IEEE 2019-2020 : Image Processing Projects

For Outstation Students, we are having online project classes both technical and coding using net-meeting software

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DHS Informatics providing latest 2019-2020 IEEE projects on Image Processing for the final year engineering students. DHS Informatics trains all students to develop their project with good idea what they need to submit in college to get good marks. DHS Informatics offers placement training in Bangalore and the program name is OJT – On Job Training, job seekers as well as final year college students can join in this placement training program and job opportunities in their dream IT companies. We are providing IEEE projects for B.E / B.TECH, M.TECH, MCA, BCA, DIPLOMA students from more than two decades.

Image processing

Abstract : Information hiding aims to embed secret data into the multimedia, such as image, audio, video,  and text. In this paper, two new quantum information hiding approaches are put forward. A  quantum stenography approach is proposed to hide a quantum secret image into a quantum  cover image. The quantum secret image is encrypted first using a controlled-NOT gate to  demonstrate the security of the embedded data. The encrypted secret image is embedded into the  quantum cover image using the two most and least significant qubits. In addition, a quantum  image watermarking approach is presented to hide a quantum watermark gray image into a  quantum carrier image. The quantum watermark image, which is scrambled by utilizing Arnold’s  cat map, is then embedded into the quantum carrier image using the two least and most  significant qubits. Only the watermarked image and the key are sufficient to extract the  embedded quantum watermark image. The proposed novelty has been illustrated using a scenario  of sharing medical imagery between two remote hospitals. The simulation and analysis  demonstrate that the two newly proposed approaches have excellent visual quality and high  embedding capacity y and security.                                                                                                                                                                                                                                                                                                                         Contact:                                                                                                                                                                                                                                                                                                                 +91-98451 66723                                                                                                                                                                                                                                                                                                       ☎ 080-413 07435  

Abstract : Reversible data hiding in encrypted images (RDHEI) is an effective technique to embed data in the encrypted domain. An original image is encrypted with a secret key and during or after its transmission, it is possible to embed additional information in the encrypted image, without knowing the encryption key or the original content of the image. During the decoding process, the secret message can be extracted and the original image can be reconstructed. In the last few years, RDHEI has started to draw research interest. Indeed, with the development of cloud computing, data privacy has become a real issue. However, none of the existing methods allows us to hide a large amount of information in a reversible manner. In this paper, we propose a new reversible method based on MSB (most significant bit) prediction with a very high capacity. We present two approaches, these are: high capacity reversible data hiding approach with correction of prediction errors (CPE-HCRDH) and high capacity reversible data hiding approach with embedded prediction errors (EPE-HCRDH). With this method, regardless of the approach used, our results are better than those obtained with current state of the art methods, both in terms of reconstructed image quality and embedding capacity                                                                                                                                                                                                                                                             Contact:                                                                                                                                                                                                                                                                                                                 +91-98451 66723                                                                                                                                                                                                                                                                                                       ☎ 080-413 07435  

Abstract : The aim of this paper is to maximize the range of the access control of visual secret sharing (VSS) schemes encrypting multiple images. First, the formulation of access structures for a single secret is generalized to that for multiple secrets. This generalization is maximal in the sense that the generalized for-mulation makes no restrictions on access structures; in particular, it includes the existing ones as special cases. Next, a sufficient condition to be satisfied by the encryption of VSS schemes realizing an access structure for multiple secrets of the most general form is introduced, and two constructions of VSS schemes with encryption satisfying this condition are provided. Each of the two constructions has its advantage against the other; one is more general and can generate VSS schemes with strictly better contrast and pixel expansion than the other, while the other has a straightforward implementation. Moreover, for threshold access structures, the pixel expansions of VSS schemes generated by the latter construction are estimated and turn out to be the same as those of the existing schemes called the threshold multiple-secret visual cryptographic schemes. Finally, the optimality of the former construction is examined, giving that there exist access structures for which it generates no optimal VSS schemes.                                                                                                                                                                                Contact:                                                                                                                                                                                                                                                                                                                 +91-98451 66723                                                                                                                                                                                                                                                                                                       ☎ 080-413 07435  

Abstract : One of the becoming popular bio metric modalities  is the palm print. This bio metric modality is rich with  information, such as minutiae, ridges, wrinkles, and creases.  This research team is interested to investigate the creases for  bio metric identification. The palm print images in this research  have been captured by using a commercially available consumer  scanner. For each palm print image, two square regions on the  palm print image are extracted for bio metric identification  purpose. One of the regions is from the hyperthyroid region,  while the another is from the inter digital region. Due to  misalignment of the hand, the process of extraction of these  regions is tedious and time-consuming. Therefore, in this paper,  a computer-aided method has been proposed to simplify the  extraction process. The user only needs to mark two points on  the palm print image. Based on these points, the palm print image  will be aligned, and those two regions are extracted  automatically.                                                                                                                                                                                        Contact:                                                                                                                                                                                                                                                                                                                 +91-98451 66723                                                                                                                                                                                                                                                                                                       ☎ 080-413 07435  

Abstract: This paper presents fast categorization or  classification of images on an animal data set using different  classification algorithm in combination with manifold learning  algorithms. The paper will focus on comparing the effects of  different non-linear dimensional reduction algorithms on  speed and accuracy of different classification algorithms. It  examines how manifold learning algorithms can improve  classification speed by reducing the number of features in the  vector representation of images while keeping the classification  accuracy high.                                                                                                                                 Contact:                                                                                                                                                                                                                                                                                                                  +91-98451 66723                                                                                                                                                                                                                                                                                                       ☎ 080-413 07435  

IEEE IMAGE PROCESSING PROJECTS (2019-2020)

Project CODE
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1. IEEE 2018: Eye Recognition with Mixed Convolutional and Residual Network(MiCoRe-Net) Title Title Title
2. IEEE 2018: Latent Fingerprint Value Prediction: Crowd-based Learning Title Title Title
3. IEEE 2018: Developing LSB Method Using Mask in Colored  Images Title Title Title
4. IEEE 2018: Efficient Quantum Information Hiding for Remote Medical Image Sharing Title Title Title
5. IEEE 2018: An Efficient MSB Prediction-Based Method for High-Capacity Reversible Data Hiding in Encrypted Images Title Title Title
6. IEEE 2018:  Visual Secret Sharing Schemes Encrypting Multiple Images Title Title Title
7.  IEEE 2018: Human Identification from Freestyle Walks using Posture-Based Gait Feature Title Title Title
8.  IEEE 2018: Computer Assisted Segmentation of Palmprint  Images for Biometric Research Title Title Title
9. IEEE 2018: Deep Convolutional Neural Networks for Human Action Recognition Using Depth Maps and Postures Title Title Title
10.  IEEE 2018: Image Classification using Manifold Learning Based  Non-Linear Dimensionality Reduction Title Title Title
11.  IEEE 2018: Conceptual view of the IRIS recognition systems in  the biometric world using image processing  techniques Title Title Title
12. IEEE 2018: Animal classification using facial images with score-level fusion. Title Title Title
13. IEEE 2018: Smile Detection in the Wild Based on Transfer Learning Title Title Title
14.  IEEE 2018: Design of biometric recognition software based on image processing Title Title Title
15. IEEE 2017: Effective and Efficient Global Context  Verification for Image Copy Detection Title Title Title
16. IEEE 2017: Face Recognition Using Sparse Fingerprint Classification Algorithm Title Title Title
17.  IEEE 2017: One-time Password for Biometric Systems:  Disposable Feature Templates Title Title Title
18.  IEEE 2017: Enhanced Password Processing Scheme  Based on Visual Cryptography and OCR Title Title Title
19.  IEEE 2017: Semi-Supervised Image-to-Video Adaptation for Video Action Recognition Title Title Title
20.  IEEE 2017: My Privacy My Decision: Control of Photo Sharing on Online Social Networks Title Title Title
21.  IEEE 2017:  MR Image classification using adaboost for brain  tumor type Title Title Title
22.  IEEE 2016: Lung lesion extraction using a toboggan based growing automatic segmentation approach Title Title Title
23.  IEEE 2016: PassBYOP: Bring Your Own Picture for Securing Graphical Passwords Title Title Title
24.  IEEE 2016: Accurate Detection and Recognition of Dirty Vehicle Plate Numbers for High-Speed Applications Title Title Title

DHS Informatics believes in students’ stratification, we first brief the students about the technologies and type of Image Processing projects and other domain projects. After complete concept explanation of the IEEE Image Processing projects, students are allowed to choose more than one IEEE Image Processing projects for functionality details. Even students can pick one project topic from Image Processing and another two from other domains like Image Processing, data mining, image process, information forensic, big data, Image Processing, Image Processing, data science, block chain etc. DHS Informatics is a pioneer institute in Bangalore / Bengaluru; we are supporting project works for other institute all over India. We are the leading final year project centre in Bangalore / Bengaluru and having office in five different main locations Jayanagar, Yelahanka, Vijayanagar, RT Nagar & Indiranagar.

We allow the ECE, CSE, ISE final year students to use the lab and assist them in project development work; even we encourage students to get their own idea to develop their final year projects for their college submission.

DHS Informatics first train students on project related topics then students are entering into practical sessions. We have well equipped lab set-up, experienced faculties those who are working in our client projects and friendly student coordinator to assist the students in their college project works.

We appreciated by students for our Latest IEEE projects & concepts on final year Image Processing projects for ECE, CSE, and ISE departments.

Latest IEEE 2019-2020 projects on Image Processing with real time concepts which are implemented using Java, MATLAB, and NS2 with innovative ideas. Final year students of computer Image Processing, computer science, information science, electronics and communication can contact our corporate office located at Jayanagar, Bangalore for Image Processing project details.

IMAGE PROCESSING

Image processing is processing of images using mathematical operations by using any form of signal processing for which the input is an image, a series of images, such as a photograph, the output of image processing may be either an image or a set of characteristics or parameters related to the image.
Most image-processing techniques involve isolating the individual color planes of an image and treating them as two-dimensional signal and applying standard signal-processing techniques to them. Images are also processed as three-dimensional signals with the third dimension being time or the z-axis.

Image processing usually refers to digital image processing, but optical and analog image processing also are possible. This article is about general techniques that apply to all of them. The acquisition of images (producing the input image in the first place) is referred to as imaging.