Superpixel-Based HDR Image Region of Interest Extraction Method

2020 ◽  
Vol 47 (9) ◽  
pp. 793-803
Author(s):  
Simon Suh ◽  
Seung-Ryeol Ohk ◽  
Young-Jin Kim
Keyword(s):  
Extraction Method ◽  
Region Of Interest ◽  
Image Region ◽  
Hdr Image

Feature Extraction by Decryption to Target

2014 ◽  
Vol 1008-1009 ◽  
pp. 1509-1512
Author(s):  
Qing E Wu ◽  
Hong Wang ◽  
Li Fen Ding
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Extraction Method ◽  
Recognition Rate ◽  
Region Of Interest ◽  
Correct Recognition ◽  
Palmprint Recognition ◽  
Feature Extraction Method ◽  
Energy Feature ◽  
Correct Recognition Rate

To carry out an effective classification and recognition for target, this paper studied the target owned characteristics, discussed a decryption algorithm, gave a feature extraction method based on the decryption process, and extracted the feature of palmprint in region of interest. Moreover, this paper used the wavelet transform to extract the energy feature of target, gave an approach on matching and recognition to improve the correctness and efficiency of existing recognition approaches, and compared it with existing approaches of palmprint recognition by experiments. The experiment results show that the correct recognition rate of the approach in this paper is improved averagely by 2.34% than that of the existing recognition approaches.

Intelligent Computer Aided Detection of Tumor in MRI Brain Images Using Cascaded Correlation Neural Network Classifier

2014 ◽  
Vol 626 ◽  
pp. 65-71
Author(s):  
V. Amsaveni ◽  
N. Albert Singh ◽  
J. Dheeba
Keyword(s):  
Neural Network ◽  
Region Of Interest ◽  
Texture Features ◽  
Neural Network Classifier ◽  
Image Region ◽  
Brain Images ◽  
Gabor Features ◽  
Computer Aided ◽  
Pixel Location ◽  
Nonlinear Classifier

In this paper, a Computer aided classification approach using Cascaded Correlation Neural Network for detection of brain tumor from MRI is proposed. Cascaded Correlation Neural Network is a nonlinear classifier which is formulated as a supervised learning problem and the classifier was applied to determine at each pixel location in the MRI if the tumor is present or not. Gabor texture features are taken from the image Region of interest (ROI). The extracted Gabor features from MRI is given as input to the proposed classifier. The method was applied to real time images from the collected from diagnostic centers. Based on the analysis the performance of the proposed cascaded correlation neural network classifier is superior when compared with other classification approaches.

scholarly journals Eye-tracking and learning experience: gaze trajectories to better understand the behavior of memorial visitors

2020 ◽  
Vol 13 (2) ◽  
Author(s):  
Salma Mesmoudi ◽  
Stanislas Hommet ◽  
Denis Peschanski
Keyword(s):  
Focus Group ◽  
Eye Tracking ◽  
Learning Experience ◽  
Region Of Interest ◽  
Viewing Time ◽  
Image Region ◽  
Mass Violence ◽  
The Gaze ◽  
Before And After ◽  
First Results

Eye-tracking technology is increasingly introduced in museums to assess their role in learning and knowledge transfer. However, their use provide limited quantitative and/or qualitative measures such as viewing time and/or gaze trajectory on an isolated object or image (Region of Interest "ROI"). The aim of this work is to evaluate the potential of the mobile eye-tracking to quantify the students’ experience and behaviors through their visit of the "Genocide and mass violence" area of the Caen memorial. In this study, we collected eye-tracking data from 17 students during their visit to the memorial. In addition, all visitors filled out a questionnaire before the visit, and a focus group was conducted before and after the visit. The first results of this study allowed us to analyze the viewing time spent by each visitor in front of 19-selected ROIs, and some of their specific sub-parts. The other important result was the reconstruction of the gaze trajectory through these ROIs. Our global trajectory approach allowed to complete the information obtained from an isolated ROI, and to identify some behaviors such as avoidance. Clustering analysis revealed some typical trajectories performed by specific sub-groups. The eye-tracking results were consolidated by the participants' answers during the focus group.  

Morphology Preserving Segmentation Method for Occluded Cell Nuclei from Medical Microscopy Image

2021 ◽  
pp. 2250019
Author(s):  
Rafflesia Khan ◽  
Rameswar Debnath
Keyword(s):  
Prior Knowledge ◽  
Single Cells ◽  
Region Of Interest ◽  
Automatic Measurement ◽  
Cell Counting ◽  
Manual Annotation ◽  
Cell Nuclei ◽  
Image Region ◽  
Microscopy Image ◽  
Optimal Approach

Nowadays, image segmentation techniques are being used in many medical applications such as tissue culture monitoring, cell counting, automatic measurement of organs, etc., for assisting doctors. However, high-level segmentation results cannot be obtained without manual annotation or prior knowledge for high variability, noise and other imaging artifacts in medical images. Furthermore, unstable and continuously changing characteristics of all human cells, tissues and organs manipulate training-based segmentation methods. Detecting appropriate contour of a region of interest and single cells from overlapping condition are extremely challenging. In this paper, we aim for a model that can detect biological structure (e.g. cell nuclei and lung contour) with their proper morphology even in overlapping or occluded condition without manual annotation or prior knowledge. We have introduced a new optimal approach for automatic medical image region segmentation. The method first clearly focuses the boundaries of all object regions in a microscopy image. Then it detects the areas by following their contours. Our model is capable of detecting and segmenting object regions from medial image using less computation effort. Our experimental results prove that our model provides better detection on several datasets of different types of medical data and ensures more than 98% segmentation rate in the case of densely connected regions.

Design of Soil-Granularity Analysis System Based on MATLAB

2015 ◽  
Vol 740 ◽  
pp. 722-726 ◽  
Author(s):  
Jian Lin Rao ◽  
Jian Shu Hou ◽  
Hao Chen ◽  
Hai Hua Li ◽  
Xue Yi Wan ◽  
...  
Keyword(s):  
Image Processing ◽  
Grain Size ◽  
Extraction Method ◽  
Detection Method ◽  
Region Of Interest ◽  
Edge Extraction ◽  
New Type ◽  
Image Edge ◽  
Analysis System ◽  
Soil Grain Size

The system in the paper based on Matlab platform. With the aid of image processing toolbox of soil image analysis and processing, the soil grain size distribution and its inclination angle can be got. It overcomes the insufficiency of the existing image edge extraction method, and proposes a new type of detection method, in order that the region of interest can be more accurately extract. The system is helpful to predict the possibility of the regional landslides.

HDR IMAGE RERENDERING USING GPU-BASED PROCESSING

2012 ◽  
Vol 12 (01) ◽  
pp. 1250007
Author(s):  
PING LI ◽  
HANQIU SUN ◽  
JIANBING SHEN ◽  
CHEN HUANG
Keyword(s):  
Graphics Processing Units ◽  
Dynamic Range ◽  
Region Of Interest ◽  
High Dynamic Range ◽  
Quality Performance ◽  
Realistic Rendering ◽  
Gradient Domain ◽  
Shading Effects ◽  
Graphics Processing ◽  
Hdr Image

One essential process in image rerendering is to replace existing texture in the region of interest by other user-preferred textures, while preserving the shading and similar texture distortion. In this paper, we propose the graphics processing units (GPU)-accelerated high dynamic range (HDR) image rerendering using revisited NLM processing in parallel on GPU-CUDA platform, to reproduce the realistic rendering of HDR images with retexturing and transparent/translucent effects. Our image-based approach using GPU-based pipeline in gradient domain provides efficient processing with easy-control image retexturing and special shading effects. The experimental results showed the efficiency and high-quality performance of our approach.

scholarly journals Classification of Liver Tumor on Ct Images using Machine Learning

2019 ◽  
Vol 8 (4) ◽  
pp. 11336-11338
Keyword(s):  
Machine Learning ◽  
Liver Tumor ◽  
Region Of Interest ◽  
Region Growing ◽  
Ct Images ◽  
Svm Classifier ◽  
Intensity Level ◽  
Image Region ◽  
Occurrence Matrix

Liver tumor is one of the most severe types of cancerous diseases which is responsible for the death of many patients. CT Liver tumor images have more noises which is difficult to diagnose the level of the tumor. It is a challenging task to automatically identify the tumor from CT images because of several anatomical changes in different patients. The tumor is difficult to find because of the presence of objects with same intensity level. In this proposed system, fully automated machine learning is used to detect the liver tumor from CT image. Region growing technique is used to segment the region of interest. The textural feature are extracted from Gray level co-occurrence matrix (GLCM) of the segmented image. Extracted textural features are given as input to the designed SVM classifier system. Performance analysis of SVM classification of CT liver tumor image is studied. This will be useful for physician in better automatic diagnosis of liver tumor from CT images.

scholarly journals Edge Location Method for Multidimensional Image Based on Edge Symmetry Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Chen Li
Keyword(s):  
Vertical Direction ◽  
Region Of Interest ◽  
Basic Feature ◽  
Vertical Gradient ◽  
Edge Region ◽  
Image Region ◽  
Location Method ◽  
Candidate Image ◽  
Positioning Method ◽  
Edge Location

The most basic feature of an image is edge, which is the junction of one attribute area and another attribute area in the image. It is the most uncertain place in the image and the place where the image information is most concentrated. The edge of an image contains rich information. So, the edge location plays an important role in image processing, and its positioning method directly affects the image effect. In order to further improve the accuracy of edge location for multidimensional image, an edge location method for multidimensional image based on edge symmetry is proposed. The method first detects and counts the edges of multidimensional image, sets the region of interest, preprocesses the image with the Gauss filter, detects the vertical edges of the filtered image, and superposes the vertical gradient values of each pixel in the vertical direction to obtain candidate image regions. The symmetry axis position of the candidate image region is analyzed, and its symmetry intensity is measured. Then, the symmetry of vertical gradient projection in the candidate image region is analyzed to verify whether the candidate region is a real edge region. The multidimensional pulse coupled neural network (PCNN) model is used to synthesize the real edge region after edge symmetry processing, and the result of edge location of the multidimensional image is obtained. The results show that the method has strong antinoise ability, clear edge contour, and precise location.

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