Shambhavi Jain

Bio: Shambhavi Jain is an academic researcher. The author has contributed to research in topics: Principal curvature-based region detector & Feature extraction. The author has an hindex of 1, co-authored 1 publications receiving 10 citations.

Comparative study on SIFT and SURF face feature descriptors

Abstract: Feature refers to some relevant information which is present on images or faces. Feature extraction used to extract those features from the face. Among that bulk of keypoints, only robust features are detected by using feature descriptors. This paper analyzes 2 robust feature detector and descriptors are: Scale-Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF). These two robust feature descriptors are invariant to scale changes, blur, rotation, illumination changes and affine transformation. SIFT is an algorithm used to extract the features from the images. SURF is an efficient algorithm is same as SIFT performance and reduced in computational complexity. SIFT algorithm presents its ability in most of the situation but still its performance is slow. SURF algorithm is same as SIFT with fastest one and good performance.

UAV and obstacle sensing techniques – a perspective

Abstract: Purpose The purpose of this paper is to provide an overview of unmanned aerial vehicle (UAV) developments, types, the major functional components of UAV, challenges, and trends of UAVs, and among the various challenges, the authors are concentrating more on obstacle sensing methods. This also highlights the scope of on-board vision-based obstacle sensing for miniature UAVs. Design/methodology/approach The paper initially discusses the basic functional elements of UAV, then considers the different challenges faced by UAV designers. The authors have narrowed down the study on obstacle detection and sensing methods for autonomous operation. Findings Among the various existing obstacle sensing techniques, on-board vision-based obstacle detection has better scope in the future requirements of miniature UAVs to make it completely autonomous. Originality/value The paper gives original review points by doing a thorough literature survey on various obstacle sensing techniques used for UAVs.

An analysis of Feature extraction and Classification Algorithms for Dangerous Object Detection

Abstract: One of the important practical applications of object detection and image classification can be for security enhancement. If dangerous objects e.g. knives can be identified automatically, then a lot of violence can be prevented. For this purpose, various different algorithms and methods are out there that can be used. In this paper, four of them have been investigated to find out which can identify knives from a dataset of images more accurately. Among Bag of Words, HOG-SVM, CNN and pre-trained Alexnet CNN, the deep learning CNN methods are found to give best results, though they consume significantly more resources.

Local Geometric Deformations in the DHT Domain With Applications

Abstract: Local rotation, translation, and scaling of the image domain represent a basic toolkit in adaptive image processing, such as image registration, template matching, local invariant feature detection, and super-resolution imaging, among others. In this paper, it is shown how the local rotation, scaling, and translations can be performed in the discrete Hermite transform (DHT) domain. As the DHT satisfies the generalized steerability property, basic geometric operations are expressed as linear mappings in the DHT domain and hence can facilitate the solution of many image processing problems. The local rotation and scaling were previously shown in the continuous domain using the Hermite Transform, the former is used here as a good approximation for discrete images, whereas the latter is extended to a discrete domain. In addition, the local translation operation is fully developed in the discrete domain. The application of these three operations is illustrated with three exemplar applications including: 1) mathematical morphology; 2) template matching; and 3) depth from defocus. The simple yet effective methods presented in the paper indicate that local image decompositions satisfying the steerability property, such as the DHT, are desirable for solving a number of interesting image processing problems.

A Comparative Study Between PCA and Sift Algorithm for Static Face Recognition

Abstract: With the rapid growth in the field of information technology, peoples depend on the technology for solving many kinds of issues that can help in running on daily bases activities, as one of information security features, face recognition became one of the business intelligence methods to identify and recognize peoples in different domains, this paper compares the well-known and reputed state of arts algorithms for static face recognition such as PCA and Sift. The two algorithms start by reading image from camera and then Pre-Process the image, extracting the features, and recognize the image. Based on the experiment the results outlined that Sift algorithm achieved better performance of face recognition compared to the PCA.

An Operational Image-Based Digital Twin for Large-Scale Structures

Abstract: This study presents a novel methodology to create an operational Digital Twin for large-scale structures based on drone inspection images. The Digital Twin is primarily used as a virtualized representation of the structure, which will be updated according to physical changes during the life cycle of the structure. The methodology is demonstrated on a wind turbine transition piece. A three-dimensional geometry reconstruction of a transition piece as manufactured is created using a large number (>500) of RGB images collected from a drone and/or several LiDAR scans. Comparing the reconstruction to the original design will locate and quantify geometric deviations and production tolerances. An artificial intelligence algorithm is used to detect and classify paint defects/damages from images. The detected and classified paint defects/damages are subsequently digitalized and mapped to the three-dimensional geometric reconstruction of the structure. These developed functionalities allow the Digital Twin of the structure to be updated with manufacturing-induced geometric deviations and paint defects/damages using inspection images at regular time intervals. The key enabling technologies to realize the Digital Twin are presented in this study. The proposed methodology can be used in different industrial sectors, such as the wind energy, oil, and gas industries, aerospace, the marine and transport sector, and other large infrastructures.