To see the other types of publications on this topic, follow the link: Colorspaces.
Journal articles on the topic 'Colorspaces'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 34 journal articles for your research on the topic 'Colorspaces.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Nardini, Pascal, Min Chen, Michael Böttinger, Gerik Scheuermann, and Roxana Bujack. "Automatic Improvement of Continuous Colormaps in Euclidean Colorspaces." Computer Graphics Forum 40, no. 3 (2021): 361–73. http://dx.doi.org/10.1111/cgf.14313.
Full text
2
Ennis, Robert, Florian Schiller, Matteo Toscani, and Karl Gegenfurtner. "Hyperspectral database of fruits and vegetables (v1.1) - Calibrated data, colorspaces, masks, and MATLAB code." Journal of the Optical Society of America A 35, no. 4 (2018): 11. https://doi.org/10.5281/zenodo.2611806.
Full textAbstract:
<em><strong>***Please note that we added a small change in the "readCompressedDAT.m" MATLAB function since some users experienced a bit of confusion. There were no errors in the spectral data, but the change makes it clearer to users which wavelengths correspond to the elements of the uncompressed hyperspectral data matrix. This should hopefully be much clearer now. We updated the version number to reflect this. All other data and files remain the same, so there is no need to use the older version.***</strong></em> We have built a hyperspectral database of 42 fruits and vegetables and this is a permanent online repository for public access to the calibrated data and its representation in different colorspaces (RGB, LAB, LUV, DKL, LMS, XYZ, xyY). For more complete details, you are advised to check the official webpage for the database and to read the journal article. The database and some accompanying documentation for the Matlab functions can be found at: http://www.allpsych.uni-giessen.de/GHIFVD (pronounced “gift”) The journal article that accompanies this repository is published in JOSA A: Ennis R., Schiller F., Toscani M., Gegenfurtner, K. (2018) "Hyperspectral database of fruits and vegetables", JOSA A, Vol. 35, No. 4., pp. B256-B266 and can be found here: https://www.osapublishing.org/josaa/abstract.cfm?uri=josaa-35-4-B256 Included here is the MATLAB code for opening the PCA compressed images. Both the outside (skin) and inside of the objects were imaged. We used a Specim VNIR HS-CL-30-V8E-OEM mirror-scanning hyperspectral camera and took pictures at a spatial resolution of ∼57 px/deg by 800 pixels at a wavelength resolution of ∼1.12 nm. A stable, broadband illuminant was used. Images and software are freely available on our webserver (http://www.allpsych.uni-giessen.de/GHIFVD; pronounced “gift”). We performed two kinds of analyses on these images. First, when comparing the insides and outsides of the objects, we observed that the insides were lighter than the skins, and that the hues of the insides and skins were significantly correlated (circular correlation 0.638). Second, we compared the color distribution within each object to corresponding human color discrimination thresholds. We found a significant correlation (0.75) between the orientation of ellipses fit to the chromaticity distributions of our fruits and vegetables with the orientations of interpolated MacAdam discrimination ellipses. This indicates a relationship between sensory processing and the characteristic of environmental objects. If for some reason you need access to the original RAW data from the camera sensor, that can be found here: https://doi.org/10.5281/zenodo.1186372 However, it is only posted for preservation purposes. You are not expected to work with the RAW data.
3
da Silva, C. C. V., K. Nogueira, H. N. Oliveira, and J. A. dos Santos. "TOWARDS OPEN-SET SEMANTIC SEGMENTATION OF AERIAL IMAGES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-3/W2-2020 (October 29, 2020): 19–24. http://dx.doi.org/10.5194/isprs-annals-iv-3-w2-2020-19-2020.
Full textAbstract:
Abstract. Classical and more recently deep computer vision methods are optimized for visible spectrum images, commonly encoded in grayscale or RGB colorspaces acquired from smartphones or cameras. A more uncommon source of images exploited in the remote sensing field are satellite and aerial images. However the development of pattern recognition approaches for these data is relatively recent, mainly due to the limited availability of this type of images, as until recently they were used exclusively for military purposes. Access to aerial imagery, including spectral information, has been increasing mainly due to the low cost of drones, cheapening of imaging satellite launch costs, and novel public datasets. Usually remote sensing applications employ computer vision techniques strictly modeled for classification tasks in closed set scenarios. However, real-world tasks rarely fit into closed set contexts, frequently presenting previously unknown classes, characterizing them as open set scenarios. Focusing on this problem, this is the first paper to study and develop semantic segmentation techniques for open set scenarios applied to remote sensing images. The main contributions of this paper are: 1) a discussion of related works in open set semantic segmentation, showing evidence that these techniques can be adapted for open set remote sensing tasks; 2) the development and evaluation of a novel approach for open set semantic segmentation. Our method yielded competitive results when compared to closed set methods for the same dataset.
4
Naik Dharavath, Haji. "Aiming for G7 Master Compliance through a Color Managed Workflow: Comparison of Compliance with Amplitude Modulated (AM) vs. Frequency Modulated (FM) Screening of Multicolor Digital Printing." Journal of graphic engineering and design 12, no. 2 (2021): 5–19. http://dx.doi.org/10.24867/jged-2021-2-005.
Full textAbstract:
The purpose of this research was to determine the influence of screening technologies (AM vs. FM) in the color reproduction aimed at the G7 master compliance. The quality of digital color printing is determined by these influential factors: screening method applied, type of printing process, ink (dry-toner or liquid-toner), printer resolution and the substrate (paper). For this research, only the color printing attributes such as the G7 colors hue and chroma, gray balance, and overall color deviations were analyzed to examine the significant differences that exist between the two screening technologies (AM vs. FM). These are the color attributes which are monitored and managed for quality accuracy during the printing. Printed colorimetry of each screening from the experiment was compared against G7 ColorSpace GRACoL 2013 (CGATS21-2-CRPC6) in CIE L* a* b* space using an IDEAlliance (Chromix/Hutch Color) Curve 4.2.4 application interface with an X-Rite spectrophotometer with an i1iO table. The measured data of each screening were run through this application (Curve 4.2.4). The data of each screening were analyzed by using the Verify Tool of the Curve 4.2.4 application to determine the pass/fail of G7 master compliance levels using G7 ColorSpace tolerances (G7 Grayscale, G7 Targeted, and G7 Colorspace). Analyzed data from the experiment revealed that the printed colorimetric values of each screening (G7 Grayscale, G7 Targeted, and G7 Colorspace) are in match (aligned) with the G7 master compliance levels (reference/target) colorimetric values (G7 Grayscale, G7 Targeted, and G7 Colorspace). Therefore, the press run was passed by the Curve 4 application for both screening technologies tested.
5
Naik Dharavath, Haji. "Aiming for G7 Master Compliance through a Color Managed Digital Printing Workflow (CMDPW): Comparison of Compliance with Output Device Profile (ODP) vs. Device Link Profile (DLP)." Journal of graphic engineering and design 12, no. 1 (2021): 23–35. http://dx.doi.org/10.24867/jged-2021-1-023.
Full textAbstract:
The purpose of this applied research was to determine the influence of device link profile (DLP) in the color reproduction aimed at the G7 master compliance. The quality of digital color printing is determined by these influential factors: screening method applied, type of printing process, ink (dry-toner or liquid-toner), printer resolution and the substrate (paper). For this research, only the color printing attributes such as the G7 colors hue and chroma, gray balance, and overall color deviations were analyzed to examine the significant differences that exist between the two output profiles [Output Device Profile (ODP) vs Device Link Profile (DLP)]. These are the color attributes which are monitored and managed for quality accuracy during the printing. Printed colorimetry of each profile from the experiment was compared against G7 ColorSpace GRACoL 2013 (CGATS21-2-CRPC6) in CIE L* a* b* space using an IDEAlliance (Chromix/Hutch Color) Curve 4.2.4 application interface with an X-Rite spectrophotometer with an i1iO table. The measured data of each profile were run through this application (Curve 4.2.4). The data were analyzed by using the Verify Tool of the Curve 4.2.4 application to determine the pass/fail of G7 master compliance levels using G7 ColorSpace tolerances (G7 Grayscale, G7 Targeted, and G7 Colorspace). Analyzed data from the experiment revealed that the printed colorimetric values of each profile (G7 Grayscale, G7 Targeted, and G7 Colorspace) are in match (aligned) with the G7 master compliance levels (reference/target) colorimetric values (G7 Grayscale, G7 Targeted, and G7 Colorspace). Therefore, the press run was passed by the Curve 4 application for both the profiles used/tested towards aiming for G7 master compliance.
6
Yue, Lin, Hao Shen, Sen Wang, et al. "Exploring BCI Control in Smart Environments." ACM Transactions on Knowledge Discovery from Data 15, no. 5 (2021): 1–20. http://dx.doi.org/10.1145/3450449.
Full textAbstract:
The brain–computer interface (BCI) control technology that utilizes motor imagery to perform the desired action instead of manual operation will be widely used in smart environments. However, most of the research lacks robust feature representation of multi-channel EEG series, resulting in low intention recognition accuracy. This article proposes an EEG2Image based Denoised-ConvNets (called EID) to enhance feature representation of the intention recognition task. Specifically, we perform signal decomposition, slicing, and image mapping to decrease the noise from the irrelevant frequency bands. After that, we construct the Denoised-ConvNets structure to learn the colorspace and spatial variations of image objects without cropping new training images precisely. Toward further utilizing the color and spatial transformation layers, the colorspace and colored area of image objects have been enhanced and enlarged, respectively. In the multi-classification scenario, extensive experiments on publicly available EEG datasets confirm that the proposed method has better performance than state-of-the-art methods.
7
Maurya, Shweta, and Vishal Shrivastava. "An Improved Novel Steganographic Technique For RGB And YCbCr Colorspace." IOSR Journal of Computer Engineering 16, no. 2 (2014): 155–57. http://dx.doi.org/10.9790/0661-1629155157.
Full text
8
Fairley, Iain, Anouska Mendzil, Michael Togneri, and Dominic Reeve. "The Use of Unmanned Aerial Systems to Map Intertidal Sediment." Remote Sensing 10, no. 12 (2018): 1918. http://dx.doi.org/10.3390/rs10121918.
Full textAbstract:
This paper describes a new methodology to map intertidal sediment using a commercially available unmanned aerial system (UAS). A fixed-wing UAS was flown with both thermal and multispectral cameras over three study sites comprising of sandy and muddy areas. Thermal signatures of sediment type were not observable in the recorded data and therefore only the multispectral results were used in the sediment classification. The multispectral camera consisted of a Red–Green–Blue (RGB) camera and four multispectral sensors covering the green, red, red edge and near-infrared bands. Statistically significant correlations (>99%) were noted between the multispectral reflectance and both moisture content and median grain size. The best correlation against median grain size was found with the near-infrared band. Three classification methodologies were tested to split the intertidal area into sand and mud: k-means clustering, artificial neural networks, and the random forest approach. Classification methodologies were tested with nine input subsets of the available data channels, including transforming the RGB colorspace to the Hue–Saturation–Value (HSV) colorspace. The classification approach that gave the best performance, based on the j-index, was when an artificial neural network was utilized with near-infrared reflectance and HSV color as input data. Classification performance ranged from good to excellent, with values of Youden’s j-index ranging from 0.6 to 0.97 depending on flight date and site.
9
Stauffer, Reto, and Achim Zeileis. "colorspace: A Python Toolbox for Manipulating and Assessing Colors and Palettes." Journal of Open Source Software 9, no. 102 (2024): 7120. http://dx.doi.org/10.21105/joss.07120.
Full text
10
Kim, Young-Ju. "Multi-Mode Reconstruction of Subsampled Chrominance Information using Inter-Component Correlation in YCbCr Colorspace." Journal of the Korea Contents Association 8, no. 2 (2008): 74–82. http://dx.doi.org/10.5392/jkca.2008.8.2.074.
Full text
11
Edwards, Tory, and Michael J. Buono. "Urine color expressed in CIE L*a*b* colorspace during rapid changes in hydration status." Current Research in Physiology 5 (2022): 251–55. http://dx.doi.org/10.1016/j.crphys.2022.06.007.
Full text
12
Poynton, Charles, Robin Atkins, Jaclyn Pytlarz, and Dale Stolitzka. "18‐3: Computing Display Color Gamut Volume using Tetrahedra." SID Symposium Digest of Technical Papers 54, no. 1 (2023): 229–32. http://dx.doi.org/10.1002/sdtp.16532.
Full textAbstract:
We describes an approach to computation of display color gamut volume (sometimes called color volume). We describe two methods of partitioning color space into tetrahedra, and methods of aggregating the volume of tetrahedra. There some mathematical subtleties that need to be addressed to efficiently obtain the correct result. We discuss the constraints that guide a suitable choice of color space in which to perform the calculation. Our goal is to characterize the range of physical stimuli produced by a display. We want a perceptual measure of this volume, but we want the metric to cover the entire range of physical display stimuli, not just the range attained at a particular state of the viewer's adaptation. We comment on using CIE LAB or BT.2124 ITP5 as the underlying colorspace, and suggest that ITP is most useful.
13
Ahmad Fauzi, Aditya, and Adisuputra Adisuputra. "Implementation of ColorSpace, GrabCut, and Watershed Methods on Digital Image Segmentation of Coral and Fish Objects." Journal of Computer Networks, Architecture and High Performance Computing 5, no. 1 (2023): 87–99. http://dx.doi.org/10.47709/cnahpc.v5i1.2012.
Full textAbstract:
Poor coral reefs rising in eastern Indonesia. The colors have disappeared from the seafloor instead, bleached branches are visible. To recognize the differences between dead and healthy coral reefs, an identification system has been created using the image processing method. Object segmentation is a step in digital image processing to separate one object from another based on specific characteristics. In this study, coral objects with various colored backgrounds and things became a problem to separate, so this study aimed to separate these various colors. This research uses color space segmentation to visualize RGB and HSV colors, Grabcut segmentation to separate the largest corals, and watershed segmentation to separate dead corals. Therefore, from this study, the RGB and HSV color visualizations were clearly visible. From Grabcut segmentation, it is found that the largest fish is detected and can be displayed in the segmentation results. At the same time, the watershed segmentation displays dead coral taken by gray segmentation with otsu.
14
Gunawan, Wawan, and Nurul Latifah. "Mahalanobis Fuzzy C-Means Clustering with Spatial Information for Image Segmentation." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 17, no. 2 (2023): 139. http://dx.doi.org/10.22146/ijccs.81521.
Full textAbstract:
A fuzzy C-Means segmentation algorithm can be implemented in an image segmentationbased on the Mahalanobis distance; However, this method only needs to consider the colorspace situation, not the neighborhood system of the image. It was an effective edge detectionprocess unwell performed and generated less accuracy in segmentation results. In this article,we propose a new method for image segmentation with Mahalanobis fuzzy C-means Spatialinformation (MFCMS). The proposed method combines feature space and images of theinformation of the neighborhood (spatial information) to improve the accuracy of the result ofsegmentation on the image. The MFCMS consists of two steps, the histogram threshold modulefor the first step and the MFCMS module for the second step. The Histogram Threshold moduleis used to get the MFCMS initialization conditions for the cluster centroid and the number ofcentroids. Test results show that this method provides better segmentation performance thanclassification errors (ME) and relative foreground area errors (RAE) of 1.61 and 3.48,respectively.
15
Zhang, Genfang, Wenfu Zhang, Ronghui Ye, et al. "Analysis of Selective Breeding of Nacre Color in Two Strains ofHyriopsis cumingiiLea Based on the Cielab Colorspace." Journal of Shellfish Research 35, no. 1 (2016): 225–29. http://dx.doi.org/10.2983/035.035.0124.
Full text
16
Bogush, R. P., I. Yu Zakharova, and S. V. Ablameyko. "ALGORITHM FOR PERSON TRACKING ON VIDEO SEQUENCES USING FACE IDENTIFICATION FOR INDOOR SURVEILLANCE." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 193 (July 2020): 3–14. http://dx.doi.org/10.14489/vkit.2020.07.pp.003-014.
Full textAbstract:
This paper discusses the algorithmic framework for tracking people on indoor video. To improve tracking accuracy was used face identification algorithm to reduce errorr rate during complicated trajectory of persons in indoor environment. Object detection was performed with CNN Yolov3 that extract rectangular area as a result. Face detection task was resolved eith Cascade CNN MTCNN with following recognition using CNN MobileFaceNetwork. To form person features we used historgrams in HSV colorspave and CNN that includes 29 convolution layers followed by fully connected layer. The Hungarian algorithm was used as decision maker for allignment problem. Experiments were conducted on five videosequences with the variable number of people in it. The main characteristics of the developed algorithm are obtained which confirmed its effectiveness and the possibility of use for indoor video surveillance.
17
Bogush, R. P., I. Yu Zakharova, and S. V. Ablameyko. "ALGORITHM FOR PERSON TRACKING ON VIDEO SEQUENCES USING FACE IDENTIFICATION FOR INDOOR SURVEILLANCE." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 193 (July 2020): 3–14. http://dx.doi.org/10.14489/vkit.2020.07.pp.003-014.
Full textAbstract:
This paper discusses the algorithmic framework for tracking people on indoor video. To improve tracking accuracy was used face identification algorithm to reduce errorr rate during complicated trajectory of persons in indoor environment. Object detection was performed with CNN Yolov3 that extract rectangular area as a result. Face detection task was resolved eith Cascade CNN MTCNN with following recognition using CNN MobileFaceNetwork. To form person features we used historgrams in HSV colorspave and CNN that includes 29 convolution layers followed by fully connected layer. The Hungarian algorithm was used as decision maker for allignment problem. Experiments were conducted on five videosequences with the variable number of people in it. The main characteristics of the developed algorithm are obtained which confirmed its effectiveness and the possibility of use for indoor video surveillance.
18
Pirhonen, Jesse, Risto Ojala, Klaus Kivekäs, Jari Vepsäläinen, and Kari Tammi. "Brake Light Detection Algorithm for Predictive Braking." Applied Sciences 12, no. 6 (2022): 2804. http://dx.doi.org/10.3390/app12062804.
Full textAbstract:
There has recently been a rapid increase in the number of partially automated systems in passenger vehicles. This has necessitated a greater focus on the effect the systems have on the comfort and trust of passengers. One significant issue is the delayed detection of stationary or harshly braking vehicles. This paper proposes a novel brake light detection algorithm in order to improve ride comfort. The system uses a camera and YOLOv3 object detector to detect the bounding boxes of the vehicles ahead of the ego vehicle. The bounding boxes are preprocessed with L*a*b colorspace thresholding. Thereafter, the bounding boxes are resized to a 30 × 30 pixel resolution and fed into a random forest algorithm. The novel detection system was evaluated using a dataset collected in the Helsinki metropolitan area in varying conditions. Carried out experiments revealed that the new algorithm reaches a high accuracy of 81.8%. For comparison, using the random forest algorithm alone produced an accuracy of 73.4%, thus proving the value of the preprocessing stage. Furthermore, a range test was conducted. It was found that with a suitable camera, the algorithm can reliably detect lit brake lights even up to a distance of 150 m.
19
Simantiris, Georgios, and Costas Panagiotakis. "Unsupervised Color-Based Flood Segmentation in UAV Imagery." Remote Sensing 16, no. 12 (2024): 2126. http://dx.doi.org/10.3390/rs16122126.
Full textAbstract:
We propose a novel unsupervised semantic segmentation method for fast and accurate flood area detection utilizing color images acquired from unmanned aerial vehicles (UAVs). To the best of our knowledge, this is the first fully unsupervised method for flood area segmentation in color images captured by UAVs, without the need of pre-disaster images. The proposed framework addresses the problem of flood segmentation based on parameter-free calculated masks and unsupervised image analysis techniques. First, a fully unsupervised algorithm gradually excludes areas classified as non-flood, utilizing calculated masks over each component of the LAB colorspace, as well as using an RGB vegetation index and the detected edges of the original image. Unsupervised image analysis techniques, such as distance transform, are then applied, producing a probability map for the location of flooded areas. Finally, flood detection is obtained by applying hysteresis thresholding segmentation. The proposed method is tested and compared with variations and other supervised methods in two public datasets, consisting of 953 color images in total, yielding high-performance results, with 87.4% and 80.9% overall accuracy and F1-score, respectively. The results and computational efficiency of the proposed method show that it is suitable for onboard data execution and decision-making during UAV flights.
20
Dawwas, Rodan Hilmi. "TBC Bacteria Detection in Microscopic Image With Watershed Countur Method." [CEPAT] Journal of Computer Engineering: Progress, Application and Technology 1, no. 03 (2022): 28. http://dx.doi.org/10.25124/cepat.v1i03.5315.
Full textAbstract:
Tuberculosis (TB) is an infectious disease that can be detected using a sputum sample. TB cases in Indonesia have spread throughout the region; the highest cases are in West Java. This problem makes the government do some handling and prevention of TB disease. The Bandung City Health Office (DKKB) conducted a cross-test to diagnose TB using a sputum sample. So in this study, a TB bacteria detection system, namely Mycobacterium Tuberculosis (MTB), will be made in sputum samples and their number to diagnose TB. Detection and calculation of the number of MTB are done by processing the image on the sputum sample using the watershed contour detection method. In this study, sputum sample data were obtained from DKKB. The acquisition of microscopic images at each point of the field of view is carried out using an SLR camera connected directly to the microscope to replace the function of the ocular lens. In this study, the microscopic sputum sample images were classified into positive and negative using the watershed and colorspace methods and were tested on a total of 90 microscopic images. From the system testing results, the system accuracy level is 100%, and the system precision is 100% for the detection of TB diagnosis. The system processing time averaged 5.811 seconds for 90 images used.
21
Sarker, Ayesha, and Tony E. Grift. "Monitoring Postharvest Color Changes and Damage Progression of Cucumbers Using Machine Vision." Journal of Food Research 12, no. 2 (2023): 37. http://dx.doi.org/10.5539/jfr.v12n2p37.
Full textAbstract:
To monitor cucumbers&#39; external quality, such as color changes or the presence of any damage during storage, a machine vision system was used. Red, Green, Blue (RGB) images were acquired in a &quot;soft box,&quot; which provided a highly diffused lighting scene for observing visual changes such as color and appearance in the skin of cucumber. The RGB images were transformed into L*, a*,b*, and HSV spaces. Histograms for each channel in each color space were evaluated for image segmentation, and the blue (B) channel in the RGB color space was found superior in terms of measuring damage progression. Damage progression plots (DPP) were made from accumulated grayscale images in each of the color channels and to observe variation over time, absolute differential damage progression (ADDP) plots were generated. Overall, the order of channel utility was [B], [R, G, V], and [H, S, L*, a*, b*]. To assess which channel, in which colorspace, was most sensitive, i.e., could capture most of the information regarding day-to-day color changes, a principal component analysis (PCA) was performed. The PCA showed that all individual components in the RGB color space were suitable for obtaining information about the external changes of cucumber. Based on the results, the machine vision approach is recommended as a non-destructive technique for monitoring the external quality of stored fresh produce.
22
Ahmad, Ijaz, Wooyeol Choi, and Seokjoo Shin. "Comprehensive Analysis of Compressible Perceptual Encryption Methods—Compression and Encryption Perspectives." Sensors 23, no. 8 (2023): 4057. http://dx.doi.org/10.3390/s23084057.
Full textAbstract:
Perceptual encryption (PE) hides the identifiable information of an image in such a way that its intrinsic characteristics remain intact. This recognizable perceptual quality can be used to enable computation in the encryption domain. A class of PE algorithms based on block-level processing has recently gained popularity for their ability to generate JPEG-compressible cipher images. A tradeoff in these methods, however, is between the security efficiency and compression savings due to the chosen block size. Several methods (such as the processing of each color component independently, image representation, and sub-block-level processing) have been proposed to effectively manage this tradeoff. The current study adapts these assorted practices into a uniform framework to provide a fair comparison of their results. Specifically, their compression quality is investigated under various design parameters, such as the choice of colorspace, image representation, chroma subsampling, quantization tables, and block size. Our analyses have shown that at best the PE methods introduce a decrease of 6% and 3% in the JPEG compression performance with and without chroma subsampling, respectively. Additionally, their encryption quality is quantified in terms of several statistical analyses. The simulation results show that block-based PE methods exhibit several favorable properties for the encryption-then-compression schemes. Nonetheless, to avoid any pitfalls, their principal design should be carefully considered in the context of the applications for which we outlined possible future research directions.
23
A., Al. Mamun, S. Hossain M., P. Em P., Tahabilder A., Sultana R., and A. Islam M. "Small intestine bleeding detection using color threshold and morphological operation in WCE images." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 4 (2021): 3040–48. https://doi.org/10.11591/ijece.v11i4.pp3040-3048.
Full textAbstract:
Wireless capsule endoscopy (WCE) is a significant modern technique for observing the whole gastroenterological tract to diagnose various diseases like bleeding, ulcer, tumor, Crohn's disease, and polyps in a non-invasive manner. However, it will make a substantial onus for physicians like human oversight errors with time consumption for manual checking of a vast amount of image frames. These problems motivate the researchers to employ a computer-aided system to classify the particular information from the image frames. Therefore, a computer-aided system based on the color threshold and morphological operation has been proposed in this research to recognize specified bleeding images from the WCE. Besides, A unique classifier, quadratic support vector machine (QSVM) has been employed for classifying the bleeding and non-bleeding images with the statistical feature vector in HSV color space. After extensive experiments on clinical data, 95.8% accuracy, 95% sensitivity, 97% specificity, 80% precision, 99% negative predicted value and 85% F1 score has been achieved, which outperforms some of the existing methods in this regard. It is expected that this methodology would bring a significant contribution to the WCE technology.
24
Vega Gutierrez, Sarath, R. Van Court, Derek Stone, Matthew Konkler, Emily Groth, and Seri Robinson. "Relationship between Molarity and Color in the Crystal (‘Dramada’) Produced by Scytalidium cuboideum, in Two Solvents." Molecules 23, no. 10 (2018): 2581. http://dx.doi.org/10.3390/molecules23102581.
Full textAbstract:
Pigments from wood-decay fungi (specifically spalting fungi) have a long history of use in wood art, and have become relevant in modern science due to their longevity and colorfastness. They are presently under investigation as colorants for wood, bamboo, oils, paints and textiles. Major hurdles to their commercialization have been color repeatability (in that the same strain of the same species of fungus may produce different colors over time), and the binding of the pigments to glass storage containers. This is persistent as they do not naturally exist in a loose form. Due to these issues, the ‘standard’ color for each was historically determined not by the amount of pigment, but by the color in a solution of dichloromethane (DCM), using the CIE L*a*b colorspace. This method of standardization severely limited the use of these pigments in industrial applications, as without a dry form, standard methodologies for repeatable color processing into other materials could not be easily implemented. Recent studies have developed a method to crystalize the red pigment from Scytalidium cuboideum (Sacc. & Ellis) Sigler & Kang, producing a highly pure (99%) solid crystal named ‘Dramada’. Herein a method is detailed to compare the molarity of this crystallized pigment to variations in the color, to determine a color saturation curve (by weight) for the pigment from S. cuboideum in DCM and acetone. The molarities for this experiment ranged from 0.024 mM to 19 mM. Each molarity was color read and assigned a CIEL*a*b* value. The results showed that there was a correlation between the molarity and color difference, with the maximum red color occurring between 0.73 mM and 7.3 mM in DCM and between 0.97 mM to 0.73 mM in acetone. Extremely low molarities of pigment produced strong coloration in the solvent, and changes in molarity significantly affected the color of the solution. Having a saturation and color curve for the crystal ‘Dramada’ from S. cuboideum will allow for the reliable production of distinct colors from a known quantity (by weight) of pigment, erasing the final hurdle towards commercial development of the crystallized pigment from S. cuboideum as an industrial dyestuff.
25
Pham, Hoan Thi, and Hao Minh Hoang. "Factors affecting acrylamide mitigation in fried potatoes." Science and Technology Development Journal 23, no. 2 (2020): 548–54. http://dx.doi.org/10.32508/stdj.v23i2.1906.
Full textAbstract:
Introduction: Recent findings of acrylamide, a carcinogenic agent to humans, in foods have led to great efforts to elucidate the mechanisms of acrylamide formation and its mitigation. The acrylamide was generated during the browning process by the Maillard reaction of amino acid asparagine and reducing sugars at temperatures above 120 °C. Asparagine was determined to be a precursor of acrylamide formation. Therefore, asparagine reduction in raw materials can be taken into account to limit the acrylamide level in prepared foods. L-asparaginase has been used to consume acrylamide precursor by catalyzing the conversion of asparagine into aspartic acid and ammonia. Several factors including enzyme concentration, pH, temperature and frying time can influence the efficiency of acrylamide mitigation by enzyme. In the present work, we selected potatoes as raw materials to investigate the effects of factors on the acrylamide mitigation in fried potatoes.
 Methods: By pre-treating potato strips in different conditions of enzyme concentrations, pH, temperature and frying time, the effects of these parameters on acrylamide levels in fried products were evaluated by measuring UV-Vis spectra of sample solutions containing acrylamide. The maximum absorbance values at 224 nm were used to determine the acrylamide concentrations by calculation from a calibration curve. Experimental data were statistically analyzed by one-way ANOVA. Colorspace measurements were performed to describe the differences in colors of the fried products after various frying times.
 Results: A calibration curve was established to determine the acrylamide content of sample solutions via their maximum absorbance values. Pre-treatment of potato strips with a solution of 1.0 IU/mL asparaginase at 37 °C, pH 7.3, for 30 min led to a 45.6% reduction of acrylamide in French fries compared to a solution without enzyme. The optimum pH value for the most efficient enzyme activity was 7.3. Frying time ranging from 1.0 to 6.0 min increased acrylamide content and induced a darker appearance product.
 Conclusions: By using UV-Vis measurements, we demonstrated the effects of factors on L-asparaginase based acrylamide mitigation in fried potatoes. The conditions which gave the lowest acrylamide concentrations were assessed. The results could be applicable for commercial processes to minimize acrylamide content in prepared potatoes.
26
Aydin, Ayberk, and Alptekin Temizel. "Adversarial image generation by spatial transformation in perceptual colorspaces." Pattern Recognition Letters, September 2023. http://dx.doi.org/10.1016/j.patrec.2023.09.003.
Full text
27
Berlinski, Joshua D., and Ranjan Maitra. "Computational Improvements to the Kernel k$$ k $$‐Means Clustering Algorithm." Statistical Analysis and Data Mining: An ASA Data Science Journal 18, no. 4 (2025). https://doi.org/10.1002/sam.70032.
Full textAbstract:
ABSTRACTKernel k‐means (kk‐means) extends the standard k‐means clustering method to identify generally shaped clusters by employing the k‐means algorithm in a higher‐dimensional space. Current implementations of kk‐means are rather naive. We present simplifications that reduce calculations, performing only those that are absolutely necessary, therefore improving the overall algorithm's run time. We also present a sampling‐based clustering and classification strategy for employing kk‐means clustering for large datasets. Results on 13 data sets illustrate the computational benefits of our new algorithm. Our sampling‐based strategy is also applied to investigate the use of ‐means in the color quantization of images in human perception‐based colorspaces.
28
Rodrigo, Montufar-Chaveznava. "Face Tracking using a Polling Strategy." June 23, 2008. https://doi.org/10.5281/zenodo.1061106.
Full textAbstract:
The colors of the human skin represent a special category of colors, because they are distinctive from the colors of other natural objects. This category is found as a cluster in color spaces, and the skin color variations between people are mostly due to differences in the intensity. Besides, the face detection based on skin color detection is a faster method as compared to other techniques. In this work, we present a system to track faces by carrying out skin color detection in four different color spaces: HSI, YCbCr, YES and RGB. Once some skin color regions have been detected for each color space, we label each and get some characteristics such as size and position. We are supposing that a face is located in one the detected regions. Next, we compare and employ a polling strategy between labeled regions to determine the final region where the face effectively has been detected and located.
29
Zeileis, Achim, Jason C. Fisher, Kurt Hornik, et al. "colorspace: A Toolbox for Manipulating and Assessing Colors and Palettes." Journal of Statistical Software 96, no. 1 (2020). http://dx.doi.org/10.18637/jss.v096.i01.
Full text
30
Yin, Zhaoxia, Li Chen, Wanli Lyu, and Bin Luo. "Reversible Attack based on Adversarial Perturbation and Reversible Data Hiding in YUV Colorspace." Pattern Recognition Letters, December 2022. http://dx.doi.org/10.1016/j.patrec.2022.12.018.
Full text
31
"Detection and Classification of Early Stage Lesions in Diabetic Retinopathy using Color Fundus Images." International Journal of Recent Technology and Engineering 8, no. 3 (2019): 4476–80. http://dx.doi.org/10.35940/ijrte.c6806.098319.
Full textAbstract:
Detection of lesions and classification of Diabetic Retinopathy (DR) play an important role in day-to-day life. In this proposed system, colour fundus image is pre-processed using morphological operations to recover from noises and it is converted into HSV colorspace. Fuzzy C-Means Clustering algorithm (FCMC) is used for segmenting the early stage lesions such as Microaneurysms (Ma), Haemorrhages (HE) and Exudates. Hybrid features such as colour correlogram and speeded up robust features (surf) are extracted to train the classifier. Cascaded Rotation Forest (CRF) classifier is used for classification of diabetic retinopathy. The proposed system increases the accuracy of detection and it has got high sensitivity.
32
Sinaga, Daurat, Cahaya Jatmoko, Erna Zuni Astuti, et al. "Improved Chaotic Image Encryption on Grayscale Colorspace Using Elliptic Curves and 3D Lorenz System." Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, July 12, 2025. https://doi.org/10.22219/kinetik.v10i3.2251.
Full textAbstract:
Digital data, especially visual content, faces significant security challenges due to its susceptibility to eavesdropping, manipulation, and theft in the modern digital landscape. One effective solution to address these issues is through the use of encryption techniques, such as image encryption algorithms that ensure the confidentiality, integrity, and authenticity of digital visual content. This study addresses these concerns by introducing an advanced image encryption method that combines Elliptic Curve Cryptography (ECC) with the 3D Lorenz chaotic system to enhance both security and efficiency. The method employs pixel permutation, ECC-based encryption, and diffusion using pseudo-random numbers generated by the Lorenz 3D system, tested on grayscale images such as MRI, Lena, and Peppers with a resolution of 512x512 pixels. The results show superior performance, with an MSE of 3032 and a PSNR of 8.87 dB for the Rice image, as well as UACI and NPCR values of 33.34% and 99.64%, respectively, indicating strong resilience to pixel intensity changes. During testing, the approach demonstrated robustness, allowing only the correct key to decrypt images accurately, while incorrect or modified keys led to distorted outputs, ensuring encryption reliability. Future work could explore extending the method to color images, optimizing processing for larger datasets, and incorporating additional chaotic systems to further fortify encryption strength.
33
Fong, James, Hannah K. Doyle, Congli Wang, et al. "Novel color via stimulation of individual photoreceptors at population scale." Science Advances 11, no. 16 (2025). https://doi.org/10.1126/sciadv.adu1052.
Full textAbstract:
We introduce a principle, Oz, for displaying color imagery: directly controlling the human eye’s photoreceptor activity via cell-by-cell light delivery. Theoretically, novel colors are possible through bypassing the constraints set by the cone spectral sensitivities and activating M cone cells exclusively. In practice, we confirm a partial expansion of colorspace toward that theoretical ideal. Attempting to activate M cones exclusively is shown to elicit a color beyond the natural human gamut, formally measured with color matching by human subjects. They describe the color as blue-green of unprecedented saturation. Further experiments show that subjects perceive Oz colors in image and video form. The prototype targets laser microdoses to thousands of spectrally classified cones under fixational eye motion. These results are proof-of-principle for programmable control over individual photoreceptors at population scale.
34
Kumar, K. Anup, and C. Vanmathi. "A hybrid parallel convolutional spiking neural network for enhanced skin cancer detection." Scientific Reports 15, no. 1 (2025). https://doi.org/10.1038/s41598-025-85627-6.
Full textAbstract:
Abstract The most widespread kind of cancer, affecting millions of lives is skin cancer. When the condition of illness worsens, the chance of survival is reduced, and thus detection of skin cancer is extremely difficult. Hence, this paper introduces a new model, known as Parallel Convolutional Spiking Neural Network (PCSN-Net) for detecting skin cancer. Initially, the input skin cancer image is pre-processed by employing Medav filter to eradicate the noise in image. Next, affected region is segmented by utilizing DeepSegNet, which is formed by integrating SegNet and Deep joint segmentation, where RV coefficient is used to fuse the outputs. Here, the segmented image is then augmented by including process, such as geometric transformation, colorspace transformation, mixing images Pixel averaging (mixup), and overlaying crops (CutMix). Then textural, statistical, Discrete Wavelet Transform (DWT) based Local Direction Pattern (LDP) with entropy, and Local Normal Derivative Pattern (LNDP) features are mined. Finally, skin cancer detection is executed using PCSN-Net, which is formed by fusing Parallel Convolutional Neural Network (PCNN) and Deep Spiking Neural Network (DSNN). In this work, the suggested PCSN-Net system shows high accuracy and reliability in identifying skin cancer. The experimental findings suggest that PCSN-Net has an accuracy of 95.7%, a sensitivity of 94.7%, and a specificity of 92.6%. These parameters demonstrate the model’s capacity to discriminate among malignant and benign skin lesions properly. Furthermore, the system has a false positive rate (FPR) of 10.7% and a positive predictive value (PPV) of 90.8%, demonstrating its capacity to reduce wrong diagnosis while prioritizing true positive instances. PCSN-Net outperforms various complex algorithms, including EfficientNet, DenseNet, and Inception-ResNet-V2, despite preserving effective training and inference times. The results obtained show the feasibility of the model for real-time clinical use, strengthening its capacity for quick and accurate skin cancer detection.