HSV

It's a fact that nonlinear color models like Hue-Saturation -Value/ Brightness/ Luminance/ Intensity (HSV/ HSB/ HSL/ HSI) have special feature for each
channel. So in this paper we propose a new hybrid compression system that deals with each channel with a suitable compression technique to obtain encoded images with less size and high decoding quality than the traditional encoding methods.

Tomatoes are best known grown fruits in greenhouses. In order to decrease the high labor cost and increase the efficiency, recently many researches have tried to harvest tomatoes automatically. To help the harvesting robot picking the fruit precisely, the main challenges are to identify and localize red mature tomato fruit accurately. In this paper, we presented a robust and effective method for challenging task of segmentation and localization of on-plant tomatoes in the greenhouse, for automatic tomato harvesting purpose. A low-cost RGB-D Kinect V2 camera was used to accomplish the task. Color images and depth images of tomatoes on-plants in the greenhouse were captured in day time using Kinect V2 camera. Along with color and depth images, the corresponding color coordinates, depth coordinates, and camera coordinates were also saved. The proposed method was divided into two stages: fruit segmentation and fruit localization. At the first stage, an improved HSV algorithm and watershed algorithm were used to segment mature fruits from a complex background. An improved HSV algorithm was used to enhance the feature of red mature fruits. After that, Otsu method and morphological operations were used to segment mature fruits from the background and then watershed was implemented to further separate touched fruits. Subsequently, each detected fruit was separately labeled to calculate the pixel coordinates values in color image. At the second stage, a fusion of color and depth information was carried out on the basis of the mapping relationship between color, depth and camera coordinates, to map the obtained fruit pixel coordinates in color image with acquired 3D point cloud data. Then the corresponding point cloud data of each labeled fruit were generated. To remove the noise in the cloud data, a filtration process was performed, and the final point cloud was generated. To verify the proposed method, 18 pairs of images were experimented containing the different numbers of measured fruits with measuring distance, ranging from 0.58 to 1.50 m. The average fruit localization error rate was 1.2 cm for depth distance and 1.9 cm for ground distance. Final results showed that the proposed method has good performance and robustness for segmentation and localization and have good potential to be implemented on the tomato harvesting robot in the greenhouse.

The detection of moving objects in a video sequence is an essential step in almost all the systems of vision by computer. However, because of the dynamic change in natural scenes, the detection of movement becomes a more difficult task. In this work, we propose a new method for the detection moving objects that is robust to shadows, noise and illumination changes. For this purpose, the detection phase of the proposed method is an adaptation of the MOG approach where the foreground is extracted by considering the HSV color space. To allow the method not to take shadows into consideration during the detection process, we developed a new shade removal technique based on a dynamic thresholding of detected pixels of the foreground. The calculation model of the threshold is established by two statistical analysis tools that take into account the degree of the shadow in the scene and the robustness to noise. Experiments undertaken on a set of video sequences showed that the method put forward provides better results compared to existing methods that are limited to using static thresholds.

With the development of multimedia technology and digital devices, it is very simple and easier to recapture a high quality images from LCD screens. In authentication, the use of such recaptured images can be very dangerous. So, it is very important to recognize the recaptured images in order to increase authenticity. Image recapture detection (IRD) is to distinguish real-scene images from the recaptured ones. An image recapture detection method based on set of physical based features is proposed in this paper, which uses combination of low-level features including texture, HSV colour and blurriness. Twenty six dimensions of features are extracted to train a support vector machine classifier with linear kernel. The experimental results show that the proposed method is efficient with good recognition rate of distinguishing real scene images from the recaptured ones. The proposed method also possesses low dimensional features compared to the state-of-the-art recaptured methods.

The purpose of this project in computer vision is to expand my knowledge of digital image processing through a review of mathematical techniques used by academic researchers in the work in multi-dimensional — i.e., colour — images. A brief review of colour imaging, as well as various uses of the assessment tools are reviewed. It is observed that many of the methods introduced in class: edges, Gaussian filters, augmentation, recognition, indexing, object identification– have considerations using HSV manipulation and with through histograms of such interpretations.

In this study 6 species of Serranidae family (Epinephelus aeneus, Epinephelus caninus, Epinephelus costae, Epinephelus marginatus, Hyporthodus haifensis, Mycteroperca rubra) were classified by using a color based feature extraction method. A database which consists of 112 fish images was used in this study. In each image, a fish was located on a white background floor with the same position and the images were taken from different distances. A combination of manual processes and automatic algorithms were applied on images until obtaining colored fish sample images with a black background. Since the presented color based feature extraction method avoids including background, these images were processed by using an automatic algorithm in order to obtain a solid texture image from the fish and extract features. The obtained solid texture image was in HSV color space and used due to extract species-specific information from the fish samples. Each of the hue, saturation and value components of the HSV color space was used separately in order to extract 7 statistical features. Hence, totally 21 features were extracted for each fish sample. The extracted features were used within Nearest Neighbor algorithm and 112 fish samples from the 6 species were classified with an overall accuracy achievement of 86%.

Citation/Export MLA Prof. Suresh C Mundhe,Prof. Trupti A Dhumal, “Computer Based System for Sleep Detection of Driver”, March 15 Volume 3 Issue 3 , International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 911 - 914, DOI: 10.17762/ijritcc2321-8169.150302 APA Prof. Suresh C Mundhe,Prof. Trupti A Dhumal, March 15 Volume 3 Issue 3, “Computer Based System for Sleep Detection of Driver”, International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 911 - 914, DOI: 10.17762/ijritcc2321-8169.150302

Common compression systems treat color image channels similarly. Nonlinear color models like Hue-Saturation -Value/ Brightness/ Luminance/ Intensity (HSV/ HSB/ HSL/ HSI) have special features for each channel. In this paper a new hybrid compression system is proposed for encoding color images in HSI color model. The proposed encoding system deals with each channel with a suitable compression technique to obtain encoded images with less size and high decoding quality than the traditional encoding methods. There are three encoding techniques will be mixed in the proposed system; Object Compression Technique for the Hue channel, Luma(Y) Intensity (I) Difference (D) - for Saturation, and the standard JPEG2000 encoding technique for the Intensity channel. The proposed system results demonstrate the proposed architecture and give considerable compression ratio with good decoding quality.

Common compression systems treat color image channels
similarly, but while nonlinear color models like Hue-Saturation -
Value/ Brightness/ Luminance/ Lightness (HSV/ HSB/ HSL/
HSI) have special feature for each channel, a new hybrid
compression system is proposed for encoding color images in
HSL color model using new transformation function (YLD). The
proposed encoding system deals with each channel with a
suitable compression technique to obtain encoded images with
less size and high decoding quality than the traditional encoding
methods. There are three encoding techniques will be mixed in
our proposed system; Object Compression Technique for the Hue
channel, Luma(Y) Lightness (L) Difference (D) - for Saturation,
and the standard Jpeg2000 encoding technique for the Lightness
channel. The proposed system results in very high compression
ratio with very good decoding quality.

Topical microbicides that block the sexual transmission of HIV and herpes simplex virus 2 (HSV-2) are desperately needed to reduce the incidence of HIV infections worldwide. Previously we completed phase 3 testing of the carrageenan-based gel Carraguard. Although the trial did not show that Carraguard is effective in preventing HIV transmission during vaginal sex, it did show that Carraguard is safe when used weekly for up to 2 years. Moreover, Carraguard has in vitro activity against human papillomavirus (HPV) and HSV-2 and favorable physical and rheological properties, which makes it a useful vehicle to deliver antiviral agents such as zinc acetate. To that end, we previously reported that a prototype zinc acetate carrageenan gel protects macaques against vaginal challenge with combined simian-human immunodeficiency virus reverse transcriptase (SHIV-RT). Herein, we report the safety and efficacy of a series of zinc acetate and/or carrageenan gels. The gels protected mice (75 to 85% survival; P < 0.001) against high-dose (10^6-PFU) HSV-2 vaginal or rectal challenge. In contrast, zinc acetate formulated in HEC (hydroxyethylcellulose; or the Universal Placebo) failed to protect mice against the high-dose vaginal HSV-2 challenge (similar to aqueous zinc acetate solution and the placebo controls). The gels were found to be effective spreading gels, exhibited limited toxicity in vitro, caused minimal damage to the architecture of the cervicovaginal and rectal mucosae in vivo, and induced no increased susceptibility to HSV-2 infection in a mouse model. Our results provide a strong rationale to further optimize and evaluate the zinc acetate/carrageenan gels for their ability to block the sexual transmission of HIV and HSV-2.

The significance of putting out a fire as soon as possible can serve to warn others about the perils of disaster and death. Common sensor fire detection systems are limited, especially in a vast location, requiring a long time to detect a fire, and are unable to work in an open environment. This study developed a fire sense approach based on visual sensitivity and colourful variables such as colour, earth filling, light, backdrop, and fire detection time to assess the likelihood of a fire. The degree of error of fire detection was calculated in this study's test.

Aptamers are in vitro selected DNA or RNA molecules that are capable of binding a wide range of nucleic and non-nucleic acid molecules with high affinity and specificity. They have been conducted through the process known as SELEX (Systematic Evolution of Ligands by Exponential Enrichment). It serves to reach specificity and considerable affinity to target molecules, including those of viral origin, both proteins and nucleic acids. Properties of aptamers allow detecting virus infected cells or viruses themselves and make them competitive to monoclonal antibodies. Specific aptamers can be used to interfere in each stage of the viral replication cycle and also inhibit its penetration into cells. Many current studies have reported possible application of aptamers as a treatment or diagnostic tool in viral infections, e.g., HIV (Human Immunodeficiency Virus), HBV (Hepatitis B Virus), HCV (Hepatitis C Virus), SARS (Severe Acute Respiratory Syndrome), H5N1 avian influenza and recently spread Ebola. This review presents current developments of using aptamers in the diagnostics and treatment of viral diseases.

We present techniques for the processing of color, high-dynamic luminance images of a type aiming for objectivity and also of a type aiming for aesthetic improvement. In the first case we start with camera raw data, propose a variant white balance, darken very light spots and lighten very dark spots. In the second case we use color spaces of the type hue-saturation-luminance; we propose a hue processing method inspired in the Bezold-Brucke effect as well as a luminance-dependant displacement of color saturation.

Medical imaging is one of the most attractive topics of image processing and understanding research fields due to the similarity between the captured body organs colors. Most medical images come in grayscale with low contrast gray values; which makes it a challenge to discriminate between the region of interest (ROI) and the background (BG) parts. Pseudo-coloring is one of the solutions to enhance the visual appeal of medical images, most literature works suggest RGB-base color palettes. In this paper, pseudo-coloring methods of different medical imaging works are investigated and a highly discriminative colorization method is proposed. The proposed colorization method employs HSV/HSI color models to generate the desired color scale. Experiments have been performed on different medical images and different assessment methods have been utilized. The results show that the proposed methodology could clearly discriminate between near grayscale organs especially in case of tumor existence. Comparisons with other literary works were performed and the results are promising.

ABSTRACT In this study, synthetic aperture radar (SAR) and multispectral data are fused with different methods in order to observe the effect of fusion methods on the accuracy of different classification techniques. At the same time, different polarizations of SAR data are included in fusion process and results are examined. The fusion methods that are used in this study are Brovey Color Normalized, Hue Saturation Value (HSV), Gram – Schmidt (GS) Spectral Sharpening and Principal Components (PC) Spectral Sharpening. Fused images are classified using k-nearest neighbor, support vector machine and radial based function neural network. The study area is chosen on Menemen Plain, which contains agricultural lands, and it is located in İzmir. Multispectral RapidEye satellite image and TerraSAR-X radar data are used for the analysis. Achieved results were presented in the tables. The highest accuracy is achieved by K-NN classification of TerraSAR-X and VH fusion with GS method as 95.74%.

Nitrogen (N) estimation and detecting weeds are two different works in the agriculture. Nitrogen is the essential nutrient in plant which gives the status of plants. Detecting weeds in the crop row is also an essential as it affects the crops yield [1]. In this work rice leaf images are considered to estimate nitrogen content based on Leaf Color Chart (LCC), this paper also discusses the detection of weeds in crop row field.

Since the start of the COVID-19 pandemic, a wide range of clinical pictures have been documented ranging from asymptomatic cases to respiratory failure. Accumulating evidence of co-infection and reactivation of viruses during COVID-19 started to emerge. Herein we report three cases of herpes virus reactivation in severely ill COVID-19 patients. With the presence of herpes virus reactivation, one should think for the possibility to screen simultaneously besides SARS-CoV-2 for other respiratory pathogens, not forgetting HSV reactivation, given its similarity in worsening respiratory functions and similarity in radiologic findings to COVID-19. Missing such diagnosis could worsen the clinical course of COVID-19 patients and worsen the prognosis.

Disease is one of the major cause for fish mortality. The identification of diseased fishes are at early stage to prevent and spreading diseases. Manually detecting fish diseases are not error free. The image of the diseased fish recognise by using PCA method. In this work diseased area segmentation of fish image based on colour features with K-means clustering. HSV images and Morphological operation open for better accuracy to diseased area detection and measurement. Taken four Epizootic Ulcerative Syndrome (EUS) diseased fish images as a case study to evaluated the proposed approach. The experimental results clear indication of the effectiveness of proposed approach to improve the diseased identification with greater precision as well as correctly compute diseased area. The simulation results of this approach is encouraging.

Common compression systems treat color image channels similarly. Nonlinear color models like Hue-Saturation -Value/ Brightness/ Luminance/ Intensity (HSV/ HSB/ HSL/ HSI) have special features for each channel. In this paper a new hybrid compression system is proposed for encoding color images in HSI color model. The proposed encoding system deals with each channel with a suitable compression technique to obtain encoded images with less size and high decoding quality than the traditional encoding methods. There are three encoding techniques will be mixed in the proposed system; Object Compression Technique for the Hue channel, Luma(Y) Intensity (I) Difference (D) - for Saturation, and the standard JPEG2000 encoding technique for the Intensity channel. The proposed system results demonstrate the proposed architecture and give considerable compression ratio with good decoding quality.