Image Processing

It is a challenging work to solve the geometric attack in the field of digital watermarking. In order to solve the synchronization between the host image and the watermark, image normalization is introduced. Firstly, the geometrically invariant space of image is constructed by using image normalization, and a region of interest (ROI) is obtained from the normalized image by utilizing the invariant centroid theory. Then, the contourlet transform is performed on the ROI. Low-pass sub-band coefficients are divided into non-overlapping blocks.


Supplementary material of the article "Precise 2D and 3D fluoroscopic Imaging by using an FMCW Millimeter-Wave Radar".


This Dataset used a non-invasive blood group prediction approach using deep learning. Rapid and meticulous prediction of blood type is a major step during medical emergency before supervising the red blood cell, platelet, and plasma transfusion. Any small mistake during transfer of blood can cause death. In conventional pathological assessment, the blood test is conducted using automated blood analyser; however, it results into time taking process.


Gorakhpur is a city located in the north-eastern region of

Uttar Pradesh state of India. It is a sub-part of Purvanchal

region of Uttar Pradesh and Bihar. In the south-western

part, Gorakhpur periphery spreads along Rapti river. In

the north-western region, Gorakhpur shares its periphery

with Chillua Tal. In the southern part, Ramgarh Tal with a

perimeter of 18 km is located.


Water Bodies:


+ Ramgarh Tal is a historically important heritage site and

is also a tourist attraction; spread over 700 hectares of


India is a sub-continent that stretches from Ladakh in

the North to Kanyakumari in the South and from

Gujrat in the West to Arunachal Pradesh, Nagaland

and Manipur in the East. India is currently the

seventh largest country by land covering an area of

approximately 32,87,263 kms.


India's Space Strengths:

India is the fourth country in the world to have

destroyed a satellite of its own. India built the

record-breaking space capability of launching 104

satellites on a single Polar Satellite Launch Vehicle


The dataset contains thermal and visible images of volunteers taken in different places to develop face detection algorithms. By providing both thermal and visible face images in a single dataset, our dataset empowers researchers, scientists, and developers to leverage the strengths of each image type. The dataset can be utilized for tasks like biometric authentication, emotion recognition, facial expression analysis, age estimation, and gender classification.


Neuroimaging methods play an important role in presurgical examinations and localization of epileptogenic lesion. Magnetic resonance imaging (MRI) is a neuroimaging technique that is essential to detect structurally abnormal tissue and thus delineate the epileptogenic lesion. Magnetic resonance imaging (MRI) provides structural data and can reveal underlying epileptogenic lesions (T1, T2, FLAIR).



The goal of this project is to leverage Amazon Web Service's machine learning services to create a dataset that automatically adds and updates files on IEEE DataPort's S3 storage. Through this process, we sought to learn and demonstrate how an ongoing data collection script can create a shared living dataset by streaming data to our IEEE DataPort dataset storage. In the process, we also hoped to gain further insights into areas including:


A long-standing problem in thermal imaging is the inherent assumption of a uniform and known emissivity across an entire image. Semantic segmentation of the materials in a thermal image can identify the pixel-wise emissivity, thus rectifying the spatially uniform emissivity assumption with no human intervention. We have created a multispectral thermal image dataset consisting of nine materials (acrylic, aluminum, bakelite, ceramic, cork, EVA, granite, maple, and silicone) at six different temperatures.


— Medical image segmentation is a crucial aspect of medical image processing, and has been widely used in the detection and clinical diagnosis for brain, lung, liver, heart and other diseases. In this paper, we propose a novel multimodal mutual attention network, called MMAUNet, for medical image segmentation. MMA-UNet is divided into two parts. The first part obtains more highdimensional features by skip connection and improved network structure.