Chapter Interactive Image Reconstruction from its Incomplete, Irregular and Imprecise Fragments

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JigsawNet: Shredded Image Reassembly using Convolutional Neural Network and Loop-based Composition

This paper proposes a novel algorithm to reassemble an arbitrarily shredded image to its original status. Existing reassembly pipelines commonly consist of a local matching stage and a global compositions stage. In the local stage, a key challenge in fragment reassembly is to reliably compute and identify correct pairwise matching, for which most existing algorithms use handcrafted features, and hence, cannot reliably handle complicated puzzles. We build a deep convolutional neural network to detect the compatibility of a pairwise stitching, and use it to prune computed pairwise matches. To improve the network efficiency and accuracy, we transfer the calculation of CNN to the stitching region and apply a boost training strategy. In the global composition stage, we modify the commonly adopted greedy edge selection strategies to two new loop closure based searching algorithms. Extensive experiments show that our algorithm significantly outperforms existing methods on solving various puzzles, especially those challenging ones with many fragment pieces

Image Processing Applications in Real Life: 2D Fragmented Image and Document Reassembly and Frequency Division Multiplexed Imaging

In this era of modern technology, image processing is one the most studied disciplines of signal processing and its applications can be found in every aspect of our daily life. In this work three main applications for image processing has been studied. In chapter 1, frequency division multiplexed imaging (FDMI), a novel idea in the field of computational photography, has been introduced. Using FDMI, multiple images are captured simultaneously in a single shot and can later be extracted from the multiplexed image. This is achieved by spatially modulating the images so that they are placed at different locations in the Fourier domain. Finally, a Texas Instruments digital micromirror device (DMD) based implementation of FDMI is presented and results are shown. Chapter 2 discusses the problem of image reassembly which is to restore an image back to its original form from its pieces after it has been fragmented due to different destructive reasons. We propose an efficient algorithm for 2D image fragment reassembly problem based on solving a variation of Longest Common Subsequence (LCS) problem. Our processing pipeline has three steps. First, the boundary of each fragment is extracted automatically; second, a novel boundary matching is performed by solving LCS to identify the best possible adjacency relationship among image fragment pairs; finally, a multi-piece global alignment is used to filter out incorrect pairwise matches and compose the final image. We perform experiments on complicated image fragment datasets and compare our results with existing methods to show the improved efficiency and robustness of our method. The problem of reassembling a hand-torn or machine-shredded document back to its original form is another useful version of the image reassembly problem. Reassembling a shredded document is different from reassembling an ordinary image because the geometric shape of fragments do not carry a lot of valuable information if the document has been machine-shredded rather than hand-torn. On the other hand, matching words and context can be used as an additional tool to help improve the task of reassembly. In the final chapter, document reassembly problem has been addressed through solving a graph optimization problem

3D Pedestrian Tracking and Virtual Reconstruction of Ceramic Vessels Using Geometric and Color Cues

Object tracking using cameras has many applications ranging from monitoring children and the elderly, to behavior analysis, entertainment, and homeland security. This thesis concentrates on the problem of tracking person(s) of interest in crowded scenes (e.g., airports, train stations, malls, etc.), rendering their locations in time and space along with high quality close-up images of the person for recognition. The tracking is achieved using a combination of overhead cameras for 3D tracking and a network of pan-tilt-zoom (PTZ) cameras to obtain close-up frontal face images. Based on projective geometry, the overhead cameras track people using salient and easily computable feature points such as head points. When the obtained head point is not accurate enough, the color information of the head tops across subsequent frames is integrated to detect and track people. To capture the best frontal face images of a target across time, a PTZ camera scheduling is proposed, where the 'best' PTZ camera is selected based on the capture quality (as close as possible to frontal view) and handoff success (response time needed by the newly selected camera to move from current to desired state) probabilities. The experiments show the 3D tracking errors are very small (less than 5 cm with 14 people crowding an area of around 4 m2) and the frontal face images are captured effectively with most of them centering in the frames. Computational archaeology is becoming a success story of applying computational tools in the reconstruction of vessels obtained from digs, freeing the expert from hours of intensive labor in manually stitching shards into meaningful vessels. In this thesis, we concentrate on the use of geometric and color information of the fragments for 3D virtual reconstruction of broken ceramic vessels. Generic models generated by the experts as a rendition of what the original vessel may have looked like are also utilized. The generic models need not to be identical to the original vessel, but are within a geometric transformation of it in most of its parts. The markings on the 3D surfaces of fragments and generic models are extracted based on their color cues. Ceramic fragments are then aligned against the corresponding generic models based on the geometric relation between the extracted markings. The alignments yield sub-scanner resolution fitting errors.Ph.D., Electrical Engineering -- Drexel University, 201

Electronic Imaging & the Visual Arts. EVA 2018 Florence

The Publication is following the yearly Editions of EVA FLORENCE. The State of Art is presented regarding the Application of Technologies (in particular of digital type) to Cultural Heritage. The more recent results of the Researches in the considered Area are presented. Information Technologies of interest for Culture Heritage are presented: multimedia systems, data-bases, data protection, access to digital content, Virtual Galleries. Particular reference is reserved to digital images (Electronic Imaging & the Visual Arts), regarding Cultural Institutions (Museums, Libraries, Palace - Monuments, Archaeological Sites). The International Conference includes the following Sessions: Strategic Issues; New Sciences and Culture Developments and Applications; New Technical Developments & Applications; Museums - Virtual Galleries and Related Initiatives; Art and Humanities Ecosystem & Applications; Access to the Culture Information. Two Workshops regard: Innovation and Enterprise; the Cloud Systems connected to the Culture (eCulture Cloud) in the Smart Cities context. The more recent results of the Researches at national and international are reported in the Area of Technologies and Culture Heritage, also with experimental demonstrations of developed Activities