Appearance Enhancement for Visually Impaired with Projector Camera Feedback

Visually impaired is a common problem for human life in the world wide. The projector-based AR technique has ability to change appearance of real object, and it can help to improve visibility for visually impaired. We propose a new framework for the appearance enhancement with the projector camera system that employed model predictive controller. This framework enables arbitrary image processing such as photo-retouch software in the real world and it helps to improve visibility for visually impaired. In this article, we show the appearance enhancement result of Peli's method and Wolffshon's method for the low vision, Jefferson's method for color vision deficiencies. Through experiment results, the potential of our method to enhance the appearance for visually impaired was confirmed as same as appearance enhancement for the digital image and television viewing

Estimation-by-Completion : A linear method for pose estimation of 3D object

本稿では三次元物体の姿勢パラメータを二次元画像から高速に推定する方法として,EbC(Estimation-by-Completion)法を提案する.EbC法は,アピアランスベース姿勢推定を,画像に埋め込まれた情報トラックの復元という問題としてとらえ,固有空間法による学習結果を基にBPLP法で画像補完することで情報トラックを復元し,パラメータ推定を実現する.また,画像補完とパラメータ推定の計算を二枚の画像(EbC画像対)に集約し,各パラメータの推定を画像の内積演算と簡単な三角関数のみで実現するため,パラメータ推定を高速に行うことができる.実験では,鉛直軸周りの回転と画像面での並進の3自由度の姿勢パラメータ推定結果を示し,EbC法の精度と計算コストについて述べる.In this paper, we propose a method called EbC, Estimation-by-Completion, a high speed estimation of pose parameters of a three-dimensional object in an image. For appearance-based pose estimation, EbC method introduces the concept of information track that represents the parameters embedded into image samples. The pose parameters are estimated by the completion of the information track with BPLP method, an image interpolation technique with eigenspace learning. EbC integrates the completion of the information track and the estimation of the parameters into pairs of two images, called EbC image pairs, and performs quickly the estimation by few inner products of images. Experimental results for pose estimation results of three degree of freedoms, rotation about vertical axis and horizontal and vertical translations, show the accuracy, and calculation cost of EbC method is described.MIRU2006 画像の認識・理解シンポジウム インタラクティブセッション優秀賞受

Estimation-by-Completion : 3次元物体の線形姿勢推定手法

In this paper, we propose a method called EbC, Estimation-by-Completion, a high speed estimation of pose parameters of a three-dimensional object in an image.MIRU2006 画像の認識・理解シンポジウム インタラクティブセッション優秀賞受

マルチポート固有空間法

本稿では、多様体の教師付き問題としての姿勢パラメータ推定手法である「マルチポート固有空間法」[1],[2]について議論する。これは欠損画素の輝度値を推定して画像を補間するBPLP[3],[4]を基にしており、群の回帰問題を空間への投影という線形演算で行うものである。まずマルチポート固有空間法の内容を説明し、その主要部分は連立方程式による最小ノルム推定であることを示す。またその方程式による空間への投影がどのようなものかを説明し、学習とサンプル数の影響について述べる。In this paper, we discusson Multi-port Eigenspace Method[1], [2], an supervised manifold learning of pose parameters. This method is based on BPLP[3], [4], a method of intensity interpolation, and operates a linear mapping of projection to a subspace as a regression to a group. First we describe the method, and show that the important part of it is a least norm solution of a system of equations. Then we illustrate the projection by the system, and the effect of the number of learning samples

マルチポート固有空間法

部分空間法研究会 2006 : Subspace 2006, 発表スライド ; 開催場所 : 東北大学, 仙台 ; 開催日 : 2006年7月18

An appearance based fast linear pose estimation

MVA 2009 : IAPR Conference on Machine Vision Applications, Slides ; Place : Tokyo, Japan ; Date : May 20-22, 200

An appearance based fast linear pose estimation

In this paper, we describe a high speed and multi degree of freedoms (DOFs) pose estimation method for a 3-D object that called Estimation-by-Completion (EbC) method. The most of employed processes are described with linear calculation, thus, whole procedure for each parameter estimation is expressed by a pair inner production, and it used only an arctangent calculation at the final part of the estimation. The accuracy evaluation by 3DOFs pose estimation that includes rotation around the object's vertical and horizontal translations is shown in experiment. We also describe its calculation cost in discussions

Estimation-by-Completion : A linear method for pose estimation of 3D object

MIRU 2006 画像の認識・理解シンポジウム, ポスター ; 開催日時 : 2006年7月19-21日 ; 開催場所 : 仙台MIRU2006 画像の認識・理解シンポジウム インタラクティブセッション優秀賞受

Linear 3-D object pose estimation with dense sample images : Discussions about limitation of parameter estimation ability by the linear regressions

In the image parameter estimation by the linear regression, it has very high degrees of freedom for the decision of regression coefficients, because the dimension of image vector is huge high. In this paper, we discuss its potential by the learning of the dense samples. For the learning process, we employed a sequential regression coefficient calculation algorithm and realize its calculation for dense samples with reasonable computational cost. Through the experimental result, we discuss about the limitation of parameter estimation ablity by the linear regression

Linear 3D object pose estimation with dense sample images

線形回帰による画像のパラメータ推定は,画像ベクトルの次元が高いため回帰係数の決定には非常に高い自由度を有する。本稿では,線形回帰において逐次更新による回帰係数の算出方法を提案し,稠密な学習サンプルに対して現実的な計算コストでの回帰係数の計算を実現する。また,この手法を3次元物体の姿勢推定に応用し,coil-20を用いた実験結果より線形回帰による3次元物体の線形姿勢推定の能力限界について考察する。In the image parameter estimation by the linear regression, it has very high degrees of freedom for the decision of regression coefficients, because the dimension of image vector is huge high. In this paper, we propose a sequential regression coefficient calculation algorithm, and we realize its calculation for dense samples with reasonable computational cost. Moreover, we apply this method to the pose estimation of the 3-D object, and we discuss about limit of parameter estimation ability by the linear regression with the coil-20 image library