In this paper, a 2d to 3d stereo imageconversion scheme is proposed for 3d content creation. The difficulty in this problem lies on depth estimation/assignment from a mono image, which actually does not have sufficie...
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ISBN:
(纸本)9783642253454
In this paper, a 2d to 3d stereo imageconversion scheme is proposed for 3d content creation. The difficulty in this problem lies on depth estimation/assignment from a mono image, which actually does not have sufficient information. To estimate the depth map, we adopt a strategy of performing foreground/background separation first, then classifying a backgrounddepth profile by neural network, estimating foregrounddepth from image cues, and finally combining them. To enhance stereoscopic perception for the synthesizedimages viewed on 3ddisplay, depth refinement based on bilateral filter and HVS-based contrast modification between the foreground and background are adopted. Subjective experiments show that the stereo images generated by using the proposed scheme can provide good3d perception.
We present a method for semi-automatically converting unconstrained2dimages and video content into stereoscopic 3d. The user is presented with the image to convert, and brushes user-defineddepth strokes in certain ...
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ISBN:
(纸本)9781622765386
We present a method for semi-automatically converting unconstrained2dimages and video content into stereoscopic 3d. The user is presented with the image to convert, and brushes user-defineddepth strokes in certain areas. These correspond to a rough estimate of the scene depths within these points. After, the rest of the depths are solved using this information, producing a depth map to create stereoscopic 3d content. For video, the user chooses several keyframes for brushing, and the depths for the entire video are found in a volumetric basis. Additionally for video, the user has the option of minimizing effort by employing a robust tracking algorithm, where the first frame only needs to be labeled. After, the labels are propagated throughout the entire video, ultimately increasing accuracy with more frames labeled. Our work combines the merits of two energy minimization techniques: Graph Cuts and Random Walks. The former respects boundaries, but does not have suitable depth diffusion, making the scene look like “cardboard cutouts”. The latter has gooddepth diffusion, but object boundaries are blurred. Therefore, combining the merits of both will lead to a higher quality result. Current efforts rely on automatic or manual conversion by rotoscopers. The former prohibits user intervention, while the latter is time consuming, prohibiting use in smaller studios. Semi-automatic is a compromise to allow for more faster and accurate conversion, decreasing the time for studios to release 3d content. The results shown in this paper generate good quality stereoscopic depth maps with minimal effort required.
In recent years, measuring three-dimensional (3d) surface information has gained a great interest in plant phenotyping because it can represent the nature of plant architecture better than conventional 2dimages. This...
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ISBN:
(纸本)9789819752119;9789819752126
In recent years, measuring three-dimensional (3d) surface information has gained a great interest in plant phenotyping because it can represent the nature of plant architecture better than conventional 2dimages. This paper presents an approach for processing 3d point clouds converted from 2d RGB images in the context of high-throughput plant phenotyping. High-resolution RGB multi-view imagery of a Chickpea plant was collected using a high-end camera. Based on these image sequences, 3d point cloud reconstruction of the canopy was conducted and analyzed. Later sophisticated3d operations were performed on these images including 3ddownsampling and after that clustering was performed on the processed point cloud. The information generated can help in the evaluation of crop traits and provide accurate statistics for the assessment of their growth parameters.
We describe a system for robustly estimating synthetic depth maps in unconstrainedimages and videos, for semi-automatic conversion into stereoscopic 3d. Currently, this process is automatic or done manually by rotosc...
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We describe a system for robustly estimating synthetic depth maps in unconstrainedimages and videos, for semi-automatic conversion into stereoscopic 3d. Currently, this process is automatic or done manually by rotoscopers. Automatic is the least labor intensive, but makes user intervention or error correction difficult. Manual is the most accurate, but time consuming and costly. Noting the merits of both, a semi-automatic method blends them together, allowing for faster and accurate conversion. This requires user-defined strokes on the image, or over several keyframes for video, corresponding to a rough estimate of the depths. After, the rest of the depths are determined, creating depth maps to generate stereoscopic 3d content, with depth image Based Rendering to generate the artificial views. depth map estimation can be considered as a multi-label segmentation problem: each class is a depth. For video, we allow the user to label only the first frame, and we propagate the strokes using computer vision techniques. We combine the merits of two well-respected segmentation algorithms: Graph Cuts and Random Walks. The diffusion from Random Walks, with the edge preserving of Graph Cuts should give good results. We generate good quality content, more suitable for perception, compared to a similar framework.
Estimating scene depth from a single monocular image is a crucial component in computer vision tasks, enabling many further applications such as robot vision, 3-d modeling, and above all, 2-d to 3-dimage/video conver...
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Estimating scene depth from a single monocular image is a crucial component in computer vision tasks, enabling many further applications such as robot vision, 3-d modeling, and above all, 2-d to 3-dimage/video conversion. Since there are an infinite number of possible world scenes, that can produce a unique image, single imagedepth estimation is a highly challenging task. This paper tackles such an ambiguous problem by using the merits of both global and local information (structures) of a scene. To this end, we formulate single imagedepth estimation as a regression problem via (on) rich depth related features which describe effective monocular cues. Exploiting the relationship between these image features anddepth values is adopted via a learning model which is inspired by modified stacked generalization scheme. The experiments demonstrate competitive results compared with existing data-driven approaches in both quantitative and qualitative analysis with a remarkably simpler approach than previous works.
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