Object recognition and 3D reconstruction of occluded objects using binocular stereo

2017 ◽  
Vol 21 (1) ◽  
pp. 29-38 ◽  
Author(s):  
L. Priya ◽  
Sheila Anand
Keyword(s):  
Object Recognition ◽  
3D Reconstruction ◽  
Occluded Objects ◽  
Binocular Stereo

scholarly journals Vision System of Mobile Robot Combining Binocular and Depth Cameras

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Yuxiang Yang ◽  
Xiang Meng ◽  
Mingyu Gao
Keyword(s):  
Mobile Robot ◽  
3D Reconstruction ◽  
Trajectory Planning ◽  
Stereo Matching ◽  
Vision System ◽  
Depth Camera ◽  
Structural Constraints ◽  
Depth Cameras ◽  
Stereo Cameras ◽  
Binocular Stereo

In order to optimize the three-dimensional (3D) reconstruction and obtain more precise actual distances of the object, a 3D reconstruction system combining binocular and depth cameras is proposed in this paper. The whole system consists of two identical color cameras, a TOF depth camera, an image processing host, a mobile robot control host, and a mobile robot. Because of structural constraints, the resolution of TOF depth camera is very low, which difficultly meets the requirement of trajectory planning. The resolution of binocular stereo cameras can be very high, but the effect of stereo matching is not ideal for low-texture scenes. Hence binocular stereo cameras also difficultly meet the requirements of high accuracy. In this paper, the proposed system integrates depth camera and stereo matching to improve the precision of the 3D reconstruction. Moreover, a double threads processing method is applied to improve the efficiency of the system. The experimental results show that the system can effectively improve the accuracy of 3D reconstruction, identify the distance from the camera accurately, and achieve the strategy of trajectory planning.

scholarly journals Invariants of the Space Point Element Structure and Their Applications

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yanping Mui ◽  
Youzheng Zhang ◽  
Guitao Cao
Keyword(s):  
Object Recognition ◽  
3D Reconstruction ◽  
Traditional Method ◽  
3D Structure ◽  
Geometric Invariant ◽  
Basic Matrix ◽  
Projective Invariants ◽  
Single Frame ◽  
Space Point ◽  
Geometric Relationship

In this paper, a new geometric structure of projective invariants is proposed. Compared with the traditional invariant calculation method based on 3D reconstruction, this method is comparable in the reliability of invariant calculation. According to this method, the only thing needed to find out is the geometric relationship between 3D points and 2D points, and the invariant can be obtained by using a single frame image. In the method based on 3D reconstruction, the basic matrix of two images is estimated first, and then, the 3D projective invariants are calculated according to the basic matrix. Therefore, in terms of algorithm complexity, the method proposed in this paper is superior to the traditional method. In this paper, we also study the projection transformation from a 3D point to a 2D point in space. According to this relationship, the geometric invariant relationships of other point structures can be easily derived, which have important applications in model-based object recognition. At the same time, the experimental results show that the eight-point structure invariants proposed in this paper can effectively describe the essential characteristics of the 3D structure of the target, without the influence of view, scaling, lighting, and other link factors, and have good stability and reliability.

Three-Dimensional Object Surface Reconstruction Based on Camera Calibration and SIFT

2015 ◽  
Vol 719-720 ◽  
pp. 1191-1197 ◽  
Author(s):  
Jun Zhang ◽  
Long Ye ◽  
Qin Zhang ◽  
Jing Ling Wang
Keyword(s):  
3D Reconstruction ◽  
Camera Calibration ◽  
Image Matching ◽  
Three Dimensional ◽  
Target Object ◽  
Object A ◽  
Binocular Stereo Vision ◽  
Binocular Stereo ◽  
Dimensional Object

This paper is focused on camera calibration, image matching, both of which are the key issues in three-dimensional (3D) reconstruction. In terms of camera calibration firstly, we adopt the method based on the method proposed by Zhengyou Zhang. In addition to this, it is selective for us to deal with tangential distortion. In respect of image matching, we use the SIFT algorithm, which is invariant to image translation, scaling, rotation, and partially invariant to illumination changes and to affine or 3D projections. It performs well in the follow-up matching the corresponding points. Lastly, we perform 3D reconstruction of the surface of the target object. A Graphical User Interface is designed to help us to realize the key function of binocular stereo vision, with better visualization. Apparently, the entire GUI brings convenience to the follow-up work.

scholarly journals Mitigation of Effects of Occlusion on Object Recognition with Deep Neural Networks through Low-Level Image Completion

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Benjamin Chandler ◽  
Ennio Mingolla
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Object Recognition ◽  
Convolutional Neural Network ◽  
Deep Neural Networks ◽  
Poor Performance ◽  
Classification Algorithms ◽  
Image Completion ◽  
Convolutional Network ◽  
Occluded Objects

Heavily occluded objects are more difficult for classification algorithms to identify correctly than unoccluded objects. This effect is rare and thus hard to measure with datasets like ImageNet and PASCAL VOC, however, owing to biases in human-generated image pose selection. We introduce a dataset that emphasizes occlusion and additions to a standard convolutional neural network aimed at increasing invariance to occlusion. An unmodified convolutional neural network trained and tested on the new dataset rapidly degrades to chance-level accuracy as occlusion increases. Training with occluded data slows this decline but still yields poor performance with high occlusion. Integrating novel preprocessing stages to segment the input and inpaint occlusions is an effective mitigation. A convolutional network so modified is nearly as effective with more than 81% of pixels occluded as it is with no occlusion. Such a network is also more accurate on unoccluded images than an otherwise identical network that has been trained with only unoccluded images. These results depend on successful segmentation. The occlusions in our dataset are deliberately easy to segment from the figure and background. Achieving similar results on a more challenging dataset would require finding a method to split figure, background, and occluding pixels in the input.

Development of 3D Reconstruction and Object Recognition Model using Video

2020 ◽  
Vol 21 (11) ◽  
pp. 2011-2019
Author(s):  
Nahyuk Lee ◽  
Kyungtaek Lee ◽  
Youngsup Park ◽  
Sanghyun Seo ◽  
Taemin Lee
Keyword(s):  
Object Recognition ◽  
3D Reconstruction ◽  
Recognition Model

Effects of Occlusion on Pigeons' Visual Object Recognition

Perception ◽  
10.1068/p3441 ◽  
2002 ◽  
Vol 31 (11) ◽  
pp. 1299-1312 ◽  
Author(s):  
Norma T DiPietro ◽  
Edward A Wasserman ◽  
Michael E Young
Keyword(s):  
Object Recognition ◽  
Laboratory Research ◽  
Visual Object ◽  
Visual Object Recognition ◽  
Occluded Objects ◽  
Novel Objects ◽  
Series Of Experiments ◽  
Different Response ◽  
Partially Occluded ◽  
Do So

Casual observation suggests that pigeons and other animals can recognize occluded objects; yet laboratory research has thus far failed to show that pigeons can do so. In a series of experiments, we investigated pigeons' ability to ‘name’ shaded, textured stimuli by associating each with a different response. After first learning to recognize four unoccluded objects, pigeons had to recognize the objects when they were partially occluded by another surface or when they were placed on top of another surface; in each case, recognition was weak. Following training with the unoccluded stimuli and with the stimuli placed on top of the occluder, pigeons' recognition of occluded objects dramatically improved. Pigeons' improved recognition of occluded objects was not limited to the trained objects but transferred to novel objects as well. Evidently, the recognition of occluded objects requires pigeons to learn to discriminate the object from the occluder; once this discrimination is mastered, occluded objects can be better recognized.

Stereoscopic Depth: Its Relation to Image Segmentation, Grouping, and the Recognition of Occluded Objects

Perception ◽  
1989 ◽  
Vol 18 (1) ◽  
pp. 55-68 ◽  
Author(s):  
Ken Nakayama ◽  
Shinsuke Shimojo ◽  
Gerald H Silverman
Keyword(s):  
Image Segmentation ◽  
Object Recognition ◽  
Visual Processing ◽  
Template Matching ◽  
Stereoscopic Depth ◽  
Spatial Relationships ◽  
Object Shape ◽  
Occluded Objects ◽  
Surface Description ◽  
The Common

Image regions corresponding to partially hidden objects are enclosed by two types of bounding contour: those inherent to the object itself (intrinsic) and those defined by occlusion (extrinsic). Intrinsic contours provide useful information regarding object shape, whereas extrinsic contours vary arbitrarily depending on accidental spatial relationships in scenes. Because extrinsic contours can only degrade the process of surface description and object recognition, it is argued that they must be removed prior to a stage of template matching. This implies that the two types of contour must be distinguished relatively early in visual processing and we hypothesize that the encoding of depth is critical for this task. The common border is attached to and regarded as intrinsic to the closer region, and detached from and regarded as extrinsic to the farther region. We also suggest that intrinsic borders aid in the segmentation of image regions and thus prevent grouping, whereas extrinsic borders provide a linkage to other extrinsic borders and facilitate grouping. Support for these views is found in a series of demonstrations, and also in an experiment where the expected superiority of recognition was found when partially sampled faces were seen in a back rather than a front stereoscopic depth plane.

scholarly journals Parameters configuration for interest points in images obtained by Drone

2018 ◽  
Author(s):  
Rodrigo A. Rebouças ◽  
Elcio H. Shiguemori ◽  
Lamartine N. F. Guimarães
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Object Recognition ◽  
3D Reconstruction ◽  
Optimization Method ◽  
Input Image ◽  
Interest Points ◽  
Image Navigation ◽  
Mosaic Generation ◽  
Best Parameters

Drone use has grown with the use of image processing and computer vision techniques, such as autonomous image navigation, mosaic generation, elevation modeling, 3D reconstruction, and object recognition. In all techniques, an important step is an extraction of features, such as methods of interest points. This work addresses the modes of application of interest points, such as BRISK, ORB, FREAK, AKAZE and LATCH with the parameters configured automatically using the optimization method for images with different textures. This process is one of the pieces of final software that selects the use of a meta heuristic the best parameters automatically according to an input image.

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