Automatic annotation algorithm based on sliding window moment feature matching

This paper aims at an elegant mixture of methods for automatic annotation, detection, clustering, segmentation and retrieval of ultrasound lung images. The annotation of lung images done by using a method called Speeded up Robust Features that is based on the Support Vector Machine classifier. For the features extraction a Fast-Hessian detector was used. The feature matching was performed with SVM. The featured images were clustered using Independent Component Analysis. Micro structure descriptor was used for segmentation of these images while extracting the features. The testing of the developed system was performed using a subset of the IRMA radiographic images. The results provided with the propsed methods were compared with independent methods. Altogether it prospectively constructed an efficient system for automatic medical image retrieval and annotation.

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Word-Finding Support From Mobile Technology Benefits a Woman With Aphasia

Perspectives on Augmentative and Alternative Communication ◽

10.1044/aac24.1.26 ◽

2015 ◽

Vol 24 (1) ◽

pp. 26-39 ◽

Cited By ~ 1

Author(s):

Yvonne Gillette

Keyword(s):

Mobile Technology ◽

Clinical Education ◽

Augmentative And Alternative Communication ◽

Feature Matching ◽

Mobile Technologies ◽

Alternative Communication ◽

Alternative Intervention ◽

Communication Assessment ◽

Word Finding ◽

Intervention Costs

Mobile technology provides a solution for individuals who require augmentative and alternative intervention. Principles of augmentative and alternative communication assessment and intervention, such as feature matching and the participation model, developed with dedicated speech-generating devices can be applied to these generic mobile technologies with success. This article presents a clinical review of an adult with aphasia who reached her goals for greater communicative participation through mobile technology. Details presented include device selection, sequence of intervention, and funding issues related to device purchase and intervention costs. Issues related to graduate student clinical education are addressed. The purpose of the article is to encourage clinicians to consider mobile technology when intervening with an individual diagnosed with mild receptive and moderate expressive aphasia featuring word-finding difficulties.

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Sliding Window-Based Transmit Antenna Selection Technique for Large-Scale MU-MIMO Networks

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences ◽

10.1587/transfun.e97.a.1640 ◽

2014 ◽

Vol E97.A (7) ◽

pp. 1640-1641 ◽

Cited By ~ 1

Author(s):

Tae-Won BAN ◽

Bang Chul JUNG

Keyword(s):

Large Scale ◽

Antenna Selection ◽

Sliding Window ◽

Transmit Antenna Selection ◽

Selection Technique ◽

Mimo Networks

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Recognition of Birds in Blurred and Illumination Images by Local Features

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse/v7i7/0128 ◽

2017 ◽

Vol 7 (7) ◽

pp. 243

Author(s):

Suresha .M ◽

. Sandeep

Keyword(s):

Computer Vision ◽

Time Complexity ◽

Feature Detection ◽

Feature Matching ◽

Fast Algorithms ◽

Local Features ◽

Experimental Demonstration ◽

Harris Corner ◽

Blurred Image ◽

Blurred Images

Local features are of great importance in computer vision. It performs feature detection and feature matching are two important tasks. In this paper concentrates on the problem of recognition of birds using local features. Investigation summarizes the local features SURF, FAST and HARRIS against blurred and illumination images. FAST and Harris corner algorithm have given less accuracy for blurred images. The SURF algorithm gives best result for blurred image because its identify strongest local features and time complexity is less and experimental demonstration shows that SURF algorithm is robust for blurred images and the FAST algorithms is suitable for images with illumination.

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