Digital image processing simulator (DIP simulator) is an interactive tool to cope with image processing simulation. It offers a sophisticated environment with in-built massive libraries and toolboxes to implement different image processing techniques smoothly.
What is simulation in image processing?
Performing computer-assisting technologies over the image with a virtualization effect is known as image processing simulation. The main aim of this system is to measure the image quality and enhance the image visualization for better human and machine understanding. Also, it quantifies the variation of system entities which create instigate the quality of the image.
This page describes significance of DIP Simulator with its supporting tools, libraries and programming languages!!!
How does image simulation works?
Digital image processing plays a significant role in the medical field. Even it has a separate research domain as medical image processing. The main objective of this field is to give realistic environs on simulating enhanced medical images for doctor’s clear-cut interpretation of patient’s physical conditions. For this purpose, DIP provides many digital simulation tools and techniques for designing numerous medical applications in simulation environs. Hence, it becomes a vibrating research domain among current scholars.
If we are performing an image reconstruction method, the simulation tool lets us model the 3D image from 2D body slices. Then, we can detect the tumor (with size, position, etc.) and recognize patterns based on effective analysis techniques.
Though the simulated image reconstruction yields an accurate result, it cannot be directly employed in real-world environs. Prior to real implementation, it should be tested in different hardware with diverse settings. On the whole, this image segmentation process detects the tumor in the processed image for further medical inspection.
When we talk about simulation tools, the programming languages are also essential to discuss. So, we have listed a few important languages widely used in developing and simulating image processing applications.
Major programming languages in DIP Simulator
Are you interested in developing an academic or research project? Then choose MATLAB software. It is furnished with pre-defined libraries that suit well all recent image processing modules. Also, it will provide you the friendly assistance in both coding and learning using dip simulator.
Are you specifically interested in implementing a project that dependent fully on ML-based image processing? Then choose OpenCV and Python. Since Python is a friendly programming tool with more ML modules and minimum modules of image processing.
Are you interested in choosing image processing as your profession? Then choose C++ and OpenCV. Since many corporates are currently using C++ for designing new applications.
Now, we can see about the various interactive simulation tools which are portable. These tools are designed with an intention to support bioelectric fields for simulating image-based geometric models and specific subject-centered models. By the by, the below tools are applied either independently or collectively to solve any type of complicated issue.
List of DIP Simulators
- Matlab: It is a DIP based simulation tool for processing any types of images with mathematical and statistical analysis of it.
- Scilab: It is one of the tool works similar by Matlab for all kinds of images
- Shapeworks: It is an image processing toolkit that supports for statistical analysis and processing
- Seg3D: It is a common tool that supports for various image processing operations like segmentation and edge detection
- Cleaver: It is used to generate the mesh model for image segmentation and volume surface creation
- ImageVis3D: It is used for large scale data collection, processing and visualization
- Python: Currently emerging programming tool that supports the any dimensional images modelling
- VXL: This tool contains libraries (C++ language) for computer vision and image processing algorithms as imaging, streaming IO, geometry for advanced imaging applications
- SCIRun: It is a modelling tool that supports to process the whole input image
- Octave: It is open source tool that supports for image processing operations
- AForge.Net: This is a C# based processing tool for various computer vision algorithms as machine learning, neural networks, evolutionary algorithms, genetic etc.
In general, DIP simulator is used to monitor and analyze the actual behavior and performance of the modelled system before the direct deployment of the system. Accordingly, infinite numbers of simulators are launched to satisfy the prerequisite of image processing applications. So, it is necessary to know each Simulator’s determination prior to confirming the development tool in implementing DIP projects.
DIP Simulators Purpose
- Supports linear and non-linear edge enhancement
- Subtractive / additive primary colours correlation
- Image subtraction and differencing operations
- Supervised classification supports
- Various digital data formats supports (BIL, BIP and BSQ)
- Noise removal (salt and pepper, white Gaussian)
- Density slicing and other enrichment operations
- Performance assessment (error matrix, commission error, and kappa statistic)
- Perform linear contrast stretching operations
- Convolution filters (low and high pass) operationssupported
One of the major simulation tools in the digital image processing field is Digital Image Processing Simulator (DIPS) since this learning tool comprises all the fundamentals of DIP and remote sensing courses for outstanding interpretation of image analysis.
As a matter of fact, it is designed to fill the learning gap between the theoretical and practical demonstration in the smart classroom. The teacher can give a realistic demo of complex image processing theories/models and make students understand easily through this tool. So, it is the preferable tool (Windows XP/7/8/10) for learning purposes, but it is quite expensive for researchers.
Similar to this tool, “Julia” is also the top-rated tool in the DIP domain. In the future, it is sure to reach incredible demand in the research world, which will become the master of all other programming languages.
DIP Simulator Libraries
Now, we can see about the other widely used libraries that can be imported into our image processing tools.
- Include NPP library for multimedia processing
- Support parallel computing
- Easy and Fast to code
- Use GPUs power for efficient performance
- TensorFlow 2.0
- Enables the pre-trained models implementation
- Easy to develop reference prototypes for own applications and solutions
- Support recommendation system, reinforced learning, recognition system (object and speech) and more
- Open-source software
- Use user-friendly APIs
- Support ML and DL libraries
- Provisioned with mathematical archives for implementing differential methods on data stream
- For instances: Applications based on neural networks approaches
- Python based DL library
- Incorporates various libraries (CNTK, Theano and Tensorflow)
- Executes on CNTK, Theano, Tensorflow and PlaidML
- Use basic and simple APIs
- Rapid deep-CNN model testing based on extensibility and quality
- Constraint: Limit the user tasks
- Numerical library in Python
- Executes on GPU or CPU
- Improve the complier for math learning, control and evaluation
- Easy to work with mathematical equations or formulae and matrix
- Multi-model Platform and Language
- Support all math libraries and functions
- Simpler than C++ code
- Rapid prototyping
- Easy Trouble shooting and error debugging
- Jave enabled open-source library
- Used for both research and industrial purpose
- Support CV and robotics realistic applications
- For instance: module alignment of camera, SFM, streamlined low-level DIP, tracking of feature and more
- Use Python and C++
- Support large-scale libraries
- Provisioned with basic image / video processing methodologies and procedures
- OpenGL ES 2.0 enabled Framework
- Easy to create custom based filter / channel
- Implements channels to live video or image streaming
- Suits for GPU based image processing
- Computer Vision Tool (with pygame and OpenCV)
- Support high-powered CV libraries
- Fast Modeling
- Suits for CV based applications
- YOLO -“You just look once”
- Utilize neural network algorithms
- Easy to recognize the objects in real environs
- For detection, implement neural network to segment the image in gird format. Then, recognize the different regions to identify the object.
MATLAB Image Apps and Visualization
Several MATLAB apps exist to provide the same functionalities and features of MATLAB tools and libraries. The main differences between them are code generation. In normal tools, you will import the code from scratch, but in these apps, it will automatically generate the MATLAB code for analyzing the image/data. In addition, we have given other special featured apps as follows,
- Image Segmentation
- Break the image into several segments (depends on similar patterns, edges, and more) using distinct algorithms such as graph-cut, active contours, lazy snapping and grabcut.
- Image and Video Labelling
- Label the information on the original source or location of the image which is also called as “ground truth”. Then, it lets to analyze and view the sequence of collected images and videos
- Camera Calibration
- Works on camera’s features to find precise relation between 3D and 2D models by predicting following camera metrics,
- Lens Distortion
- Works on camera’s features to find precise relation between 3D and 2D models by predicting following camera metrics,
Our experts always passionately work on all sorts of current research advancements. Also, they are familiarized to handle the latest development technologies and dip simulators. So, we assure you that we will tackle any kind of complex problem in fewer efforts through our smart skills. If it is necessary, we will design our own algorithm or pseudocode to crack the challenging issues. Below, we have suggested few commonly used algorithms and methods in DIP projects.
Important Digital Image Processing Algorithms
- STEAL Algorithm
- Uses one more layer for loss computation and semantic features computation
- Object boundaries or edges detection from the low resolution and blurring images
- Maximize the performance of labeled objects detection
- REASONING based RCNN
- Determines the end-to-end architecture for object detection
- Uses 1000’s of object classes for image classification
- Small, medium and large objects are detected by this method
- MESH RCNN
- Background and foreground analysis model
- Uses layers for 2D / 3D object modeling
- Predicts the actual shape and orientation of the 3D objects
- 3D Bo-Net
- 10x faster algorithm for object segmentation
- Uses 3D and 2D types of input images for object boundary detection
- Consists of limited number of layers for object detection and segmentation
- Can be suited for smart devices and other devices
- It can be the portable model for update the machine learning frameworks, but not release the latest version of it.
- Mobile Net
- Consumes less delay and power
- Used for facial recognition in real-world apps
- Use geolocation features for location prediction
- Contains lots of functions for optimization of DIP and computer vision operations
If you are curious to know about the new update on Digital image processing projects, then make contact with our team. For your benefit, our experts have listed some current ongoing research trends in DIP
DIP Research Topics
- Multiresolution Techniques
- Analysis of Various multi-resolution approaches
- Modeling and Representation of video / image
- Extraction of features on beyond one scale (in non-local and local phenomena)
- Scientific Imaging
- 2D and 3D Imaging based on scientific technologies
- Analysis different images: remote sensed images and microscope images
- Biomedical Imaging
- Analyzing different kinds of medical images (Ultrasound, CT, PET, MRI, OCT)
- Various anatomy approaches for investigating human internal organs
- Image Segmentation/Classification
- Identify and extract the features in image
- Segment the image based on homogenous regions
- Classy the segmented objects into different classes
- Integrate the pattern recognition and DIP methodologies
- Video Analysis
- Algorithms for auto-analysis of digital video
- Enhance the interpretation of video / image
- Evolutionary Deep Intelligence
- Enhancing the accuracy of different complex problems using DL methods
- DL is efficient than the other ML approaches
- Computer Vision
- Improving the image interpretation based on suitable scientific approaches
- Enhancing image and video visualization
- Also, it supports other fields as DSP, AI, pattern recognition, medical imaging, VR/AR, 2D/3D modeling, etc.
- Remote Sensing
- Examining remote sensed images (from satellites / aircrafts)
- Remote observation of ocean, atmosphere, land areas
- Efficient enhancement of remote sensed data
- Stochastic Models
- Analyze the large-scale uncertainty in CV, pattern recognition and image processing related to external features
- Features: physical structures (size, color, shape), object location, background history of data, etc.
Aspiringly, we ensure that we will direct you to the fullest in your selected research topic using dip simulator under the guidance of our expert committee. So, don’t miss this successful opportunity. Let’s walk together to create a masterwork in your research career.
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