scholarly journals Invisibility Cloak using Color Detection and Segmentation with Open CV

2020 ◽  
Vol 9 (4) ◽  
pp. 1621-1624
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Harry Potter ◽  
Invisibility Cloak ◽  
Invisible Cloak ◽  
The World ◽  
Color Detection ◽  
False Sense ◽  
Collective Imagination ◽  
Science Fantasy

Have you ever thought of making visible things invisible, just like the Harry Potter? Have you ever thought how does one supersede backgrounds and add effects in a movie? The cloak was magical and invisible in Harry Potter, the movie. As we know there is no magic and no invisible cloak which exists in the world. It’s all about the graphics tricks. The concept of an invisibility cloak is a mixture of science, fantasy, and the collective imagination. This paper helps to create one’s own ‘Invisibility Cloak’.It will make use of Python and OpenCV module specifically targeting Image Processing and Image Segmentation to create a false sense of invisibility in the frame. It will explore how an object of a specific color or texture can be manipulated using the OpenCV library of python. To achieve this, initially we’ll be capturing and storing the backdrop frame . Thereafter we’ll be identifying the red coloured fabric by making use of the above mentioned algorithms. Then we’ll segment out the red colored fabric by generating a mask and then finally , we’ll generate the final augmented(magical) output to create Invisibility cloak. These steps are discussed deeper in the paper

scholarly journals Invisibility Cloak using Color Detection and Segmentation with open CV

2020 ◽  
Vol 6 (12) ◽  
pp. 440-444
Author(s):  
Puneet and Vasudha Bahl
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Harry Potter ◽  
Invisibility Cloak ◽  
Invisible Cloak ◽  
The World ◽  
Color Detection ◽  
False Sense ◽  
Collective Imagination ◽  
Science Fantasy

Have you ever thought of making visible things invisible, just like the Harry Potter? Have you ever thought how does one supersede backgrounds and add effects in a movie? The cloak was magical and invisible in Harry Potter, the movie. As we know there is no magic and no invisible cloak which exists in the world. It’s all about the graphicstricks. The concept of an invisibility cloak is a mixture of science, fantasy, and the collective imagination. This paper helps to create one’s own ‘Invisibility Cloak’.It will make use of Python and OpenCV module specifically targeting Image Processing and Image Segmentation to create a false sense of invisibility in the frame. It will explore how an object of a specific color or texture can be manipulated using the OpenCV library of python. To achieve this, initially we’ll be capturing and storing the backdrop frame . Thereafter we’ll be identifying the red coloured fabric by making use of the above mentioned algorithms. Then we’ll segment out the red colored fabric by generating a mask and then finallywe’ll generate the final augmented(magical) output to create Invisibility cloak. These steps are discussed deeper in thepaper.

A basic introduction to image processing using NIH-Image as a model

1994 ◽  
Vol 52 ◽  
pp. 392-393
Author(s):  
John Mansfield
Keyword(s):  
Image Processing ◽  
Digital Image ◽  
Image Acquisition ◽  
Digital Instrument ◽  
Novice User ◽  
Nih Image ◽  
The Past ◽  
Digital World ◽  
The World ◽  
Instrument Control

Advances in camera technology and digital instrument control have meant that in modern microscopy, the image that was, in the past, typically recorded on a piece of film is now recorded directly into a computer. The transfer of the analog image seen in the microscope to the digitized picture in the computer does not mean, however, that the problems associated with recording images, analyzing them, and preparing them for publication, have all miraculously been solved. The steps involved in the recording an image to film remain largely intact in the digital world. The image is recorded, prepared for measurement in some way, analyzed, and then prepared for presentation.Digital image acquisition schemes are largely the realm of the microscope manufacturers, however, there are also a multitude of “homemade” acquisition systems in microscope laboratories around the world. It is not the mission of this tutorial to deal with the various acquisition systems, but rather to introduce the novice user to rudimentary image processing and measurement.

scholarly journals LeLePhid: An Image Dataset for Aphid Detection and Infestation Severity on Lemon Leaves

Data ◽  
2021 ◽  
Vol 6 (5) ◽  
pp. 51
Author(s):  
Jorge Parraga-Alava ◽  
Roberth Alcivar-Cevallos ◽  
Jéssica Morales Carrillo ◽  
Magdalena Castro ◽  
Shabely Avellán ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Plant Disease ◽  
Automatic Identification ◽  
Health State ◽  
Classification Problems ◽  
Affected Area ◽  
White Spots ◽  
The World ◽  
Image Dataset ◽  
Plant Sap

Aphids are small insects that feed on plant sap, and they belong to a superfamily called Aphoidea. They are among the major pests causing damage to citrus crops in most parts of the world. Precise and automatic identification of aphids is needed to understand citrus pest dynamics and management. This article presents a dataset that contains 665 healthy and unhealthy lemon leaf images. The latter are leaves with the presence of aphids, and visible white spots characterize them. Moreover, each image includes a set of annotations that identify the leaf, its health state, and the infestation severity according to the percentage of the affected area on it. Images were collected manually in real-world conditions in a lemon plant field in Junín, Manabí, Ecuador, during the winter, by using a smartphone camera. The dataset is called LeLePhid: lemon (Le) leaf (Le) image dataset for aphid (Phid) detection and infestation severity. The data can facilitate evaluating models for image segmentation, detection, and classification problems related to plant disease recognition.

A Survey on Solar Image Segmentation Techniques

2014 ◽  
Vol 945-949 ◽  
pp. 1899-1902
Author(s):  
Yuan Yuan Fan ◽  
Wei Jiang Li ◽  
Feng Wang
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Image Analysis ◽  
Image Segmentation ◽  
Image Retrieval ◽  
Fundamental Issue ◽  
Solar Image ◽  
Segmentation Methods

Image segmentation is one of the basic problems of image processing, also is the first essential and fundamental issue in the solar image analysis and pattern recognition. This paper summarizes systematically on the image segmentation techniques in the solar image retrieval and the recent applications of image segmentation. Then the merits and demerits of each method are discussed in this paper, in this way we can combine some methods for image segmentation to reach the better effects in astronomy. Finally, according to the characteristics of the solar image itself, the more appropriate image segmentation methods are summed up, and some remarks on the prospects and development of image segmentation are presented.

Near-Band IR Small Scene Star Image Segmentation Based on Partial Histogram Grey Level

2014 ◽  
Vol 496-500 ◽  
pp. 1834-1839
Author(s):  
Zhe Wang ◽  
Zhe Yan ◽  
Wei Tan
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Real Time ◽  
Grey Level ◽  
Star Image ◽  
Star Images

The near-band IR star images segmentation and recognition is key technique in day time star navigation. Due to the scene of near-band IR star imaging relative small and stellar with high star grade are limited. Pertinence and dynamic grey level threshold is necessary for image processing arithmetic. In order to enhance near-band IR star images segmentation and recognition in real-time, this paper present the process of partial histogram grey level threshold and improve for actually near-band IR star images with scene of no more than 1.5°×1.5°. It can reduce the calculation of near-band IR star images with adjustable threshold. And get rid of disturbance of small imaging square stars and noise points.

Image Segmentation Using Rough Set Theory

2014 ◽  
Vol 1 (2) ◽  
pp. 62-74 ◽  
Author(s):  
Payel Roy ◽  
Srijan Goswami ◽  
Sayan Chakraborty ◽  
Ahmad Taher Azar ◽  
Nilanjan Dey
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Gray Level ◽  
Rule Generation ◽  
Local Minima ◽  
Fuzzy Correlation ◽  
Segmentation Methods

In the domain of image processing, image segmentation has become one of the key application that is involved in most of the image based operations. Image segmentation refers to the process of breaking or partitioning any image. Although, like several image processing operations, image segmentation also faces some problems and issues when segmenting process becomes much more complicated. Previously lot of work has proved that Rough-set theory can be a useful method to overcome such complications during image segmentation. The Rough-set theory helps in very fast convergence and in avoiding local minima problem, thereby enhancing the performance of the EM, better result can be achieved. During rough-set-theoretic rule generation, each band is individualized by using the fuzzy-correlation-based gray-level thresholding. Therefore, use of Rough-set in image segmentation can be very useful. In this paper, a summary of all previous Rough-set based image segmentation methods are described in detail and also categorized accordingly. Rough-set based image segmentation provides a stable and better framework for image segmentation.

AN OBJECT LOCATION SPECIFYING METHODOLOGY USING ONE CAMERA

2001 ◽  
Vol 09 (06) ◽  
pp. 685-702 ◽  
Author(s):  
WIRAT KESRARAT ◽  
THOTSAPON SORTRAKUL
Keyword(s):  
Image Processing ◽  
Image Plane ◽  
Video Camera ◽  
Object Location ◽  
Target Point ◽  
Target Area ◽  
Digital Video Camera ◽  
The World ◽  
The Difference ◽  
Point Center

This research proposed a methodology for specifying the location of an object with image processing. The objectives of this methodology are to capture the target area, and specify the location of the object by using image. In order to locate the dropping object on the image plane efficiently, consecutive images are analyzed and a threshold operation is proposed. Because the accuracy of the dropping objects location on the difference of consecutive images image plane is usually influenced by noise. Moreover, transformation unit is adopted to map the XY coordinate on image plane into the world coordinate for an accuracy of the dropping objects position. After we get the actual XY coordinate of the dropping object, we can find the distance from the target point (center) and clock direction of the dropping object related to the center also. In addition, by using one digital video camera set on the tower and pan to capture the image on the target area to detect the dropping object from the air to the ground. It made the proposed methodology provide easier portability to detect the dropping object in any area.

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