scholarly journals Robust Attendance System using RFID and Human Detection by Image Processing

2021 ◽  
Vol 6 (4) ◽  
Keyword(s):  
Image Processing ◽  
Human Detection

scholarly journals Detection of Masses in Mammograms using Bayesian Method and Machine learning

2018 ◽  
Vol 7 (4.6) ◽  
pp. 108
Author(s):  
Priyadarshini Chatterjee ◽  
Ch. Mamatha ◽  
T. Jagadeeswari ◽  
Katha Chandra Shekhar
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Bayesian Method ◽  
Learning Algorithm ◽  
Correct Diagnosis ◽  
Machine Learning Algorithms ◽  
Human Detection ◽  
Breast Masses ◽  
Learning Machine ◽  
Vast Area

Every 100th cases in cancer we come across are of breasts cancer cases. It is becoming very common in woman of all ages. Correct detection of these lesions in breast is very important. With less of human intervention, the goal is to do the correct diagnosis. Not all the cases of breast masses are futile. If the cases are not dealt properly, they might create panic amongst people. Human detection without machine intervention is not hundred percent accurate. If machines can be deeply trained, they can do the same work of detection with much more accuracy. Bayesian method has a vast area of application in the field of medical image processing as well as in machine learning. This paper intends to use Bayesian probabilistic in image segmentation as well as in machine learning. Machine learning in image processing means application in pattern recognition. There are various machine learning algorithms that can classify an image at their best. In the proposed system, we will be firstly segment the image using Bayesian method. On the segmented parts of the image, we will be applying machine learning algorithm to diagnose the mass or the growth.  

scholarly journals DRONE BASED NEAR REAL-TIME HUMAN DETECTION WITH GEOGRAPHIC LOCALIZATION

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 49-53 ◽  
Author(s):  
P. J. Baeck ◽  
N. Lewyckyj ◽  
B. Beusen ◽  
W. Horsten ◽  
K. Pauly
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Real Time ◽  
Search And Rescue ◽  
Human Detection ◽  
The Other ◽  
Terrain Model ◽  
Aerial Photogrammetry ◽  
3D Terrain ◽  
Processing Chain

<p><strong>Abstract.</strong> Detection of humans, e.g. for search and rescue operations has been enabled by the availability of compact, easy to use cameras and drones. On the other hand, aerial photogrammetry techniques for inspection applications allow for precise geographic localization and the generation of an overview orthomosaic and 3D terrain model. The proposed solution is based on nadir drone imagery and combines both deep learning and photogrammetric algorithms to detect people and position them with geographical coordinates on an overview orthomosaic and 3D terrain map. The drone image processing chain is fully automated and near real-time and therefore allows search and rescue teams to operate more efficiently in difficult to reach areas.</p>

scholarly journals THE CALCULATION SYSTEM OF NUMBER OF PEOPLE IN A ROOM BASED ON HUMAN DETECTION USING HAAR-CASCADE CLASSIFIER

2021 ◽  
Vol 2 (2) ◽  
pp. 75-84
Author(s):  
Gusti Ngurah Rama Putra Atmaja ◽  
Koredianto Usman ◽  
Muhammad Ary Murti
Keyword(s):  
Image Processing ◽  
Computation Time ◽  
Scale Factor ◽  
Human Detection ◽  
Raspberry Pi ◽  
Cascade Classifier ◽  
Final Project ◽  
Object Distance ◽  
Haar Cascade ◽  
Commercial Applications

Data of number of people in the room, calculations are usually carried out by assigning someone to oversee a room. In this final project, a system for calculating the number of people in the room is designed with image processing based on human detection that can be used in rooms, both for commercial applications and for security. This system uses Raspberry Pi device that already has an image processing method Haar-Cascade Classifier.   Input data is in the form of video taken directly via webcam to be captured into a frame so that it   can be used as a input the Haar-Cascade Classifier method and perform the counting process will be sent to the Antares platform. The system design has been tested with five scenarios. Scenario 1 the effect of the distance of the object, scenario 2 the effect of the pose of the object, scenario 3 the effect of the amount the object in the frame, scenario 4 affects the scale factor and scenario 5 measurement computation time. Scenarios 1 to 3 will do the best configuration for minimum neighbour. The system gets the best accuracy of 98,5% when the object distance 4 meters, the best accuracy of 96,6% when the object is facing forward and accuracy the best is 97,7% when the object in the frame is more than two objects with the best configuration use the minimum neighbour 5. Scenario 4 gets accuracy the best is 76,2% when using the scale factor 1.1. Scenario 5 gets the average computation time of the system is under one second, meaning the detection process done pretty fast.

scholarly journals Comparison of human detection using background subtraction and frame difference

2020 ◽  
Vol 9 (1) ◽  
pp. 345-353 ◽  
Author(s):  
Mohd Saifulnizam Zaharin ◽  
Norazlin Ibrahim ◽  
Tengku Mohd Azahar Tuan Dir
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Background Subtraction ◽  
Human Movement ◽  
Human Detection ◽  
Support Vector ◽  
Frame Difference ◽  
Image Technique ◽  
The Difference ◽  
High Level

Image processing is mostly used for exploring image behaviour. There are several steps in image processing. Image acquisition, pre-processing, feature extraction, and classification are the processes used for the detection of human movement based on high-level feature extraction (HLFE), in which HLFE was used for feature extraction in this paper. This study proposed the use of background subtraction and frame difference. This research was conducted to analyse the difference of background subtraction and frame difference methods based on movement of human. Movement of human detected by using feature extraction were centroid image technique used. Furthermore, support vector machine (SVM) was used for classification.

Some comments about observations and image processing of comet 29P/Schwassmann-Wachmann 1

1999 ◽  
Vol 173 ◽  
pp. 243-248
Author(s):  
D. Kubáček ◽  
A. Galád ◽  
A. Pravda
Keyword(s):  
Image Processing ◽  
Large Scale ◽  
Harvard College ◽  
Oak Ridge ◽  
Large Scale Structures ◽  
Short Period Comet ◽  
Short Period ◽  
Scale Structures ◽  
Harvard College Observatory ◽  
Period Comet

AbstractUnusual short-period comet 29P/Schwassmann-Wachmann 1 inspired many observers to explain its unpredictable outbursts. In this paper large scale structures and features from the inner part of the coma in time periods around outbursts are studied. CCD images were taken at Whipple Observatory, Mt. Hopkins, in 1989 and at Astronomical Observatory, Modra, from 1995 to 1998. Photographic plates of the comet were taken at Harvard College Observatory, Oak Ridge, from 1974 to 1982. The latter were digitized at first to apply the same techniques of image processing for optimizing the visibility of features in the coma during outbursts. Outbursts and coma structures show various shapes.

Analysis of the 9th November 1990 flare

2000 ◽  
Vol 179 ◽  
pp. 229-232
Author(s):  
Anita Joshi ◽  
Wahab Uddin
Keyword(s):  
Image Processing ◽  
Two Dimensional ◽  
Temporal Behaviour ◽  
Contour Maps ◽  
Dimensional Measurements ◽  
Processing Software ◽  
Image Processing Software

AbstractIn this paper we present complete two-dimensional measurements of the observed brightness of the 9th November 1990Hαflare, using a PDS microdensitometer scanner and image processing software MIDAS. The resulting isophotal contour maps, were used to describe morphological-cum-temporal behaviour of the flare and also the kernels of the flare. Correlation of theHαflare with SXR and MW radiations were also studied.

The 'well-known' theory of electron image formation

1983 ◽  
Vol 41 ◽  
pp. 288-289
Author(s):  
M.A. O'Keefe ◽  
W.O. Saxton
Keyword(s):  
Image Processing ◽  
Image Formation ◽  
Source Profiles ◽  
Electron Image

A recent paper by Kirkland on nonlinear electron image processing, referring to a relatively new textbook, highlights the persistence in the literature of calculations based on incomplete and/or incorrect models of electron imageing, notwithstanding the various papers which have recently pointed out the correct forms of the appropriate equations. Since at least part of the problem can be traced to underlying assumptions about the illumination coherence conditions, we attempt to clarify both the assumptions and the corresponding equations in this paper, illustrating the effects of an incorrect theory by means of images calculated in different ways.The first point to be made clear concerning the illumination coherence conditions is that (except for very thin specimens) it is insufficient simply to know the source profiles present, i.e. the ranges of different directions and energies (focus levels) present in the source; we must also know in general whether the various illumination components are coherent or incoherent with respect to one another.

Recent Applications of High Resolution Electron Microscopy to Crystalline Arrays of Virus Particles

1978 ◽  
Vol 36 (3) ◽  
pp. 470-482 ◽  
Author(s):  
R.W. Horne
Keyword(s):  
Electron Microscopy ◽  
Image Processing ◽  
Analytical Techniques ◽  
Staining Technique ◽  
High Resolution Electron Microscopy ◽  
Optical Diffraction ◽  
Full Potential ◽  
Virus Particles ◽  
Resolution Electron ◽  
Wide Range

The technique of surrounding virus particles with a neutralised electron dense stain was described at the Fourth International Congress on Electron Microscopy, Berlin 1958 (see Home & Brenner, 1960, p. 625). For many years the negative staining technique in one form or another, has been applied to a wide range of biological materials. However, the full potential of the method has only recently been explored following the development and applications of optical diffraction and computer image analytical techniques to electron micrographs (cf. De Hosier & Klug, 1968; Markham 1968; Crowther et al., 1970; Home & Markham, 1973; Klug & Berger, 1974; Crowther & Klug, 1975). These image processing procedures have allowed a more precise and quantitative approach to be made concerning the interpretation, measurement and reconstruction of repeating features in certain biological systems.

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