IEEE 2014 JAVA MOBILE COMPUTING PROJECTS Tag sense leveraging smartphones for automatic image tagging

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Mobile phones are becoming platforms for personal sensing, computing and communication. This paper proposes TagSense, a mobile phone system that uses sensing to automatically tag images. TagSense senses people, activities and contexts in pictures and merges them to create tags on-the-fly. It was tested on 8 Android phones with 200 pictures, showing it can effectively tag images. Compared to Apple and Google tagging systems, TagSense provides valuable tagging by using additional sensing dimensions, especially as devices and sensing algorithms improve.

Engineering

picture from those that are not. We deploy a prototype of TagSense on 8 Android phones,
and demonstrate its effectiveness through 200 pictures, taken in various social settings.
While research in face recognition continues to improve image tagging, TagSense is an
attempt to embrace additional dimensions of sensing towards this end goal. Performance
comparison with Apple iPhoto and Google Picasa shows that such an out-of-band approach
is valuable, especially with increasing device density and greater sophistication in
sensing/learning algorithms.
SYSTEM CONFIGURATION:-
HARDWARE CONFIGURATION:-
 Processor - Pentium –IV
 Speed - 1.1 Ghz
 RAM - 256 MB(min)
 Hard Disk - 20 GB
 Key Board - Standard Windows Keyboard
 Mouse - Two or Three Button Mouse
 Monitor - SVGA
SOFTWARE CONFIGURATION:-
 Operating System : Windows XP
 Programming Language : JAVA
 Java Version : JDK 1.6 & above.

IEEE 2014 JAVA MOBILE COMPUTING PROJECTS Tag sense leveraging smartphones for automatic image tagging

This document describes a proposed smart home security system called AstroBell. The system uses a Wi-Fi enabled device with a push button, LCD screen, and USB camera located on the front door. It allows users to see and interact with visitors via their smartphone. Messages can be sent to the LCD screen and photos of visitors can be emailed. The system is powered by a cloud server that enables communication between the door device and smartphone. It aims to provide home security and visitor identification through internet of things technology in an early stage of development.

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TagSense: Leveraging Smartphones For Automatic Image Tagging

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TagSense is a smartphone-based system that leverages built-in sensors to automatically tag images with descriptions of when, where, who, and what is happening in the picture. It uses sensors like GPS, light sensors, accelerometers, microphones, and cameras to determine the date, location, activities, and people present. TagSense then organizes these tags into a "When-Where-Who-What" format to automatically describe pictures. While promising, TagSense has limitations like a limited vocabulary of tags, inability to generate full captions, and complex methods for identifying people.

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The document discusses tackling steganography apps on Android devices. It presents work on detecting stego images from mobile apps using two approaches: signature detection and machine learning methods. A key challenge is generating a large database of stego images from different apps at various embedding rates. The authors developed tools to automatically generate such a database using Android emulators and reverse engineering techniques. They analyzed several stego apps and found the embedding algorithms were often simple, like least significant bit changes. Some apps had detectable signatures. The authors tested signature detection and machine learning on images from seven stego apps to evaluate detecting stego images from apps.

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This document describes a proposed sign language interpreter system that uses machine learning and computer vision techniques. It aims to enable deaf and mute users to communicate through computers and the internet by recognizing static hand gestures from camera input and translating them to text. The proposed system extracts features from captured images of signs and uses a support vector machine model to classify the gestures by comparing to a dataset of labeled images. If implemented, this system could help overcome communication barriers for deaf users in an increasingly digital world.

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https://www.datatobiz.com/blog/computer-vision-guide/ To most, they consist of pixels only, but digital images, like any other form of content, can be mined for data by computers. Further, they can also be analyzed afterward. Use image processing methods, including computers, to retrieve the information from still photographs, and even videos. Here we are going to discuss everything you must know about computer vision. There are two forms-Machine Vision, which is this tech’s more “traditional” type, and Computer Vision (CV), a digital world offshoot. While the first is mostly for industrial use, as an example are cameras on a conveyor belt in an industrial plant, the second is to teach computers to extract and understand “hidden” data inside digital images and videos. Facebook this August said it was open-sourcing its work to improve its Computer Visiontechnology software for users further. This image was posted by FB Research scientist Piotr Dollar to explain the difference between human and computer vision. Thanks to advances in artificial intelligence and innovations in deep learning and neural networks, the field has been able to take big leaps in recent years, and in some tasks related to detection and labeling of objects has been able to surpass humans. One of the driving factors behind computer vision development is the amount of data we produce now, which will then get used to educate and develop computer vision.

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This document discusses a real-time object detection system using the Caffe model. The authors used OpenCV, Caffe model, Python and NumPy to build a system that can detect objects like humans and vehicles in images and videos. It discusses how deep learning techniques like convolutional neural networks can be used for tasks like object localization, classification and feature extraction. Specifically, it explores using the Caffe framework to implement real-time object detection with OpenCV by accessing the webcam and applying detection to each frame.

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With the vigorous development of emerging information technology, artificial intelligence application scenarios are everywhere. When it comes to AI, the first thing we think of is machine learning and deep learning. However, they are only part of the field of artificial intelligence research. The scope of artificial intelligence is extremely wide. This presentation describes the hot topics in artificial intelligence research and ten major technical categories.

IRJET - Hand Gestures Recognition using Deep Learning

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This document discusses a proposed system to allow deaf or mute individuals to use voice-controlled digital assistants through hand gesture recognition. The system would use deep learning models to recognize hand gestures in real-time and convert them to text to query an assistant, and then convert the assistant's audio response to text for the user to read. This approach aims to provide an accessible alternative to audio-based assistants for those unable to use voice commands. The proposed system is designed to accurately recognize a series of gestures in real-time without requiring the user to wear any hardware.

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Computer vision can be used for many applications like facial expression detection, camera mice that move the cursor based on head movements, detecting text and defects. It allows those with limited mobility to interact with computers. Computer vision tasks include image processing, feature extraction, object detection and more. Major applications include manufacturing defect detection, barcode and text reading, and computer vision is a key technology enabling self-driving cars.

SIGN LANGUAGE INTERFACE SYSTEM FOR HEARING IMPAIRED PEOPLE

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The document describes a proposed sign language interface system for hearing impaired people. The system aims to use machine learning algorithms like convolutional neural networks to classify hand gestures captured by a webcam into corresponding letters or words. The system would preprocess the images, extract features, then use a trained CNN model to predict the sign and output it as text and speech for better understanding by users. The goal is to help bridge communication between deaf/mute and normal people without requiring specialized gloves or sensors.

IRJET- Survey on Face-Recognition and Emotion Detection

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The document summarizes a research paper on developing a real-time security system using face recognition, motion detection, tracking, and emotion detection. The proposed system monitors an area using a network camera and detects any motion. If motion is detected, it captures live images and sends notifications to listed individuals. The system provides safety against unauthorized access or misbehavior using computer vision techniques like face recognition, motion detection and tracking implemented on a Raspberry Pi board with a camera module and OpenCV library.

IRJET- Detection and Recognition of Hypertexts in Imagery using Text Reco...

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The document discusses a technique for detecting and recognizing hyperlink text in images using text recognition. It proposes analyzing images on handheld devices to recognize patterns like URLs, emails, etc. and allow users to select them to open in a browser or app. It reviews related work on text recognition challenges. The proposed system would scan active images, recognize hyperlink patterns, and give users an option to select them and redirect to the appropriate destination. This could help simplify accessing links from product images and other media. Future work aims to build a more accurate model and implement the technique on a large set of images.

Hand Gesture Identification

IRJET Journal

This document describes a study on hand gesture identification using the Mediapipe framework. The goal is to develop a system to translate American Sign Language (ASL) gestures into text by recognizing 21 3D landmarks on the hand. It discusses related work on sign language recognition using both vision-based and sensor-based approaches. The implementation methodology section describes using Mediapipe's hand tracking model to detect hand landmarks and then using KNN classification to identify the ASL alphabet gestures. Results show the system can currently recognize ASL alphabet signs in real-time with 86-91% accuracy on average. Future work includes improving the system with more training data to increase accuracy and expand the vocabulary of signs recognized.

ASSISTANCE SYSTEM FOR DRIVERS USING IOT

IRJET Journal

This document describes a driver assistance system that uses computer vision and IoT technologies. It consists of three main sections: 1) object detection to identify obstacles in front of the vehicle using a convolutional neural network, 2) lane detection to identify the lane the driver should follow, and 3) an IoT component using a Raspberry Pi camera to send images to the neural network for analysis and display warnings. The system is intended to help reduce accidents by detecting objects and lanes and warning drivers. It applies techniques like YOLO for real-time object detection using neural networks to analyze camera footage and assist drivers.

POPULAR MACHINE LEARNING SOFTWARE TOOLS

rahul804591

The current world and activities are highly dependent on technology and its various devices. In this technological era, one can find it extremely normal for us to come across certain tech terms for instance Digital Marketing, Artificial Intelligence, Python, Machine Learning and many more. Here, we will be focusing on Machine Learning plus its interesting productive tools. visit us :- https://kvch.in/best-machine-learning-training-noida

IRJET- Review on Text Recognization of Product for Blind Person using MATLAB

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This document summarizes a research paper that proposes a system to help blind people read text on product labels and documents using a camera and MATLAB software. The system uses image processing techniques like converting images to grayscale, binarization, and filtering to isolate text from complex backgrounds. It then applies optical character recognition to identify the text and provide information to blind users. The proposed system aims to address limitations of prior methods that struggled with non-horizontal text, complex backgrounds, and positioning objects in the camera view. It extracts a region of interest around a product using motion detection and recognizes text regardless of orientation.

IOT Consulting Service in Bangalore

Embitel Technologies (I) PVT LTD

Embitel Technologies has been working on an IoT-based corporate wellness reward platform for a US-based client for the past two years. The platform allows companies to track and reward employee wellness efforts. Embitel implemented various fitness tracker syncing protocols to make the platform compatible with many devices. The platform is accessible worldwide and can support up to 2000 users. Embitel has gained expertise in technologies like Zigbee and EnOcean through their IoT work. They are using machine learning algorithms and automated controls based on collected sensor data. Embitel has also made progress in IoT for commerce using beacon technology and geofencing applications. Facing challenges has made their learning experience more exciting.

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