Neural network + image processing + matlab program + thesis

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There are various thesis topics in image processing using Matlab.

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It is one of the core research areas and is growing rapidly day by day. Image Processing is of two types namely — Analogue and Digital.

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Digital Image Processing is the trending research area and is used to perform operations on digital images. Matlab provides tools for automation of image processing. Signal Processing — Signal Processing is the process of performing operations on signals for modification and amplification. Signal processing improves the quality and efficiency of signals.

Signal Processing is also of two types — Analogue and Digital. Signal Processing tools in Matlab can perform operations on the signals. These tools also provide algorithms for visualization, amplification, synchronization, and resampling. The signals can also be compared and analyzed in real-time. The main applications of digital signal processing are audio signal processing, audio compression, speech recognition, digital communication, seismology, and biomedicine. This is also a good topic for thesis implementation in Matlab. Data Compression — Data Compression is the process of encoding and modifying data in such a way that it covers less memory space on the storage disk.

In data compression, the number of bits is reduced than the original data. The compressed data can be sent quickly over the internet to the required destination. In this process, the repetitive elements of data and symbols are replaced and removed. This will save storage space, and reduce the cost.

Top 100+ Image Processing Projects – Source Code and Abstracts

There are also certain algorithms designed for data compression. Data Compression is also known as Data Compaction. There are mainly two types of data compression — Lossless Compression, Lossy Compression. It finds its application in Image Compression. Computer Vision — Computer Vision is a field that deals with the study to make computer highly intelligent in understanding digital images and videos. It tends to make computers visualize things just the way human visualization does. The Matlab tools provide algorithms and functions for designing and simulating computer vision. Other functions that can be performed using these tools include object detection, extraction and tracking.

Along with these, Matlab also provides tools for 3D computer vision, 3D reconstruction, and 3D point cloud processing. For computer vision, machine learning and deep learning algorithms are applied. Thus, computer vision is a very good topic for research, project, and thesis in Matlab and Machine Learning. Face Detection — Face Detection is another application of Matlab and a good topic for a thesis.

This position is subject to the collective agreement for workers and employees in the electrical and electronics industry. Master students receive a compensation of 2. Part of your life. Part of tomorrow. We make life easier, safer and greener - with technology that achieves more, consumes less and is accessible to everyone. Microelectronics from Infineon is the key to a better future. Our ultimate aim is to enable users to navigate through digital cultural and scientific resources through its images.

It will reduce complexity by the provision of intuitive navigation method. It will exploit the context of resources stored in its knowledge space by combining text-mining, image segmentation and image recognition algorithms.

Age Detection by Neural Network - File Exchange - MATLAB Central

This combination will cause a synergy effect and will result in semi-automatically generated, high-level semantic metadata. A major outcome of the project will be the new and intuitive approach of navigation trough images and a set of technologies and tools to support the annotation of images by manual, semi-automatic and automatic techniques. BOEMIE will pave the way towards automation of the process of knowledge acquisition from multimedia content, by introducing the notion of evolving multimedia ontologies, which will be used for the extraction of information from multimedia content in networked sources, both public and proprietary.

BOEMIE advocates a synergistic approach that combines multimedia extraction and ontology evolution in a bootstrapping process involving, on the one hand, the continuous extraction of semantic information from multimedia content in order to populate and enrich the ontologies and, on the other hand, the deployment of these ontologies to enhance the robustness of the extraction system. The ambitious scope of the BOEMIE project and the proven specialized competence of the carefully composed project consortium ensure that the project will achieve the significant advancement of the state of the art needed to successfully merge the component technologies.

Multimedia Semantic Syndication for Enhanced News Services MESH will apply multimedia analysis and reasoning tools, network agents and content management techniques to extract, compare and combine meaning from multiple multimedia sources, and produce advanced personalized multimedia summaries, deeply linked among them and to the original sources to provide end users with an easy-to-use "multimedia mesh" concept, with enhanced navigation aids.

A step further will empower users with the means to reuse available content by offering media enrichment and semantic mixing of both personal and network content, as well as automatic creation from semantic descriptions. Encompassing all the system, dynamic usage management will be included to facilitate agreement between content chain players content providers, service providers and users. In a sentence, the project will create multimedia content brokers acting on behalf of users to acquire, process, create and present multimedia information personalized to user and adapted to usage environment.

These functions will be fully exhibited in the application area of news, by creation of a platform that will unify news organizations through the online retrieval, editing, authoring and publishing of news items. X-Media addresses the issue of knowledge management in complex distributed environments. It will study,develop and implement large scale methodologies and techniques for knowledge management able to support sharing and reuse of knowledge that is distributed in different media images, documents and data and repositories data bases, knowledge bases, document repositories, etc.

All the developed methodologies aim at seamlessly integrating with current work practices. Usability will be a major concern together with ease of customization for new applications. The expected impact on organizations is to dramatically improve access to, sharing of and use of information by humans as well as by and between machines.

Expected benefits are a dramatic reduction of management costs and increasing feasibility of complex knowledge management tasks.

K-Space is a network of leading research teams from academia and industry conducting integrative research and dissemination activities in semantic inference for automatic and semi-automatic annotation and retrieval of multimedia content. K-Space exploits the complementary expertise of project partners, enables resource optimization and fosters innovative research in the field.

The aim of K-Space research is to narrow the gap between low-level content descriptions that can be computed automatically by a machine and the richness and subjectivity of semantics in high-level human interpretations of audiovisual media: The Semantic Gap. Specifically, K-Space integrative research focus on three areas:. An objective of the Network is to implement an open and expandable framework for collaborative research based on a common reference system. Due to the convergence of several strands of scientific and technological progress we are witnessing the emergence of unprecedented opportunities for the creation of a knowledge driven society.

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Indeed, databases are accruing large amounts of complex multimedia documents, networks allow fast and almost ubiquitous access to an abundance of resources and processors have the computational power to perform sophisticated and demanding algorithms. However, progress is hampered by the sheer amount and diversity of the available data. As a consequence, access can only be efficient if based directly on content and semantics, the extraction and indexing of which is only feasible if achieved automatically.

Given the above, we feel that there is both a need and an opportunity to systematically incorporate machine learning into an integrated approach to multimedia data mining.

Neural network image processing matlab program thesis

Indeed, enriching multimedia databases with additional layers of automatically generated semantic metadata as well as with artificial intelligence to reason about these meta data, is the only conceivable way that we will be able to mine for complex content, and it is at this level that MUSCLE will focus its main effort. Realizing this vision will require breakthrough progress to alleviate a number of key bottlenecks along the path from data to understanding.

Long term market viability of multimedia services requires significant improvements to the tools, functionality, and systems to support target users. The ACE may be created by a commercial content provider, to enable personalized self-announcement and automatic content collections, or may be created in a personal content system in order to make summaries of personal content, or automatically create personal albums of linked content.

The ACE concept will be verified by two user focused application prototypes, enabled for both home network and mobile communication environments. This enables the aceMedia partners to evaluate the technical feasibility and user acceptance of the ACE concept, with a view to market exploitation after the end of the project. MIRROR aims to create a collection of components and tools for a distributed knowledge management system that will support physical and social interactions.

Mirror aims to establish a Europe-wide community of practice for learning and innovation in the area of natural sciences museums by developing a novel learning methodology and by implementing state-of-the-art tools, techniques and systems. From a technical point of view, the proposed system will play the role of an intermediate access server residing between the end users and multiple heterogeneous audiovisual archives organized according to new MPEG standards. The major final product will be an integrated software system consisting of the two, semantic unification and personalization subsystems, together with two types of interfaces.

Systematic principles for integrating symbolic and subsymbolic processing will be developed in the project. Key aims are to ensure that the resulting total hybrid system retains desirable properties of both processing levels. On the one side the signal processing abilities, robustness and learning capability of neural networks should be preserved.

On the other side advantage should be taken of the ability of rule-based systems to exploit high level knowledge and existing algorithms and to explain to a user why conclusions were reached in particular cases. The methodologies to be developed in the project will be tested in a challenging application related to human computer interaction, which is recognition of emotion based on both voice and visual cues. Low level features will be extracted from signals using neural networks and subsequent formulation of rules will provide a conceptual framework, substantial for emotion analysis.

This EU-funded project aims to establish a pilot European multimedia network and organization with the purpose of motivating encouraging school pupils to take up a career in technology and related businesses and industries, and to assist with the learning of languages within the context of learning about technological subjects in school at both primary and secondary level. The network will take the structural form of a group of European universities with skills in the development and use of multimedia materials and experience in teacher training working together with teachers and pupils in schools to provide a unified range of user configurable, multi-lingual, multimedia courses on topics in advanced technology for primary and secondary schools.

Course materials will be developed in at least 6 topics with all materials available in English, German, and Greek. The project will be of 2 years duration starting in early ISDN links will be established between the 3 participating universities and from each of the universities to its group of schools 14 schools in total. Telecommunications service providers will establish these connections and be involved as partners in the project to assist with technical developments as well as with commercial aspects for exploitation of results.

The project will form the pilot study for the creation of a self-funding European educational multimedia network and organization for the teaching and learning of advanced technology which has the capability of assisting language teaching. The M.

It promotes the production of European multimedia and supports producers, companies, publishers and other cultural and technological bodies interested in applying multimedia technology to the cultural field. The aim of the project is to create a multimedia support network, based on four Mediterranean regions, dedicated to fostering the development of European multimedia applications in the area of culture and arts. The main objectives of M. CUBE are to i improve the competitiveness of the European multimedia industry, enhancing the quality of its products, improving the business capabilities of the enterprises, and attracting funds for new developments, ii create a critical mass to penetrate the consumer market at international level, putting together many small developers to address the market through big distributors, and iii exploit the enormous European potential in terms of cultural contents.

Supplying multimedia support services to cultural users is a strategic task for M.

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Museums, galleries, collections, public administrations, etc. CUBE contribution is to: First, contribute to the development of a whole editorial line, defining technical and artistic contents, market targets and channels, certification process, methodological recommendations, policy and programmes. Second, create a network of manufacturers, through setting up local associations and the development of international exchanges.