Structure Preserving Large Imagery Reconstruction

With the explosive growth of web-based cameras and mobile devices, billions of photographs are uploaded to the internet. We can trivially collect a huge number of photo streams for various goals, such as image clustering, 3D scene reconstruction, and other big data applications. However, such tasks are not easy due to the fact the retrieved photos can have large variations in their view perspectives, resolutions, lighting, noises, and distortions. Fur-thermore, with the occlusion of unexpected objects like people, vehicles, it is even more challenging to find feature correspondences and reconstruct re-alistic scenes. In this paper, we propose a structure-based image completion algorithm for object removal that produces visually plausible content with consistent structure and scene texture. We use an edge matching technique to infer the potential structure of the unknown region. Driven by the estimated structure, texture synthesis is performed automatically along the estimated curves. We evaluate the proposed method on different types of images: from highly structured indoor environment to natural scenes. Our experimental results demonstrate satisfactory performance that can be potentially used for subsequent big data processing, such as image localization, object retrieval, and scene reconstruction. Our experiments show that this approach achieves favorable results that outperform existing state-of-the-art techniques

Edge Potential Functions (EPF) and Genetic Algorithms (GA) for Edge-Based Matching of Visual Objects

Edges are known to be a semantically rich representation of the contents of a digital image. Nevertheless, their use in practical applications is sometimes limited by computation and complexity constraints. In this paper, a new approach is presented that addresses the problem of matching visual objects in digital images by combining the concept of Edge Potential Functions (EPF) with a powerful matching tool based on Genetic Algorithms (GA). EPFs can be easily calculated starting from an edge map and provide a kind of attractive pattern for a matching contour, which is conveniently exploited by GAs. Several tests were performed in the framework of different image matching applications. The results achieved clearly outline the potential of the proposed method as compared to state of the art methodologies. (c) 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works

Learning Regularization Weight for CRF Optimization

In recent years, convolutional neural networks (CNNs) are leading the way in many computer vision problems. Since the development of fully convolutional networks, CNNs have been widely employed for low-level pixel-labeling problems, and successfully pushed the performance to a new level. Although CNNs are able to extract highly discriminative features, they typically assign a class label to each image pixel individually. This leads to various spatial inconsistencies. Therefore, CNNs are commonly combined with graphical models, such as conditional random fields (CRFs), to impose spatial coherence. CRFs were invented precisely for the task of imposing spatial coherence among image pixels. The coherence regularization weight serves an important role of controlling the regularization strength in the CRF optimization, and has a great influence on the quality of the final result. Traditionally this weight value is set to a fixed number for all images. In this thesis, we propose a novel approach to learn the coherence regularization weight for each individual image using a CNN, and then apply this per-image-learned weight in the CNN+CRF system. We first construct a dataset where the optimal regularization weight for the CRF optimization has been pre-computed for each image. We adopt convolutional regression networks with standard architecture for learning, and tailor the input according to our problem. We test the effectiveness of our approach on the task of salient object segmentation where a graph-cut based CRF optimizer can generate globally optimal solution. We show that consistent performance improvements can be achieved by using the regularization weight learned on per-image basis as opposed to a fixed regularization weight for all images in the dataset