An in Depth Review Paper on Numerous Image Mosaicing Approaches and Techniques

Image mosaicing is one of the most important subjects of research in computer vision at current. Image mocaicing requires the integration of direct techniques and feature based techniques. Direct techniques are found to be very useful for mosaicing large overlapping regions, small translations and rotations while feature based techniques are useful for small overlapping regions. Feature based image mosaicing is a combination of corner detection, corner matching, motion parameters estimation and image stitching.Furthermore, image mosaicing is considered the process of obtaining a wider field-of-view of a scene from a sequence of partial views, which has been an attractive research area because of its wide range of applications, including motion detection, resolution enhancement, monitoring global land usage, and medical imaging. Numerous algorithms for image mosaicing have been proposed over the last two decades.In this paper the authors present a review on different approaches for image mosaicing and the literature over the past few years in the field of image masaicing methodologies. The authors take an overview on the various methods for image mosaicing.This review paper also provides an in depth survey of the existing image mosaicing algorithms by classifying them into several groups. For each group, the fundamental concepts are first clearly explained. Finally this paper also reviews and discusses the strength and weaknesses of all the mosaicing groups

Shift Estimation Algorithm for Dynamic Sensors With Frame-to-Frame Variation in Their Spectral Response

This study is motivated by the emergence of a new class of tunable infrared spectral-imaging sensors that offer the ability to dynamically vary the sensor\u27s intrinsic spectral response from frame to frame in an electronically controlled fashion. A manifestation of this is when a sequence of dissimilar spectral responses is periodically realized, whereby in every period of acquired imagery, each frame is associated with a distinct spectral band. Traditional scene-based global shift estimation algorithms are not applicable to such spectrally heterogeneous video sequences, as a pixel value may change from frame to frame as a result of both global motion and varying spectral response. In this paper, a novel algorithm is proposed and examined to fuse a series of coarse global shift estimates between periodically sampled pairs of nonadjacent frames to estimate motion between consecutive frames; each pair corresponds to two nonadjacent frames of the same spectral band. The proposed algorithm outperforms three alternative methods, with the average error being one half of that obtained by using an equal weights version of the proposed algorithm, one-fourth of that obtained by using a simple linear interpolation method, and one-twentieth of that obtained by using a nai¿ve correlation-based direct method

Utilizing radiation for smart robotic applications using visible, thermal, and polarization images.

The domain of this research is the use of computer vision methodologies in utilizing radiation for smart robotic applications for driving assistance. Radiation can be emitted by an object, reflected or transmitted. Understanding the nature and the properties of the radiation forming an image is essential in interpreting the information in that image which can then be used by a machine e.g. a smart vehicle to make a decision and perform an action. Throughout this work, different types of images are used to help a robotic vehicle make a decision and perform a certain action. This work presents three smart robotic applications; the first one deals with polarization images, the second one deals with thermal images and the third one deals with visible images. Each type of these images is formed by light (radiation) but in a way different from other types where the information embedded in an image depends on the way it was formed and how the light was generated. For polarization imaging, a direct method utilizing shading and polarization for unambiguous shape recovery without the need for nonlinear optimization routines is proposed. The proposed method utilizes simultaneously polarization and shading to find the surface normals, thus eliminating the reconstruction ambiguity. This can be useful to help a smart vehicle gain knowledge about the terrain surface geometry. Regarding thermal imaging, an automatic method for constructing an annotated thermal imaging pedestrian dataset is proposed. This is done by transferring detections from registered visible images simultaneously captured at day-time where pedestrian detection is well developed in visible images. Histogram of Oriented Gradients (HOG) features are extracted from the constructed dataset and then fed to a discriminatively trained deformable part based classifier that can be used to detect pedestrians at night. The resulting classifier was tested for night driving assistance and succeeded in detecting pedestrians even in the situations where visible imaging pedestrian detectors failed because of low light or glare of oncoming traffic. For visible images, a new feature based on HOG is proposed to be used for pedestrian detection. The proposed feature was augmented to two state of the art pedestrian detectors; the discriminatively trained Deformable Part based models (DPM) and the Integral Channel Features (ICF) using fast feature pyramids. The proposed approach is based on computing the image mixed partial derivatives to be used to redefine the gradients of some pixels and to reweigh the vote at all pixels with respect to the original HOG. The approach was tested on the PASCAL2007, INRIA and Caltech datasets and showed to have an outstanding performance

Hierarchical Estimation of Oceanic Surface Velocity Fields From Satellite Imagery.

Oceanic surface velocity fields are objectively estimated from time-sequential satellite images of sea-surface temperature from the Advanced Very High Resolution Radiometey on board the National Oceanic and Atmospheric Administration\u27s polar orbiters. The hierarchical technique uses the concept of image pyramids and multi-resolution grids for increased computational efficiency. Images are Gaussian filtered and sub-sampled from fine to coarse grid scales. The number of pyramid levels is selected such that the maximum expected velocity in the image results in a displacement of less than one pixel at the coarsest spatial scale. Maximum Cross-Correlation at the sub-pixel level with orthogonal polynomial approximation is used to compute a velocity field at each level of the pyramid which is then iterated assuming a locally linear velocity field. The first image at the next finer level of the pyramid is warped towards the second image by the calculated velocity field. At each succeeding finer grid scale, the velocity field is updated and the process repeated. The final result is an estimated velocity at each pixel at the finest resolution of the imagery. There are no free parameters as used in some gradient-based approaches and the only assumption is that the velocity field is locally linear. Test cases are shown using both simulated and real images with numerically simulated velocity fields which demonstrate the accuracy of the technique. Results are compared to gradient-based techniques using concepts of optical flow and projection onto convex sets and to the standard Maximum Cross-Correlation technique. The hierarchical computations for a real satellite image numerically advected by a rotational sheared flow recover the original field with a rms speed error of 12.6% and direction error of 4.9\sp\circ. Hierarchically-estimated velocity fields from real image pairs are compared to ground-truth estimates of the velocity from satellite-tracked drifters in the eastern Gulf of Mexico. Results indicate the technique underestimates daily mean buoy vector speeds, but with reasonably good direction. The problems of ground truth relations to hierarchically computed flows are discussed with regard to mismatches of time and space scales of measurement

A real-time computer vision library for heterogeneous processing environments

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 69-70).With a variety of processing technologies available today, using a combination of different technologies often provides the best performance for a particular task. However, unifying multiple processors with different instruction sets can be a very ad hoc and difficult process. The Open Component Portability Infrastructure (OpenCPI) provides a platform that simplifies programming heterogeneous processing applications requiring a mix of processing technologies. These include central processing units (CPU), graphics processing units (GPU), field-programmable gate arrays (FPGA), general-purpose processors (GPP), digital signal processors (DSP), and high-speed switch fabrics. This thesis presents the design and implementation of a computer vision library in the OpenCPI framework, largely based on Open Source Computer Vision (OpenCV), a widely used library of optimized software components for real-time computer vision. The OpenCPI-OpenCV library consists of a collection of resource-constrained C language (RCC) workers, along with applications demonstrating how these workers can be combined to achieve the same functionality as various OpenCV library functions. Compared with applications relying solely on OpenCV, analogous OpenCPI applications can be constructed from many workers, often resulting in greater parallelization if run on multi-core platforms. Future OpenCPI computer vision applications will be able to utilize these existing RCC workers, and a subset of these workers can potentially be replaced with alternative implementations, e.g. on GPUs or FPGAs.by Tony J. Liu.M.Eng

Development Of A High Performance Mosaicing And Super-Resolution Algorithm

In this dissertation, a high-performance mosaicing and super-resolution algorithm is described. The scale invariant feature transform (SIFT)-based mosaicing algorithm builds an initial mosaic which is iteratively updated by the robust super resolution algorithm to achieve the final high-resolution mosaic. Two different types of datasets are used for testing: high altitude balloon data and unmanned aerial vehicle data. To evaluate our algorithm, five performance metrics are employed: mean square error, peak signal to noise ratio, singular value decomposition, slope of reciprocal singular value curve, and cumulative probability of blur detection. Extensive testing shows that the proposed algorithm is effective in improving the captured aerial data and the performance metrics are accurate in quantifying the evaluation of the algorithm

High dynamic range video merging, tone mapping, and real-time implementation

Although High Dynamic Range (High Dynamic Range (HDR)) imaging has been the subject of significant research over the past fifteen years, the goal of cinemaquality HDR video has not yet been achieved. This work references an optical method patented by Contrast Optical which is used to capture sequences of Low Dynamic Range (LDR) images that can be used to form HDR images as the basis for HDR video. Because of the large diverence in exposure spacing of the LDR images captured by this camera, present methods of merging LDR images are insufficient to produce cinema quality HDR images and video without significant visible artifacts. Thus the focus of the research presented is two fold. The first contribution is a new method of combining LDR images with exposure differences of greater than 3 stops into an HDR image. The second contribution is a method of tone mapping HDR video which solves potential problems of HDR video flicker and automated parameter control of the tone mapping operator. A prototype of this HDR video capture technique along with the combining and tone mapping algorithms have been implemented in a high-definition HDR-video system. Additionally, Field Programmable Gate Array (FPGA) hardware implementation details are given to support real time HDR video. Still frames from the acquired HDR video system which have been merged used the merging and tone mapping techniques will be presented