Value Color Space Research Articles

National Retail Benchmarking for Pork 2018

ObjectivesThe objective of this study was to quantify pork quality attributes in the retail meat case nationwide. Similar evaluations were conducted in 2012 and 2015. The comparisons of the benchmarking projects show trends in pork products in nationwide retail stores.Materials and MethodsEighty-five stores representing 26 retailers in 15 cities were visited. Subjective color score (1–6) and subjective marbling score (1–10) on center-cut loin chops was assessed by an experienced grader under lighting of the self-serve meat case. Four individual loin chops with at least 50% lean exposed were randomly selected for evaluation from ten retail packages for each brand and enhancement type. Preference was given to boneless, 2.5 cm-thick center-cut loin chops where available. Ten additional packages of chops for each brand and enhancement type were evaluated in-store and purchased. Each of the purchased packages were labeled and each chop within a package was identified for later evaluation. The purchased packages were subsequently placed inside a cooler and transported to a temporary lab station where chops were again subjectively evaluated for color and marbling under controlled lighting and evaluated in the center of the loin muscle for instrumental color (CIE L*, a*, and b* color space values) using a calibrated Minolta Colorimeter and a Hunter Colorimeter. Chops were then vacuum sealed and immediately placed in the freezer. The samples were later packed in a cooler with dry ice and shipped to the University of Florida on dry ice. After arrival, they were placed in frozen storage until thawed for cooking and shearing. Chops were weighed prior to and after cooking on open hearth, adjustable heat grills until reaching 65°C then evaluated for slice shear.Data was analyzed using the mixed procedure in SAS (v. 9.4, SAS Institute, Cary, NC). The model included enhancement type (EN or NON), and the interaction between enhancement type as fixed effects. Package nested within store, brand, and purchased (yes or no) was fit as a random effect.ResultsThe USDA AMS defined “Tender” as having a minimum threshold values for slice shear as 20.0 kg and “Very Tender” at 15.3 kg, respectively. Sixteen percent of the instore evaluated and purchased retail packages were enhanced (247 and 119 retail packages, respectively). Means for instore enhanced (EN) and non-enhanced (NON) chops were 2.52 and 2.70 for subjective color and 2.25 and 2.65 for subjective marbling, respectively. Means for chops purchased for EN and NON chops under controlled lighting were: subjective color (2.07 and 2.25), subjective marbling (1.89 and 2.21), Minolta L* (56.35 and 56.86), Minolta a* (15.48 and 15.61), Hunter L (56.99 and 58.69), Hunter a (13.98 and 15.08), cook loss (13.02 and 15.64%), and slice shear (12.68 and 15.63 kg).ConclusionResults indicate extensive variation for color, marbling, cooking loss and tenderness exists in the pork meat case. Enhancement continues to improve tenderness though less enhanced pork is found in the marketplace. However, regardless of enhancement, the average value for slice shear of center loin chops were quite tender.Table 5.

An improved luminosity and contrast enhancement framework for feature preservation in color fundus images

Insufficient luminosity and poor local contrast are the major hurdles affecting the visual quality of the fundus images. A suitable framework is proposed for the enhanced visual perception of color fundus images based on a hybrid approach that combines gamma correction and singular value equalization for luminosity enhancement and contrast-limited adaptive histogram equalization (CLAHE) for local contrast enhancement. Luminosity enhancement is done by performing singular value equalization of the low-frequency component of the original value channel of the image in hue, saturation, and value color space using the low-frequency component of the gamma-corrected value channel of the same image. Discrete wavelet transform is applied for extracting the corresponding low-frequency components from the original and gamma-corrected value channels. Local contrast enhancement is achieved using CLAHE performed on the luminosity channel in \(L^*a^*b^*\) color space. The performance of the proposed method is analyzed qualitatively based on visual assessment and quantitatively with the parameters such as peak signal-to-noise ratio, absolute mean brightness error, discrete entropy and measure of enhancement. Experiments conducted on the color fundus images show improved results with sufficient detail preservation and enhanced visual perception compared to the existing methods.

A machine learning texture model for classifying lung cancer subtypes using preliminary bronchoscopic findings.

Bronchoscopy is useful in lung cancer detection, but cannot be used to differentiate cancer types. A computer-aided diagnosis (CAD) system was proposed to distinguish malignant cancer types to achieve objective diagnoses. Bronchoscopic images of 12 adenocarcinoma and 10 squamous cell carcinoma patients were collected. The images were transformed from a red-blue-green (RGB) to a hue-saturation-value (HSV) color space to obtain more meaningful color textures. By combining significant textural features (P < 0.05) in a machine learning classifier, a prediction model of malignant types was established. The performance of the CAD system achieved an accuracy of 86% (19/22), a sensitivity of 90% (9/10), a specificity of 83% (10/12), a positive predictive value of 82% (9/11), and a negative predictive value of 91% (10/11) in distinguishing lung cancer types. The area under the receiver operating characteristic curve was 0.82. On the basis of extracted HSV textures of bronchoscopic images, the CAD system can provide recommendations for clinical diagnoses of lung cancer types.

A Deep Structure of Person Re-Identification Using Multi-Level Gaussian Models

Person re-identification is being widely used in the forensic, and security and surveillance system, but person re-identification is a challenging task in real life scenario. Hence, in this work, a new feature descriptor model has been proposed using a multilayer framework of Gaussian distribution model on pixel features, which include color moments, color space values and Schmid filter responses. An image of a person usually consists of distinct body regions, usually with differentiable clothing followed by local colors and texture patterns. Thus, the image is evaluated locally by dividing the image into overlapping regions. Each region is further fragmented into a set of local Gaussians on small patches. A global Gaussian encodes, these local Gaussians for each region creating a multi-level structure. Hence, the global picture of a person is described by local level information present in it, which is often ignored. Also, we have analyzed the efficiency of earlier metric learning methods on this descriptor. The performance of the descriptor is evaluated on four public available challenging datasets and the highest accuracy achieved on these datasets are compared with similar state-of-the-arts, which demonstrate the superior performance.

Digital imaging devices as sensors for iron determination

Portable, sensitive and cost-effective sensors represent an unmet need, especially in resource-limited settings and locally deprived communities. Digital imaging devices can fill the gap. Thus, we have tested a desktop scanner, a digital camera and a smartphone to determine iron using three standard colour reactions. Images of reacting solutions were analysed to obtain the RGB (red, green and blue) non-uniform colour space parameters. To improve the calibration linearity, sensitivity, and detection limit, we converted the RGB intensities into six uniform colour space values and two colour differences attributes. The converted signals surpassed the RGB signals and compared well with reference spectrophotometric signals. The simplicity and sensitivity of this approach make digital imaging devices as excellent competitors to field-monitoring instruments and sophisticated spectrophotometers. Our approach was successfully applied to the assessment of iron in Nile river water, soils, plant materials and meat and liver samples.

Field Programmable Gate Arrays Based Real Time Robot Arm Inverse Kinematic Calculations and Visual Servoing

DOI: 10.26650/electrica.2018.49877 Reliability and precision are very important in space, medical, and industrial robot control applications. Recently, researchers have tried to increase the reliability and precision of the robot control implementations. High precision calculation of inverse kinematic, color based object recognition, and parallel robot control based on field programmable gate arrays (FPGA) are combined in the proposed system. The precision of the inverse kinematic solution is improved using the coordinate rotation digital computer (CORDIC) algorithm based on double precision floating point number format. Red, green, and blue (RGB) color space is converted to hue saturation value (HSV) color space, which is more convenient for recognizing the object in different illuminations. Moreover, to realize a smooth operation of the robot arm, a parallel pulse width modulation (PWM) generator is designed. All applications are simulated, synthesized, and loaded in a single FPGA chip, so that the reliability requirement is met. The proposed method was tested with different objects, and the results prove that the proposed inverse kinematic calculations have high precision and the color based object recognition is quite successful in finding coordinates of the objects.

Monitoring System for Persons With Alzheimer's Disease via Video-Object Tracking

This article presents a framework for a food intake monitoring system intended for use with persons with Alzheimer's disease and other dementias. Alzheimer's disease has a significant impact on the individual's ability to perform their daily activities including eating. Providing assistance with feeding is a major challenge for caregivers, including a significant time commitment. We present a vision-based system that tracks moving objects, such as the hand, using a combined optical flow and skin region detection algorithms. Skin detection is implemented using two different methods. Hue, saturation, and value (HSV) color space, which is on separation of the illuminance component from chrominance one as the first method and skin color information is extracted from subject's face detected using Viola-Johns algorithm for the second method. Once face and other moving skin regions are detected, bounding boxes are created and used to track all moving regions over the video frames, recognizing eating behavior or the lack of it. Based on experimental results the proposed method using optical flow and skin regions segmentation using HSV color detects the hand to mouth eating motion with 92.12% accuracy. The optical flow and skin region segmentation based on face color information achieves a higher accuracy of 94.29%.

Supervised Classification of RGB Aerial Imagery to Evaluate the Impact of a Root Rot Disease

Aerial imaging provides a landscape view of crop fields that can be utilized to monitor plant diseases. Phymatotrichopsis root rot (PRR) is a serious root rot disease affecting several dicotyledonous hosts, including the perennial forage crop alfalfa. PRR disease causes stand loss by spreading as circular to irregular diseased areas that increase over time, but disease progression in alfalfa fields is poorly understood. The objectives of this study were to develop a workflow to produce PRR disease maps from sets of high-resolution red, green and blue (RGB) images acquired from two different platforms and to assess the feasibility of using these PRR disease maps to monitor disease progression in alfalfa fields. Aerial RGB images, two from unmanned aircraft systems (UAS) and four images from a manned aircraft platform were acquired at different time points during the 2014–2015 growing seasons from a center-pivot irrigated, PRR-infested alfalfa field near Burneyville, OK. Supervised classification of images acquired from both platforms were performed using three spectral signatures: image-specific, UAS-platform-specific and manned-aircraft platform-specific. Our results showed that the UAS-platform-specific spectral signature was most efficient for classifying images acquired with the UAS, with accuracy ranging from 90 to 96%. In contrast, manned-aircraft-acquired images classified using image-specific spectral signatures yielded 95 to 100% accuracy. The effect of hue, saturation and value color space transformations (HSV and Hrot60SV) on classification accuracy was determined, but the accuracy estimates showed no improvement in their efficiency compared to the RGB color space. Finally, the data showed that the classification of the bare ground increased by 74% during the study period, indicating the extent of alfalfa stand loss caused by PRR disease. Thus, this study showed the utility of high-resolution RGB aerial images for monitoring PRR disease spread in alfalfa.

Classification of lung cancer subtypes based on autofluorescence bronchoscopic pattern recognition: A preliminary study

Background and objectivesLung cancer is the leading cause of cancer deaths worldwide. With current use of autofluorescent bronchoscopic imaging to detect early lung cancer and limitations of pathologic examinations, a computer-aided diagnosis (CAD) system based on autofluorescent bronchoscopy was proposed to distinguish different pathological cancer types to achieve objective and consistent diagnoses. MethodsThe collected database consisted of 12 adenocarcinomas and 11 squamous cell carcinomas. The corresponding autofluorescent bronchoscopic images were first transformed to a hue (H), saturation (S), and value (V) color space to obtain better interpretation of the color information. Color textural features were respectively extracted from the H, S, and V channels and combined in a logistic regression classifier to classify malignant types by machine learning. ResultsAfter feature selection, the proposed CAD system achieved an accuracy of 83% (19/23), a sensitivity of 73% (8/11), a specificity of 92% (11/12), a positive predictive value of 89% (8/9), a negative predictive value of 79% (11/14), and an area under the receiver operating characteristic curve of 0.81 for distinguishing lung cancer types. ConclusionsThe proposed CAD system based on color textures of autofluorescent bronchoscopic images provides a diagnostic method of malignant types in clinical use.

Approaches to quantitating the results of differentially dyed cottons

The differential dyeing (DD) method has been a subjective method for visually determining immature cotton fibers. We attempted to quantitate DD results and offer an efficient means of elucidating cotton maturity, without visual discretion. Image analysis of cotton color obtained with a scanner was performed and compared to spectrophotometrically measured color. Low, Medium and High micronaire (Mic) cotton standards were dyed, and we determined that blending the cotton yielded more precise color measurement; with the mean red, green and blue (RGB) values for a low Mic sample being: 101.6 ± 23.9, 83.1 ± 22.0 and 98.3 ± 23.1, respectively; while blending yielded RGB values: 121.6 ± 9.0, 98.8 ± 8.6 and 116.0 ± 8.9, respectively. Comparing RGB values to Mic, it was found that R and B color parameters do not show a trend, but G values decrease with increasing Mic indicating a decrease in G dye uptake with an increase in maturity. The conventional L*a*b* color space values were also obtained for the dyed cottons and compared to RGB and Mic results. The %CV for a* values are higher compared to L* and b* measurements: ∼31.9%, 18.9% and 8.6% for low, medium and high Mic samples, respectively. L* and b* decrease while a* increases with Mic, indicating an increase in R dye with an increase in maturity. The DD Bath method was found to be reliable, with R showing the greatest variances. G and a* values obtained were found to be the best correlation of differences in Mic.

Application of Computer-Aided Tongue Inspection for Preliminary Screening of Esophageal Cancer.

To differentiate patients with esophageal cancer or premalignant lesions from the high-risk population for preliminary screening of esophageal cancer using a feature index determined by a computer-aided tongue information acquisition and processing system (DS01-B). Totally, 213 patients diagnosed with esophageal cancer or premalignant lesions and 2,840 normal subjects were collected including primarily screened and reexamined, all of them were confirmed with histological examinations. Their tongue color space values and manifestation features were extracted by DS01-B and analyzed. Firstly, the analysis of variance was performed to differentiate normal subjects from patients with esophageal cancer and premalignant lesions. Secondly, the logistic regression was conducted using 10 features and gender, age to get a predictive equation of the possibility of esophageal cancer or premalignant lesions. Lastly, the equation was tested by subjects undergoing primary screening. Saturation (S) values in the HSV color space showed significant differences between patients with esophageal cancer and normal subjects or those with mild atypical hyperplasia (P<0.05); blue-to-yellow (b) values in the Lab color space showed significant differences between patients with esophageal cancer or premalignant lesions and normal subjects (P<0.05). Logistic regression analysis showed that the computer-aided tongue inspection approach had an accuracy of 72.3% (2008/2776) in identifying patients with esophageal cancer or premalignant lesions for preliminary screening in high-risk population. Computer-aided tongue inspection, with descriptive and quantitative profile as described in this study, could be applied as a cost- and timeefficient, non-invasive approach for preliminary screening of esophageal cancer in high-risk population.

A New Timbre Visualization Technique Based on Semantic Descriptors

Abstract This article introduces the concept of Sound Signature audio visualization, a new form of amplitude waveform that also visualizes perceptually salient spectral features and their evolution over time. A brief review of existing research into timbre description and visualization is given. This is followed by an in-depth description of the algorithm. Rationale is given for the various visual mappings with reference to existing literature. The results of an online subjective evaluation survey are reported and discussed. The survey examined user preferences for the visual mappings used in Sound Signature visualizations. Results show a preference for inverse mapping of spectral centroid to the first component of the hue, saturation, value (HSV) color space.

A Fruit Quality Survey of Peach Cultivars Grown in the Southeastern United States

Peach (Prunus persica) production in the southeastern United States extends from mid-May to mid-September. There are ≈60 peach cultivars commercially grown. Each cultivar has unique fruit quality characteristics, which could influence consumer perception and likability. The present study is a survey of chemical and physical characteristics of mature, commercially grown, fresh peaches in Georgia. A collection of 30 cultivars was evaluated in 2015 and 2016 for soluble solids concentration (SSC), total titratable acidity (TTA), SSC/TTA ratio, texture (compression, puncture, and Kramer shear), and skin and flesh color (CIE L*, chroma, and hue color space values). There was significant variation between seasons for all variables (P &lt; 0.05) except for TTA (P = 0.12), and flesh hue values (P = 0.38). Statistical differences among cultivars within each year were reported for all variables (P &lt; 0.0001). SSC showed variation seasonally and among cultivars, whereas TTA variation was mainly attributed to cultivar differences. Similarly, cultivar-to-cultivar differences were found when comparing the different texture tests evaluated with cultivars such as Goldprince, Early August Prince, Flameprince, Majestic, and Red Globe having the most variation between seasons. Other cultivars analyzed had little variation between seasons. Moreover, firmness differences observed across the three texture tests were inconsistent. In peach skin color, significant variation was observed for L*, chroma, and hue among cultivars. Skin hue and chroma were highly correlated within each season (r = 0.77 for 2015, r = 0.72 for 2016). The results of this survey demonstrate the variation of quality characteristics for a large selection of peach cultivars grown in Georgia. The information reported in this paper will be used as a baseline for further examining and understanding peach fruit quality.

A hybrid colour model based land cover classification using random forest and support vector machine classifiers

Land cover monitoring using remotely sensed data requires robust classification methods for the accurate mapping of complex land cover and land use categories. Classification is a supervised learning method which maps a data item into predefined classes. Colour is an important feature used in image classification since humans tend to distinguish images mostly based on colour feature. This paper proposes a hybrid colour model for land cover classification in which colour features are extracted by combining the hue (H) values of HSV colour space and luminance (L) values of LUV colour space. The extracted features are trained and tested with random forest (RF) and support vector machine (SVM) classifiers. The performance of the proposed hybrid colour model is compared with the existing HSV colour space model using RF and SVM classifiers based on several metrics such as accuracy, sensitivity, specificity and f-score. Hyper spectral dataset of Pavia University and an IRS LISS IV orthorectified dataset are chosen as the input image for this experiment.

A hybrid colour model based land cover classification using random forest and support vector machine classifiers

Land cover monitoring using remotely sensed data requires robust classification methods for the accurate mapping of complex land cover and land use categories. Classification is a supervised learning method which maps a data item into predefined classes. Colour is an important feature used in image classification since humans tend to distinguish images mostly based on colour feature. This paper proposes a hybrid colour model for land cover classification in which colour features are extracted by combining the hue (H) values of HSV colour space and luminance (L) values of LUV colour space. The extracted features are trained and tested with random forest (RF) and support vector machine (SVM) classifiers. The performance of the proposed hybrid colour model is compared with the existing HSV colour space model using RF and SVM classifiers based on several metrics such as accuracy, sensitivity, specificity and f-score. Hyper spectral dataset of Pavia University and an IRS LISS IV orthorectified dataset are chosen as the input image for this experiment.

A Naturalness Preserved Fast Dehazing Algorithm Using HSV Color Space

Visibility of outdoor scenes is often degraded by haze. The atmosphere particles absorb and scatter the light, causing failure in various computer vision applications. In this paper, we propose a naturalness preserved fast dehazing algorithm using hue, saturation, and value (HSV) color space. First, we process hazy images in HSV color space instead of red, green, and blue (RBG) in order to preserve hue and reduce computational complexity. Second, we use the modified morphological opening operation for estimating the transmission map. In this way, the halo effects are greatly suppressed, and it costs less time than the specially designed filters. The experimental results demonstrate that the proposed algorithm can effectively remove haze. Also, our algorithm maintains naturalness by preserving hue and suppressing halo effects. Moreover, the computational complexity has been largely reduced, thus making our algorithm appropriate for real-time applications.

Impact of Multiple Antimicrobial Interventions on Ground Beef Quality

Multiple antimicrobial intervention strategies are employed in beef manufacturing, and some may impact quality and palatability of ground beef patties. Combinations of antimicrobial treatments – hot water (at least 82 °C), lactic acid (4.0 to 5.0%), acidified sodium chlorite (pH of 2.7 to 2.8) and Beefxide (lactic/citric acid mixture; up to 2.5%) – were applied to hot or chilled carcasses and trimmings before manufacturing ground beef patties, which were designated for color evaluation, consumer panel, or trained panel evaluation. Few significant changes were seen in color space values for each treatment combination. Consumer scores for overall liking, flavor liking, and beefy flavor liking were impacted (P &lt; 0.05) by combined antimicrobial treatment effects. Trained panelists detected 18 out of 33 attributes with only scores for fat-like (P = 0.0391) and cardboardy (P &lt; 0.0001) being impacted by treatments. No clear trends were related to any single or combined antimicrobial treatment, and findings support that these food safety interventions have minimal negative impacts on beef patty quality.

Vegetation interpretation and classification from high resolution true colour map images of shanghai city

Vegetation interpretation and classification is the core work of regional ecological monitoring and carbon sinks calculating. Different from traditional remote sensing data, the development of high resolution true colour map provides a new possibility of vegetation interpretation and classification, especially for scattered, small-scale vegetation distributions in city regions. In Hue, Saturation and Value (HSV) colour space, colour model and texture model are combined to extract vegetation features, adjusting weights of the two features as a whole to achieve a better identification effect. Based on the nearest neighbour method, the model matches the features of candidate images with typical training vegetation samples. The main researches and innovations are as fol- lows: (i) The research uses high resolution true colour map images to provide real-time and more convenient data, making the study less limited to low spatial resolution of sensing images. (ii) It explores vegetation cover in city regions in an effective way, which is dispersed in size, variable in type and difficult to be located pre- cisely. (iii) The results of simulation show that this method is feasible and the feature-weighted model im- proves the precision to 83.3 % around by adjusting weight parameters, much better than single feature model. (iv) Combined with the annual Net primary productivity (NPP) values in different vegetation types, the carbon storage of carbon sinks in one area of 23,373 m2 is calculated, ranging from 9,000 - 12,000 kg, providing a new way to track the carbon footprints in city regions.

Protection of blue color in a spirulina derived phycocyanin extract from proteolytic and thermal degradation via complexation with beet-pectin

The focus of the presented work was on improvement of spirulina based phycocyanin (PhyC) extracts as a potential natural blue colorant by enhancement of color stability via polysaccharide complexation. Beet pectin (β-pectin), guar gum and soluble soy polysaccharides (SSPS) were assessed for their ability to protect the color of a commercial phycocyanin (PhyC) extract from thermal and proteolytic degradation. Both the beet pectin and guar gum studied formed stable and homogenous solutions with the PhyC extract; SSPS did not. Color change was assessed throughout by measurement of L, a, and b color space values (Hunter, 1948) before and after thermal or proteolytic treatment. Beet pectin improved color stability, over PhyC alone, with respect to heating at 65 °C (20 min) and proteolysis by Alcalase 2.4L, papain and bromelain. For these treatments beet pectin reduced color change values (ΔEab) from 13.0 ± 0.1 to 6.2 ± 0.4 following thermal treatment and from 12.7 ± 0.2 to 5.9 ± 0.7, 19.6 ± 1.3 to 12.5 ± 0.8, and 10.8 ± 0.4 to 4.6 ± 0.6 for the Alcalase, papain and bromelain treatments, respectively. Intrinsic fluorescence (excitation at 285 nm, emission at 325 nm) of beet pectin complexed PhyC indicates an increased presence of exposed tryptophan residues compared to PhyC alone. Furthermore, measured zeta-potentials following complex heating generally diverged to slightly more negative values for PhyC-beet pectin complexes at elevated temperatures compared to PhyC alone, suggesting that maintenance of a negatively charged environment may be important to pigment color stability in PhyC complexes.

Deep Belief Network For Smoke Detection

Forest fire is an serious hazard in many places around the world. For such threats, video-based smoke detection would be particularly important for early warning because smoke arises in any forest fire and can be seen from a long distance. This paper presents a novel and robust approach for smoke detection that employs Deep Belief Networks. The proposed method is divided into three phases. In the preprocessing phase, the region of high motion is extracted by background subtraction method. During the next phase, smoke pixel intensities are extracted from the Red, Green and Blue and Luminance; Chroma:Blue; Chroma:Red color spaces for foreground regions. Subsequently, second feature which is based on texture is computed for detecting smoke regions in which Local Extrema Co-occurrence Pattern, an improved version of local binary patterns are extracted from different foreground regions which compute not only texture of smoke but also intensity and color of smoke using Hue Saturation Value color space. Finally, Deep Belief Network is employed for classification. The proposed method proves its accuracy and robustness when tested on different varieties of scenarios whether wildfire-smoke video, hill base smoke video, indoor or outdoor smoke videos.