Adherent raindrop detection and removal in video

Abstract Raindrops adhered to a windscreen or window glass can significantly degrade the visibility of a scene. Detecting and removing raindrops will, therefore, benefit many computer vision applications, particularly outdoor surveillance systems and intelligent vehicle systems. In this paper, a method that automatically detects and removes adherent raindrops is introduced. The core idea is to exploit the local spatiotemporal derivatives of raindrops. First, it detects raindrops based on the motion and the intensity temporal derivatives of the input video. Second, relying on an analysis that some areas of a raindrop completely occludes the scene, yet the remaining areas occludes only partially, the method removes the two types of areas separately. For partially occluding areas, it restores them by retrieving as much as possible information of the scene, namely, by solving a blending function on the detected partially occluding areas using the temporal intensity change. For completely occluding areas, it recovers them by using a video completion technique. Experimental results using various real videos show the effectiveness of the proposed method

Evaluation of the difference-correction effect of the gamma camera systems used by easy Z-score Imaging System (eZIS) analysis

Objective: We examined the difference of the effect by data to revise a gamma camera difference. The difference-correction method of the camera is incorporated in eZIS analysis. Methods: We acquired single photon emission computed tomography (SPECT) data from the three-dimensional (3D) Hoffman brain phantom (Hoffman), the three-dimensional brain phantom (3D-Brain), Pool phantom (pool) and from normal subjects (Normal-SPECT) to investigate compensating for a difference in gamma camera systems. We compared SPECT counts of standard camera with the SPECT counts that revised the difference of the gamma camera system (camera). Furthermore, we compared the "Z-score map (Z-score)". To verify the effect of the compensation, we examined digitally simulated data designed to represent a patient with Alzheimer\u27s dementia. We carried out both eZIS analysis and "Specific Volume of interest Analysis (SVA)". Results: There was no great difference between the correction effect using Hoffman phantom data and that using 3D-Brain phantom data. Furthermore, a good compensation effect was obtained only over a limited area. The compensation based on the pool was found to be less satisfactory than any of the other compensations according to all results of the measurements examined in the study. The compensation based on the Normal-SPECT data resulted in a Z-score map (Z-score) for the result that approximated that from the standard camera. Therefore, we concluded that the effect of the compensation based on Normal-SPECT data was the best of the four methods tested. Conclusions: Based on eZIS analysis, the compensation using the pool data was inferior to the compensations using the other methods tested. Based on the results of the SAV analysis, the effect of the compensation using the Hoffman data was better than the effect of the compensation using the 3D-Brain data. By all end-point measures, the compensation based on the Normal-SPECT data was more accurate than the compensation based on any of the other three phantoms. © 2014 The Author(s).発行後1年より全文公

Surface Color Estimation of Large Scale Diffuse Objects under Outdoor Environment

Digital three-dimensional models created by computer vision and graphics techniques are becoming widely used for a variety of purposes. Specifically, modeling cultural heritage objects has attracted considerable attention, since such objects are worth preserving, and the data can be utilized for restoration when an object faces the crisis of collapse. Automation for creating 3D models has therefore attracted much interest, since most models are currently created by manual operation, adding significantly to the cost. Creating an accurate model of an object requires knowledge of the object's shape and surface reflectance. Acquiring shape information is facilitated by the development of sensors and the progress of data processing algorithms, but acquiring surface reflectance properties remains a challenge, specifically with outdoor objects. This paper targets large-scale objects such as architectural structures in an outdoor environment. The size of target objects may be as much as 100 m by 100 m by 50 m. Measuring the surface properties of such huge objects is a challenge. The appearance of an object can be modeled by mapping image textures to the known shape of the object. However, to achieve consistent colors among image textures, the effect of illumination has to be removed before mapping these textures by using surface color estimation and surface reflectance estimation techniques. Two methods that calculate a surface color by a pixel-based operation are presented. Most previous methods assume uniform illumination in a scene, but this is not always true in images with shadows or with curved objects. The proposed methods enable pixel-based operation by utilizing illumination change. Two models of illumination colors that we introduce enable a surface color to be uniquely determined from two pixel values. First, the paper proposes a method that uses blackbody radiation and analyzes the stability and practicality of the method. Then, a more practical method is proposed that can perform robust estimation using a statistical model derived from outdoor illumination data. Robust estimation is achieved by introducing the plausible range of outdoor illumination colors. In practical situation, surface reflectance would be required for relighting purposes. A method is presented to estimate surface reflectance from spherical images with known shape information. Spherical images have nearly a 360-degree field of view; they capture target objects and surrounding illumination at one shot. Therefore they do not require specific apparatus or calibration of exposure times, apertures, and camera gain factors. Furthermore, geometric calibration between an image and shape information becomes robust owing to the characteristic of a spherical camera. Measurement and data-processing cost will be decreased by the method compared to previous methods that need elaborate procedures. This is critical specifically for large-scale objects. The main contribution of this thesis is that the author has proposed three methods that estimate surface properties of an object. It can be summarized by the three following points: First, the research provides insights into the stability and practicality of pixel-based surface color estimation. Second, a pixel-based method for surface color estimation has been developed that is robust and accurate even for real outdoor objects. None of the conventional methods can perform a pixel-based operation with higher accuracy than the proposed method. Third, an efficient method has been developed that estimates surface reflectance of large-scale objects under outdoor environment. The proposed techniques form the foundation for developing a system that models the appearance of a large-scale object in an outdoor environment.報告番号: 甲23943 ; 学位授与年月日: 2008-03-24 ; 学位の種別: 課程博士 ; 学位の種類: 博士(情報理工学) ; 学位記番号: 博情第188号 ; 研究科・専攻: 情報理工学系研究科電子情報学専