scholarly journals De pixel a voxel. Arquitectura entre identidad líquida y imagen física.

2019 ◽  
Vol 24 (36) ◽  
pp. 232
Author(s):  
Emanuela Lanzara
Keyword(s):  
Picture Element

<p>La actividad de investigación dedicada a la gestión y comunicación de formas arquitectónicas complejas favorece al voxel, volumetric pixel (picture element), como principal herramienta tecnológica que caracteriza la producción física y virtual de la segunda era digital. El proceso de voxelización distorsiona y fragmenta la imagen fluida y abstracta de la arquitectura líquida que caracteriza la primera era digital. Desde la arquitectura calculada a la construida, el proceso de voxelización manipula la imagen del proyecto para encontrar una solución tecnológicamente más accesible. Las imágenes de los experimentos artísticos y arquitectónicos más recientes, resultado de herramientas digitales generativas y computacionales consolidadas, testifican un enfoque de diseño caracterizado por un tecnicismo avanzado.Por lo tanto, esta comunicación nos invita a reflexionar sobre el impacto visual de la necesidad actual de inmediatez resolutiva y optimización económica constructiva, expresiones de la sociedad contemporánea, que son determinantes en la figuración del concepto de complejidad arquitectónica.</p>

Radiation Damage, Contrast and Statistics in High Resolution Biological Electron Microscopy

1970 ◽  
Vol 28 ◽  
pp. 260-261
Author(s):  
Robert M. Glaeser
Keyword(s):  
High Resolution ◽  
Particle Flux ◽  
Unit Area ◽  
Signal To Noise ◽  
Resolution Image ◽  
High Resolution Image ◽  
Pass Through ◽  
Counting Statistics ◽  
The Relationship ◽  
Picture Element

It is well known that a large flux of electrons must pass through a specimen in order to obtain a high resolution image while a smaller particle flux is satisfactory for a low resolution image. The minimum particle flux that is required depends upon the contrast in the image and the signal-to-noise (S/N) ratio at which the data are considered acceptable. For a given S/N associated with statistical fluxtuations, the relationship between contrast and “counting statistics” is s131_eqn1, where C = contrast; r2 is the area of a picture element corresponding to the resolution, r; N is the number of electrons incident per unit area of the specimen; f is the fraction of electrons that contribute to formation of the image, relative to the total number of electrons incident upon the object.

scholarly journals Techniques for Image Segmentation: A Critical Review

2021 ◽  
Vol 9 (3) ◽  
pp. 1-4
Author(s):  
Harshita Mishra ◽  
Anuradha Misra
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Image Segmentation ◽  
Digital Image ◽  
Electronic Devices ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Mathematical Algorithms ◽  
Picture Element

In today’s world there is requirement of some techniques or methods that will be helpful for retrieval of the information from the images. Information those are important for finding solution to the problems in the present time are needed. In this review we will study the processing involved in the digitalization of the image. The set or proper array of the pixels that is also called as picture element is known as image. The positioning of these pixels is in matrix which is formed in columns and rows. The image undergoes the process of digitalization by which a digital image is formed. This process of digitalization is called digital image processing of the image (D.I.P). Electronic devices as such computers are used for the processing of the image into digital image. There are various techniques that are used for image segmentation process. In this review we will also try to understand the involvement of data mining for the extraction of the information from the image. The process of the identifying patterns in the large stored data with the help of statistic and mathematical algorithms is data mining. The pixel wise classification of the image segmentation uses data mining technique.

scholarly journals Color detection in RGB-modeled images using MAT LAB

2018 ◽  
Vol 7 (2.31) ◽  
pp. 29 ◽  
Author(s):  
P Sudharshan Duth ◽  
M Mary Deepa
Keyword(s):  
Median Filter ◽  
Research Work ◽  
Detection Algorithm ◽  
Color Model ◽  
Connected Component ◽  
Black And White ◽  
Security Applications ◽  
Color Detection ◽  
Connected Area ◽  
Picture Element

This research work introduces a method of using color thresholds to identify two-dimensional images in MATLAB using the RGB Color model to recognize the Color preferred by the user in the picture. Methodologies including image color detection convert a 3-D RGB Image into a Gray-scale Image, at that point subtract the two pictures to obtain a 2-D black-and-white picture, filtering the noise picture elements using a median filter, detecting with a connected component mark digital pictures in the connected area and utilize the bounding box and its properties to calculate the metric for every marking area. In addition, the shade of the picture element is identified by examining the RGB value of every picture element present in the picture. Color Detection algorithm is executed utilizing the MATLAB  Picture handling Toolkit. The result of this implementation can be used in as a bit of security applications such as spy robots, object tracking, Color-based object isolation, and intrusion detection. 

scholarly journals LEARNING INVARIANT COLOUR FEATURES FOR PERSON REIDENTIFICATION

2020 ◽  
Vol 5 (5) ◽  
pp. 65-70 ◽  
Author(s):  
D.D. Chaudhary ◽  
Nikita Jadhav
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Body Part ◽  
Human Face ◽  
Direct Transformation ◽  
Colour Feature ◽  
Colour Features ◽  
Picture Element

In this examination we have proposed Learning invariant shading highlights for individual recognizable proof utilizing human face for high proficient flag exchange framework applications. In this paper, we have a tendency to propose an information driven approach for taking in shading designs from pixels examined from pictures crosswise over to camera sees. The instinct behind this work is that, even assuming picture element values of same colour would wander across views, they thought to be encoded with indistinguishable qualities. We tend to model colour feature age as a learning drawback by together learning a direct transformation and a wordbook to write in code picture component esteems. We have a tendency to conjointly dissect entirely unexpected estimating invariant shading zones. Abuse shading in light of the fact that the exclusively prompt, we tend to contrast our approach and all the estimating invariant shading zones and show better execution over every one of them. Overwhelming pivoted nearby double example is anticipated yields higher execution. This paper proposes a totally exceptional strategy of characterizing the outer body part misuse Convolutional Neural Network.

scholarly journals Combined with Local Neighborhood Characteristics and Remote Sensing Image Fusion Method of C-BEMD

2021 ◽  
Vol 15 ◽  
pp. 936-944
Author(s):  
Peili Fan
Keyword(s):  
Remote Sensing ◽  
Image Fusion ◽  
Remote Sensing Image ◽  
Neighborhood Characteristics ◽  
Feature Points ◽  
Matching Method ◽  
Local Neighborhood ◽  
Remote Sensing Image Fusion ◽  
Image Fusion Method ◽  
Picture Element

For the sake of ameliorate the high resolution recognition capacity building remote sensing images, a remote sensing image fusion method based on local neighborhood characteristics and C-BEMD is advanced. The building remote sensing image acquisition model and the building remote sensing image picture element edge feature detection model are designed. The wavelet multi-scale denoising method is used to suppress the fuzzy spread of picture element feature points between image residual units, extract the geometric feature points of image sequence, and process the building remote sensing image block by block. The global residual learning and message fusion of building remote sensing image are implemented. The local neighborhood feature matching method is used to reconstruct the building remote sensing image region. Combined with the C-BEMD empirical mode decomposition method, the building remote sensing image fusion and feature point matching in affine region are implemented, and the block image template matching method is used to realize the automatic fusion and recognition of building remote sensing image. Simulation results show that this method has high precision in constructing remote sensing image fusion and good positioning performance in constructing remote sensing image feature points.

Optimized Hybrid DCT-SVD Computation over Extremely Large Images

2021 ◽  
Vol 13 (2) ◽  
pp. 56-61
Author(s):  
Iwan Setiawan ◽  
Akbari Indra Basuki ◽  
Didi Rosiyadi
Keyword(s):  
High Performance ◽  
Singular Values ◽  
Block Matrix ◽  
Original Image ◽  
Discrete Cosine Transformation ◽  
Computational Burden ◽  
Cosine Transformation ◽  
Single Matrix ◽  
Performance Computing ◽  
Picture Element

High performance computing (HPC) is required for image processing especially for picture element (pixel) with huge size. To avoid dependence to HPC equipment which is very expensive to be provided, the soft approach has been performed in this work. Actually, both hard and soft methods offer similar goal which are to reach time computation as short as possible. The discrete cosine transformation (DCT) and singular values decomposition (SVD) are conventionally performed to original image by consider it as a single matrix. This will result in computational burden for images with huge pixel. To overcome this problem, the second order matrix has been performed as block matrix to be applied on the original image which delivers the DCT-SVD hybrid formula. Hybrid here means the only required parameter shown in formula is intensity of the original pixel as the DCT and SVD formula has been merged in derivation. Result shows that when using Lena as original image, time computation of the singular values using the hybrid formula is almost two seconds faster than the conventional. Instead of pushing hard to provide the equipment, it is possible to overcome computational problem due to the size simply by using the proposed formula.

Physics and modeling of an ac-plasma display picture element

1994 ◽  
Author(s):  
William W. Williamson, Jr. ◽  
P. J. Drallos ◽  
V. P. Nagorny
Keyword(s):  
Plasma Display ◽  
Picture Element
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