Image Processing Projects Using Deep Learning

Mainly Convolutional Neural Networks (CNNs) in deep learning techniques have developed image processing and computer vision. Novel, unique and innovative dissertation ideas are shared for PhD and MS scholars for all Image Processing Projects. The success of your Image Processing Projects are guaranteed by our professionals. All types of methodologies and algorithms our  expert are well versed with so it will easy for us to finish of the work before the estimated time with affordable price. We also merge different techniques to get the result that pleases the readers.

Here we give some impactful project plans in image processing by utilizing deep learning.

  1. 1. Image Classification:
  • Objective: Our Work allocates an image to one of the predefined labels.
  • Dataset Examples: Some of the dataset we utilize are CIFAR-10, CIFAR-100, and ImageNet.
  • Applications: We arrange the images into grouping, object recognition into photos.
  1. Object Detection:
  • Objective: In our work, we find the objects in images and their locations.
  • Dataset Examples: We utilize some of the datasets like COCO, Pascal VOC.
  • Applications: Our work uses various applications of object detection such as face detection, vehicle detection in satellite images, defecting defects in manufacturing lines.
  1. Image Segmentation:
  • Objective: In our work, we divide an image into multiple segments, each of them that resemble an object or a part of it.
  • Dataset Examples: Some of the datasets we utilize are COCO-Stuff, ADE20K.
  • Applications: our work utilizes some of the image segmentation applications are autonomous vehicles (Finding road, pedestrians, vehicles), medical imaging (finding tumor regions).
  1. Image Generation:
  • Objective: Our work utilizes a set of training images that corresponds to creating new images.
  • Techniques: In our work, we utilize the image generation techniques like Generative Adversarial Networks (GANs), Variational Autoencoders.
  • Applications: We use various image generation applications like art generation, data augmentation, game design.
  1. Style Transfer:
  • Objective: To convert one image to another image, our work applies the artistic style.
  • Applications: Our work uses some of the applications for style transfer are photo styling, video styling, creating digital art.
  1. Facial Recognition:
  • Objective: From a digital image, we find or validate a person.
  • Dataset Examples: We use facial recognition datasets like LFW (Labeled Faces in the Wild), CelebA.
  • Applications: Some of the applications for facial recognition are security systems and photo tagging on social media.
  1. Image-to-Image Translation:
  • Objective: Our work utilizes some of the Image-to-Image translation methods to alter one type of images into other types like photos into paintings, or black & white photos into color.
  • Techniques: We can utilize the technique conditional GANs for Image-to-Image translation.
  • Applications: In our work, we use some of the applications like medical image translation (e.g., MRI to CT), photo enhancement,
  1. Super-Resolution:
  • Objective: Our work makes the super-resolution sharper, to improve the resolution of images.
  • Applications: Some of the applications we utilized are improving old movies, satellite image refinement, medical image improvement etc.
  1. Image Captioning:
  • Objective: For an image, we create textual explanations.
  • Dataset Examples: In our work, we utilize images captioning datasets like MSCOCO.
  • Applications: Our work helps for visually impaired and automatic generation for websites.
  1. Anomaly Detection in images:
  • Objective: We detect uncommon patterns that do not conform to normal behavior.
  • Applications: Our work utilizes some of the applications such as defect detection in production, observing security applications.
  1. Pose Estimation:
  • Objective: In images, our work defines the position or orientation of objects, often humans.
  • Dataset Examples: We use some of the datasets for pose estimation as MPII Human Pose, COCO Keypoints.
  • Applications: Gesture recognition, gaming and fitness tracking are some of the applications we use for pose estimation.

Steps to Approach These Projects:

  1. Understand the Problem: Our work describes the project’s aim and scope clearly.
  2. Data Collection and Preparation: To create our own or to get labeled datasets. We clean and preprocess the data.
  3. Model selection and Training: CNNs, RNNs or GANs are the relevant deep learning techniques we utilize. GPU resources are also utilized to train.
  4. Evaluation: By utilizing appropriate metrics (accuracy, F1score, IoU, etc. we calculate the framework’s achievements.
  5. Fine-Tuning and Optimization: Optimize our model, experiment with various architectures, and utilize methods like transfer learning.
  6. Deployment: We can deploy our model depending on our goal, deploy the model on a server, combine it into applications or we utilize it for further research.

As the field of deep learning is quickly developing, we recall often reference recent research and publications. To improve the project’s quality and applicability, we work together with the field specialists.

Image Processing Research Topics using Deep Learning

Image Processing Projects Using Deep Learning Thesis Topics

  1. An automatic progressive chromosome segmentation approach using deep learning with traditional image processing

Abstract

With the help of conventional image processing, overall metaphase chromosome image segmentation is performed by suggesting an automatic progressive segmentation technique in our study. To split the chromosomes as single and group of chromosomes, thresholding based and geometric based approaches are utilized. To categorize the chromosome clusters, CCI-Net is suggested. We integrated conventional image processing with CNN for segmentation.

Keywords

Fully automatic chromosome analysis, Automatic progressive segmentation, Chromosome cluster identification, Chromosome instance segmentation, deep learning

  1. Covert Timing Channels Detection Based on Image Processing Using Deep Learning

Abstract

A new methodology is recommended in our article by employing deep neural network to enhance the Covert Timing Channels identification precisely. Our approach includes transformation of congestion inter-arrival times into colored images, after that, feature extraction procedure and categorizations of images are carried out by utilizing CNN method. Results showed that, the suggested CNN framework outperforms other existing models.

Keywords

Covert timing channels detection, Convolutional neural networks, Image processing

  1. An autonomous system design for mold loading on press brake machines using a camera platform, deep learning, and image processing

Abstract

Our paper suggested a model by utilizing YOLOv4 and image processing for autonomous identification of molds and for managing robotic arm. To identify the locations, types and holes of molds, a DL method named YOLOv4 is employed. To detect the center axis, Classical image processing techniques are utilized. We conclude that, from the use of DL and image processing techniques, press brake machines utilized in industries can be converted into smart machines.

Keywords

Mold, Press brake, YOLOv4

  1. A sugar beet leaf disease classification method based on image processing and deep learning

Abstract

For the autonomous identification of leaf spot disease and categorization of severity, an efficient model is suggested in our paper that includes Faster R-CNN, SSD, VGG16, Yolov4 DL methods. YOLOv4 is utilized for image processing technique in an integrated way. An integrated approach that is image processing with DL provides best results than utilizing DL methods alone. Here, Time consumption for diagnosis is reduced and human faults are removed.

Keywords

Leaf spot disease, Sugar Beet, Faster RCNN, SSD, VGG16

  1. Deep learning and image processing-based early detection of Alzheimer disease in cognitively normal individuals

Abstract

This study proposed a framework to categorize a person as normal or Alzheimer’s disease affected person by utilizing a deep learning method. Several brain features are extracted from the MRI scans. We concluded that, our framework is noninvasive and cost efficient than other existing approaches. We enhanced the accuracy of the framework by integrating LSTM and CNN with Adam optimization and it achieved highest results.

Keywords

Alzheimer, Adam optimization, Softmax, MRI

  1. Integrated digital image processing techniques and deep learning approaches for wheat stripe rust disease detection and grading

Abstract

A combined deep learning techniques and image processing is suggested in our research for wheat rust disease identification and grading. It comprises single-band and dual-band processing, computation of Visual Atmospheric Resistance Index (VARI), image segmentation, and image cropping.  Several DL methods like transfer learning and CNN are examined. Results show that, we can optimally categorize the wheat stripe rust because of image processing steps.

Keywords

Wheat stripe rust disease, Feature extraction, Vegetation indices

  1. Detection of counterfeit banknotes by security components based on image processing and GoogLeNet deep learning network

Abstract

To detect the fake currency notes by employing GoogLeNet deep learning model and image processing techniques, a new system is constructed in our approach. Image processing and ML approaches are utilized to extract highly accurate security factors from the banknote images. Then the GoogLeNet evaluated the degree of authenticity of every security factors. Therefore, this system has the ability to differentiate fake and original banknotes precisely.

Keywords

Counterfeit banknotes detection, Security component, GoogLeNet network

  1.   Research on Image Processing Methods and Deep Learning Models in Structural Exterior Inspection

Abstract

A deep learning framework and image processing techniques are recommended in our approach to attain the better detection accuracy. To detect whether the paint on the column is peeling or not, several DL methods such as CNN and autoencoder, various image processing steps like binarized, canny, and gray scale images, and original images are utilized. As a consequence, we get better results when gray scale images and autoencoder method is utilized.

Keywords

Gray scale, Autoencoder

  1. Exploration of Deep Learning Based Underwater Image Processing Techniques

Abstract

To identify and categorize the living things and objects in underwater, an efficient model is suggested in our study. We also planned to suggest a new methodology to filter the imprecise underwater and identify objects easily. To enhance the application and range of the framework, a new method is constructed not only to identify single class but also to identify and categorize the various classes.

Keywords

Underwater image processing, image classification, transfer learning, image enhancement

  1. Avoiding Shortcut-Learning by Mutual Information Minimization in Deep Learning-Based Image Processing

Abstract

A new model named Mutual Information Minimization Model (MIMM) is recommended in our paper to minimize the impact of fraudulent or false correlations and to forecast the desired actual results. It states that, our approach overcome the issue of traditional deep learning frameworks while examining the clinical image data. By doing so, our model learns actual correlations instead of learning false correlations and it leads to avoid shortcut learning.

Keywords

Causality, medical image analysis, mutual information, shortcut learning.