scholarly journals RGB Colors and Ecological Optics

2021 ◽  
Vol 3 ◽  
Author(s):  
Jan Koenderink ◽  
Andrea van Doorn ◽  
Karl Gegenfurtner
Keyword(s):  
Spectral Reflectance ◽  
Color Space ◽  
Convex Geometry ◽  
Image Data ◽  
Coarse Grained ◽  
Ecological Context ◽  
Object Properties ◽  
Natural Geometry ◽  
Formal Framework ◽  
Source Variation

Object color space is highly structured due to optical constraints (radiant power non-negative, reflectance factors between zero and unity) and ecological context (daylight illuminant). In this setting trichromacy induces a natural geometry through a unique spectral tripartition. Different from null-context colorimetry, one gains two desirable relations: The colorimetric coordinates are coarse-grained spectral reflectance factors and there is a direct link to color experiences, since RGB–coordinates provide ostensive definitions. The framework allows one to deal with subtractive color mixture, source variation, effects of metamerism and relations between scenes and image data in a unified, structured manner. In ecological contexts, colors are effectively object properties. The formal framework is linear algebra and convex geometry. Applications in human biology, computer graphics, design, etc., are immediate.

Pencarian File Gambar Berdasarkan Dominasi Warna

2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Vina Chovan Epifania ◽  
Eko Sediyono
Keyword(s):  
Digital Image ◽  
Retrieval System ◽  
Color Image ◽  
Color Space ◽  
Image Data ◽  
Image Search ◽  
Test Results ◽  
Application Form ◽  
Space Model ◽  
Image File

Abstract. Image File Searching Based on Color Domination. One characteristic of an image that can be used in image searching process is the composition of the colors. Color is a trait that is easily seen by man in the picture. The use of color as a searching parameter can provide a solution in an easier searching for images stored in computer memory. Color images have RGB values that can be computed and converted into HSL color space model. Use of HSL images model is very easy because it can be calculated using a percent, so that in each pixel of the image can be grouped and named, this can give a dominant values of the colors contained in one image. By obtaining these values, the image search can be done quickly just by using these values to a retrieval system image file. This article discusses the use of the HSL color space model to facilitate the searching for a digital image in the digital image data warehouse. From the test results of the application form, a searching is faster by using the colors specified by the user. Obstacles encountered were still searching with a choice of 15 basic colors available, with a limit of 33% dominance of the color image search was not found. This is due to the dominant color in each image has the most dominant value below 33%.   Keywords: RGB, HSL, image searching Abstrak. Salah satu ciri gambar yang dapat dipergunakan dalam proses pencarian gambar adalah komposisi warna. Warna adalah ciri yang mudah dilihat oleh manusia dalam citra gambar. Penggunaan warna sebagai parameter pencarian dapat memberikan solusi dalam memudahkan pencarian gambar yang tersimpan dalam memori komputer. Warna gambar memiliki nilai RGB yang dapat dihitung dan dikonversi ke dalam model HSL color space. Penggunaan model gambar HSL sangat mudah karena dapat dihitung dengan menggunakan persen, sehingga dalam setiap piksel gambar dapat dikelompokan dan diberi nama, hal ini dapat memberikan suatu nilai dominan dari warna yang terdapat dalam satu gambar. Dengan diperolehnya nilai tersebut, pencarian gambar dapat dilakukan dengan cepat hanya dengan menggunakan nilai tersebut pada sistem pencarian file gambar. Artikel ini membahas tentang penggunaan model HSL color space untuk mempermudah pencarian suatu gambar digital didalam gudang data gambar digital. Dari hasil uji aplikasi yang sudah dibuat, diperoleh pencarian yang lebih cepat dengan menggunakan pilihan warna yang ditentukan sendiri oleh pengguna. Kendala yang masih dijumpai adalah pencarian dengan pilihan 15 warna dasar yang tersedia, dengan batas dominasi warna 33% tidak ditemukan gambar yang dicari. Hal ini disebabkan warna dominan disetiap gambar kebanyakan memiliki nilai dominan di bawah 33%. Kata Kunci: RGB, HSL, pencarian gambar

scholarly journals IMPLEMENTASI JARINGAN SYARAF TIRUAN (JST) DAN PENGOLAHAN CITRA UNTUK KlASIFIKASI KEMATANGAN TBS KELAPA SAWIT

2018 ◽  
Vol 15 (1) ◽  
pp. 36
Author(s):  
Minarni Minarni ◽  
Roni Salumbae ◽  
Zilhan Hasbi
Keyword(s):  
Oil Palm ◽  
Color Space ◽  
Human Perception ◽  
Image Data ◽  
Cost Effective ◽  
Fresh Fruit ◽  
Human Vision ◽  
Palm Fruit ◽  
Color Parameters ◽  
Fresh Fruit Bunches

The clasification of ripeness stages of oil palm fresh fruit bunches (FFBs) can be done using color parameters. These parameters are often evaluated by human vision, whose degree of accuracy is subjective which can cause doubt in judgement. Automatic clasifications offreshfruit bunches (FFBs) based on color parameters can be done using computer vision. This method is known as a nondestructive, fast and cost effective method. In this research, a MATLAB computer program has been developed which consists of RGB and HSV GUI which is used to record, display, and process FFB image data. The backpropagation artificial neural network (ANN) program is also developed which is used to classify the oil palm fruit fresh bunches (FFBs). Samples are fresh fruit bunches (FFB) of oil palm varieties of Tenera which comprise of Topaz, Marihat, and Lonsum clones. Each clone composed of three levels of ripeness represented by five fractions. The measurements were started by capturing images of oil palm, extracting RGB and HSV values, calculating weight values from the image database to make anANN program, preparing grid programs for oil palm FFBs, and comparing grading levels of oil palm FFBs using program and by harvester. This program successfully classified oil palm (FFBs) into three categories of ripeness which are unripe (F0 and F1), ripe (F1 and F1) and over ripe (F4 and F5). The RGB and HSV programs successfully classified 79 out of 216 FFBs or 36.57% and 106 out of 216 TBS or 49.07%. Respectively the HSV program is better than RGB program because the representation of HSV color space are more understood by human perception hence can be used in calibration and color comparison.

Digital Camera Color Resolution Test Based on Knife-Edge Method

2013 ◽  
Vol 469 ◽  
pp. 246-250 ◽  
Author(s):  
Yue Hong Song ◽  
Jun Qing Xu
Keyword(s):  
Transfer Function ◽  
Modulation Transfer Function ◽  
Color Space ◽  
Digital Camera ◽  
Image Data ◽  
Knife Edge ◽  
Modulation Transfer ◽  
Lab Color Space ◽  
Edge Method

A resolution test-chart for digital camera has been developed based on the knife-edge method. RAW images have been acquired with NikonD7000 DSLR and Canon G12. Set the device color-space to Adobe RGB, and convert the image data to LAB color-space using Adobe RGB profile. The Fourier transfer of the LAB tonal value were computed under MATLAB environment. Then by analyzing the L/C/H (Derived from LAB space ) and the Modulation Transfer Function (MTF), we can get the resolution characteristics of the Nikon D7000 and Canon G12 camera.

scholarly journals VAE-SNE: a deep generative model for simultaneous dimensionality reduction and clustering

2020 ◽  
Author(s):  
Jacob M. Graving ◽  
Iain D. Couzin
Keyword(s):  
Dimensionality Reduction ◽  
Image Data ◽  
Stereotyped Behavior ◽  
General Purpose ◽  
Coarse Grained ◽  
High Dimensional ◽  
Nonlinear Dimensionality Reduction ◽  
Multi Scale ◽  
Variational Autoencoder ◽  
Real World Datasets

AbstractScientific datasets are growing rapidly in scale and complexity. Consequently, the task of understanding these data to answer scientific questions increasingly requires the use of compression algorithms that reduce dimensionality by combining correlated features and cluster similar observations to summarize large datasets. Here we introduce a method for both dimension reduction and clustering called VAE-SNE (variational autoencoder stochastic neighbor embedding). Our model combines elements from deep learning, probabilistic inference, and manifold learning to produce interpretable compressed representations while also readily scaling to tens-of-millions of observations. Unlike existing methods, VAE-SNE simultaneously compresses high-dimensional data and automatically learns a distribution of clusters within the data — without the need to manually select the number of clusters. This naturally creates a multi-scale representation, which makes it straightforward to generate coarse-grained descriptions for large subsets of related observations and select specific regions of interest for further analysis. VAE-SNE can also quickly and easily embed new samples, detect outliers, and can be optimized with small batches of data, which makes it possible to compress datasets that are otherwise too large to fit into memory. We evaluate VAE-SNE as a general purpose method for dimensionality reduction by applying it to multiple real-world datasets and by comparing its performance with existing methods for dimensionality reduction. We find that VAE-SNE produces high-quality compressed representations with results that are on par with existing nonlinear dimensionality reduction algorithms. As a practical example, we demonstrate how the cluster distribution learned by VAE-SNE can be used for unsupervised action recognition to detect and classify repeated motifs of stereotyped behavior in high-dimensional timeseries data. Finally, we also introduce variants of VAE-SNE for embedding data in polar (spherical) coordinates and for embedding image data from raw pixels. VAE-SNE is a robust, feature-rich, and scalable method with broad applicability to a range of datasets in the life sciences and beyond.

Computational Model of DIC Microscopy for Reconstructing 3-D Specimens

2000 ◽  
Vol 39 (02) ◽  
pp. 105-109 ◽  
Author(s):  
F. Lanni ◽  
T. Kanade ◽  
F. Kagalwala
Keyword(s):  
Three Dimensional ◽  
Image Data ◽  
Visualization Tool ◽  
Differential Interference Contrast ◽  
Real Image ◽  
Biological Cells ◽  
Virtual Objects ◽  
Object Properties ◽  
Simulated Images ◽  
Dic Microscopy

Abstract:Differential Interference Contrast (DIC) microscopy is a powerful visualization tool used to study live biological cells. Its use, however, has been limited to qualitative observations. The inherent non-linear relation between the object properties and the image intensity makes quantitative analysis difficult. As a first step towards measuring optical properties of objects from DIC images, we develop a model for the image formation process using methods consistent with energy conservation laws. We verify our model by comparing real image data of manufactured specimens to simulated images of virtual objects. As the next step, we plan to use this model to reconstruct the three-dimensional properties of unknown specimens.

scholarly journals Coarse-graining ecological dynamics in the face of unknown microscopic details

2021 ◽  
Author(s):  
Jacob Moran ◽  
MIKHAIL TIKHONOV
Keyword(s):  
Resource Competition ◽  
Evolutionary Model ◽  
Coarse Graining ◽  
Species Level ◽  
Coarse Grained ◽  
Ecological Context ◽  
Evolutionary Context ◽  
The Face ◽  
Natural Diversity ◽  
Functionally Diverse

Any description of an ecosystem necessarily ignores some details of the underlying diversity. What predictions can be robust to such omissions? Here, building on the theoretical framework of resource competition, we introduce an eco-evolutionary model that allows organisms to be described at an arbitrary, potentially infinite, level of detail, enabling us to formally study the hierarchy of possible coarse-grained descriptions. Within this model, we demonstrate that a coarse-graining scheme may enable ecological predictions despite grouping together functionally diverse strains. However, this requires two conditions: the strains we study must remain in a diverse ecological context, and this diversity must be derived from a sufficiently similar environment. Our model suggests that studying individual strains of a species away from their natural eco-evolutionary context may eliminate the very reasons that make a species-level characterization an adequate coarse-graining of the natural diversity.

scholarly journals Study of contrast between satellite image data and ground data

2011 ◽  
Vol 8 (3) ◽  
pp. 717-722
Author(s):  
Baghdad Science Journal
Keyword(s):  
River Water ◽  
Satellite Data ◽  
Spectral Reflectance ◽  
Satellite Image ◽  
Image Data ◽  
Sources Of Pollution ◽  
Tigris River ◽  
Satellite Image Data ◽  
The Difference ◽  
Remote Sensing Techniques

Spot panchromatic satellite image had been employed to study and know the difference Between ground and satellite data( DN ,its values varies from 0-255) where it is necessary to convert these DN values to absolute radiance values through special equations ,later it converted to spectral reflectance values .In this study a monitoring of the environmental effect resulted from throwing the sewage drainages pollutants (industrial and home) into the Tigris river water in Mosul, was achieved, which have an effect mostly on physical characters specially color and turbidity which lead to the variation in Spectral Reflectance of the river water ,and it could be detected by using many remote sensing techniques. The contaminated areas within the water of the river which represents the difference in the reflectance values were isolated and signed, as well as the field estimations, which had been done by using spectrometer device, which gave an acceptable agreement with satellite data considering time difference between these estimations. satellite imagery analysis program ERDAS version 8.4 was used to determine the values of Spectral Reflectance in the satellite image. A geographic information systems through the ARC INFO has been used to draw photo map of the study area determined it specific sites of measuring the Reflectance, which represent areas that are near the sources of pollution and the other various regions along the river.

Image Compression and Reconstruction based on Fuzzy Relation and Soft Computing Technology

2004 ◽  
Vol 8 (1) ◽  
pp. 72-80
Author(s):  
Kaoru Hirota ◽  
◽  
Hajime Nobuhara ◽  
Kazuhiko Kawamoto ◽  
Shin’ichi Yoshida
Keyword(s):  
Image Reconstruction ◽  
Image Compression ◽  
Soft Computing ◽  
Signal To Noise Ratio ◽  
Color Space ◽  
Image Data ◽  
Compression Rate ◽  
Reconstruction Method ◽  
Image Reconstruction Method ◽  
Fuzzy Relational Equation

A fast image reconstruction method for Image Compression method based on Fuzzy relational equation (ICF) and soft computing is proposed. In experiments using 20 images (Standard Image DataBAse), the decrease in image reconstruction time to 1/132.02 and 1/382.29 are obtained when the compression rate is 0.0156 and 0.0625, respectively, and the proposed method outperforms the conventional one in the Peak Signal to Noise Ratio (PSNR). ICF using nonuniform coders over YUV color space is proposed in order to achieve effective compression. Linear quantization of compressed image data is introduced in order to improve the compression rate. Through experiments using 100 typical images (Corel Gallery, Arizona Directory), PSNR increases at 7.9-14.1% compared with the conventional method under the condition that compression rates are 0.0234-0.0938.

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