Depth estimation from single image using Defocus and Texture cues

Abstract: Significant recent developments in 3D display technology have focused on techniques for converting 2D media into 3D. Depth map is an integral part of 2D-to-3D conversion. Combining multiple depth cues results in a more accurate depth map as it compensates for the errors caused by one depth cue as well as its absence by other depth cues. In this paper, we present a novel framework to generate a more accurate depth map for video using defocus and motion cues. The moving objects present in the scene are the source of errors in both defocus and motion-based depth map estimation. The proposed method rectifies these errors in the depth map by integrating defocus blur and motion cues. In addition, it also corrects the errors in other parts of depth map caused by inaccurate estimation of defocus blur and motion. Since the proposed integration approach relies on the characteristics of point spread functions of defocus and motion blur along with their relations to camera parameters, it is more accurate and reliable.

Abstract: Depth map estimation forms an integral part of many applications such as 2D-to-3D creation. There exists various methods in literature for depth map estimation using different cues and structure. Usually, depth information is decoded from these cues at the edges and matting is applied to spread it over neighboring regions. Defocus is one such cue due to its natural existence and does not require any precondition compared to other cues. However, there can exist regions in images with no edges. These regions are referred to hole regions and are the main source of error in estimated depth map. In this paper, we propose a method to correct some of these errors to obtain an accurate depth map. The proposed method uses color uniformity principle to detect hole regions present in depth map. We also provide a framework to identify falsely detected holes in order to increase effectiveness of our method.

Abstract: The occurrence of motion blur along with defocus blur is a common phenomena in natural images. Usually, these blurs are spatially varying in nature for any general image and estimation of one type of blur is affected by presence of other. In this paper, we propose a novel method to estimate the concurrent defocus and motion blurs in a single image. Unlike the recent methods, which perform well only on simulated conditions or in presence of single type of blur, proposed method works well for real images as well as for compressed images. In this paper, we consider only commonly associated motion and defocus blurs for analysis. Decoupling of motion and defocus blur provides a fundamental tool that can be used for various analysis and applications.

Abstract: Depth information is essential for on-board perception in autonomous driving and driver assistance. Monocular depth estimation (MDE) is very appealing since it allows for appearance and depth being on direct pixelwise correspondence without further calibration. Best MDE models are based on Convolutional Neural Networks (CNNs) trained in a supervised manner, i.e., assuming pixelwise ground truth (GT). Usually, this GT is acquired at training time through a calibrated multi-modal suite of sensors. However, also using only a monocular system at training time is cheaper and more scalable. This is possible by relying on structure-from-motion (SfM) principles to generate self-supervision. Nevertheless, problems of camouflaged objects, visibility changes, static-camera intervals, textureless areas, and scale ambiguity, diminish the usefulness of such self-supervision. In this paper, we perform monocular depth estimation by virtual-world supervision (MonoDEVS) and real-world SfM self-supervision. We compensate the SfM self-supervision limitations by leveraging virtual-world images with accurate semantic and depth supervision and addressing the virtual-to-real domain gap. Our MonoDEVSNet outperforms previous MDE CNNs trained on monocular and even stereo sequences.

Abstract: Tree trunk diameter and tree species are two of the most important parameters in analyzing trees in urban areas and forests. Conventionally, diameters have been measured manually, and the species were determined by sight. An automated tool for these assessments was developed. Tree trunks are automatically detected from captured stereo images. Then, tree trunk diameters are estimated, and the species are determined. The developed graphical user interface tool enables fast and accurate estimation even while one is walking, which reduces the time spent in measuring trees.

Abstract: 1. Introduction. 2. Fundamentals. 3. Intensity Transformations and Spatial Filtering. 4. Frequency Domain Processing. 5. Image Restoration. 6. Color Image Processing. 7. Wavelets. 8. Image Compression. 9. Morphological Image Processing. 10. Image Segmentation. 11. Representation and Description. 12. Object Recognition.

Abstract: Interactive digital matting, the process of extracting a foreground object from an image based on limited user input, is an important task in image and video editing. From a computer vision perspective, this task is extremely challenging because it is massively ill-posed - at each pixel we must estimate the foreground and the background colors, as well as the foreground opacity ("alpha matte") from a single color measurement. Current approaches either restrict the estimation to a small part of the image, estimating foreground and background colors based on nearby pixels where they are known, or perform iterative nonlinear estimation by alternating foreground and background color estimation with alpha estimation. In this paper, we present a closed-form solution to natural image matting. We derive a cost function from local smoothness assumptions on foreground and background colors and show that in the resulting expression, it is possible to analytically eliminate the foreground and background colors to obtain a quadratic cost function in alpha. This allows us to find the globally optimal alpha matte by solving a sparse linear system of equations. Furthermore, the closed-form formula allows us to predict the properties of the solution by analyzing the eigenvectors of a sparse matrix, closely related to matrices used in spectral image segmentation algorithms. We show that high-quality mattes for natural images may be obtained from a small amount of user input.

Abstract: We consider the problem of estimating detailed 3D structure from a single still image of an unstructured environment. Our goal is to create 3D models that are both quantitatively accurate as well as visually pleasing. For each small homogeneous patch in the image, we use a Markov random field (MRF) to infer a set of "plane parametersrdquo that capture both the 3D location and 3D orientation of the patch. The MRF, trained via supervised learning, models both image depth cues as well as the relationships between different parts of the image. Other than assuming that the environment is made up of a number of small planes, our model makes no explicit assumptions about the structure of the scene; this enables the algorithm to capture much more detailed 3D structure than does prior art and also give a much richer experience in the 3D flythroughs created using image-based rendering, even for scenes with significant nonvertical structure. Using this approach, we have created qualitatively correct 3D models for 64.9 percent of 588 images downloaded from the Internet. We have also extended our model to produce large-scale 3D models from a few images.

Abstract: Colorization is a computer-assisted process of adding color to a monochrome image or movie The process typically involves segmenting images into regions and tracking these regions across image sequences Neither of these tasks can be performed reliably in practice; consequently, colorization requires considerable user intervention and remains a tedious, time-consuming, and expensive taskIn this paper we present a simple colorization method that requires neither precise image segmentation, nor accurate region tracking Our method is based on a simple premise; neighboring pixels in space-time that have similar intensities should have similar colors We formalize this premise using a quadratic cost function and obtain an optimization problem that can be solved efficiently using standard techniques In our approach an artist only needs to annotate the image with a few color scribbles, and the indicated colors are automatically propagated in both space and time to produce a fully colorized image or sequence We demonstrate that high quality colorizations of stills and movie clips may be obtained from a relatively modest amount of user input

Abstract: We consider the task of depth estimation from a single monocular image. We take a supervised learning approach to this problem, in which we begin by collecting a training set of monocular images (of unstructured outdoor environments which include forests, trees, buildings, etc.) and their corresponding ground-truth depthmaps. Then, we apply supervised learning to predict the depthmap as a function of the image. Depth estimation is a challenging problem, since local features alone are insufficient to estimate depth at a point, and one needs to consider the global context of the image. Our model uses a discriminatively-trained Markov Random Field (MRF) that incorporates multiscale local- and global-image features, and models both depths at individual points as well as the relation between depths at different points. We show that, even on unstructured scenes, our algorithm is frequently able to recover fairly accurate depthmaps.