Image Processing Projects Using Machine Learning

Integration of image processing with machine learning techniques is considered as an innovative approach for several research concepts. Your research paper will be written by our subject matter professionals in perfect academic language for your dissertation on Image Processing Project. You can undertake PhD and MS dissertation ideas, topics, proposal and writing services from as we  are well experienced in this field for more than 19+ years.

Here we discuss about various possible research topics that we can carry out:

  1. Image Categorization:
  • Aim: Our aim is to define a label to an image from an already defined group of categories.
  • Dataset: We utilize datasets like ImageNet, MNIST, CIFAR-10, Fashion MNIST and CIFAR-100.
  • Applications: Our project detects objects in images and classifies images for search engines.
  1. Image Segmentation:
  • Aim: We segment the image into relevant parts to categorize every pixel in the image.
  • Dataset: Our approach considers ADE20k and COCO-Stuff.
  • Applications: It helps us to identify tumors in clinical imaging and segment roads in automatic vehicles.
  1. Image Captioning:
  • Aim: For an image, we create a text based definition.
  • Datasets: Flickr30k and COCO Captions datasets assist us to carry out this approach.
  • Applications: We make use of this concept to create descriptions for websites and also aids for visually defective individuals.
  1. Style Transfer:
  • Aim: Our goal is to change one image by utilizing the stylistic pattern of another image.
  • Applications: We apply this concept in various applications like personalized image filtering and artistic description development.
  1. Anomaly Identification in Images:
  • Aim: In sequential images, we identify abnormalities and outliers.
  • Applications: Our work involves several applications including identifying defects in products and quality control in the production field.
  1. Pose Estimation:
  • Aim: Mostly in humans or in images, we estimate the ordering or pose of architecture.
  • Datasets: MPII human Pose dataset assists us to accomplish this concept.
  • Applications: We apply this in different fields like action recognition and gesture recognition.
  1. Image Generation with GANs:
  • Aim: Similar to an offered dataset, our research aims to create new images.
  • Datasets: We utilize different datasets for particular fields and for faces, we use CelebA.
  • Applications: In this, we consider various applications like art development and creation of video games.
  1. Object Identification:
  • Aim: In an image, our approach detects and locates objects.
  • Datasets: Several datasets help us for this research such as Google Open Images, Pascal VOC, and COCO.
  • Applications: We apply object identification approach in various applications like retail for detecting goods on shelves and autonomous cars for identifying other vehicles or pedestrians.
  1. Face Recognition:
  • Aim: By analyzing a digital image, we detect and validate a person.
  • Datasets: We make use of datasets like CelebA and LFW (contains labeled faces in the Wild).
  • Applications: For the purpose of photo tagging in social media and security framework, we apply this concept.
  1. Super-resolution:
  • Aim: Our research improves the resolution of images and offers clearer images.
  • Application: We apply this to improve satellite based images and enhance the video quality.
  1. Image-to-Image translation:
  • Aim: Our goal is to transform one kind of images into another ( for instance: conversion of sketch image to color image)
  • Application: It assists us in the field of clinical imaging (for instance: CT scan and MRI), creativity and art.
  1. Image Improvement & Restoration:
  • Aim: We aim to restore the previous / altered images or enhance the image quality.
  • Applications: This research helps us to improve satellite images or less effective images and restore previous images.
  1. Visual Question Answering:
  • Aim: Our objective is to answer text based queries related to images.
  • Datasets: We utilize VQA dataset.
  • Applications: Various fields gain benefits through our approach such as communicative image based models and visually defective persons.

Limitations & Considerations:

  • Confidentiality of Data: We must ensure the data confidentiality when dealing with facial images or other personal data.
  • Computational Resources: Deep learning methods always require extensive resources particularly Convolutional Neural Networks (CNNs) and GANs.
  • Fairness & Bias: To prevent unfairness in recognition based projects, we check the dataset diversity.
  • Overfitting: We make sure whether our framework doesn’t learn the training data and effectively generalizes.
  • Real-time Processing: Various applications need actual-time reviews because of this, we may experience computational complexity.

We conclude that dealing with these concepts is effective and also provides practical experience with complicated ML frameworks, specifically with deep learning frameworks. Our project improves its effect and significance by integrating certain field-based knowledge such as art, medical or autonomous skills.

We provide you with a complete Image Processing research end to end support. Moreover, accurate information where there will be no plagiarism and in good grammar quality content will be written. In reputable journal like IEEE, SCI, SCOPUS, ACM etc….we publish your paper .

Image Processing Projects using Machine Learning

Image Processing Projects Using Machine Learning Thesis Ideas

Choosing the best thesis topic on Image Processing is the first choice for scholars. But selecting the best topic on Image Processing is not an easy task. Our thesis expert stay updated on the current topics that is in trend by referring the international journal. So, we can guide you with interesting Image Processing ideas and topics.

Some of the projects that we have worked on Image Processing are.

  1. Automotive Scenarios for Trajectory Tracking using Machine Learning Techniques and Image Processing


Support vector machines, Machine learning algorithms, Image processing, Adaptive filters, Machine learning, Cameras, Safety

            In automotive traffic situations to improve a vehicle’s level of self-determination our paper utilizes innovative Machine Learning methods. We compared two various methods classic tracking method and Kalman filter (an adaptive filter) was used first and at second a machine learning method Support vector machine was used. Our paper get the data from the camera targets on tracking selected object and validate their location using image processing.   

  1. Investigation of Machine Learning Algorithms for Pattern Recognition in Image Processing


Deep learning, Histograms, Handwriting recognition, Image recognition, Forestry

            In this paper we accomplish many ML methods and feature extraction methods. We have to compare four ML methods namely Deep learning, Support Vector machine, Decision tree and Random forests and two feature extraction method raw pixel values and histogram of oriented gradients. We use some metrics as accuracy, precision, F1score and recall with each of the algorithms. Deep learning and SVM gives the better outcomes.

  1. Rice Quality Analysis Based on Physical Attributes Using Image Processing and Machine Learning


Training, Manuals, Quality control, Object detection, Software

            In our paper we used methods namely Digital Image Processing, Computer vision- that include pre-processing, morphological operation, edge detection, object detection and object measurement. ML can serve all data outcome to image processing to CSV files. In this the machine based training, and test to classify the quality into High or low by using SVM method.

  1. FASBM: FPGA-specific Approximate Sum-based Booth multipliers for energy efficient Hardware Acceleration of Image Processing and Machine Learning Applications


Energy efficiency, Resource management, Field programmable gate arrays, Hardware acceleration

            We enhance the achievement of FPGA based resource- constrained embedded image processing and ML methods were used. Adaptive Logic Modules (ALM) on Intel FPGAs are designed from energy efficient soft core Booth Multipliers. Two products of multiplier partial product matrix are combined together. We also added a Karnaugh map reduction in a individual way.

  1. Lung cancer detection and nodule type classification using image processing and machine learning


Wireless communication, Image segmentation, Lung cancer, DNA, Lung, Reinforcement learning

            Our paper proposes a ML and image processing method to detect and classify the cancer. Our method comprises of two processes, to detect features we used preprocessing at first and next we find the type of lung cancer like benign of malignant by rhe discrimination process.

  1. Determine the Blood Group by using Image Processing and Machine Learning


Data communication, Blood

            Finding one’s blood type is essential in life-or-death surroundings like blood transfusions, blood donation, roadside disasters and other similar emergencies. Now a day it is important to do the tests and this saves time. Our aim is to give the outcome fastly and precisely to different users. We used parallel Image processing to calculate this. This can be helpful in emergency situation without any mistakes.  

  1. Forest Wildfire Detection and Forecasting Utilizing Machine Learning and Image Processing


Satellites, Wind speed, Fires, Vegetation mapping

            Our paper aims to detect wildfires by using ML methods. Our paper uses image processing to detect wildfires earlier and we also used remote sensed satellite images of specific geographical region. To train and test many numbers of images and data we used various supervised ML techniques as Logistic Regression, KNN and Random Forest.  

  1. Nonlinear analysis of shell structures using image processing and machine learning


Convolutional neural networks, Nonlinear finite element analysis, Shell structures, Stress prediction

            Using image processing techniques our paper solves non-linear stress analysis problems in shell structure. To train ML methods our paper converts the mechanical behaviour of shell structure into images. Conditional generative adversial neural network is also used to train a group of images. Two different structures were used and the outcome of trained network agrees the outcome of nonlinear finite element analysis. 

  1. Printed Circuit Board Defect Detection Methods Based on Image Processing, Machine Learning and Deep Learning: A Survey


 Defect detection, PCB

                                        Our paper analyse the different fault detection in Printed Circuit Boards (PCBs). At first the outline of PCB defect detection is presented and at second manual fault detection can be viewed in detail. Then ML, traditional image processing and DL methods are explained briefly. Then at last the performance, methods, advantages, procedures and limitations are compared and clarified.

  1. Weedy Rice Classification Using Image Processing and a Machine Learning Approach


Machine vision; weedy rice; paddy seed; seed quality; classification

            Our study offers ML and image processing to categorize weedy rice seed variants and cultivated rice seeds. We used Red, Green and Blue (RGB) for scanning camera to image acquisition. Images were removed from three main parameters as morphology, colour and texture and they were used as an input. We also used seven ML classifiers and the classification achievement was analysed.  

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