IEEE ICAPR 2009
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The document summarizes a novel approach for multisensor biometric fusion of face and palmprint images using wavelet decomposition and SIFT features for person authentication. Face and palmprint images are decomposed using wavelets and fused to create an enhanced fused image. SIFT features are extracted from the fused image and used for matching based on a monotonic-decreasing graph approach. Experimental results on a 150 person database show the proposed fusion method achieves 98.19% accuracy, outperforming individual face and palmprint recognition.
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![“ Multisensor Biometric Evidence Fusion for Person Authentication using Wavelet Decomposition and Monotonic-Decreasing Graph” *D. R. Kisku, J. K. Sing, M. Tistarelli, P. Gupta *Department of Computer Science and Engineering, Dr. B. C. Roy Engineering College, Durgapur – 713206, India [email_address]](https://image.slidesharecdn.com/kiskusingtistaguptaicapr2009-090908211329-phpapp01/85/IEEE-ICAPR-2009-1-320.jpg)
![Agenda of discussion: ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
A feature selection method for automatic image annotation
ABSTRACT: Automatic image annotation (AIA) is the bridge of high-level semantic information and the low-level feature. AIA is an effective method to resolve the problem of “Semantic Gap”. According to the intrinsic character of AIA, some common features are selected from the labeled images by multiple instance learning method. The feature selection method is applied into the task of automatic image annotation in this paper. Each keyword is analyzed hierarchically in low-granularity-level under the framework of feature selection. Through the common representative instances are mined, the semantic similarity of images can be effectively expressed and the better annotation results are able to be acquired, which testifies the effectiveness of the proposed annotation method. Gaussian mixture model is built by the selected feature method to characterize the labeled keyword. The experimental results illustrate the good perfromance of AIA.
D018112429
This document presents a method for tracking moving objects in video sequences using affine flow parameters combined with illumination insensitive template matching. The method extracts affine flow parameters from frames to model local object motion using affine transformations. It then applies template matching with illumination compensation to track objects across frames while being robust to illumination changes. The method is evaluated on various indoor and outdoor database videos and is shown to effectively track objects without false detections, handling issues like illumination variations, camera motion and dynamic backgrounds better than other methods.
DETECTION OF CONCEALED WEAPONS IN X-RAY IMAGES USING FUZZY K-NN
Scanning baggage by x-ray and analysing such images have become important technique for detecting
illicit materials in the baggage at Airports. In order to provide adequate security, a reliable and fast
screening technique is needed for baggage examination.This paper aims at providing an automatic method
for detecting concealed weapons, typically a gun in the baggage by employing image segmentation method
to extract the objects of interest from the image followed by applying feature extraction methods namely
Shape context descriptor and Zernike moments. Finally the objects are classified using fuzzy KNN as illicit
or non-illicit object.
IEEE 2014 MATLAB IMAGE PROCESSING PROJECTS Seamless view synthesis through te...
IEEE 2014 MATLAB IMAGE PROCESSING PROJECTS Seamless view synthesis through te...IEEEBEBTECHSTUDENTPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.orgComparative between global threshold and adaptative threshold concepts in ima...
A digital image can be considered as a discrete representation of data possessing both spatial (layout) and
intensity (colour) information. Pixel intensities form a gateway communication between human perception
of things and digital image processing.
Image thresholding is a simple form of image segmentation. It is a way to create a binary image from a
grayscale or full-color image. This is typically done in order to separate "object" or foreground pixels from
background pixels to aid in image processing.
In this paper we aim to present a small and modest comparative between two kind of image thresholding.
The local and adapatative concepts may not give the same correct results at the end of a process, and we
aim to demonstrate which kind of the two
Ijarcet vol-2-issue-3-1280-1284
This document discusses image registration using mutual information. It describes mutual information as a similarity measure that is robust to illumination changes, modality differences, and occlusions. It can accurately register both monomodal and multimodal images in real-time. The document evaluates three optimization techniques for mutual information-based registration - gradient descent, conjugate gradient, and random search. Gradient descent produced the most accurate registrations with the lowest error values. Entropy, mutual information, and error values are calculated and reported to evaluate the registration results for both mono and multimodal images. Gradient descent optimization achieved the best alignment between images as indicated by the increased mutual information and decreased error values after registration.
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Biometric sensor image fusion for identity verification: A case study with wavelet-based fusion rules and graph matching
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APPLYING R-SPATIOGRAM IN OBJECT TRACKING FOR OCCLUSION HANDLING
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A comparative analysis of retrieval techniques in content based image retrieval
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A SURVEY ON VARIOUS APPROACHES TO FINGERPRINT MATCHING FOR PERSONAL VERIFICAT...
Automatic Fingerprint authentication for personal identification and verification has received considerable
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local texture, ridge geometry etc. All these different techniques have their pros and cons. This paper also
provides the performance comparison of several existing methods proposed by researchers in fing
A novel tool for stereo matching of images
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A017330108
1) The document presents a new touchless palmprint verification method that uses shock filtering for preprocessing, SIFT feature extraction and matching, and I-RANSAC and LPD refinement for feature matching.
2) Shock filtering is used to enhance the palmprint images by dividing them clearly at edges and flattening signals within areas. This improves SIFT feature detection by producing more keypoints.
3) SIFT is used to extract scale and rotation invariant features from the palmprint images. I-RANSAC and LPD refinement are then used to match features between images and improve matching accuracy.
Touchless Palmprint Verification using Shock Filter, SIFT, I-RANSAC, and LPD
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
E010513037
This document summarizes and compares two image registration techniques: Scale Invariant Feature Transform (SIFT) and Curvature Scale-Space (CSS) Corner Detection with robust corner matching.
SIFT detects and describes local features in images that are invariant to changes in scale, rotation, and illumination. It finds key points at multiple scales and assigns orientations based on local image gradients. CSS corner detection uses the Canny edge detector to find edges, then detects corners as curvature maxima along edges. It parameterizes edges using affine-length for stability to affine transformations. Robust corner matching finds candidate matches based on curvature and affine-length, then estimates transformation matrices from triangle areas to find the best matches.
The document shows
OPTIMIZED FINGERPRINT COMPRESSION WITHOUT LOSS OF DATAProposed workblessy up...
OPTIMIZED FINGERPRINT COMPRESSION WITHOUT LOSS OF DATAProposed workblessy up...feature software solutions pvt ltd
This document summarizes research on optimized fingerprint compression without loss of data. It discusses how fingerprint recognition works by extracting minutiae features from fingerprints. It then describes the proposed fingerprint recognition method using minutiae score matching (FRMSM), which uses block filtering for thinning fingerprints to preserve image quality while extracting minutiae. Experimental results showed the false matching ratio was better than existing algorithms. The document also provides background on biometric systems and fingerprint recognition. It reviews related work on fingerprint enhancement, orientation field estimation, and minutiae extraction. The proposed system describes a line extraction and graph matching approach for fingerprint matching with improved robustness. Modules for the system include authentication, image capturing, fingerprint matching, binarization, andGait Based Person Recognition Using Partial Least Squares Selection Scheme
The document summarizes a research paper on gait-based person recognition using partial least squares selection. It presents an Arbitrary View Transformation Model (AVTM) that uses gait energy images and partial least squares (PLS) feature selection to improve gait recognition accuracy under varying viewing angles, clothing, and other conditions. The proposed AVTM PLS method is evaluated on the CASIA gait database and shown to achieve higher recognition rates compared to other existing methods, especially when there are changes in viewing angle, clothing, or whether the person is carrying something. Tables of results demonstrate the proposed method outperforms alternatives across different test conditions and ranges of gallery and probe viewing angles.
B017360516
The document presents a new approach called Linear Curvature Empirical Coding (LCEC) for image retrieval. LCEC aims to improve upon existing curvature-based coding approaches by linearly representing the curvature scale space plot and then applying empirical coding to select descriptive shape features. The linear representation considers variations across all smoothing factors rather than discarding information below a threshold. Empirical coding is used to select features based on variation density rather than just magnitudes. The results show LCEC performs better than previous methods for image retrieval.
Empirical Coding for Curvature Based Linear Representation in Image Retrieval...
The document presents a new approach called Linear Curvature Empirical Coding (LCEC) for image retrieval. LCEC aims to improve upon existing curvature-based coding approaches by linearly representing the curvature scale space plot and then applying empirical coding to select descriptive shape features. The linear representation considers variations across all smoothing factors rather than discarding information below a threshold. Empirical coding is used to select features based on variation density rather than just magnitude. The results show LCEC performs better than previous methods for image retrieval.
E017443136
This document summarizes an analysis of iris recognition based on false acceptance rate (FAR) and false rejection rate (FRR) using the Hough transform. It first provides an overview of iris recognition and its typical stages: image acquisition, localization/segmentation, normalization, feature extraction, and pattern matching. It then describes existing methods used in each stage, including the Hough transform and rubber sheet model for localization and normalization. The proposed methodology applies Canny edge detection, Hough transform for boundary detection, normalization with the rubber sheet model, and calculates metrics like mean squared error, root mean squared error, signal-to-noise ratio, and root signal-to-noise ratio to evaluate the accuracy of iris recognition using FAR
JBSC_online
This paper proposes a novel technique for detecting point landmarks in 3D medical images based on phase congruency (PC). A bank of 3D log-Gabor filters is used to compute energy maps from the images. These energy maps are combined to form the PC measure, which is invariant to intensity variations and provides good feature localization. Significant 3D point landmarks are detected by analyzing the eigenvectors of PC moments computed at each point. The method is demonstrated on head and neck images for radiation therapy planning.
Af35178182
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Comparison of various Image Registration Techniques with the Proposed Hybrid ...
Image Registration is termed as the method to
transform different forms of image data into one coordinate
system. Registration is a important part in image processing
which is used for matching the pictures which are obtained at
different time intervals or from various sensors. A broad range
of registration techniques have been developed for the various
types of image data. These techniques are independently
studied for many applications resulting in the large body of
result. Vision is the most advanced of human sensors, so
naturally images play one of the most important roles in
human perception. Image registration is one of the branches
encompassed by the diverse field of digital image processing.
Due to its importance in many application areas as well as
since its nature is complicated; image registration is now the
topic of much recent research. Registration algorithms tend
to compute transformations to set correspondence betweenthe two images. In this paper the survey is done on various
image registration techniques. Also the different techniques
are compared with the proposed system of the projec
Multi Wavelet for Image Retrival Based On Using Texture and Color Querys
This document summarizes a research paper on using multi-wavelet transforms for content-based image retrieval. The paper proposes extracting multi-wavelet features from images in a database and query images to measure similarity. It calculates energy levels from multi-wavelet sub-bands and uses Canberra distance between feature vectors to retrieve similar images. The method achieves 98.5% accuracy and is faster than using Gabor wavelets. In conclusion, multi-wavelet transforms provide good performance for content-based image retrieval applications.
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Touchless Palmprint Verification using Shock Filter, SIFT, I-RANSAC, and LPD
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Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
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