<i>2D projectivity</i> is an invertible mapping to present the perspective imaging of a world plane by projective translation, called homography. Good image feature have to be robust under 2D projectivity caused by any camera movements. In the standard performance evaluation of template matching, many real captured images of many scenes are ordinarily used. However it is not enough to evaluate the robustness under 2D projectivity in detail because the variations of real camera pose and position in the 3D world are limited and the capturing cost is expensive. During the early stage of the template matching development, an easy performance evaluation method is required to examine the behavior. We propose a self-diagnosis method to measure the robustness of local descriptor base template matching between a template image and reference images which are created by projective translation of the template image. We focus on the template matching consisting of a feature point extraction and a local descriptor matching. The proposed method evaluates the spatial accuracy of the feature points and the estimated template positions in the reference images with local descriptor matchings. Four metrics, feature point precision (PP), feature point recall (PR), local descriptor matching precision (MP) and local descriptor matching recall (MR) are introduced to evaluate the performance. The experiment results will be appeared in the final manuscript to show the effectiveness of our method.
Nighttime images of a scene from a surveillance camera have lower contrast and higher noise than their corresponding daytime images of the same scene due to low illumination. Denighting is an image enhancement method for improving nighttime images, so that they are closer to those that would have been taken during daytime. The method exploits the fact that background images of the same scene have been captured all day long with a much higher quality. We present several results of the enhancement of low quality nighttime images using denighting.
We propose a <i>digital scorebook </i>for football game which digitizes a football game video and presents it as an animation.
The proposed system consists of player position estimation from the game video, event selection interface
and player movement animation. Player position estimation allows for flexible movements and angles of the cameras
including zoom in and out, pan, tilt, and yaw. This reliable and robust estimation of the player movement
is based on image analysis by synthesis and Generalized Hough Transform(GHT). The operator can annotate
game scenes based on player movement data using event selection interface. The player movement is represented
by the animation whose characters have number or letter figures to emphasize the data. We demonstrate the
applicability of player position estimation and play annotation scheme via the character behaviors in animation.
Spot observation by computer vision is the one of fundamental key technology. In this paper, we propose a moving object color learning and robust recognition with Hidden Markov Model(HMM) from various scenes under different light conditions. Feature box which is a small area in a image is defined to observe a spot. The time series data of such as averages of R, G, B intensities in feature boxes are the input signals of our system. The HMMs learn correspondences of input signals with object color of moving object and background. Baum-Welch and Vi-terbi algorithms are used to learning and interpret the spot scene transition. In moving object color interpretation, the system selects a best HMM model for input signals using maximum likelihood method based on a given object color appearance grammar. In the experiment, we examine the number of feature boxes and its shapes under some light conditions. The feature boxes adjoining in vertical column whose height is almost same as objects results best score in the experiment. It shows the effectiveness of our method.