biswajitcsecu

-Color-multi-focus-image-fusion-using-Guided-and-Bilateral-filter-based-on-focus-region-detection-in

This code impliments a multi-focus color image fusion method based on focus region detection with edge preseve using bilateral filter and guided filter in discrete wavelet transform (2DWT). Firstly, a novel focus region detection method is estimated, which uses guided filter to refine the rough focus maps obtained by bilateral filter and difference operator. Then, An initial decision map is got via the pixel-wise maximum rule, and optimized to generate final decision map by using guided filter again. Finally, the fused image is obtained by the pixel-wise weighted-averaging rule with the final decision map via inverse transform.

2D-Heat-Equation-Using-Finite-Difference-Method-with-CUDA

This code is designed to solve the heat equation in a 2D plate with CUDA-Opengl. After solution, graphical simulation appears to show that how the heat diffuses throughout the medium within time interval selected in the code. Solving the 2 Dimensional Heat conduction equation in the generalized form, we used FEM technique.

Binary-Image-classification

Binary Image classification using ImageDataGenerator in Keras

CFD-Simulation-in-Porous-Medium

This code is capable of simulating of flows through materials with complex, porous structure. In this way, the whole process of importing files containing the porous geometry, generating the computational mesh and running CFD simulation can be performed in MATLAB

Code-for-parallel-Recursive-Newton-s-Algorithm-using-MPI

we have just coded to implement parallel Newton’s recursive algorithm. To accomplish this, we first partition the problem and fitted each partition into master-slave by MPI mode. First, we need to have: (a) The number of interpolation points. (b) A grid of points at which the interpolation is to be exact. (c) An array containing the function we wish to interpolate evaluated at the interpolating grid. (d) An array to store the Newton differencing coefficients

Guided-Bilateral-Filter-based-Medical-Image-Fusion-Using-Visual-Saliency-Map-in-the-Wavelet-Domain

Guided-Bilateral Filter-based Medical Image Fusion Using Visual Saliency Map in the Wavelet Domain

Integration-of-cloud-and-cgal-c-

Integration of PCL and CGAL library

Polyp-Detection-and-Segmentation-from-Capsule-Endoscopy

To analyze the large scale CE data exams automatic image processing, computer vision, and learning algorithms. An automatic polyp detection algorithms have been implineted with deep learning approach. The polyp detection in colonoscopy in CE is a challenging problem still now.

Surface-Reconstruction-from-Point-Clouds

Surface Reconstruction from Point Clouds by Point Cloud Library (PCL) and CGAL

mfem

Lightweight, general, scalable C++ library for finite element methods

biswajitcsecu/Deep-learning-practice-codes-

Several deep learning applications have design using Python on GPU on Linux

biswajitcsecu/Display-video-using-OpenGL

To display very high resolution video directly with OpenGL and OpenCV on Qt5

biswajitcsecu/Gastrointestinal-Endoscopy-Anomaly-Segmentation-on-Colonoscopy-Images-Using-U-Net

A deep learning method, ingraft-U-Net, is proposed to segment polyps using Melanoma Skin frames. Ingraft-U-Net is a modified version of UNet, which comprises three stages, including the preprocessing, encoder, and decoder stages.

biswajitcsecu/Melanoma-segmentation-using-deep-learning

This code proposes a novel deep learning-based, fully automated approach to skin lesion segmentation, including sophisticated pre and postprocessing approaches. We use three deep learning models, including UNet, deep residual U-Net (ResUNet), and improved ResUNet (ResUNet++).

biswajitcsecu/Color-Correction-and-Preserving-on-GPU

Run a CUDA kernel that writes image data to a GL buffer or texture image. A live display via CUDA Graphics Interop mode on Ubuntu and CUDA-11.0 Driver.

biswajitcsecu/Endoscopy-image-denoising-using-patch-correction-with-deep-convolutional-neural-networks-DCNNs-

Deep Image Patch (DIP) correction method uses to reconstruct a clean image by deep convolutional neural networks (DCNNs) traing on noisy images with different noise model and ground truth.

biswajitcsecu/Java-Computer-Vision-Codes

Java Codes for different Computer Vision algorithms

biswajitcsecu/Low-light-Image-Enhancement-Using-Deep-Convolutional-Network

A Convolutional Neural Networks (CNNs) is directly learns an end-to-end mapping between low light and bright images. The low-light image enhancement in this code solved as a machine learning problem using tensorflow library. This model considred different metrics SSIM, PSNR and entropy as loss function for improve contrast lowand hazy images.

biswajitcsecu/Medical-image-colorization-using-deep-GANs

Deep GANs color correction method uses to reconstruct a clean color image by deep convolutional neural networks (CNNs) traing on gray images with ground truth.

biswajitcsecu/Melanoma-Skin-Anomaly-Segmentation-on-Colonoscopy-Images-Using-U-Net

A deep learning method, ingraft-U-Net, is proposed to segment polyps using Melanoma Skin frames. Ingraft-U-Net is a modified version of UNet, which comprises three stages, including the preprocessing, encoder, and decoder stages.

biswajitcsecu/Polyp-Anomaly-Segmentation-on-Colonoscopy-Images-Using-U-Net

A deep learning method, ingraft-U-Net, is proposed to segment polyps using colonoscopy frames. Ingraft-U-Net is a modified version of UNet, which comprises three stages, including the preprocessing, encoder, and decoder stages.

biswajitcsecu/Semantic-Segmentation-deep-learning

Semantic segmentation describes the process of associating each pixel of an image with a class label, (such as flower, person, road, sky, ocean, or car).

biswajitcsecu/-Simulating-Particle-Effects-using-NVIDIA-Cg-Tool-Kits-and-OpenMP

I have implemented a particle effect in NVIDIA Cg Tool Kit using OpenMP to render the effect. This demo uses the vertex and fragment shader pipeline and the device processor (GPU) to perform the n-particles simulation.

biswajitcsecu/A-Simple-CNN-framework-Binary-and-Multi-Image-Classifier

Using Tensorflow and transfer learning, easily make a labeled image classifier with convolutional neural network

biswajitcsecu/Automatic-Polyps-DeepCNNs-Segmentation-

Automatic Polyps Segmentation using Deep CNNs

biswajitcsecu/Automatic-SAR-Segmentation-using-Deep-CNNs-

Automatic SAR Segmentation using Deep CNNs

biswajitcsecu/Color-Edge-Preserving-on-GPU

Run a CUDA kernel that writes image data to a GL buffer or texture image. A live display via CUDA Graphics Interop mode on Ubuntu and CUDA-11.0 Driver.

biswajitcsecu/Color-image-quantization

Color image quantization is a process of selecting a set of colors to display an image with some representative colors, this without noticeable perceived difference. This algorithm deals with the problem of the quantization errors evaluation in taking into account a probabilistic measure on color space. Developed a imageJ plugin to minimize the possible degradationscolor profile.

biswajitcsecu/Common-deep-learning-framework-for-computer-vision-and-image-processing

This gives a list of tools that will improve different computer vision projects . A number of concepts were developed, including approaches to solve computer vision and image processing algorithms.

biswajitcsecu/Computer-Vision-algorithms

C++ Codes for different Computer Vision algorithms

biswajitcsecu/Deep-learning-for-computer-vision

Building a real-time computer vision algorithms using Tensorflow and OpenCV library in Python

biswajitcsecu/GPU-based-parallel-image-sharpening-method-

biswajitcsecu/Gray-Image-Edge-Detection-in-Cuda-openGL

Run a CUDA kernel that writes Gray image data to a GL buffer or texture image. A live display via CUDA Graphics Interop mode on Ubuntu and CUDA-11.0 Driver.

biswajitcsecu/Gray-Image-Load-in-Cuda-openGL

Run a CUDA kernel that writes Gray image data to a GL buffer or texture image. A live display via CUDA Graphics Interop mode on Ubuntu and CUDA-11.0 Driver.

biswajitcsecu/Gray-Image-Sharpening-in-Cuda-openGL

Run a CUDA kernel that writes Gray image data to a GL buffer or texture image. A live display via CUDA Graphics Interop mode on Ubuntu and CUDA-11.0 Driver.

biswajitcsecu/ImageJ-plugin-for-COVID-19-image-enhancement

An imageJ plugin developed for type-2 fuzzy COVID-19 image enhancement approach.

biswajitcsecu/Opencv-Java-framework-computer-vision-algorithms

Using opencv and java, easily make best results with computer vision algorithms

biswajitcsecu/Parallel-watershed-transformation-algorithms-for-image-segmentation

Parallel watershed transformation algorithms for image segmentation

biswajitcsecu/Semantic-segmentation-using-deep-learning

A semantic segmentation network classifies every pixel in an image, resulting in an image that is segmented by class. Applications for semantic segmentation include road segmentation for autonomous driving and cancer cell segmentation for medical diagnosis.

biswajitcsecu/U-Net-Res-Net-based-Semantic-Segmentation

Image segmentation is the method to partition the image into various segments with each segment having a different entity. Convolutional Neural Networks are successful for simpler images but not good for complex images. Here, this code implimentes the semantic segmentation using both Res-Nets and U-Nets for complex scenes.