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

Detecting, Tracking, And Recognizing Activities In Aerial Video

In this dissertation, we address the problem of detecting humans and vehicles, tracking them in crowded scenes, and finally determining their activities in aerial video. Even though this is a well explored problem in the field of computer vision, many challenges still remain when one is presented with realistic data. These challenges include large camera motion, strong scene parallax, fast object motion, large object density, strong shadows, and insufficiently large action datasets. Therefore, we propose a number of novel methods based on exploiting scene constraints from the imagery itself to aid in the detection and tracking of objects. We show, via experiments on several datasets, that superior performance is achieved with the use of proposed constraints. First, we tackle the problem of detecting moving, as well as stationary, objects in scenes that contain parallax and shadows. We do this on both regular aerial video, as well as the new and challenging domain of wide area surveillance. This problem poses several challenges: large camera motion, strong parallax, large number of moving objects, small number of pixels on target, single channel data, and low frame-rate of video. We propose a method for detecting moving and stationary objects that overcomes these challenges, and evaluate it on CLIF and VIVID datasets. In order to find moving objects, we use median background modelling which requires few frames to obtain a workable model, and is very robust when there is a large number of moving objects in the scene while the model is being constructed. We then iii remove false detections from parallax and registration errors using gradient information from the background image. Relying merely on motion to detect objects in aerial video may not be sufficient to provide complete information about the observed scene. First of all, objects that are permanently stationary may be of interest as well, for example to determine how long a particular vehicle has been parked at a certain location. Secondly, moving vehicles that are being tracked through the scene may sometimes stop and remain stationary at traffic lights and railroad crossings. These prolonged periods of non-motion make it very difficult for the tracker to maintain the identities of the vehicles. Therefore, there is a clear need for a method that can detect stationary pedestrians and vehicles in UAV imagery. This is a challenging problem due to small number of pixels on the target, which makes it difficult to distinguish objects from background clutter, and results in a much larger search space. We propose a method for constraining the search based on a number of geometric constraints obtained from the metadata. Specifically, we obtain the orientation of the ground plane normal, the orientation of the shadows cast by out of plane objects in the scene, and the relationship between object heights and the size of their corresponding shadows. We utilize the above information in a geometry-based shadow and ground plane normal blob detector, which provides an initial estimation for the locations of shadow casting out of plane (SCOOP) objects in the scene. These SCOOP candidate locations are then classified as either human or clutter using a combination of wavelet features, and a Support Vector Machine. Additionally, we combine regular SCOOP and inverted SCOOP candidates to obtain vehicle candidates. We show impressive results on sequences from VIVID and CLIF datasets, and provide comparative quantitative and qualitative analysis. We also show that we can extend the SCOOP detection method to automatically estimate the iv orientation of the shadow in the image without relying on metadata. This is useful in cases where metadata is either unavailable or erroneous. Simply detecting objects in every frame does not provide sufficient understanding of the nature of their existence in the scene. It may be necessary to know how the objects have travelled through the scene over time and which areas they have visited. Hence, there is a need to maintain the identities of the objects across different time instances. The task of object tracking can be very challenging in videos that have low frame rate, high density, and a very large number of objects, as is the case in the WAAS data. Therefore, we propose a novel method for tracking a large number of densely moving objects in an aerial video. In order to keep the complexity of the tracking problem manageable when dealing with a large number of objects, we divide the scene into grid cells, solve the tracking problem optimally within each cell using bipartite graph matching and then link the tracks across the cells. Besides tractability, grid cells also allow us to define a set of local scene constraints, such as road orientation and object context. We use these constraints as part of cost function to solve the tracking problem; This allows us to track fast-moving objects in low frame rate videos. In addition to moving through the scene, the humans that are present may be performing individual actions that should be detected and recognized by the system. A number of different approaches exist for action recognition in both aerial and ground level video. One of the requirements for the majority of these approaches is the existence of a sizeable dataset of examples of a particular action from which a model of the action can be constructed. Such a luxury is not always possible in aerial scenarios since it may be difficult to fly a large number of missions to observe a particular event multiple times. Therefore, we propose a method for v recognizing human actions in aerial video from as few examples as possible (a single example in the extreme case). We use the bag of words action representation and a 1vsAll multi-class classification framework. We assume that most of the classes have many examples, and construct Support Vector Machine models for each class. Then, we use Support Vector Machines that were trained for classes with many examples to improve the decision function of the Support Vector Machine that was trained using few examples, via late weighted fusion of decision values

Automatic Alignment of 3D Multi-Sensor Point Clouds

Automatic 3D point cloud alignment is a major research topic in photogrammetry, computer vision and computer graphics. In this research, two keypoint feature matching approaches have been developed and proposed for the automatic alignment of 3D point clouds, which have been acquired from different sensor platforms and are in different 3D conformal coordinate systems. The first proposed approach is based on 3D keypoint feature matching. First, surface curvature information is utilized for scale-invariant 3D keypoint extraction. Adaptive non-maxima suppression (ANMS) is then applied to retain the most distinct and well-distributed set of keypoints. Afterwards, every keypoint is characterized by a scale, rotation and translation invariant 3D surface descriptor, called the radial geodesic distance-slope histogram. Similar keypoints descriptors on the source and target datasets are then matched using bipartite graph matching, followed by a modified-RANSAC for outlier removal. The second proposed method is based on 2D keypoint matching performed on height map images of the 3D point clouds. Height map images are generated by projecting the 3D point clouds onto a planimetric plane. Afterwards, a multi-scale wavelet 2D keypoint detector with ANMS is proposed to extract keypoints on the height maps. Then, a scale, rotation and translation-invariant 2D descriptor referred to as the Gabor, Log-Polar-Rapid Transform descriptor is computed for all keypoints. Finally, source and target height map keypoint correspondences are determined using a bi-directional nearest neighbour matching, together with the modified-RANSAC for outlier removal. Each method is assessed on multi-sensor, urban and non-urban 3D point cloud datasets. Results show that unlike the 3D-based method, the height map-based approach is able to align source and target datasets with differences in point density, point distribution and missing point data. Findings also show that the 3D-based method obtained lower transformation errors and a greater number of correspondences when the source and target have similar point characteristics. The 3D-based approach attained absolute mean alignment differences in the range of 0.23m to 2.81m, whereas the height map approach had a range from 0.17m to 1.21m. These differences meet the proximity requirements of the data characteristics and the further application of fine co-registration approaches

Robust Modular Feature-Based Terrain-Aided Visual Navigation and Mapping

The visual feature-based Terrain-Aided Navigation (TAN) system presented in this thesis addresses the problem of constraining inertial drift introduced into the location estimate of Unmanned Aerial Vehicles (UAVs) in GPS-denied environment. The presented TAN system utilises salient visual features representing semantic or human-interpretable objects (roads, forest and water boundaries) from onboard aerial imagery and associates them to a database of reference features created a-priori, through application of the same feature detection algorithms to satellite imagery. Correlation of the detected features with the reference features via a series of the robust data association steps allows a localisation solution to be achieved with a finite absolute bound precision defined by the certainty of the reference dataset. The feature-based Visual Navigation System (VNS) presented in this thesis was originally developed for a navigation application using simulated multi-year satellite image datasets. The extension of the system application into the mapping domain, in turn, has been based on the real (not simulated) flight data and imagery. In the mapping study the full potential of the system, being a versatile tool for enhancing the accuracy of the information derived from the aerial imagery has been demonstrated. Not only have the visual features, such as road networks, shorelines and water bodies, been used to obtain a position ’fix’, they have also been used in reverse for accurate mapping of vehicles detected on the roads into an inertial space with improved precision. Combined correction of the geo-coding errors and improved aircraft localisation formed a robust solution to the defense mapping application. A system of the proposed design will provide a complete independent navigation solution to an autonomous UAV and additionally give it object tracking capability

Video-based motion detection for stationary and moving cameras

In real world monitoring applications, moving object detection remains to be a challenging task due to factors such as background clutter and motion, illumination variations, weather conditions, noise, and occlusions. As a fundamental first step in many computer vision applications such as object tracking, behavior understanding, object or event recognition, and automated video surveillance, various motion detection algorithms have been developed ranging from simple approaches to more sophisticated ones. In this thesis, we present two moving object detection frameworks. The first framework is designed for robust detection of moving and static objects in videos acquired from stationary cameras. This method exploits the benefits of fusing a motion computation method based on spatio-temporal tensor formulation, a novel foreground and background modeling scheme, and a multi-cue appearance comparison. This hybrid system can handle challenges such as shadows, illumination changes, dynamic background, stopped and removed objects. Extensive testing performed on the CVPR 2014 Change Detection benchmark dataset shows that FTSG outperforms most state-of-the-art methods. The second framework adapts moving object detection to full motion videos acquired from moving airborne platforms. This framework has two main modules. The first module stabilizes the video with respect to a set of base-frames in the sequence. The stabilization is done by estimating four-point homographies using prominent feature (PF) block matching, motion filtering and RANSAC for robust matching. Once the frame to base frame homographies are available the flux tensor motion detection module using local second derivative information is applied to detect moving salient features. Spurious responses from the frame boundaries and other post- processing operations are applied to reduce the false alarms and produce accurate moving blob regions that will be useful for tracking

BEMDEC: An Adaptive and Robust Methodology for Digital Image Feature Extraction

The intriguing study of feature extraction, and edge detection in particular, has, as a result of the increased use of imagery, drawn even more attention not just from the field of computer science but also from a variety of scientific fields. However, various challenges surrounding the formulation of feature extraction operator, particularly of edges, which is capable of satisfying the necessary properties of low probability of error (i.e., failure of marking true edges), accuracy, and consistent response to a single edge, continue to persist. Moreover, it should be pointed out that most of the work in the area of feature extraction has been focused on improving many of the existing approaches rather than devising or adopting new ones. In the image processing subfield, where the needs constantly change, we must equally change the way we think. In this digital world where the use of images, for variety of purposes, continues to increase, researchers, if they are serious about addressing the aforementioned limitations, must be able to think outside the box and step away from the usual in order to overcome these challenges. In this dissertation, we propose an adaptive and robust, yet simple, digital image features detection methodology using bidimensional empirical mode decomposition (BEMD), a sifting process that decomposes a signal into its two-dimensional (2D) bidimensional intrinsic mode functions (BIMFs). The method is further extended to detect corners and curves, and as such, dubbed as BEMDEC, indicating its ability to detect edges, corners and curves. In addition to the application of BEMD, a unique combination of a flexible envelope estimation algorithm, stopping criteria and boundary adjustment made the realization of this multi-feature detector possible. Further application of two morphological operators of binarization and thinning adds to the quality of the operator

Dense Point-Cloud Representation of a Scene using Monocular Vision

We present a three-dimensional (3-D) reconstruction system designed to support various autonomous navigation applications. The system presented focuses on the 3-D reconstruction of a scene using only a single moving camera. Utilizing video frames captured at different points in time allows us to determine the depths of a scene. In this way, the system can be used to construct a point-cloud model of its unknown surroundings. We present the step-by-step methodology and analysis used in developing the 3-D reconstruction technique. We present a reconstruction framework that generates a primitive point cloud, which is computed based on feature matching and depth triangulation analysis. To populate the reconstruction, we utilized optical flow features to create an extremely dense representation model. With the third algorithmic modification, we introduce the addition of the preprocessing step of nonlinear single-image super resolution. With this addition, the depth accuracy of the point cloud, which relies on precise disparity measurement, has significantly increased. Our final contribution is an additional postprocessing step designed to filter noise points and mismatched features unveiling the complete dense point-cloud representation (DPR) technique. We measure the success of DPR by evaluating the visual appeal, density, accuracy, and computational expense and compare with two state-of-the-art techniques

Adaptive Methods for Point Cloud and Mesh Processing

Point clouds and 3D meshes are widely used in numerous applications ranging from games to virtual reality to autonomous vehicles. This dissertation proposes several approaches for noise removal and calibration of noisy point cloud data and 3D mesh sharpening methods. Order statistic filters have been proven to be very successful in image processing and other domains as well. Different variations of order statistics filters originally proposed for image processing are extended to point cloud filtering in this dissertation. A brand-new adaptive vector median is proposed in this dissertation for removing noise and outliers from noisy point cloud data. The major contributions of this research lie in four aspects: 1) Four order statistic algorithms are extended, and one adaptive filtering method is proposed for the noisy point cloud with improved results such as preserving significant features. These methods are applied to standard models as well as synthetic models, and real scenes, 2) A hardware acceleration of the proposed method using Microsoft parallel pattern library for filtering point clouds is implemented using multicore processors, 3) A new method for aerial LIDAR data filtering is proposed. The objective is to develop a method to enable automatic extraction of ground points from aerial LIDAR data with minimal human intervention, and 4) A novel method for mesh color sharpening using the discrete Laplace-Beltrami operator is proposed. Median and order statistics-based filters are widely used in signal processing and image processing because they can easily remove outlier noise and preserve important features. This dissertation demonstrates a wide range of results with median filter, vector median filter, fuzzy vector median filter, adaptive mean, adaptive median, and adaptive vector median filter on point cloud data. The experiments show that large-scale noise is removed while preserving important features of the point cloud with reasonable computation time. Quantitative criteria (e.g., complexity, Hausdorff distance, and the root mean squared error (RMSE)), as well as qualitative criteria (e.g., the perceived visual quality of the processed point cloud), are employed to assess the performance of the filters in various cases corrupted by different noisy models. The adaptive vector median is further optimized for denoising or ground filtering aerial LIDAR data point cloud. The adaptive vector median is also accelerated on multi-core CPUs using Microsoft Parallel Patterns Library. In addition, this dissertation presents a new method for mesh color sharpening using the discrete Laplace-Beltrami operator, which is an approximation of second order derivatives on irregular 3D meshes. The one-ring neighborhood is utilized to compute the Laplace-Beltrami operator. The color for each vertex is updated by adding the Laplace-Beltrami operator of the vertex color weighted by a factor to its original value. Different discretizations of the Laplace-Beltrami operator have been proposed for geometrical processing of 3D meshes. This work utilizes several discretizations of the Laplace-Beltrami operator for sharpening 3D mesh colors and compares their performance. Experimental results demonstrated the effectiveness of the proposed algorithms

Super resolution and dynamic range enhancement of image sequences

Camera producers try to increase the spatial resolution of a camera by reducing size of sites on sensor array. However, shot noise causes the signal to noise ratio drop as sensor sites get smaller. This fact motivates resolution enhancement to be performed through software. Super resolution (SR) image reconstruction aims to combine degraded images of a scene in order to form an image which has higher resolution than all observations. There is a demand for high resolution images in biomedical imaging, surveillance, aerial/satellite imaging and high-definition TV (HDTV) technology. Although extensive research has been conducted in SR, attention has not been given to increase the resolution of images under illumination changes. In this study, a unique framework is proposed to increase the spatial resolution and dynamic range of a video sequence using Bayesian and Projection onto Convex Sets (POCS) methods. Incorporating camera response function estimation into image reconstruction allows dynamic range enhancement along with spatial resolution improvement. Photometrically varying input images complicate process of projecting observations onto common grid by violating brightness constancy. A contrast invariant feature transform is proposed in this thesis to register input images with high illumination variation. Proposed algorithm increases the repeatability rate of detected features among frames of a video. Repeatability rate is increased by computing the autocorrelation matrix using the gradients of contrast stretched input images. Presented contrast invariant feature detection improves repeatability rate of Harris corner detector around %25 on average. Joint multi-frame demosaicking and resolution enhancement is also investigated in this thesis. Color constancy constraint set is devised and incorporated into POCS framework for increasing resolution of color-filter array sampled images. Proposed method provides fewer demosaicking artifacts compared to existing POCS method and a higher visual quality in final image