Image mosaicing of panoramic images

Image mosaicing is combining or stitching several images of a scene or object taken from different angles into a single image with a greater angle of view. This is practised a developing field. Recent years have seen quite a lot of advancement in the field. Many algorithms have been developed over the years. Our work is based on feature based approach of image mosaicing. The steps in image mosaic consist of feature point detection, feature point descriptor extraction and feature point matching. RANSAC algorithm is applied to eliminate variety of mismatches and acquire transformation matrix between the images. The input image is transformed with the right mapping model for image stitching. Therefore, this paper proposes an algorithm for mosaicing two images efficiently using Harris-corner feature detection method, RANSAC feature matching method and then image transformation, warping and by blending methods

A Minimalist Approach to Type-Agnostic Detection of Quadrics in Point Clouds

This paper proposes a segmentation-free, automatic and efficient procedure to detect general geometric quadric forms in point clouds, where clutter and occlusions are inevitable. Our everyday world is dominated by man-made objects which are designed using 3D primitives (such as planes, cones, spheres, cylinders, etc.). These objects are also omnipresent in industrial environments. This gives rise to the possibility of abstracting 3D scenes through primitives, thereby positions these geometric forms as an integral part of perception and high level 3D scene understanding. As opposed to state-of-the-art, where a tailored algorithm treats each primitive type separately, we propose to encapsulate all types in a single robust detection procedure. At the center of our approach lies a closed form 3D quadric fit, operating in both primal & dual spaces and requiring as low as 4 oriented-points. Around this fit, we design a novel, local null-space voting strategy to reduce the 4-point case to 3. Voting is coupled with the famous RANSAC and makes our algorithm orders of magnitude faster than its conventional counterparts. This is the first method capable of performing a generic cross-type multi-object primitive detection in difficult scenes. Results on synthetic and real datasets support the validity of our method.Comment: Accepted for publication at CVPR 201

Multi-Image Semantic Matching by Mining Consistent Features

This work proposes a multi-image matching method to estimate semantic correspondences across multiple images. In contrast to the previous methods that optimize all pairwise correspondences, the proposed method identifies and matches only a sparse set of reliable features in the image collection. In this way, the proposed method is able to prune nonrepeatable features and also highly scalable to handle thousands of images. We additionally propose a low-rank constraint to ensure the geometric consistency of feature correspondences over the whole image collection. Besides the competitive performance on multi-graph matching and semantic flow benchmarks, we also demonstrate the applicability of the proposed method for reconstructing object-class models and discovering object-class landmarks from images without using any annotation.Comment: CVPR 201

Video-based iris feature extraction and matching using Deep Learning

This research is initiated to enhance the video-based eye tracker’s performance to detect small eye movements.[1] Chaudhary and Pelz, 2019, created an excellent foundation on their motion tracking of iris features to detect small eye movements[1], where they successfully used the classical handcrafted feature extraction methods like Scale InvariantFeature Transform (SIFT) to match the features on iris image frames. They extracted features from the eye-tracking videos and then used patent [2] an approach of tracking the geometric median of the distribution. This patent [2] excludes outliers, and the velocity is approximated by scaling by the sampling rate. To detect the microsaccades (small, rapid eye movements that occur in only one eye at a time) thresholding was used to estimate the velocity in the following paper[1]. Our goal is to create a robust mathematical model to create a 2D feature distribution in the given patent [2]. In this regard, we worked in two steps. First, we studied a large number of multiple recent deep learning approaches along with the classical hand-crafted feature extractor like SIFT, to extract the features from the collected eye tracker videos from Multidisciplinary Vision Research Lab(MVRL) and then showed the best matching process for our given RIT-Eyes dataset[3]. The goal is to make the feature extraction as robust as possible. Secondly, we clearly showed that deep learning methods can detect more feature points from the iris images and that matching of the extracted features frame by frame is more accurate than the classical approach

Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC

Plane model extraction from three-dimensional point clouds is a necessary step in many different applications such as planar object reconstruction, indoor mapping and indoor localization. Different RANdom SAmple Consensus (RANSAC)-based methods have been proposed for this purpose in recent years. In this study, we propose a novel method-based on RANSAC called Multiplane Model Estimation, which can estimate multiple plane models simultaneously from a noisy point cloud using the knowledge extracted from a scene (or an object) in order to reconstruct it accurately. This method comprises two steps: first, it clusters the data into planar faces that preserve some constraints defined by knowledge related to the object (e.g., the angles between faces); and second, the models of the planes are estimated based on these data using a novel multi-constraint RANSAC. We performed experiments in the clustering and RANSAC stages, which showed that the proposed method performed better than state-of-the-art methods

On the sample consensus robust estimation paradigm: comprehensive survey and novel algorithms with applications.

Master of Science in Statistics and Computer Science.University of KwaZulu-Natal, Durban 2016.This study begins with a comprehensive survey of existing variants of the Random Sample Consensus (RANSAC) algorithm. Then, five new ones are contributed. RANSAC, arguably the most popular robust estimation algorithm in computer vision, has limitations in accuracy, efficiency and repeatability. Research into techniques for overcoming these drawbacks, has been active for about two decades. In the last one-and-half decade, nearly every single year had at least one variant published: more than ten, in the last two years. However, many existing variants compromise two attractive properties of the original RANSAC: simplicity and generality. Some introduce new operations, resulting in loss of simplicity, while many of those that do not introduce new operations, require problem-specific priors. In this way, they trade off generality and introduce some complexity, as well as dependence on other steps of the workflow of applications. Noting that these observations may explain the persisting trend, of finding only the older, simpler variants in ‘mainstream’ computer vision software libraries, this work adopts an approach that preserves the two mentioned properties. Modification of the original algorithm, is restricted to only search strategy replacement, since many drawbacks of RANSAC are consequences of the search strategy it adopts. A second constraint, serving the purpose of preserving generality, is that this ‘ideal’ strategy, must require no problem-specific priors. Such a strategy is developed, and reported in this dissertation. Another limitation, yet to be overcome in literature, but is successfully addressed in this study, is the inherent variability, in RANSAC. A few theoretical discoveries are presented, providing insights on the generic robust estimation problem. Notably, a theorem proposed as an original contribution of this research, reveals insights, that are foundational to newly proposed algorithms. Experiments on both generic and computer-vision-specific data, show that all proposed algorithms, are generally more accurate and more consistent, than RANSAC. Moreover, they are simpler in the sense that, they do not require some of the input parameters of RANSAC. Interestingly, although non-exhaustive in search like the typical RANSAC-like algorithms, three of these new algorithms, exhibit absolute non-randomness, a property that is not claimed by any existing variant. One of the proposed algorithms, is fully automatic, eliminating all requirements of user-supplied input parameters. Two of the proposed algorithms, are implemented as contributed alternatives to the homography estimation function, provided in MATLAB’s computer vision toolbox, after being shown to improve on the performance of M-estimator Sample Consensus (MSAC). MSAC has been the choice in all releases of the toolbox, including the latest 2015b. While this research is motivated by computer vision applications, the proposed algorithms, being generic, can be applied to any model-fitting problem from other scientific fields