| JaLCDOI | 10.18926/14153 |
|---|---|
| FullText URL | Mem_Fac_Eng_39_1_56.pdf |
| Author | Sugaya, Yasuyuki| Kanatani, Kenichi| |
| Abstract | We present a new method for extracting objects moving independently of the background from a video sequence taken by a moving camera. We first extract and track feature points through the sequence and select the trajectories of background points by exploiting geometric constraints based on the affine camera model. Then, we generate a panoramic image of the background and compare it with the individual frames. We describe our image processing and thresholding techniques. |
| Publication Title | Memoirs of the Faculty of Engineering, Okayama University |
| Published Date | 2005-01 |
| Volume | volume39 |
| Issue | issue1 |
| Start Page | 56 |
| End Page | 62 |
| ISSN | 0475-0071 |
| language | English |
| File Version | publisher |
| NAID | 120002308594 |
| JaLCDOI | 10.18926/14155 |
|---|---|
| FullText URL | Mem_Fac_Eng_39_1_63.pdf |
| Author | Kanatani, Kenichi| |
| Abstract | Geometric fitting is one of the most fundamental problems of computer vision. In [8], the author derived a theoretical accuracy bound (KCR lower bound) for geometric fitting in general and proved that maximum likelihood (ML) estimation is statistically optimal. Recently, Chernov and Lesort [3] proved a similar result, using a weaker assumption. In this paper, we compare their formulation with the author’s and describe the background of the problem. We also review recent topics including semiparametric models and discuss remaining issues. |
| Publication Title | Memoirs of the Faculty of Engineering, Okayama University |
| Published Date | 2005-01 |
| Volume | volume39 |
| Issue | issue1 |
| Start Page | 63 |
| End Page | 70 |
| ISSN | 0475-0071 |
| language | English |
| File Version | publisher |
| NAID | 120002308366 |
| Author | Kanatani, Kenichi| |
|---|---|
| Published Date | 2004-10 |
| Publication Title | Pattern Analysis and Machine Intelligence |
| Content Type | Journal Article |
| JaLCDOI | 10.18926/46953 |
|---|---|
| FullText URL | mfe_38_1-2_061_071.pdf |
| Author | Kanatani, Kenichi| Sugaya, Yasuyuki| |
| Abstract | The Tomasi-Kanade factorization for reconstructing the 3-D shape of the feature points tracked through a video stream is widely regarded as based on factorization of a matrix by SVD (singular value decomposition). This paper points out that the core principle is the affine camera approximation to the imaging geometry and that SVD is merely one means of numerical computation. We first describe the geometric structure of the problem and then give a complete programming scheme for 3-D reconstruction. |
| Publication Title | Memoirs of the Faculty of Engineering, Okayama University |
| Published Date | 2004-03 |
| Volume | volume38 |
| Issue | issue1-2 |
| Start Page | 61 |
| End Page | 71 |
| ISSN | 0475-0071 |
| language | English |
| File Version | publisher |
| NAID | 80016889443 |
| JaLCDOI | 10.18926/46952 |
|---|---|
| FullText URL | mfe_38_1-2_039_059.pdf |
| Author | Kanatani, Kenichi| |
| Abstract | We investigate the meaning of "statistical methods" for geometric inference based on image feature points. Tracing back the origin of feature uncertainty to image processing operations, we discuss the implications of asymptotic analysis in reference to "geometric fitting" and "geometric model selection", We point out that a correspondence exists between the standard statistical analysis and the geometric inference problem. We also compare the capability of the "geometric AIC" and the "geometric MDL' in detecting degeneracy. Next, we review recent progress in geometric fitting techniques for linear constraints, describing the "FNS method", the "HEIV method", the "renormalization method", and other related techniques. Finally, we discuss the "Neyman-Scott problem" and "semiparametric models" in relation to geometric inference. We conclude that applications of statistical methods requires careful considerations about the nature of the problem in question. |
| Publication Title | Memoirs of the Faculty of Engineering, Okayama University |
| Published Date | 2004-03 |
| Volume | volume38 |
| Issue | issue1-2 |
| Start Page | 39 |
| End Page | 59 |
| ISSN | 0475-0071 |
| language | English |
| File Version | publisher |
| NAID | 80016889442 |
| JaLCDOI | 10.18926/46969 |
|---|---|
| FullText URL | mfe_37_1_015_023.pdf |
| Author | Kanatani, Kenichi| |
| Abstract | In order to facilitate smooth communications with researchers in other fields including statistics, this paper investigates the meaning of "statistical methods" for geometric inference based on image feature points, We point out that statistical analysis does not make sense unless the underlying "statistical ensemble" is clearly defined. We trace back the origin of feature uncertainty to image processing operations for computer vision in general and discuss the implications of asymptotic analysis for performance evaluation in reference to "geometric fitting", "geometric model selection", the "geometric AIC", and the "geometric MDL". Referring to such statistical concepts as "nuisance parameters", the "Neyman-Scott problem", and "semiparametric models", we point out that simulation experiments for performance evaluation will lose meaning without carefully considering the assumptions involved and intended applications. |
| Publication Title | Memoirs of the Faculty of Engineering, Okayama University |
| Published Date | 2002-11 |
| Volume | volume37 |
| Issue | issue1 |
| Start Page | 15 |
| End Page | 23 |
| ISSN | 0475-0071 |
| language | English |
| File Version | publisher |
| NAID | 80015664455 |
| JaLCDOI | 10.18926/46970 |
|---|---|
| FullText URL | mfe_37_1_025_032.pdf |
| Author | Kanazawa, Yasushi| Kanatani, Kenichi| |
| Abstract | We present a new method for detecting point matches between two images without using any combinatorial search. Our strategy is to impose various local and non-local constraints as "soft" constraints by introducing their "confidence" measures via "mean-field approximations". The computation is a cascade of evaluating the confidence values and sorting according to them. In the end, we impose the "hard" epipolar constraint by RANSAC. We also introduce a model selection procedure to test if the image mapping can be regarded as a homography. We demonstrate the effectiveness of our method by real image examples. |
| Publication Title | Memoirs of the Faculty of Engineering, Okayama University |
| Published Date | 2002-11 |
| Volume | volume37 |
| Issue | issue1 |
| Start Page | 25 |
| End Page | 32 |
| ISSN | 0475-0071 |
| language | English |
| File Version | publisher |
| NAID | 80015664456 |
| JaLCDOI | 10.18926/46971 |
|---|---|
| FullText URL | mfe_37_1_041_049.pdf |
| Author | Sugaya, Yasuyuki| Kanatani, Kenichi| |
| Abstract | We study the problem of segmenting independently moving objects in a video sequence. Several algorithms exist for classifying the trajectories of the feature points into independent motions, but the performance depends on the validity of the underlying camera imaging model. In this paper, we present a scheme for automatically selecting the best model using the geometric AIC before the segmentation stage, Using real video sequences, we confirm that the segmentation accuracy indeed improves if the segmentation is based on the selected model. We also show that the trajectory data can be compressed into low-dimensional vectors using the selected model. This is very effective in reducing the computation time for a long video sequence. |
| Publication Title | Memoirs of the Faculty of Engineering, Okayama University |
| Published Date | 2002-11 |
| Volume | volume37 |
| Issue | issue1 |
| Start Page | 41 |
| End Page | 49 |
| ISSN | 0475-0071 |
| language | English |
| File Version | publisher |
| NAID | 120003457326 |
| Author | Kanatani, Kenichi| |
|---|---|
| Published Date | 2001-7 |
| Publication Title | Computer Vision |
| Content Type | Journal Article |
| JaLCDOI | 10.18926/47004 |
|---|---|
| FullText URL | mfe_36_1_107_116.pdf |
| Author | Kanatani, Kenichi| Ohta, Naoya| |
| Abstract | We present a new method for automatically detecting circular objects in images: we detect an osculating circle to an elliptic arc using a Hough transform, iteratively deforming it into an ellipse, removing outlier pixels, and searching for a separate edge. The voting space is restricted to one and two dimensions for efficiency, and special weighting schemes are introduced to enhance the accuracy. We demonstrate the effectiveness of our method using real images. Finally, we apply our method to the calibration of a turntable for 3-D object shape reconstruction. |
| Publication Title | Memoirs of the Faculty of Engineering, Okayama University |
| Published Date | 2001-12 |
| Volume | volume36 |
| Issue | issue1 |
| Start Page | 107 |
| End Page | 116 |
| ISSN | 0475-0071 |
| language | English |
| File Version | publisher |
| NAID | 80012855284 |
| JaLCDOI | 10.18926/47003 |
|---|---|
| FullText URL | mfe_36_1_091_106.pdf |
| Author | Kanatani, Kenichi| Ohta, Naoya| |
| Abstract | We present a theoretically optimal linear algorithm for 3-D reconstruction from point correspondences over two views. We also present a similarly constructed optimal linear algorithm for 3-D reconstruction from optical flow. We then compare the performance of the two algorithms by simulation and real-image experiments using the same data. This is the first impartial comparison ever done in the sense that the two algorithms are both optimal, extracting the information contained in the data to a maximum possible degree. We observe that the finite motion solution is always superior to the optical flow solution and conclude that the finite motion algorithm should be used for 3-D reconstruction. |
| Publication Title | Memoirs of the Faculty of Engineering, Okayama University |
| Published Date | 2001-12 |
| Volume | volume36 |
| Issue | issue1 |
| Start Page | 91 |
| End Page | 106 |
| ISSN | 0475-0071 |
| language | English |
| File Version | publisher |
| NAID | 120003497029 |
| JaLCDOI | 10.18926/47002 |
|---|---|
| FullText URL | mfe_36_1_079_090.pdf |
| Author | Kanatani, Kenichi| |
| Abstract | We first present an improvement of Kanatani's subspace separation [8] for motion segmentation by newly introducing the affine space constraint. We point out that this improvement does not always fare well due to the effective noise it introduces. In order to judge which solution to adopt if different segmentations are obtained, we present two criteria: one is the standard F test; the other is model selection using the geometric AIC of Kanatani [7] and the geometric MDL of Matsunaga and Kanatani [13]. We test these criteria doing real image experiments. |
| Publication Title | Memoirs of the Faculty of Engineering, Okayama University |
| Published Date | 2001-12 |
| Volume | volume36 |
| Issue | issue1 |
| Start Page | 79 |
| End Page | 90 |
| ISSN | 0475-0071 |
| language | English |
| File Version | publisher |
| NAID | 120003497028 |
| JaLCDOI | 10.18926/47001 |
|---|---|
| FullText URL | mfe_36_1_059_077.pdf |
| Author | Kanatani, Kenichi| |
| Abstract | Contrasting "geometric fitting", for which the noise level is taken as the asymptotic variable, with "statistical inference", for which the number of observations is taken as the asymptotic variable, we give a new definition of the "geometric AIC" and the "geometric MDL" as the counterparts of Akaike's AIC and Rissanen's MDL. We discuss various theoretical and practical problems that emerge from our analysis. Finally, we show, doing experiments using synthetic and real images, that the geometric MDL does not necessarily outperform the geometric AIC and that the two criteria have very different characteristics. |
| Publication Title | Memoirs of the Faculty of Engineering, Okayama University |
| Published Date | 2001-12 |
| Volume | volume36 |
| Issue | issue1 |
| Start Page | 59 |
| End Page | 77 |
| ISSN | 0475-0071 |
| language | English |
| File Version | publisher |
| NAID | 80012855281 |
