Recovering Occlusion Boundaries from a Single Image

Robust Real-Time Face Detection

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A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics

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Understanding Line drawings of Scenes with Shadows

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Putting Objects in Perspective

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Contextual Priming for Object Detection

Torralba, A. B. (2003) · International Journal of Computer Vision · doi:10.1023/a:1023052124951 · Cited by 792

Logistic Regression, AdaBoost and Bregman Distances

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On seeing things

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Recovering Surface Layout from an Image

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Using Multiple Segmentations to Discover Objects and their Extent in Image Collections

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Occlusion-based depth ordering on monocular images with Binary Partition Tree

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Monocular Depth Estimation: A Survey

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The Logistic Random Field — A convenient graphical model for learning parameters for MRF-based labeling

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A systematic review of monocular depth estimation for autonomous driving: Methods and dataset benchmarking

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Superpixels of RGB-D Images for Indoor Scenes Based on Weighted Geodesic Driven Metric

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Occlusion cues for image scene layering

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Building a dictionary of image fragments

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Real-time estimation of 3D scene geometry from a single image

Jung, C., & Kim, C. (2012) · Pattern Recognition · doi:10.1016/j.patcog.2012.02.028 · Cited by 13

Building a database of 3D scenes from user annotations

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Contour Grouping Based on Contour-Skeleton Duality

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Deep Learning based Monocular Depth Prediction: Datasets, Methods and ApplicationsPreprint

Li, Q., Zhu, J., Liu, J., Cao, R., Li, Q., Jia, S., & Qiu, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.04123 · Cited by 12

ColorSketch: A Drawing Assistant for Generating Color Sketches from Photos

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Learning Two-View Stereo Matching

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Trajectory bundle estimation For perception-driven planning

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Mobile robot 3D map building and path planning based on multi-sensor data fusion

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Cusp Pixel Labelling Model for Objects Outline Using R-CNN

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Virtual Occlusions Through Implicit Depth

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A visual attention model for stereoscopic 3D images using monocular cues

Iatsun, I., Larabi, M., & Fernández-Maloigne, C. (2015) · Signal Processing Image Communication · doi:10.1016/j.image.2015.05.009 · Cited by 8

Depth Extraction from Videos Using Geometric Context and Occlusion Boundaries

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An Efficient Multi-View Generation Method From a Single-View Video Based on Affine Geometry Information

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Finding Temporally Consistent Occlusion Boundaries in Videos Using Geometric Context

Raza, S. H., Humayun, A., Essa, I. A., Grundmann, M., & Anderson, D. (2015) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2015.141 · Cited by 7

RGBD image segmentation

Mirkamali, S., & Nagabhushan, P. N. (2015) · Iranian Conference on Machine Vision and Image Processing · doi:10.1109/iranianmvip.2015.7397500 · Cited by 6

Single image ground plane estimation

Rahimi, A. M., Moradi, H., & Zoroofi, R. A. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738443 · Cited by 6

Perceptual and Computational Categories in Art

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Image Segmentation by Discounted Cumulative Ranking on Maximal CliquesPreprint

Carreira, J., Ion, A., & Sminchisescu, C. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1009.4823 · Cited by 6

Amodal Scene Analysis via Holistic Occlusion Relation Inference and Generative Mask Completion

Zhang, B., Liu, Q., Zhang, J., Wang, Y., Liu, L., Lin, Z., & Liu, Y. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i7.28526 · Cited by 6

Figure-Ground Image Segmentation Helps Weakly-Supervised Learning of Objects

Fragkiadaki, K., & Shi, J. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15567-3_41 · Cited by 5

Occlusion Boundary: A Formal Definition & Its Detection via Deep Exploration of Context

Wang, C., Fu, H., Tao, D., & Black, M. D. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3039478 · Cited by 5

3D Reconstruction from a Single Still Image Based on Monocular Vision of an Uncalibrated Camera

Yu, T., Zou, J., & Song, Q. (2017) · ITM Web of Conferences · doi:10.1051/itmconf/20171201018 · Cited by 5

Place recognition from disparate views

Frampton, R., & Calway, A. D. (2013) · British Machine Vision Conference (BMVC) · doi:10.5244/c.27.111 · Cited by 5

Cooperative disparity and object boundary estimation

Narasimha, R., Arnaud, É., Forbes, F., & Horaud, R. (2008) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2008.4712122 · Cited by 5

Learning to Refine Object Contours with a Top-Down Fully Convolutional Encoder-Decoder NetworkPreprint

Liu, Y., Yao, J., Li, L., Lu, X., & Han, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.04456 · Cited by 5

Multilevel affinity graph for unsupervised image segmentation

Li, A., Wang, X., Yan, K., Li, C., & Feng, D. D. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7532561 · Cited by 4

RCFNet: Related cross-level feature network with cascaded self-distillation for monocular depth estimation

Xia, C., Zhang, M., Gao, X., Ge, B., Li, K., Fang, X., Liang, X., & Zhang, Y. (2024) · Digital Signal Processing · doi:10.1016/j.dsp.2024.104681 · Cited by 4

Object Detection Under Occlusion in Aerial Images: A Review

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Pascal VOC 2008 Challenge

Hoiem, D., Divvala, S., & Hays, J. H. (2009) · Cited by 4

Depth prediction from a single image based on non-parametric learning in the gradient domain

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Extended set of superpixel features

Egorova, A. A., Sergeyev, V. V., & RAS, I. R. –. B. O. T. F. “. A. P. (2021) · Computer Optics · doi:10.18287/2412-6179-co-876 · Cited by 4

Automatic depth profiling of 2D cinema - and photographic images

Burazerovic, D., Vandewalle, P., & Berretty, R.-P. (2009) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2009.5413347 · Cited by 4

Multi-layered image retargeting

Sugimoto, S., Shimizu, S., Kimata, H., & Kojima, A. (2012) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2012.6467531 · Cited by 4

Structured Depth Prediction in Challenging Monocular Video SequencesPreprint

Liu, M., Salzmann, M., & He, X. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.06070 · Cited by 4

Single image depth estimation using local gradient-based features

Kostadinov, D., & Ivanovski, Z. A. (2012) · International Conference on Systems, Signals and Image Processing · Cited by 3

Generation of Random Fields for Image Segmentation Using Manifold Learning Technique

Pemula, R., & Nagaraju, C. H. (2016) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001416540070 · Cited by 3

Mobile Robot Tracking with Deep Learning Models under the Specific Environments

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Scene image segmentation based on Perceptual Organization

Chang, C., Koschan, A. F., Page, D. L., & Abidi, M. A. (2009) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2009.5414627 · Cited by 3

Estimation of Underwater Monocular Depth Using Laplacian Pyramid-Based Depth Residuals

Chen, W., Luo, X., Li, F., & Wang, D. (2023) · International Conference on Information Science and Technology · doi:10.1109/icist59754.2023.10367166 · Cited by 2

Architectural epistemology, existing buildings, and single-image modeling

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Comprehensive depth estimation algorithm for efficient stereoscopic content creation in three-dimensional video systems

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Neighborhood Filters and the Recovery of 3D Information

Digne, J., Dimiccoli, M., Sabater, N., & Salembier, P. (2015) · Springer eBooks · doi:10.1007/978-1-4939-0790-8_27 · Cited by 2

Learning boundaries with color and depth

Jia, Z., Gallagher, A., & Chen, T. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738628 · Cited by 2

Boundary extraction using supervised edgelet classification

Zhao, J. Y., Ma, J., Ma, J., & Zheng, S. (2012) · Optical Engineering · doi:10.1117/1.oe.51.1.017002 · Cited by 2

Question interface for 3D picture creation on an autostereoscopic digital picture frame

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Depth estimation algorithm based on data-driven approach and depth cues for stereo conversion in three-dimensional displays

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Noise-Robust SLIC Superpixel for Natural Images

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Hash based manifold learning technique to generating random fields for image segmentation

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A 3D Pseudo-Reconstruction from Single ImageBased on Vanishing Point

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A Survey on Outdoor Scene Image Segmentation

SamaSam, E., Prabhavathy, A. K., & Shree, J. D. (2012) · International Journal of Computer Applications · doi:10.5120/8781-2752 · Cited by 2

Enhanced depth estimation by using object placement relation

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Occlusion-Ordered Semantic Instance Segmentation

Baselizadeh, S., Yu, C.-T., Veksler, O., & Boykov, Y. Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31335-5_27 · Cited by 1

Single-image 3-D depth estimation for urban scenes

Cheng, H.-M., Tseng, C., Hsin, C., & Wang, S. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738437 · Cited by 1

Motion field estimation in images based on segmentation

Jiang, Y. (2015) · International Conference on Intelligent Control and Information Processing · doi:10.1109/icicip.2015.7388222 · Cited by 1

Bas-Relief Ambiguity

Chandraker, M. K. (2014) · Springer eBooks · doi:10.1007/978-0-387-31439-6_542 · Cited by 1

Depth extraction of partially occluded objects using deformable net

Shaaban, K. M., & Omar, N. M. (2016) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2016.05.004 · Cited by 1

Neighborhood Filters and the Recovery of 3D Information

Digne, J., Dimiccoli, M., Salembier, P., & Sabater, N. (2010) · Springer eBooks · doi:10.1007/978-0-387-92920-0_27 · Cited by 1

Estimating object region from local contour configuration

Suzuki, T., & Hebert, M. (2009) · 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2009.5204212 · Cited by 1

Tapping motion blur for robust normal estimation of planar scenes

Vasu, S., Rajagopalan, A. N., & Seetharaman, G. S. (2015) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2015.7351305 · Cited by 1

Category independent object discovery via background modeling

Chen, G., & Zhang, H. (2016) · Pattern Recognition Letters · doi:10.1016/j.patrec.2016.05.004 · Cited by 1

A Neurally Inspired Model of Figure Ground Organization with Local and Global Cues

Ramenahalli, S. (2020) · AI · doi:10.3390/ai1040028 · Cited by 1

Journal of Scientific Research and Advances

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Constrained Parametric Proposals and Pooling Methods for Semantic Segmentation in RGB-D ImagesPreprint

Banica, D., & Sminchisescu, C. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1312.7715 · Cited by 1

2D-to-3D conversion of single-view video based on view-distance classification

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Layered graphical models for tracking partially-occluded moving objects in video

Sclaroff, S., & Ablavsky, V. (2011) · OpenBU (Boston University)

An image query-based approach for urban modeling

Kanazawa, K., 浦辺, 一., Moriya, T., & Takahashi, T. (2010) · ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia (SIGGRAPH Asia) · doi:10.1145/1899950.1899964

A novel 2D-to-3D conversion method based on blocks world

Ji, P., Wang, L., Li, D., & Zhang, M. (2012) · International Conference on Audio, Language and Image Processing · doi:10.1109/icalip.2012.6376676

Photo magician: Controlling point of focus and depth of field on an all-focused image

Rahmah, D. N., Hua, K., Lai, Y., & Yao, C. (2013) · IEEE International Symposium on Consumer Electronics (ISCE) · doi:10.1109/isce.2013.6570253

Segment image with depth in a bayesian framework

Gao, R., & Wang, M. (2010) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp.2010.5647996

Embedded Implementation of Non-uniformity Correction using Kalman Filter Algorithm

Goyal, P. (2011) · International Journal of Computer Applications · doi:10.5120/2297-2985

Vistas: Hierarchial boundary priors using multiscale conditional random fields

Warrell, J., Moore, A. P., & Prince, S. J. D. (2009) · British Machine Vision Conference (BMVC) · doi:10.5244/c.23.88

OCCLUSION BOUNDARIES ESTIMATION FROM A HIGH-RESOLUTION SAR IMAGE

He, W. P., Jägaer, M., & Hellwich, O. (2009)

A highly parallel design of image surface layout recovering on GPGPU

Li, G., & Lai, B.-C. C. (2012) · International Symposium on VLSI Design, Automation and Test · doi:10.1109/vlsi-dat.2012.6212628

Image Segmentation and Filtering

Dimiccoli, M., & Salembier, P. (2009)

Rock Image Segmentation Based on Improved Entropyrate Superpixel and Region Merging

李, 杰. (2016) · Journal of Image and Signal Processing · doi:10.12677/jisp.2016.51003

Combining recognition and geometry for data-driven 3D reconstruction

Owens, A. (2013) · DSpace@MIT (Massachusetts Institute of Technology)

Silhouette-Aware Warping for Image-Based Rendering - Supplementary material

Chaurasia, G., Sorkine‐Hornung, O., & George Drettakis, B. (2011)

Blending Learning and Inference in Structured PredictionPreprint

Hazan, T., Schwing, A. G., McAllester, D., & Urtasun, R. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1210.2346

Techniques for extracting contours and merging maps

Latecki, L. J., & Adluru, N. (2008)

Automatic Face Recognition Using Gabor Wavelet and Robust Structured Spares Representation

Madhura, R., & Devi, P. S. (2014) · IOSR Journal of Computer Engineering · doi:10.9790/0661-16198285

Monocular-Cues Based 3-D Reconstruction

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Depth refinement on sparse-depth images using visual perception cues

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Automatic Depth Prediction from 2D Videos Based on Non-parametric Learning and Bi-directional Depth Propagation

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APPLICATION OF COMPUTER VISION TECHNOLOGIES FOR AUTONOMOUS PILE MANIPULATION

Supoņenkovs, A., Kovaļovs, M., & Markovičs, Z. (2019) · Environment Technology Resources Proceedings of the International Scientific and Practical Conference · doi:10.17770/etr2019vol2.4033

Boundary Detection

Ren, X. (2014) · Springer eBooks · doi:10.1007/978-0-387-31439-6_215

Neighborhood Filters and the Recovery of 3D Information

Digne, J., Dimiccoli, M., Sabater, N., & Salembier, P. (2014) · Springer eBooks · doi:10.1007/978-3-642-27795-5_27-5

Boundary Detection

Ren, X. (2021) · Springer eBooks · doi:10.1007/978-3-030-63416-2_215

Occlusion

Sinha, S. N. (2021) · Springer eBooks · doi:10.1007/978-3-030-03243-2_806-1

Occlusion

Sinha, S. N. (2021) · Springer eBooks · doi:10.1007/978-3-030-63416-2_806

2.1D Sketch and Layered Representation

Zhu, S. C., & Wu, Y. N. (2023) · Springer eBooks · doi:10.1007/978-3-030-96530-3_7

Scene Segmentation and Object Classification for Place Recognition

Chang, C. (2010)

EDFIDepth: enriched multi-path vision transformer feature interaction networks for monocular depth estimation

Xia, C., Zhang, M., Gao, X., Ge, B., Li, K., Fang, X., Zhang, Y., & Liang, X. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06205-7

Enhanced Holistically Nested Edge Detection (eHED) Algorithm: A Reliable Edge Detection in Unconstrained Scenarios

Ramalakshmi, S., & Vani, V. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-6726-7_7

Recovering Occlusion Boundaries from an Image

Hoiem, D., Efros, A. A., & Hebert, M. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-010-0400-4 · Cited by 177

Occlusion relationship reasoning with a feature separation and interaction network

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Occlusion boundaries: low-level detection to high-level reasoning

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DOC: Deep OCclusion Recovering From A Single Image.Preprint

Wang, P., & Yuille, A. (2015) · arXiv (Cornell University) · Cited by 2

Occlusion Boundaries from Motion: Low-Level Detection and Mid-Level Reasoning

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