Accurate and discernible photocollages

There currently exist several techniques for selecting and combining images from a digital image library into a single image so that the result meets certain prespecified visual criteria. Image mosaic methods, first explored by Connors and Trivedi[18], arrange library images according to some tiling arrangement, often a regular grid, so that the combination of images, when viewed as a whole, resembles some input target image. Other techniques, such as Autocollage of Rother et al.[78], seek only to combine images in an interesting and visually pleasing manner, according to certain composition principles, without attempting to approximate any target image. Each of these techniques provide a myriad of creative options for artists who wish to combine several levels of meaning into a single image or who wish to exploit the meaning and symbolism contained in each of a large set of images through an efficient and easy process. We first examine the most notable and successful of these methods, and summarize the advantages and limitations of each. We then formulate a set of goals for an image collage system that combines the advantages of these methods while addressing and mitigating the drawbacks. Particularly, we propose a system for creating photocollages that approximate a target image as an aggregation of smaller images, chosen from a large library, so that interesting visual correspondences between images are exploited. In this way, we allow users to create collages in which multiple layers of meaning are encoded, with meaningful visual links between each layer. In service of this goal, we ensure that the images used are as large as possible and are combined in such a way that boundaries between images are not immediately apparent, as in Autocollage. This has required us to apply a multiscale approach to searching and comparing images from a large database, which achieves both speed and accuracy. We also propose a new framework for color post-processing, and propose novel techniques for decomposing images according to object and texture information

融合主题和视觉特征的图片拼贴画合成方法

为辅助设计师进行图片拼贴画艺术创作,提出一种融合主题和视觉特征的图片拼贴画合成方法.给定容器图片和素材图片集,将容器图片划分为若干子区域;并使用圆填充算法及Delaunay三角剖分算法生成子区域的Voronoi图,得到补丁集;之后,计算主题-颜色相似性矩阵,进行素材图片与补丁之间的映射;最后,使用颜色线性融合方法进一步优化拼贴画视觉效果.在保留图片视觉特征的同时,该方法亦能保证容器和素材之间具有相似的主题信息.与市面流行拼贴画制作软件的对比实验表明,该方法合成的图片拼贴画能取得更好的视觉效果,在数字媒体和装饰领域中均有着潜在的应用价值.国家自然科学基金(61772440);;\n光电控制技术重点实验室和航空科学基金联合资助项目(20165168007);;\n浙江大学CAD&CG国家重点实验室开放课题(A1706

Large-area visually augmented navigation for autonomous underwater vehicles

Submitted to the Joint Program in Applied Ocean Science & Engineering in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2005This thesis describes a vision-based, large-area, simultaneous localization and mapping (SLAM) algorithm that respects the low-overlap imagery constraints typical of autonomous underwater vehicles (AUVs) while exploiting the inertial sensor information that is routinely available on such platforms. We adopt a systems-level approach exploiting the complementary aspects of inertial sensing and visual perception from a calibrated pose-instrumented platform. This systems-level strategy yields a robust solution to underwater imaging that overcomes many of the unique challenges of a marine environment (e.g., unstructured terrain, low-overlap imagery, moving light source). Our large-area SLAM algorithm recursively incorporates relative-pose constraints using a view-based representation that exploits exact sparsity in the Gaussian canonical form. This sparsity allows for efficient O(n) update complexity in the number of images composing the view-based map by utilizing recent multilevel relaxation techniques. We show that our algorithmic formulation is inherently sparse unlike other feature-based canonical SLAM algorithms, which impose sparseness via pruning approximations. In particular, we investigate the sparsification methodology employed by sparse extended information filters (SEIFs) and offer new insight as to why, and how, its approximation can lead to inconsistencies in the estimated state errors. Lastly, we present a novel algorithm for efficiently extracting consistent marginal covariances useful for data association from the information matrix. In summary, this thesis advances the current state-of-the-art in underwater visual navigation by demonstrating end-to-end automatic processing of the largest visually navigated dataset to date using data collected from a survey of the RMS Titanic (path length over 3 km and 3100 m2 of mapped area). This accomplishment embodies the summed contributions of this thesis to several current SLAM research issues including scalability, 6 degree of freedom motion, unstructured environments, and visual perception.This work was funded in part by the CenSSIS ERC of the National Science Foundation under grant EEC-9986821, in part by the Woods Hole Oceanographic Institution through a grant from the Penzance Foundation, and in part by a NDSEG Fellowship awarded through the Department of Defense

Revealing the Invisible: On the Extraction of Latent Information from Generalized Image Data

The desire to reveal the invisible in order to explain the world around us has been a source of impetus for technological and scientific progress throughout human history. Many of the phenomena that directly affect us cannot be sufficiently explained based on the observations using our primary senses alone. Often this is because their originating cause is either too small, too far away, or in other ways obstructed. To put it in other words: it is invisible to us. Without careful observation and experimentation, our models of the world remain inaccurate and research has to be conducted in order to improve our understanding of even the most basic effects. In this thesis, we1 are going to present our solutions to three challenging problems in visual computing, where a surprising amount of information is hidden in generalized image data and cannot easily be extracted by human observation or existing methods. We are able to extract the latent information using non-linear and discrete optimization methods based on physically motivated models and computer graphics methodology, such as ray tracing, real-time transient rendering, and image-based rendering

Seabed fluid flow-related processes: evidence and quantification based on high-resolution imaging techniques and GIS analyses

This work provides new insights on different aspects of seabed fluid flow processes based on seafloor observations. The methods used entirely rely on ROV-based high-resolution imaging and mapping techniques. Optical data are used to produce visual maps of the seafloor, in the form of geo-referenced video- and photo-mosaics, whereas acoustic techniques allow mapping the micro-bathymetry of the seabed, as well as the signal reflectivity of the sediment surface and of the water column. This work presents three case studies, about two sites of seabed fluid flow: the Menez Gwen hydrothermal vent on the MAR and the REGAB pockmark in the Lower Congo Basin. On the technical side, some of the high-resolution techniques used in this thesis are not commonly used by the marine scientific community. This is particularly the case for large-area photo-mosaics. Although the interest in mosaicking is growing, there are still no tools freely and readily available to scientists to routinely construct large-area photo-mosaics. Therefore, this work presents a MATLAB toolbox for large-area photo-mosaicking (LAPM toolbox), which was developed as part of this thesis

Interfaces with the Ineffable

In recent years, Human Computer Interaction (HCI) designers and researchers have shifted focus from a primary concern with procedural, generic, and task based applications to applications that address messy, personal, and aesthetic experiences. These difficult to formalize experiences, such as feelings of intimacy, spirituality, or a sense of place, are conceptualized as experiences of the ineffable. In this work, I use a reflective design practice to look at two primary approaches to designing interfaces with the ineffable, one emphasizes reduction and the other openness to interpretation. I discuss issues of control and reification that result from the reduction approach and develop the interpretation approach as a viable alternative requiring a re-thinking design and evaluation strategies and criteria. These issues and approaches are explored in detail through the development of two case studies. Case study one addresses the ineffable experience of art and presents a series of applications for interfacing with the ineffable in the art museum. Case study two details the ineffable experience of affect and presents a system designed for augmenting affective presence in an office environment. To further this work, I examine new thinking in both HCI and Communication for understanding every day interpretive acts and the implications for design. In addition, I advance reflective design as a new process based practice for the field of Communication