Digital Image Processing

Ramesh M. Kagalkar ¹, S. V. Gumaste ²

Affiliations
1 VTU, Belgaum, Karnataka, IN
2 Department of Computer Engg, R. H. Sapat College of Engineering, Nashik, Pune, Maharashtra, IN

Abstract

This paper reviews the intensive state of the art in automatic recognition of continuous signs, from  different languages, supported the information  sets used, features computed, technique used, and recognition rates achieved. In this paper discover that, in the past, most work has been tired finger-spelled words and isolated sign recognition, but recently, there has been vital progress within the recognition of signs embedded briefly continuous sentences. Paper tend to conjointly realize that researchers are getting down addressing the necessary downside of extracting and integration non-manual data that is gift in face and head movement and present results from experiments integration of non-manual options.

Keywords

American Sign Language (ASL), Hidden Marko Model (HMM) and Extended Multi Modal Annotation (EMMA).

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Amitkumar Shinde ¹, Ramesh M. Kagalkar ¹

Affiliations
1 Department of Computer Engineering, Savitribai Phule University of Pune, IN

Abstract

In later year's gesture based communication acknowledgment has turned in a standout amongst the most developing fields of examination and it is the most characteristic method of correspondence for the individuals with listening to issues. A hand signal acknowledgment framework can give a chance to hard of hearing persons speak with typical individuals without the need of a translator or middle. We are going to construct a framework and techniques for the programmed acknowledgment of the Marathi communication via gestures. Through that we are giving instructing classes to the reason for preparing the hard of hearing sign client in Marathi. The framework does oblige hand to be appropriately adjusted to the camera and does not require any wearable sensors. A substantial arrangement of tests has been utilized as a part of the proposed framework to perceive confined words from the standard Marathi communication through signing, which are taken before the camera with distinctive hard of hearing sign client. In our proposed framework, we mean to perceive some extremely essential components of gesture based communication and to make an interpretation of them to content and the other way around. The proposed framework utilizing 46 Marathi letters in order for acknowledgment.

Keywords

Marathi Sign Language, Hand Gesture Recognition, Canny’s Edge Detection, Processing, Feature Extraction, Pattern Recognition/Matching, Gray Scale Image, Database.

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M. Siddappa ¹, Ramesh M. Kagalkar ², M. Z. Kurian ³

Affiliations
1 Department of Computer Science, Sri Siddhartha Institute of Technology, Tumkur, Karnataka, IN
2 Department of Computer Science, Rural Engineering College, Hulkoti, Karnataka, IN
3 Department of Electronics & Communication Engineering, Sri Siddhartha Institute of Technology, Tumkur, Karnataka, IN

Abstract

This paper presents a tumor detection algorithm from mammogram. The proposed system focuses on the solution of two problems. One is how to detect tumors as suspicious regions with a very weak contrast to their background and another is how to extract features which categorize tumors. The tumor detection method follows the scheme of mammogram enhancement, the segmentation of the tumor area, the extraction of features from the segmented tumor area and the use of SVM classifier. The enhancement can be defined as conversion of the image quality to a better and more understandable level. The mammogram enhancement procedure includes filtering, top hat operation, DWT. Then the contrast stretching is used to increase the contrast of the image. The segmentation of mammogram images has been playing an important role to improve the detection and diagnosis of breast cancer. The most common Segmentation method used is thresholding. The features are extracted from the segmented breast area. Next stage include, which classifies the regions using the SVM classifier. The method was tested on 75 mammographic images, from the mini-MIAS database. The methodology achieved a sensitivity of 88.75%.

Keywords

Computer Aided Diagnosis CAD, Computed Tomography CT, Support Vector Machine SVM and Discrete Wavelet Transform DWT.

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Ramesh M. Kagalkar ¹, S. V. Gumaste ²

Affiliations
1 Department of Computer Engineering, Dr. D Y Patil School of Engineering and Technology, Pune, IN
2 Department of Computer Engineering, R. H. Sapat College of Engineering, Nashik, Maharashtra, IN

Abstract

In later year's gesture based communication acknowledgment has turned in a standout amongst the most developing fields of examination and it is the most characteristic method of correspondence for the individuals with listening to issues. A hand signal acknowledgment framework can give a chance to hard of hearing persons speak with typical individuals without the need of a translator or middle. Proposed system is going to construct a framework and techniques for the programmed acknowledgment of the Kannada communication via gestures. Through that we are giving instructing classes to the reason for preparing the hard of hearing sign client in Kannada. The framework does oblige hand to be appropriately adjusted to the camera and does not require any wearable sensors. A substantial arrangement of tests has been utilized as a part of the proposed framework to perceive confined words from the standard Kannada communication through signing, which are taken before the camera with distinctive hard of hearing sign client. In proposed framework, we mean to perceive some extremely essential components of gesture based communication and to make an interpretation of them to content and the other way around. The proposed framework utilizing 36 Kannada letters in order for acknowledgment.

Keywords

Kannada Sign Language, Hand Gesture Recognition, Canny’s Edge Detection, Processing, Feature Extraction, Pattern Recognition/Matching, Gray Scale Image, Database.

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