Image Processing MATLAB Projects

CMAG: IoT Baby Monitor

Sudden Infant Death Syndrome, also known as SIDS, is the leading cause of mortality in infants from one month to one year of age. We propose a video – based baby monitoring system with Internet of Things (IoT) capabilities to help shorten the response time of SIDS cases. Using a video amplification technique developed at MIT dubbed “Eulerian Magnification” to amplify subtle movements we can compare pixel color differences in frames for breathing detection in a recorded video of a baby. In the event that abnormal movement is detected from the baby an alarm will be generated to notify the parents or guardians.

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Hand Reader – A Hand Geometry Based Personal Verification System

the Department of Applied Physics and Electronics at Umeå University. The Digital Media Lab was the assigner, supervising and assisting me during the project.

A simple and smart system to verify a person’s identity using hand geometry as a biometrical identity is developed. The system should tolerate some deviation in hand placement and will not use pins or pegs. A complete system, hardware and software, is delivered. A webcam based hand-scanner is constructed and designed to operate on a PC utilizing Matlab for image processing.

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Strategy for the Development of a Smart NDVI Camera System for Outdoor Plant Detection and Agricultural Embedded Systems

The application of (smart) cameras for process control, mapping, and advanced imaging in agriculture has become an element of precision farming that facilitates the conservation of fertilizer, pesticides, and machine time. This technique addition ally reduces the amount of energy required in terms of fuel. Although research activities have increased in this field, high camera prices reflect low adaptation to applications in all fields of agriculture.

Smart, low-cost cameras adapted for agricultural applications can overcome this drawback. The normalized difference vegetation index (NDVI) for each image pixel is an applicable algorithm to discriminate plant information from the soil background enabled by a large difference in the reflectance between the near infrared (NIR) and the red channel optical frequency band. Two aligned charge coupled device (CCD) chips for the red and NIR channel are typically used, but they are expensive because of the precise optical alignment required.

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Image Decomposition for Low-Dose CT Image Processing with the aid of Feature extraction and Machine learning algorithm

X-ray computed tomography (CT) is now a widely used imaging modality for numerous medical purposes. The risk of high X-ray radiation may induce genetic, cancerous and other diseases, demanding the development of new image processing methods that are able to enhance the quality of low-dose CT images. However, lowering the radiation dose increases the noise in acquired images and hence affects important diagnostic information. This paper contributes an efficient denoising method for low-dose CT images. A noisy image is decomposed into three component images of low, medium and high frequency bands; noise is mainly presented in the medium and high component images. Then, by exploiting the fact that a small image patch of the noisy image can be approximated by a linear combination of several elements in a given dictionary of noise-free image patches generated from noise-free images taken at nearly the same position with the noisy image, noise in these medium and high component images are effectively eliminated

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Method to Extract Roads from Satellite Images

A robust and efficient method for extraction of roads from a given set of database is explored in this project. The other applications of road extraction are: identification of isolated buildings that require to be detected and updating of GIS database based on the requirements of the expertise that is human.

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MULTISCALE EDGE-BASED TEXT EXTRACTION

A multiscale edge-based text extraction algorithm, which can automatically detect and extract text in complex images has been proposed in this project. The proposed method is a text that is general-purpose and extraction algorithm, which can deal not only with printed document images but additionally with scene text.

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Detection of Objects in Crowded Environments

The recognition of four sub-events that characterize the activity of interest in this project. When an bag that is unaccompanied detected, the system analyzes its history to determine its probably owner, in which the owner is defined as the one who brought the case to the scene before leaving it unattended. The system keeps a lookout for the owner, whose presence in or disappearance from the scene defines the status of the bag, and decides the appropriate course of action through subsequent frames.

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Hand Gesture Recognition

In this project hand gesture recognition system is proposed using a hand that is database-driven recognition based upon skin tone model approach and thresholding approach along with an effective template matching with may be efficiently used for human robotics applications and similar other applications. Initially, hand region is segmented by applying skin tone model in YCbCr color area. Within the stage that is next is applied to separate foreground and history. Finally, template based technique that is matching developed utilizing Principal Component Analysis (PCA) for recognition.

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Digital Image Arnold Transformation and RC4 Algorithms

This project has three phases; the First phase may be the design and implementation of digital image scrambling using Arnold change according to iteration that is best. The 2nd phase is the design and utilization of digital image encryption using RC4 flow cipher and the 3rd phase makes use of both Arnold transform and RC4 algorithm based on iteration that is best which applies Arnold transform to scramble an electronic image and then encrypt It utilizing RC4. The input key to RC4 is generated using pseudo random bit generator algorithm.

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JPEG COMPRESSOR

In this project JPEG algorithms have been used for image compression. The quantity of MATLAB code can be production to a quantized DCT version of the input image and techniques used to accomplish manner that is expeditious algorithm had been investigated procedures.

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Inverse Data Hiding in a Classical Image

In this project to supply data that is efficient technique and Image Encryption in that the data and the image can be retrieved independently is carried away using MATLAB. In the encryption phase, initially the images are encrypted with the encryption key connected with data to be embedded on that encrypted image with data key that is hiding. The embedded image is extracted and decrypted with encryption key and encrypted data is extracted and decryption is made using data hiding key in the decryption phase.

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VEHICLE NUMBER PLATE RECOGNI TION USING MATLAB

A method for the detection and identification of vehicle number plate has been proposed in this project. This method can be used within the detection of number dishes of authorized and vehicles that are unauthorized. Further, it presents an approach based on easy but efficient operation that is morphological Sobel side detection method. The letters and numbers utilized within the number plate by making use of box method that is bounding. After the segmentation of numbers and figures present on number plate, template approach that is matching used to recognition of figures.

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Mammogram of Breast Cancer detection

The simulated results show that the high potential to advantageously enhance the image contrast hence giving extra aid to radiologists to detect and classify mammograms of breast cancer in this project. This method aims to achieve the benefits of sharpening and enhance procedure. The proposed technique is further tested using mammograms.

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Brain Tumour Extraction from MRI Images

In this project strategy to extraction detect of brain tumour from patient's MRI scan images of the brain is explored using the MATLAB. The method that is proposed the basic concepts of image processing such as sound removal functions, segmentation and morphological operations. The extraction and detection of tumour from MRI scan pictures of the brain has been carried out by utilizing MATLAB software.

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Unimodal and Multimodal Biometric Sensing Systems using MATLAB : A Review

Biometric systems are used for the verification and identification of individuals using their physiological or behavioral features. These features can be categorized into unimodal and multimodal systems, in which the former have several deficiencies that reduce the accuracy of the system, such as noisy data, inter-class similarity, intra-class variation, spoofing, and non-universality. However, multimodal biometric sensing and processing systems, which make use of the detection and processing of two or more behavioral or physiological traits, have proved to improve the success rate of identification and verification significantly. This paper provides a detailed survey of the various unimodal and multimodal biometric sensing types providing their strengths and weaknesses. It discusses the stages involved in the biometric system recognition process and further discusses multimodal systems in terms of their architecture, mode of operation, and algorithms used to develop the systems. It also touches on levels and methods of fusion involved in biometric systems and gives researchers in this area a better understanding of multimodal biometric sensing and processing systems and research trends in this area. It furthermore gives room for research on how to find solutions to issues on various unimodal biometric systems.

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Workplace Posture Assessment and Biofeedback With Kinect Using MATLAB

With the prevalence of computing, many workers today are confined to desk within an office. By sitting in these positions for long periods of time, workers are prone to develop one of many musculoskeletal disorders (MSDs), such as carpal tunnel syndrome. In order to prevent MSDs in the long term, workers must employ good sitting habits. One promising method to ensure good workplace posture is through camera monitoring. To date, camera systems have been used in determining posture in a clean environment. However, an occluded and cluttered background, which is typical in an office setting, imposes a great challenge for a computer vision system to detect desired objects. In this thesis, we design and propose components that assess good posture using information gathered from a Microsoft Kinect camera. To do so, we generate a data set of posture captures to test and train, applying crowd-sourced voting to determine ratings for a subset of these captures. Leveraging this data set, we apply machine learning to develop a classification tool. Finally, we explore and compare the usage of depth information in conjunction with a traditional RGB sensor array and present novel implementations of a wrist locating method.

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Perspective Distortion Modeling in Face Images and Object Tracking Library using MATLAB

In a first chapter we describe a method to model perspective distortion as a one- parameter family of warping functions. This can be used to mitigate its effects on visual recognition, or interactively manipulate the perceived personality. The warps are learned from a novel face data set and, by comparing orbits spanned by images instead of images themselves, we improve face recognition when small focal lengths are used. Additional applications are presented to image editing, video conference, and multi-view validation of recognition systems. A second chapter is devoted to a new versatile and modular open-source cross- platform online object tracking library, designed to be easily usable by the vision community. Object tracking plays a central part in a number of vision problems, and there is no, to date, a ready-to-use and extensible tracking library at the object level.

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Pest Control in Agricultural Plantations using Image Processing using MATLAB

Monocropped plantations are unique to India and a handful of countries throughout the globe. Essentially, the FOREST approach of growing coffee along with in India has enabled the plantation to fight many outbreaks of pests and diseases. Mono cropped Plantations are under constant threat of pest and disease incidence because it favours the build up of pest population. To cope with these problems, an automatic pest detection algorithm using image processing techniques in MATLAB has been proposed in this paper. Image acquisition devices are used to acquire images of plantations at regular intervals. These images are then subjected to pre-processing, transformation and clustering.

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Intelligent Traffic Light Control Using Image Processing using MATLAB

An intelligent system is proposed for the control of traffic lights. The system is based on the techniques of digital image processing. Morphological image processing techniques are used for the detection and removal of background noise. The images of two roads are captured in the RGB format. Afterwards, they are converted into gray scale monochromatic images. The captured images are then subtracted from the reference images of the respective roads. Edge detection is applied on the resultant image for object detection. Morphological dilation and erosion is applied for the removal of noise components in the background. This process leaves only the objects of interest. Finally, the detected objects are counted and classified on the basis of pixels it contained and accordingly traffic lights are controlled. In comparison to other method this technique gives us better results and can be implemented on real time hardware.

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Face Detection Project using MATLAB

Object detection and tracking are important in many computer vision applications, including activity recognition, automotive safety and surveillance. Presented here is an face detection using MATLAB system that can detect not only a human face but also eyes and upper body. Face detection is an easy and simple task for humans, but not so for computers. It has been regarded as the most complex and challenging problem in the field of computer vision due to large intra-class variations caused by the changes in facial appearance, lighting and expression. Such variations result in the face distribution to be highly nonlinear and complex in any space that is linear to the original image space. Face detection is the process of identifying one or more human faces in images or videos. It plays an important part in many biometric, security and surveillance systems, as well as image and video indexing systems.

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A MATLAB based Face Recognition System using Image Processing and Neural Networks using MATLAB

Automatic recognition of people is a challenging problem which has received much attention during recent years due to its many applications in different fields. Face recognition is one of those challenging problems and up to date, there is no technique that provides a robust solution to all situations. This paper presents a new technique for human face recognition. This technique uses an image-based approach towards artificial intelligence by removing redundant data from face images through image compression using the two-dimensional discrete cosine transform (2D-DCT). The DCT extracts features from face images based on skin color. Feature-vectors are constructed by computing DCT coefficients. A self-organizing map (SOM) using an unsupervised learning technique is used to classify DCT-based feature vectors into groups to identify if the subject in the input image is "present" or "not present" in the image database. Face recognition with SOM is carried out by classifying intensity values of grayscale pixels into different groups. Evaluation was performed in MATLAB using an image database of 25 face images, containing five subjects and each subject having 5 images with different facial expressions. After training for approximately 850 epochs the system achieved a recognition rate of 81.36% for 10 consecutive trials. The main advantage of this technique is its high-speed processing capability and low computational requirements, in terms of both speed and memory utilization.

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Wavelet based Image Compression on the Texas Instrument Video Processing Board TMS320dM6437 using MATLAB

In this project, mainly I demonstrated two different methods of image compression DCT based image compression and WAVELET based image compression on JPEG2000 image standard. I designed DCT based image compression and WAVELET based image compression codes in matlab and compared their results. After that, I implemented the wavelet algorithm using C and C# in visual studio to verify the design. Finally I implemented the same algorithm on TI’s digital signal processing board EVM320DM6437, based on C language.

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Application of Image Processing For Development of Automated Inspection System using MATLAB

In manufacturing industry, machine vision is very important nowadays. Computer vision has been developed widely in manufacturing for accurate automated inspection. A model of automated inspection system is presented in this conceptual paper. Image processing is used for inspection of part. It is assumed that the part after going through many previous operations comes to inspection system where the weight of the part as well as geometry made on that part is detected and later decided whether it is to be accepted or rejected with the help of image processing technique. Using MATLAB software a program is developed and pattern or geometry is detected.

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Image Slider Using MATLAB using MATLAB

In this project we are going to control the wallpapers with our hands motion. This is done with help of MATLAB tool by using some algorithms. The aim of this project is to let the experiment with control the wallpapers with our hands motion in MATLAB. To complete the task, a number of functions have to be combined.

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Gabor Filter Design for Fingerprint Application Using Matlab and Verilog HDL using MATLAB

This paper demonstrates the application of Gabor Filter technique to enhance the fingerprint image. This work produces change in Gabor filter design by increasing the quality of an output which helps in higher security applications. The incoming signal in form of image pixel will be convoluted by Gabor filter to define the Edge and vale regions of fingerprint. The main characteristic of this paper is to store image pixel in memory if convolution signal is low and if the signal is high image is filtered.

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Smart Farm: Automated Classifying and Grading System of Tomatoes using Fuzzy Logic using MATLAB

Manual operation is considered as a big factor in a low production and the Smart Farm System is one way that can address this problem by improving and increasing the quality and quantity of production by making farms more intelligent and more connected through the precision agriculture. With that, the proponents will develop a system through smart farm system that is capable of classifying and grading the tomatoes. This process will be done automatically using image processing and fuzzy logic. There will be a Fuzzy Inference Systems sto be established using MATLAB software to classify and grade the tomato fruit. In classifying, system will determine if tomato is damaged or not. On the other hand, system will distinguish if a specific fruit or crop is under ripe, ripe or overripe in grading. It is believed that this study is of great help to farmers for high yield and productive plant harvests.

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A Leaf Recognition of Vegetables Using Matlab

Recognizing plants is a vital problem especially for biologists, agricultural researchers, and environmentalists. Plant recognition can be performed by human experts manually but it is a time consuming and low-efficiency process. Automation of plant recognition is an important process for the fields working with plants. This paper presents an approach for plant recognition using leaf images. In this study, the proponents demonstrated the development of the system that gives users the ability to identify vegetables based on photographs of the leaves taken with a high definition camera.

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Intelligent Traffic Light Control Using Image Processing using MATLAB

An intelligent system is proposed for the control of traffic lights. The system is based on the techniques of digital image processing. Morphological image processing techniques are used for the detection and removal of background noise. The images of two roads are captured in the RGB format. Afterwards, they are converted into gray scale monochromatic images. The captured images are then subtracted from the reference images of the respective roads. Edge detection is applied on the resultant image for object detection. Morphological dilation and erosion is applied for the removal of noise components in the background. This process leaves only the objects of interest. Finally, the detected objects are counted and classified on the basis of pixels it contained and accordingly traffic lights are controlled. In comparison to other method this technique gives us better results and can be implemented on real time hardware.

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