Registration and categorization of camera captured documents

Camera captured document image analysis concerns with processing of documents captured with hand-held sensors, smart phones, or other capturing devices using advanced image processing, computer vision, pattern recognition, and machine learning techniques. As there is no constrained capturing in the real world, the captured documents suffer from illumination variation, viewpoint variation, highly variable scale/resolution, background clutter, occlusion, and non-rigid deformations e.g., folds and crumples. Document registration is a problem where the image of a template document whose layout is known is registered with a test document image. Literature in camera captured document mosaicing addressed the registration of captured documents with the assumption of considerable amount of single chunk overlapping content. These methods cannot be directly applied to registration of forms, bills, and other commercial documents where the fixed content is distributed into tiny portions across the document. On the other hand, most of the existing document image registration methods work with scanned documents under affine transformation. Literature in document image retrieval addressed categorization of documents based on text, figures, etc. However, the scalability of existing document categorization methodologies based on logo identification is very limited. This dissertation focuses on two problems (i) registration of captured documents where the overlapping content is distributed into tiny portions across the documents and (ii) categorization of captured documents into predefined logo classes that scale to large datasets using local invariant features. A novel methodology is proposed for the registration of user defined Regions Of Interest (ROI) using corresponding local features from their neighborhood. The methodology enhances prior approaches in point pattern based registration, like RANdom SAmple Consensus (RANSAC) and Thin Plate Spline-Robust Point Matching (TPS-RPM), to enable registration of cell phone and camera captured documents under non-rigid transformations. Three novel aspects are embedded into the methodology: (i) histogram based uniformly transformed correspondence estimation, (ii) clustering of points located near the ROI to select only close by regions for matching, and (iii) validation of the registration in RANSAC and TPS-RPM algorithms. Experimental results on a dataset of 480 images captured using iPhone 3GS and Logitech webcam Pro 9000 have shown an average registration accuracy of 92.75% using Scale Invariant Feature Transform (SIFT). Robust local features for logo identification are determined empirically by comparisons among SIFT, Speeded-Up Robust Features (SURF), Hessian-Affine, Harris-Affine, and Maximally Stable Extremal Regions (MSER). Two different matching methods are presented for categorization: matching all features extracted from the query document as a single set and a segment-wise matching of query document features using segmentation achieved by grouping area under intersecting dense local affine covariant regions. The later approach not only gives an approximate location of predicted logo classes in the query document but also helps to increase the prediction accuracies. In order to facilitate scalability to large data sets, inverted indexing of logo class features has been incorporated in both approaches. Experimental results on a dataset of real camera captured documents have shown a peak 13.25% increase in the F–measure accuracy using the later approach as compared to the former

Design and development of a generalized LIDAR point cloud streaming framework over the web

LIDAR data, retrieved by laser systems carried by airplanes, helicopters or cars, require the storage of millions of point information concerning the space we are scanning. This work illustrates the implementation of a web application which gets LIDAR data from a PostreSQL database and creates n different levels of detail of the point cloud data and saves them into related files. A first operation implemented on the data is linear point to point measurementope

Aggregating Local Features into Bundles for High-Precision Object Retrieval

Due to the omnipresence of digital cameras and mobile phones the number of images stored in image databases has grown tremendously in the last years. It becomes apparent that new data management and retrieval techniques are needed to deal with increasingly large image databases. This thesis presents new techniques for content-based image retrieval where the image content itself is used to retrieve images by visual similarity from databases. We focus on the query-by-example scenario, assuming the image itself is provided as query to the retrieval engine. In many image databases, images are often associated with metadata, which may be exploited to improve the retrieval performance. In this work, we present a technique that fuses cues from the visual domain and textual annotations into a single compact representation. This combined multimodal representation performs significantly better compared to the underlying unimodal representations, which we demonstrate on two large-scale image databases consisting of up to 10 million images. The main focus of this work is on feature bundling for object retrieval and logo recognition. We present two novel feature bundling techniques that aggregate multiple local features into a single visual description. In contrast to many other works, both approaches encode geometric information about the spatial layout of local features into the corresponding visual description itself. Therefore, these descriptions are highly distinctive and suitable for high-precision object retrieval. We demonstrate the use of both bundling techniques for logo recognition. Here, the recognition is performed by the retrieval of visually similar images from a database of reference images, making the recognition systems easily scalable to a large number of classes. The results show that our retrieval-based methods can successfully identify small objects such as logos with an extremely low false positive rate. In particular, our feature bundling techniques are beneficial because false positives are effectively avoided upfront due to the highly distinctive descriptions. We further demonstrate and thoroughly evaluate the use of our bundling technique based on min-Hashing for image and object retrieval. Compared to approaches based on conventional bag-of-words retrieval, it has much higher efficiency: the retrieved result lists are shorter and cleaner while recall is on equal level. The results suggest that this bundling scheme may act as pre-filtering step in a wide range of scenarios and underline the high effectiveness of this approach. Finally, we present a new variant for extremely fast re-ranking of retrieval results, which ranks the retrieved images according to the spatial consistency of their local features to those of the query image. The demonstrated method is robust to outliers, performs better than existing methods and allows to process several hundreds to thousands of images per second on a single thread