, image noise may seriously degrade the performance of the restored images. To deal with this problem, we propose a Pyramid-Based Adaptive Restoration (PBAR) method, which restores the intermediate image with an adaptive noise suppression module to improve the performance of the phase-coded imaging system for Dof extension.
DYNAMIC CALIBRATION METHOD FOR SINGLE AND MULTIPLE VEDIO CAPTURE DEVICES
(2009-08-20)
Chen, I-Hsien
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Wang, Sheng-Jyh
The present invention discloses a dynamic calibration method for a single and multiple video capture devices. The present invention can acquire the variations of the pan angle and tilt angle of a single video capture device according to the displacement of the feature points between successive images. For a plurality of video capture devices, the present invention includes the epipolar-plane constraint between a plurality of video capture devices to achieve the goal of dynamical calibration. The calibration method in the present invention does not require specific calibration patterns or complicated correspondence of feature points, and can be applied to surveillance systems with wide-range coverage.
Generalized histogram equalization based on local characteristics
(2006)
Jen, Tzu-Cheng
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Wang, Sheng-Jyh
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電子工程學系及電子研究所
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Department of Electronics Engineering and Institute of Electronics
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 f(1) and f(2) 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.
Hierarchical Image Segmentation Based on Iterative Contraction and Merging
(2017-05-01)
Syu, Jia-Hao
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Wang, Sheng-Jyh
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Wang, Li-Chun
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電機學院
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電信工程研究所
In this paper, we propose a new framework for hierarchical image segmentation based on iterative contraction and merging. In the proposed framework, we treat the hierarchical image segmentation problem as a sequel of optimization problems, with each optimization process being realized by a contraction-and-merging process to identify and merge the most similar data pairs at the current resolution. At the beginning, we perform pixel-based contraction and merging to quickly combine image pixels into initial region-elements with visually indistinguishable intra-region color difference. After that, we iteratively perform region-based contraction and merging to group adjacent regions into larger ones to progressively form a segmentation dendrogram for hierarchical segmentation. Comparing with the state-of-the-art techniques, the proposed algorithm can not only produce high-quality segmentation results in a more efficient way, but also keep a lot of boundary details in the segmentation results.