10.1.10.1 Dense Matching for Stereo, Dense Stereo Matching

Chapter Contents (Back)
Stereo, Matching. Dense Stereo, Matching.

Gallo, I.[Ignazio], Binaghi, E.[Elisabetta], Raspanti, M.[Mario],
Neural disparity computation for dense two-frame stereo correspondence,
PRL(29), No. 5, 1 April 2008, pp. 673-687.
Elsevier DOI 0802
Stereo matching; Occlusion; Disparity space; Neural networks BibRef

Vanetti, M.[Marco], Gallo, I.[Ignazio], Binaghi, E.[Elisabetta],
Dense Two-Frame Stereo Correspondence by Self-organizing Neural Network,
CIAP09(1035-1042).
Springer DOI 0909
BibRef

Gallo, I.[Ignazio], Binaghi, E.[Elisabetta],
Dense Stereo Matching with Growing Aggregation and Neural Learning,
VISAPP06(343-353).
Springer DOI 0711
BibRef

Agarwal, A.[Ankur], Blake, A.[Andrew],
Dense Stereo Matching over the Panum Band,
PAMI(32), No. 3, March 2010, pp. 416-430.
IEEE DOI 1002
BibRef
Earlier:
The Panum Proxy Algorithm for Dense Stereo Matching over a Volume of Interest,
CVPR06(II: 2339-2346).
IEEE DOI 0606
Panum Band: human stereo works over a narrow band of disparities. Using narrower range of disparities improves computation times. BibRef

Stentoumis, C., Grammatikopoulos, L., Kalisperakis, I., Karras, G.E.,
On accurate dense stereo-matching using a local adaptive multi-cost approach,
PandRS(91), No. 1, 2014, pp. 29-49.
Elsevier DOI 1404
BibRef
Earlier:
Implementing An Adaptive Approach For Dense Stereo-matching,
ISPRS12(XXXIX-B5:309-314).
DOI Link 1209
BibRef

Stentoumis, C., Grammatikopoulos, L., Kalisperakis, I., Petsa, E., Karras, G.E.,
A Local Adaptive Approach for Dense Stereo Matching in Architectural Scene Reconstruction,
3DARCH13(219-226).
DOI Link 1308
BibRef

Kalisperakis, I.[Ilias], Karras, G.E.[George E.], Grammatikopoulos, L.[Lazaros],
3D Aspects of 2D Epipolar Geometry,
PCV06(xx-yy).
PDF File. 0609
BibRef

Joglekar, J., Gedam, S.S., Mohan, B.K.,
Image Matching Using SIFT Features and Relaxation Labeling Technique: A Constraint Initializing Method for Dense Stereo Matching,
GeoRS(52), No. 9, September 2014, pp. 5643-5652.
IEEE DOI 1407
Bayes methods BibRef

Mahato, M., Gedam, S., Joglekar, J., Buddhiraju, K.M.,
Dense Stereo Matching Based on Multiobjective Fitness Function: A Genetic Algorithm Optimization Approach for Stereo Correspondence,
GeoRS(57), No. 6, June 2019, pp. 3341-3353.
IEEE DOI 1906
Optimization, Genetic algorithms, Image matching, Sociology, Statistics, Remote sensing, Geometry, Feature extraction, stereo vision BibRef

Gonzalez-Huitron, V.[Victor], Ponomaryov, V.[Volodymyr], Ramos-Diaz, E.[Eduardo], Sadovnychiy, S.[Sergiy],
Parallel framework for dense disparity map estimation using Hamming distance,
SIViP(12), No. 2, February 2018, pp. 231-238.
Springer DOI 1802
BibRef

Liu, J., Zhang, L., Wang, Z., Wang, R.,
Dense Stereo Matching Strategy for Oblique Images That Considers the Plane Directions in Urban Areas,
GeoRS(58), No. 7, July 2020, pp. 5109-5116.
IEEE DOI 2006
Distortion, Urban areas, Machine learning, Image reconstruction, Robustness, Buildings, Image resolution, Dense matching, oblique images BibRef

Han, Y.L.[Yi-Long], Liu, W.[Wei], Huang, X.[Xu], Wang, S.G.[Shu-Gen], Qin, R.J.[Rong-Jun],
Stereo Dense Image Matching by Adaptive Fusion of Multiple-Window Matching Results,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Zhang, C.X.[Cong-Xuan], Wu, J.J.[Jun-Jie], Chen, Z.[Zhen], Liu, W.[Wen], Li, M.[Ming], Jiang, S.F.[Shao-Feng],
Dense-CNN: Dense convolutional neural network for stereo matching using multiscale feature connection,
SP:IC(95), 2021, pp. 116285.
Elsevier DOI 2106
Stereo matching, Cost volume, Multiscale features, Dense convolutional neural network BibRef


Mehltretter, M.[Max], Kleinschmidt, S.P.[Sebastian P.], Wagner, B.[Bernardo], Heipke, C.[Christian],
Multimodal Dense Stereo Matching,
GCPR18(407-421).
Springer DOI 1905
BibRef

Mao, W., Wang, M., Zhou, J., Gong, M.,
Semi-Dense Stereo Matching Using Dual CNNs,
WACV19(1588-1597)
IEEE DOI 1904
convolutional neural nets, image matching, learning (artificial intelligence), stereo image processing, BibRef

Tan, P., Chambolle, A., Monasse, P.,
Occlusion detection in dense stereo estimation with convex optimization,
ICIP17(2543-2547)
IEEE DOI 1803
Cameras, Estimation, Image color analysis, Minimization, Robustness, TV, Weight measurement, Stereo-matching, convex relaxation, variational method BibRef

Poggi, M.[Matteo], Mattoccia, S.[Stefano],
Evaluation of variants of the SGM algorithm aimed at implementation on embedded or reconfigurable devices,
IC3D16(1-8)
IEEE DOI 1703
Semi Global Matching. Dense depth from stereo. computer vision BibRef

Nahar, S., Joshi, M.V.,
Dense disparity estimation based on feature matching and IGMRF regularization,
ICPR16(3804-3809)
IEEE DOI 1705
BibRef
Earlier:
A regularization framework for stereo matching using IGMRF prior and sparseness learned from autoencoder,
ICIP16(3434-3438)
IEEE DOI 1610
BibRef
Earlier:
A learning based approach for dense stereo matching with IGMRF prior,
NCVPRIPG13(1-4)
IEEE DOI 1408
Gaussian processes. Estimation, Feature extraction, Image edge detection, Image segmentation, Minimization, Robustness, Training. Discrete cosine transforms BibRef

Hamilton, O.K., Breckon, T.P.[Toby P.],
Generalized dynamic object removal for dense stereo vision based scene mapping using synthesised optical flow,
ICIP16(3439-3443)
IEEE DOI 1610
Cameras BibRef

Chuang, T.Y., Ting, H.W., Jaw, J.J.,
Hybrid-based Dense Stereo Matching,
ISPRS16(B3: 495-501).
DOI Link 1610
BibRef

Monteiro, N.B.[Nuno Barroso], Barreto, J.P.[Jo„o Pedro], Gaspar, J.[Josť],
Dense Lightfield Disparity Estimation Using Total Variation Regularization,
ICIAR16(462-469).
Springer DOI 1608
BibRef

Ma, R.[Rui], Au, O.C., Wan, P.F.[Peng-Fei], Sun, W.X.[Wen-Xiu], Xu, L.F.[Ling-Feng], Jia, L.H.[Lu-Heng],
Solving dense stereo matching via quadratic programming,
VCIP14(370-373)
IEEE DOI 1504
computational complexity BibRef

Ha, J.[Jeong_Mok], Jeon, J.[Jea_Young], Bae, G.[Gi_Yeong], Jo, S.[Sung_Yong], Jeong, H.[Hong],
Cost Aggregation Table: Cost Aggregation Method Using Summed Area Table Scheme for Dense Stereo Correspondence,
ISVC14(I: 815-826).
Springer DOI 1501
BibRef

Irschara, A., Rumpler, M., Meixner, P., Pock, T., Bischof, H.,
Efficient and Globally Optimal Multi View Dense Matching for Aerial Images,
AnnalsPRS(I-3), No. 2012, pp. 227-232.
DOI Link 1209
BibRef

Hu, T., Wu, H.,
Dense Corresponding Pixel Matching Using A Fixed Window with RGB Independent Information,
AnnalsPRS(I-4), No. 2012, pp. 89-93.
DOI Link 1209
BibRef

Li, Y.J.[Yu-Jun], Au, O.C.[Oscar C.], Xu, L.F.[Ling-Feng], Sun, W.X.[Wen-Xiu], Chui, S.H.[Sung-Him], Kwok, C.W.[Chun-Wing],
A convex-optimization approach to dense stereo matching,
ICIP11(1005-1008).
IEEE DOI 1201
BibRef

Lang, H.T.[Hai-Tao], Wang, Y.T.[Yong-Tian], Qi, X.[Xin], Pan, W.Q.[Wei-Qing],
Enhanced point descriptors for dense stereo matching,
IASP10(228-231).
IEEE DOI 1004
BibRef

Zhao, G.Q.[Gang-Qiang], Chen, L.[Ling], Chen, G.C.[Gen-Cai],
A Speeded-Up Local Descriptor for dense stereo matching,
ICIP09(2101-2104).
IEEE DOI 0911
BibRef

Somanath, G.[Gowri], Rohith, M.V., Metaxas, D.N.[Dmitris N.], Kambhamettu, C.[Chandra],
D-Clutter: Building object model library from unsupervised segmentation of cluttered scenes,
CVPR09(2783-2789).
IEEE DOI 0906
BibRef

Rohith, M.V., Kambhamettu, C.[Chandra],
Learning Image Structures for Optimizing Disparity Estimation,
ACCV10(III: 627-640).
Springer DOI 1011
BibRef

Rohith, M.V., Somanath, G.[Gowri], Kambhamettu, C.[Chandra], Geiger, C.[Cathleen], Finnegan, D.[David],
Modified Region Growing for Stereo of Slant and Textureless Surfaces,
ISVC10(I: 666-677).
Springer DOI 1011
BibRef
Earlier: A1, A2, A3, A4, Only:
Towards estimation of dense disparities from stereo images containing large textureless regions,
ICPR08(1-5).
IEEE DOI 0812
BibRef

Payeur, P.[Pierre], Desjardins, D.[Danick],
Structured Light Stereoscopic Imaging with Dynamic Pseudo-random Patterns,
ICIAR09(687-696).
Springer DOI 0907
BibRef
Earlier: A2, A1:
Dense Stereo Range Sensing with Marching Pseudo-Random Patterns,
CRV07(216-226).
IEEE DOI 0705
BibRef

Jagmohan, A., Singh, M., Ahuja, N.,
Dense stereo matching using kernel maximum likelihood estimation,
ICPR04(III: 28-31).
IEEE DOI 0409
BibRef

Bovyrin, A., Eruhimov, V., Molinov, S., Mosyagin, V., Pisarevsky, V.,
Fast and robust dense stereo correspondence by column segmentation,
ICIP03(III: 1033-1036).
IEEE DOI 0312
BibRef

Kostkova, J., Sara, R.,
Stratified Dense Matching for Stereopsis in Complex Scenes,
BMVC03(xx-yy).
HTML Version. 0409
BibRef

Jin, K., Boufama, B.,
Towards a Fast and Reliable Dense Matching Algorithm,
VI02(178).
PDF File. 0208
BibRef

Koschan, A.F., Rodehorst, V., Spiller, K.,
Color Stereo Vision Using Hierarchical Block Matching and Active Color Illumination,
ICPR96(I: 835-839).
IEEE DOI 9608
(Technical Univ. of Berlin, D) BibRef

Koschan, A.F., Rodehorst, V.,
Towards Real-Time Stereo Employing Parallel Algorithms for Edge-Based and Dense Stereo Matching,
CAMP95(xx). BibRef 9500

Koschan, A.F.[Andreas F.],
Dense stereo correspondence using polychromatic block matching,
CAIP93(538-542).
Springer DOI 9309
BibRef

Chapter on Stereo: Three Dimensional Descriptions from Two or More Views, Binocular, Trinocular continues in
Matching for Stereo, Occlusion, Discontinuity Analysis .


Last update:Apr 18, 2024 at 11:38:49