QR Images Optimized Image Embedding in QR Codes

Existing System:

In the existing System, the concept shown was to how to embed an image with a QR Code, which can be implemented much easily with the currently online tools then the explained algorithm.

Proposed System:

In the QR Images Optimized Image Embedding in QR Codes Proposed System, we are going to develop a new approach GUI based algorithm to read the data embedded into the QR Code using the open source tools live USB camera will continuously stream the video with the data recognition algorithm running background, whenever to the system a QR Code is displayed in front of the Camera, it will decode the data of the QR image onto the screen.

BLOCK DIAGRAM

Hardware:

Raspberry pi (ARM11), USB Camera

Software:

Raspbian OS, QR Image Recognition algorithm

Applications:

Data hiding, Security, advertisements.

Advantages:

1) Developed on ARM 11 Based processor, which runs at high speed due to which no data lapse will be done
2) Low power consumption and high accuracy.

Real Time Hand Gesture Recognition for Computer Interaction

ABSTRACT

The Real Time Hand Gesture Recognition system presented, uses only a webcam and algorithms which are developed using OpenCV computer vision, image and the video processing algorithms.

Existing Work:

The Existing work carried was done a PC based MATLAB software, where it has readily available all the algorithms and such system is not used to full extent in embedded side.

Proposed Work:

Proposed Real Time Hand Gesture Recognition for Computer Interaction work will be carried out on Linux based single board computer with ARM 11 architecture. This board will be ported with Raspbian Operation System and OpenCV Image processing Library. Using which will design an algorithm such that the system will identify the finger tips and count them how many fingers have been displayed.

BLOCK DIAGRAM


Hardware:

ARM11, USB Camera, Power supply.

Software:

OS: Embedded Linux, Language: C/ C++, IDE: Qt Creator, Opencv.

Applications:

Computer Interaction, Gesture recognition based control

Advantages:

• Elimination of external hardware like mouse
• Easy to access and control appliances using gestures.

Hand Gesture Recognition based on Depth map

ABSTRACT

In this Hand Gesture Recognition based on Depth map paper a proposed method for gesture recognition using depth map image using Opencv is presented.  Using feature extraction method based on Radon transform will identify the hand posture recognition.

This method reduces the feature vector of Radon transform by averaging its values. For the classification of features vectors, the support vector machine is used.

Progress in depth map calculation of later year’s leads to exploitation then for several objects of research, one of depth map usage is gesture recognition since it provides information about shape of captured hand as well as position in frame. Depth maps have several advantages over traditional color pictures.

Existing Work:

In the existing the work, in order to capture the image  they have used an external hardware i.e Microsoft Kinect system, using the implementation will on higher cost side as well this system only has done all the preprocessing of the images ,no exact algorithm was implemented to detect the hand.

Proposed Work:

Proposed Hand Gesture Recognition work will be based on a Open Computer Vision library, using an Linux based real time device, will capture the images using a USB Camera connected to the device. And with our algorithms will detect the Open palm and Closed Palm in the capture images and draw a rectangle around it.

 

BLOCK DIAGRAM

Hardware:

ARM11, USB Camera,  Power supply.

Software: OS:

Embedded Linux, Language: C/ C++, IDE: Qt Creator.

Applications:

 Computer Vision system, Gesture control devices

Advantages:

  • Open source algorithm are used to implement the concept
  • Low cost and has future scope of controlling the appliances on gesture. 

Wireless Sensor Based Energy Conservation via Bluetooth

Existing Work:

In the existing Wireless Sensor Based Energy Conservation work, the communication protocol was limited to Bluetooth only which is very short distance and should require a device interface in order to view or control the data which is the drawback of the developed system.

Proposed Work:

The proposed Wireless Sensor Based Energy Conservation via Bluetooth work will include the low power high accuracy controllers through the Bluetooth communication as well we also control and view the data over the Intranet Network.

Load contains a Bulb, the current and Voltage consumed by the load will be monitored and an automated operation of the load based on PIR and LDR values will be done from remote location.

BLOCK DIAGRAM

Hardware:

ARM11, PIR Sensor, LDR Sensor, Bluetooth module, Wi-Fi Router, 89s52, Current Sensor, Voltage Sensor, Relay Driver, Load.

Software:

OS: Embedded Linux, Language: C/ C++, IDE: Qt Creator.

Applications:

Home automation, Industrial Power Control

Advantages:

Load Control, Remote location access,

Face Identification Implementation in a Standalone Embedded System

ABSTRACT

In this Face Identification Implementation in a Standalone Embedded System paper is described an embedded system for face identification. The system, running on ARM processor, is built around BCM2835 processor and consists of several IP (Intellectual Property) modules designed as bus peripherals.

The face detection and recognition is accelerated with the help of a hardware and software algorithm modules. The system has been designed on the criteria of resources optimization, low power consumption and improved operation speed

Existing Work:

The Existing work has been implemented on FPGA based processor device, which is complex and high cost of implementation when compared to an embedded chips. As well in the entire system description only procedure have been explained no exact output results where shown.

Proposed Work:

The host target for the proposed face detection system is an embedded environment based on ARM 11 architecture. Which has much higher RAM and Clock speech compared to an FPGA based Devices. Here using Raspbian Operating System, Open Computer Vision algorithm and Qt based GUI interface will be used to implement the face detection and recognition.

Whenever the authenticated face is identified the system will provided login access or else SMS will be sent to the concerned person with GPS location simultaneously a buzzer will be turned on to indicating the unauthorized entry.

BLOCK DIAGRAM

Hardware:

ARM11, USB Camera, GSM, GPS, Buzzer, Power supply.

Software:

OS: Embedded Linux, Language: C/ C++, IDE: Qt Creator, Image Processing Algorithm.

Applications:

Home, Security, Authentication sites

Advantages:

• Hand Held System and online face training can be done.
• Easy installation and usage