Hand Gesture Recognition in MATLAB

 Hand Gesture Recognition in MATLAB

Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms. Gestures can originate from any bodily motion or state but commonly originate from the face or hand. Current focuses in the field include emotion recognition from face and hand gesture recognition. Many approaches have been made using cameras and computer vision algorithms to interpret sign language. However, the identification and recognition of posture, gait, proxemics, and human behaviors is also the subject of gesture recognition techniques.Gesture recognition can be seen as a way for computers to begin to understand human body language, thus building a richer bridge between machines and humans than primitive text user interfaces or even GUIs (graphical user interfaces), which still limit the majority of input to keyboard and mouse.

Sample Implementation:

Depending on the type of the input data, the approach for interpreting a gesture could be done in different ways. However, most of the techniques rely on key pointers represented in a 3D coordinate system. Based on the relative motion of these, the gesture can be detected with a high accuracy, depending on the quality of the input and the algorithm’s approach. 

                                 
                       


                       

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Lung Cancer Detection Using Image Processing Techniques

Lung cancer Detection in matlab

Recently, image processing techniques are widely used in several medical areas for image improvement in earlier detection and treatment stages, where the time factor is very important to discover the abnormality issues in target images, especially in various cancer tumours such as lung cancer, breast cancer, etc. Image quality and accuracy is the core factors of this research, image quality assessment as well as improvement are depending on the enhancement stage where low pre-processing techniques is used based on Gabor filter within Gaussian rules. Following the segmentation principles, an enhanced region of the object of interest that is used as a basic foundation of feature extraction is obtained. Relying on general features, a normality comparison is made. In this research, the main detected features for accurate images comparison are pixels percentage and mask-labelling. Keywords Cancer Detection; Image processing; Feature extraction; Enhancement Watershed; Masking.

Some of the methods used:

Gabor filter

Image presentation based on Gabor function constitutes an excellent local and multiscale decomposition in terms of logons that are simultaneously (and optimally) localization in space and frequency domains [5]. A Gabor filter is a linear filter whose impulse response is defined by a harmonic function multiplied by a Gaussian function. Because of the multiplication-convolution property (Convolution theorem), the Fourier transform of a Gabor filter's impulse response is the convolution of the Fourier transform of the harmonic function and the Fourier transform of the Gaussian function [6]. Figure 2 describes (a) the original image and (b) the enhanced image using Gabor Filter

Thresholding approach

Thresholding is one of the most powerful tools for image segmentation. The segmented image obtained from thresholding has the advantages of smaller storage space, fast processing speed and ease in manipulation, compared with gray level image which usually contains 256 levels. Therefore, thresholding techniques have drawn a lot of attention during the past 20 years [10]. Thresholding is a non-linear operation that converts a gray-scale image into a binary image where the two levels are assigned to pixels that are below or above the specified threshold value. In this research, Otsu’s method that uses (gray thresh) function to compute global image threshold is used. Otsu’s method is based on threshold selection by statistical criteria.



Sample Matlab Implementation

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Super resolution in matlab

Super resolution In matlab

Single-image super-resolution refers to the task of constructing a high-resolution enlargement of a given low-resolution image. Usual interpolation-based magnification introduces blurring. Then, the problem cast into estimating missing high-frequency details. Based on the framework of Freeman et al. [1], we investigate a regression-based approach. The system consists of four components:

1.       interpolation of the input low-resolution image into the desired scale

2.       generation of a set of candidate images based on patch-wise regression: kernel ridge regression is utilized; To reduce the time complexity (around 200,000 data points), a sparse basis is found by combining kernel matching pursuit and gradient descent

3.       combining candidates to produce an image: patch-wise regression of output results in a set of candidates for each pixel location; An image output is obtained by combining the candidates based on estimated confidences for each pixel.

4.       post-processing based on the discontinuity prior of images: as a regularization method, kernel ridge regression tends to smooth major edges; The natural image prior proposed by Tappen et al. [2] is utilized to post-process the regression result such that the discontinuity at major edges are preserved.

: Overview of super-resolution shown with an example: (a) input image is interpolated into the desired scale, (b) a set of candidate images is generated as the result of regression, (c) candidates are combined based on estimated confidences; The combined result is sharper and less noisy than individual candidates, which however shows ringing artifacts, and (d) post-processing removes ringing artifacts and further enhances edges.

Sample Implementation

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Project Tiltles

MATLAB PROJECTS

Image Processing

1.  To Perform and Demonistrate The Image Histogram Streching and Histogram Equalization
2. Finger Print Authentication.
3.  Image Reconstruction by using MATLAB.
4.  DCT Based Image Water Marking.
5.  Image compression using Wavelets.
6.  Facial Recognization by using MATLAB.
7.  Face Detection System by using MATLAB.
8.  Image Segmentation using MATLAB.
9.  CANOPY Image Analysis by using MATLAB.
10.  Vehicle Traffic Pattern at an Inter section by using Simulink Events.
11.  Vehicle Number plate Recognization.
12.  Tank level Controller.
13.  Analysis of AWGN channel by using Adaptive Equalizer.
14.  Image Recognization to Deforms using MATLAB.
15.  Performance Analysis of Channel Estimation and Adaptive Equalization in

slow fading  Channel.

16.  Image Compression with Neural Network using MATLAB.
17.  Image Mosaicing using MATLAB.
18.  Finger Print Recognization System.
19.  LSB Steganography Using MATLAB Simulation.
20.  Fourier-Mellin based Image Registration (with GUI) using MATLAB

Simulation.

21.  Local Adaptive Thresholding.
22.  Rigid and non rigid image registration using sumulink.
23.  Image masking Recognization.
24.  Character Recognition Using Neural Networks.
25.  One-To-One Face Matching.
26.  Development of Medical Image Compression.
27.  Iris Recognition System.
28.  Webcam based facial recognition.
29.  Facial Expression Recognition.
30.  Facial Recognition System Based on Eigen Faces.
31.  Shape Recognition.
32.  Mosaic random-images.
33.  High Performance Face Recognition Based on Wavelet and Neural Networks.
34.  High Speed Face Recognition Based on Discrete Cosine Transforms and Neural

Networks.

35.  Cheque Number Reader.
36.  Reverseble Data Hiding Based Histogram Modification of Pixel Difference.

Signal Processing

1. Performance of OFDM over AWGN Channels Using MATLAB Simulation.
2. Implementation of GMSK in Radio Communication Using MATLAB.
3.  Preprocessing of Speech signal using LPC and Enhancing using wiener filter.
4.  Automatic Speaker Recognition System by using MATLAB.
5.  Acoustic Echo Cancellation in Hand-free Communication System.
6.  Noise Reduction by using Adaptive Filters.
7.  Channel Estimation and Detection based on DS-CDMA.
8.  Speech Compression Using Wavelets.
9.  DS-CDMA in Wireless Handset Communication Using MATLAB.
10.  Cellular Traffic Calculation.
11.  Implementation of Speech Enhancement using Wiener Filter.
12.  LDPC Decoder and BER using MATLAB Simulation.
13.  Wimax physical Llayer simulation by using MATLAB.
14.  Radar System Design by using MATLAB Simulation.
15.  Adaptive Filtering(Adaptive Channel Equalization & Channel Enhancement &

 Noise Cancellation).

16.  Adaptive Time Frequency Analysis by using MATLAB.
17.  Speaker Independent Digit Recognition System using MATLAB.
18.  Design of Speaker Recognization System.
19.  Speaker ID Identification using MATLAB.
20.  Binary Step Size based LMS Algorithm Developed by using MATLAB.
21.  Speech coding Implemented by using Sub-Band Coders.
22.  Synthetic Aperture Radar Signal Processing with MATLAB Algorithms.
23.  LPC (Linear Predictive Coding) VOCODER Simulated using MATLAB.
24.  Digital Communication Implemented by using MATLAB Simulation.
25.  Voice Audio Processing by using MATLAB.
26.  Removal of noise from ECG Signal using MATLAB Simulation.
27.  Channel coding  Developed using Hamming Code Techniques by Using

MATLAB Simulation.

28.  Smart Antenna System for Mobile Communication using MATLAB.
29.  Modern Communication System Implementation using MATLAB.
30.  To find Signal Short-time Energy and Zero-crossing MATLAB Simulation.
31.  Adaptive Equalizers and Smart Antenna Systems using MATLAB Simulation.
32.  Synthetic Aperture Radar Signal Processing with MATLAB Algorithm.
33.  RF Design and Analysis using MATLAB Simulation.
34.  Frequency Hopping Spread Spectrum, Direct Sequence Spread Spectrum and

             CDMA.

35.  Speech compression using Linear Predictive Coding.
36.  Developing a Financial Market Index Tracker using MATLAB OOP and

             Genetic Algorithms.

37.  Speak the recognized character using MATLAB simulation.
38.  Audio Signal Implementation using DCT and FFT.
39.  Generation of QPSK Wave forms Using MATLAB Simulation.
40.  Speech Recognition System for isolated words using MATLAB.
41.  Speech Processing Using Kalman filter.
42.  Implementation of Speech De-Noising using Wavelets.
43.  Implementation of Alien Voices with GUI Audio Perturbations
44.  DCT based Video Processing A Focus Compression using Simulink.
45.  Text Independent Speaker Recognition Based on Neaural Network.
46.  RADAR simulation.
47.  Performance Analysis of Channel Estimation and Adaptive Equalization in Slow Fading Channel.
48.  Development of Adaptive Band Width Filter for Tracking Radar Sub-System.
49.  WiMAX DL waveform Basic PUSC with preamble boosting.
50.  Auto Associative memory.
51.  Exact Histogram Specification-Equalization.
52.  DTMF Generator and Receiver.
53.  DSSS Enhanced with a coarse time Synchronization loop using MATLAB.
54.  Direct Sequence Spread spectrum using MATLAB Simulation.
55.  Slow Frequency Hopped Spread Spectrum Implementation.
56.  Array of Antenna System using MATLAB Simulation.
57.  Implementation of Sound Editing System by using MATLAB
58.  Text to Speech Conversion Using MATLAB.
59.  Speech Compression Using Linear Predictive Coding Technique.
60.  Speech Enhancement Using Soft Thresholding with DCT
61.  Speaking Environment Modeling Approach To Robust Speech Recognition

             Using Correlation Method.

EEE MATLAB PROJECTS LIST

1.  Simulation of PID controllers with Neural Networks.
2.  Simulation of Transformer with short circuit and RL load termination.
3.  Simulation of different Bracking Methods of DC Machine.
4.  Analysis of sub synchronous resonance in a power system.
5.  Simulation of power system stabilizers.
6.  Modeling Flexible Bodies in Sim-Mechanics.
7.  Skyhook Surface Sliding Mode Control on Semi-Active Vehicle.
8.  Electronic AC voltage Regulator.
9.  Single Phase Active Power Filter (for High Voltage DC Power Supply).
10.  Stewart Platform Mechanical System.
11.  Modeling of a PWM VSI induction motor drive.
12.  Simulation of PID controllers with Neural Networks.
13.  Emergency System in Power Substation.
14.  GUI for fuzzy based Washing Machine.
15.  Torque of Electromagnetic Torque motor by using MATLAB Simulink.
16.  System Identification of a synchronous wind turbine system using a modified MlMO ARX structure.
17.  Fuzzy Controller of Semi-active control for 1by4 Suspension System.
18.  Semi-active Control of Skyhook for 1by4 Suspension System.
19.  Tolerance Analysis of Electronic Circuits Using MATLAB.
20.  PSO solution to Economic dispatch with Multiple fuel Options.
21.  Structural Vibration Control System.
22.  General Purposes Linear and Non Linear Fuzzy Logic Controller.
23.  Armature and Field Control of DC Motor.
24.  Hydraulic-Electric (EMB) Hybrid Vehicle.
25.  An algorithm for radial distribution power flow in Complex mode including voltage controlled buses.
26.  Optimal Power flow by Genetic Algorithm.
27.  Analysis of suspension system using MATLAB, SIMULINK and Simscape.
28.  Steam Condenser Model and PI Control.
29.  Tune PID controller with your hand.
30.  Selective Harmonic Elimination Based PWM for 3-Phase.
31.  Simulation of the SPWM.
32.  Implementation of Delta Sigma Modulator – Simulink.
33.  Torque of electro magnetic torque motor.
34.  Sinusoidal PWM for Three Phase Inverter.
35.  Rotatory Gantry Simulation.
36.  Design and analysis of zero voltage switching DC-DC.
37.  Multi-Input Inverter for Grid-Connected Hybrid PV/Wind Power System.
38.  Application of Synchronous Static Series Compensator (SSSC) on Enhancement of Voltage.
39.  Sensorless Model of Permanent Magnet Synchronous Motor. 

Sonar image processing for underwater object detection in matlab

Sonar image obtained from such sonar instruments are used in different fields to realize seafloor task, such as navigation, seabed mapping, fishing, ocean drilling barrier, oil exploration, mines’ detection, and so on
Because of the particular optical properties of light in water and the presence of suspended particles, sonar images are very noisy, the lighting is not uniform, the colors are muted and the contrast is low. Most methods dedicated to reduce the noise apply different filtering and are often classified in two categories: the methods acting in the spatial domain and those acting in the transformed domain. Stéphane Bazeille and Isabelle Quidu [6] proposed an algorithm for image preprocessing sonar which allows correction of lighting, noise and color and requires no user intervention or a priori information on the acquisition conditions. This algorithm is a combination of four different filters each of which aims to revise a special defect. To eliminate the defects of non-uniformity of illumination, the Homomorphic filtering is used.

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Background Subtraction in MATLAB

Background subtraction, also known as Foreground Detection, is a technique in the fields of image processing and computer vision wherein an image's foreground is extracted for further processing (object recognition etc.). Generally an image's regions of interest are objects (humans, cars, text etc.) in its foreground. After the stage of image preprocessing (which may include image denoising, post processing like morphology etc.) object localisation is required which may make use of this technique. Background subtraction is a widely used approach for detecting moving objects in videos from static cameras. The rationale in the approach is that of detecting the moving objects from the difference between the current frame and a reference frame, often called “background image”, or “background model”. Background subtraction is mostly done if the image in question is a part of a video stream. Background subtraction provides important cues for numerous applications in computer vision, for example surveillance tracking or human poses estimation. However, background subtraction is generally based on a static background hypothesis which is often not applicable in real environments. With indoor scenes, reflections or animated images on screens lead to background changes. In a same way, due to wind, rain or illumination changes brought by weather, static backgrounds methods have difficulties with outdoor scenes.

• Background subtraction (BS) is a common and widely used technique for generating a foreground mask (namely, a binary image containing the pixels belonging to moving objects in the scene) by using static cameras.
• As the name suggests, BS calculates the foreground mask performing a subtraction between the current frame and a background model, containing the static part of the scene or, more in general, everything that can be considered as background given the characteristics of the observed scene.
  

• Background modeling consists of two main steps:
  1. Background Initialization;
  2. Background Update.

                             

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Brain signal data by EEG signal processing technique using MATLAB

 EEG is brain signal processing technique that allows gaining the understanding of the complex inner mechanisms of the brain and abnormal brain waves have shown to be associated with particular brain disorders. The analysis of brain waves plays an important role in diagnosis of different brain disorders. MATLAB provides an interactive graphic user interface (GUI) allowing users to flexibly and interactively process their high-density EEG dataset and other brain signal data different techniques

Results After Compressive Sensing:-

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