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Author: search.filters.author.K, Geethalakshmi

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    A SURVEY ON DEEP LEARNING APPROACHES IN RETINAL VESSEL SEGMENTATION FOR DISEASE IDENTIFICATION
    (Sankara College of Science and Commerce, 2018-10-10) K, Geethalakshmi
    Human retinal image plays a vital role in detection and diagnosis of various eye diseases for ophthalmologist. Automated blood vessel segmentation diagnoses many eye diseases like diabetic retinopathy, hypertension retinopathy, retinopathy of prematurity or glaucoma based on the feature extraction. Automated image analysis tool based on machine learning algorithms are the key point to improve the quality of image analysis. Deep learning (DL) is a subset of machine learning which is completely based on artificial neural network. It helps a machine to analyze the data efficiently. Deep learning is one extensively applied techniques that provides state of the art accuracy. Different types of neural network and platform used for DL also discussed. This paper reviews the different DL approaches for blood vessels segmentation. It concludes that the deep learning methods produces high level of accuracy in disease identification
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    A REVIEW ON CONTENT BASED IMAGE RETRIEVAL SYSTEM TECHNIQUES
    (PKR Arts College for Women, 2018-08-16) K, Geethalakshmi
    A database is a collection of information that is structured for easy storage, retrieval and update. This information is represented in many forms like text, table, image, chart and graph etc. Content Based Image Retrieval (CBIR) technique explores various methodologies in extracting implicit knowledge, patterns and relationships found in the images from the collection of images. As the network and development of multimedia technologies are becoming more popular, users are not satisfied with the traditional information retrieval techniques. Nowadays the content based image retrieval (CBIR) are becoming a source of exact and fast retrieval. This paper focuses on the overview of CBIR and various techniques that were proposed in earlier literature.
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    STUDY ON IMAGE PROCESSING AND SEGMENTATION TECHNIQUES
    (St.Aloysius College, 2017-11-16) K, Geethalakshmi
    Digital Image Processing is always an interesting field as it gives improved pictorial information for human interpretation & processing of image data for storage, transmission and representation for machine perception. Image processing is a technique to enhance raw images received from cameras/sensors placed on satellites, space probes and aircrafts or pictures taken in normal day-to-ay life for various applications. This field of image processing significantly improved in recent times and extended to various fields of science & technology. The Image Processing mainly deals with image acquisition, Image Enhancement, Image Segmentation, Feature Extraction, and Image Classification. A number of Image Processing techniques, in addition to enhancement techniques can be applied to improve the data usefulness. Techniques include convolution edge detection, mathematics, filters, trend removal & image analysis. The various image enhancements and image processing techniques will be discussed in this paper.
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    IMAGE PROCESSING TECHNIQUES
    (Dr.N.G.P Arts and Science College, 2017-02-24) K, Geethalakshmi; A, Shivyaa
    Digital Image Processing is always an interesting field as it gives improved pictorial information for human interpretation & processing of image data for storage, transmission and representation for machine perception. Image processing is a technique to enhance raw images received from cameras/sensors placed on satellites, space probes and aircrafts or pictures taken in normal day-to-ay life for various applications. This field of image processing significantly improved in recent times and extended to various fields of science & technology. The Image Processing mainly deals with image acquisition, Image Enhancement, Image Segmentation, Feature Extraction, and Image Classification. A number of Image Processing techniques, in addition to enhancement techniques can be applied to improve the data usefulness. Techniques include convolution edge detection, mathematics, filters, trend removal & image analysis. The various image enhancements and image processing techniques will be discussed in this paper.
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    PRIVACY RISK IN RECOMMENDED SYSTEM
    (PSGR Krishnammal College for Women, 2014-01-09) K, Geethalakshmi; R, Divya
    In many on-line applications, the range of content that is offered to users is so wide that a requirement for automatic recommender systems arises. Such systems will give a personalized selection of relevant things to users. In practice, this may facilitate people realize fun movies, boost sales through targeted advertisements, or facilitate social network users meet new friends. To produce correct personalized recommendations, recommender systems depend on detailed personal information on the preferences of users. Ratings, consumption histories and personal profiles are examples. Recommender systems are useful, but the privacy risks associated in aggregation and process personal information are typically underestimated or neglected. Many users are not sufficiently aware if and the way a lot of their information is collected, if such information is sold-out to third parties or how securely it is saved and for how long. This paper aims to provide insight into privacy in recommender systems. First, we shall discuss different varieties of existing recommender systems. Second, an overview of the data that is employed in recommender systems is given. Third, I analyze the associated risks to information privacy. Finally, relevant research areas for privacy-protection techniques and their relevancy to recommender systems are mentioned.