scholarly journals Panoramic Image Stitching: A Survey

2020 ◽  
Vol 6 (12) ◽  
pp. 93-97
Author(s):  
Vanshul Bhasker
Keyword(s):  
Feature Detection ◽  
Feature Matching ◽  
Current Status ◽  
Image Stitching ◽  
Panoramic Image ◽  
Electronic Document ◽  
Image Processing Techniques ◽  
Image Blending ◽  
Homography Estimation ◽  
Processing Techniques

This electronic document is a report on Image Stitching. Image stitching is the process of creating an image panorama from a given set of images that have some common(overlapping) area in them. Previous researches done on this topic show that there is still a lot of scope for improvement in this field as although we are able to achieve good results but we haven’t really been able to achieve perfection. There are a lot of factors that are to be blamed here. While Stitching Images, there could be many challenges such as images being corrupt by noise and/or presence of parallax in the images. Image Stitching process is divided into 5 major steps: Image Registration, Feature Detection, Feature Matching, Homography Estimation and Image Blending. In this document we are going to discuss the current status of image processing techniques and what are the challenges being faced.

scholarly journals Simulation of GNSS Availability in Urban Environments Using a Panoramic Image Dataset

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Sakpod Tongleamnak ◽  
Masahiko Nagai
Keyword(s):  
Urban Areas ◽  
Accuracy Assessment ◽  
Satellite System ◽  
Urban Environments ◽  
Panoramic Image ◽  
Image Processing Techniques ◽  
Gnss Positioning ◽  
Image Dataset ◽  
Global Navigation Satellite ◽  
Processing Techniques

Performance of Global Navigation Satellite System (GNSS) positioning in urban environments is hindered by poor satellite availability because there are many man-made and natural objects in urban environments that obstruct satellite signals. To evaluate the availability of GNSS in cities, this paper presents a software simulation of GNSS availability in urban areas using a panoramic image dataset from Google Street View. Photogrammetric image processing techniques are applied to reconstruct fisheye sky view images and detect signal obstacles. Two comparisons of the results from the simulation and real world observation in Bangkok and Tokyo are also presented and discussed for accuracy assessment.

A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters

2009 ◽  
Vol 33 (2) ◽  
pp. 183-207 ◽  
Author(s):  
Karen E. Joyce ◽  
Stella E. Belliss ◽  
Sergey V. Samsonov ◽  
Stephen J. McNeill ◽  
Phil J. Glassey
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Emergency Services ◽  
Spatial Information ◽  
Current Status ◽  
Data Types ◽  
Image Processing Techniques ◽  
Rapid Acquisition ◽  
The Status ◽  
Processing Techniques

In the event of a natural disaster, remote sensing is a valuable source of spatial information and its utility has been proven on many occasions around the world. However, there are many different types of hazards experienced worldwide on an annual basis and their remote sensing solutions are equally varied. This paper addresses a number of data types and image processing techniques used to map and monitor earthquakes, faulting, volcanic activity, landslides, flooding, and wildfire, and the damages associated with each. Remote sensing is currently used operationally for some monitoring programs, though there are also difficulties associated with the rapid acquisition of data and provision of a robust product to emergency services as an end-user. The current status of remote sensing as a rapid-response data source is discussed, and some perspectives given on emerging airborne and satellite technologies.

Bridge-surface panoramic-image generation for automated bridge-inspection using deepmatching

2020 ◽  
pp. 147592172093038
Author(s):  
Jongbin Won ◽  
Jong-Woong Park ◽  
Changsu Shim ◽  
Man-Woo Park
Keyword(s):  
Image Processing ◽  
Image Matching ◽  
Parametric Model ◽  
Image Generation ◽  
Panoramic Image ◽  
Image Processing Techniques ◽  
Matching Pair ◽  
Image Pairs ◽  
Processing Techniques ◽  
Surface Images

Visual inspection is important for the efficient maintenance of bridge structures and has recently been supplemented with the use of image-processing techniques that can localize and quantify damages using images captured from bridges. A series of overlapping bridge images can be combined for constructing a panoramic bridge-surface image in which the locations and sizes of the damages can be noted. Despite the excellent performance of image-processing techniques, generating panoramic images from a series of bridge-surface images is challenging as bridge-surface images may not possess distinct patterns or patterns that can act as reference feature points for stitching adjacent images. To address this issue, this paper presents a general method for stitching bridge-surface images using Deepmatching, which determines a pixel-wise correspondence between an image pair in comparison with conventional feature-wise matching methods. To employ Deepmatching for panoramic-image generation, (1) image matching pair search using 2D Delaunay triangulation, (2) parametric model for optimal image stitching were developed, and (3) field validation was conducted in this study. First, possible image matching pairs are organized using the two-dimensional Delaunay triangulation, and then Deepmatching is used to determine the matching points between possible image pairs. The developed parametric model refines the valid image matching pair, which is used for obtaining optimal global homographies for panoramic-image generation. For the validation of the proposed method, a lab-scale experiment on a flat concrete wall and a field experiment on a concrete bridge were conducted. The experimental validation demonstrates that the proposed method successfully identifies dense matching points between image pairs and generates a panoramic image while minimizing the occurrence of ghosting and drift.

Application of Image Processing and Fuzzy Logic to Mobile Robots Providing Assistance to Fire Fighters

2013 ◽  
Vol 389 ◽  
pp. 740-746
Author(s):  
Ayman Abbas ◽  
Khaled El-Geneidy
Keyword(s):  
Image Processing ◽  
Feature Detection ◽  
Robot Vision ◽  
Region Of Interest ◽  
Influential Factors ◽  
Fire Fighting ◽  
Fire Fighters ◽  
Image Processing Techniques ◽  
Image Processing Algorithms ◽  
Processing Techniques

The motive behind this research project is to devise a method for overcoming some of the challenges faced by fire fighters in Egypt while accomplishing their duties. This is achieved by utilizing robot vision technology as one of the approaches used for task automation. Based on a study of different methods of automation in human tracking and fire fighting applications, image processing techniques with the highest potential in a fire fighting environment were identified. A system has been developed which fusses the selected image processing algorithms with fuzzified readings from distance sensors, to extract the major blue areas in acquired images that is more likely to correspond to the uniform worn by fire fighters in Egypt. Subsequently the extracted blue area is used to identify a region of interest within the image in order to reduce the computations. The feature detection process constrains its search for a feature found on the back of the target fire fighter to the identified region of interest. Based on the location and area of this feature, the system will calculate the required velocity components to control the motion of the robot and the camera pan and tilt mechanism, in order to continue tracking the target along its path. The system has been validated by conducting an experiment which simulates the key influential factors in a fire fighting environment.

Performance Enhancement of Image Stitching Process Under Bound Energy Aided Feature Matching and Varying Noise Environments

2020 ◽  
Vol 17 (9) ◽  
pp. 4419-4424
Author(s):  
Venkat P. Patil ◽  
C. Ram Singla
Keyword(s):  
Computer Vision ◽  
Feature Detection ◽  
Feature Matching ◽  
Performance Enhancement ◽  
Computation Time ◽  
Automatic Target Recognition ◽  
Image Features ◽  
Image Stitching ◽  
Scale Invariant ◽  
Image Mosaicing

Image mosaicing is a method that combines several images or pictures of the superposing field of view to create a panoramic high-resolution picture. In the field of medical imagery, satellite data, computer vision, military automatic target recognition can be seen the importance of image mosaicing. The present domains of studies in computer vision, computer graphics and photo graphics are image stitching and video stitching. The registration of images includes five primary phases: feature detection and description; matching feature; rejection of outliers; transformation function derivation; image replication. Stitching images from specific scenes is a difficult job when images can be picked up under different noise. In this paper, we examine an algorithm for seamless stitching of images in order to resolve all such problems by employing dehazing methods to the collected images, and before defining image features and bound energy characteristics that match image-based features of the SIFT-Scale Invariant Feature Transform. The proposed method experimentation is compared with the conventional methods of stitching of image using squared distance to match the feature. The proposed seamless stitching technique is assessed on the basis of the metrics, HSGV and VSGV. The analysis of this stitching algorithm aims to minimize the amount of computation time and discrepancies in the final stitched results obtained.

scholarly journals ROBUST FEATURE MATCHING IN TERRESTRIAL IMAGE SEQUENCES

2018 ◽  
Vol XLII-3 ◽  
pp. 3-7
Author(s):  
A. Abbas ◽  
S. Ghuffar
Keyword(s):  
Feature Detection ◽  
Feature Matching ◽  
Image Sequences ◽  
Image Stitching ◽  
Urban Scenes ◽  
Structure Estimation ◽  
Scene Structure ◽  
Computer Vision Applications ◽  
Matching Techniques ◽  
Repeating Patterns

From the last decade, the feature detection, description and matching techniques are most commonly exploited in various photogrammetric and computer vision applications, which includes: 3D reconstruction of scenes, image stitching for panoramic creation, image classification, or object recognition etc. However, in terrestrial imagery of urban scenes contains various issues, which include duplicate and identical structures (i.e. repeated windows and doors) that cause the problem in feature matching phase and ultimately lead to failure of results specially in case of camera pose and scene structure estimation. In this paper, we will address the issue related to ambiguous feature matching in urban environment due to repeating patterns.

Automatic Screening of Diabetic Maculopathy Using Image Processing

2019 ◽  
Vol 15 (4) ◽  
pp. 30-37
Author(s):  
Shweta Reddy
Keyword(s):  
Image Processing ◽  
Feature Detection ◽  
Vision Loss ◽  
Accurate Result ◽  
Screening Method ◽  
Automated System ◽  
Diabetic Maculopathy ◽  
Image Processing Techniques ◽  
Regular Screening ◽  
Processing Techniques

Retinal imaging is a challenging screening method for detection of retinal abnormalities. Diabetic Maculopathy (DM) is a condition that can result from retinopathy. Regular screening is necessary for diabetic maculopathy in order to identify the risk of vision loss. Maculopathy is damage to macula, the key region responsible for high sharp colour vision. Diabetic Retinopathy and Diabetic Maculopathy needs regular observation in order to indicate visual impairment risk. In this article, the author first presents a brief summary of diabetic maculopathy and its causes. Then, an exhaustive literature review of different automated DM diagnosis systems offered. It is important for ophthalmologists to have an automated system which detects early symptoms of the disease and yields a high accurate result. A vital assessment of the image processing techniques used for DM feature detection is projected in this paper. Various methods have been proposed to identify and classify DM based on severity level.

Three-Dimensional Structure of Rotavirus-Fab Complex

1990 ◽  
Vol 48 (1) ◽  
pp. 238-239
Author(s):  
B.V.V. Prasad ◽  
E. Marietta ◽  
J.W. Burns ◽  
M.K. Estes ◽  
W. Chiu
Keyword(s):  
Image Processing ◽  
Smooth Surface ◽  
Three Dimensional ◽  
Outer Shell ◽  
Dimensional Structure ◽  
Structural Studies ◽  
Cell Penetration ◽  
Electron Cryomicroscopy ◽  
Image Processing Techniques ◽  
Processing Techniques

Rotaviruses are spherical, double-shelled particles. They have been identified as a major cause of infantile gastroenteritis worldwide. In our earlier studies we determined the three-dimensional structures of double-and single-shelled simian rotavirus embedded in vitreous ice using electron cryomicroscopy and image processing techniques to a resolution of 40Å. A distinctive feature of the rotavirus structure is the presence of 132 large channels spanning across both the shells at all 5- and 6-coordinated positions of a T=13ℓ icosahedral lattice. The outer shell has 60 spikes emanating from its relatively smooth surface. The inner shell, in contrast, exhibits a bristly surface made of 260 morphological units at all local and strict 3-fold axes (Fig.l).The outer shell of rotavirus is made up of two proteins, VP4 and VP7. VP7, a glycoprotein and a neutralization antigen, is the major component. VP4 has been implicated in several important functions such as cell penetration, hemagglutination, neutralization and virulence. From our earlier studies we had proposed that the spikes correspond to VP4 and the rest of the surface is composed of VP7. Our recent structural studies, using the same techniques, with monoclonal antibodies specific to VP4 have established that surface spikes are made up of VP4.

A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

2020 ◽  
pp. 967-973
Author(s):  
V. Deepika ◽  
T. Rajasenbagam
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Soft Tissues ◽  
Tumor Detection ◽  
Tumor Classification ◽  
Normal Brain ◽  
Image Processing Techniques ◽  
Normal Brain Function ◽  
Processing Techniques ◽  
The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

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