Digital Color Camera Research Articles

Laser Conoscopy Study of Optical Anomalies in Uniaxial Crystals

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Theoretical Determination of a Critical Nitrogen Dilution Curve Based on the Carrot Case Study

The objectives of the present study were to: (a) formulate a general dilution equation for a critical nitrogen percentage in a leaf (Nc); (b) monitor dilution function of carrot as a case study; (c) demonstrate applicability of digital color imaging to monitor total nitrogen (Ntot) in crops. Carrot (Daucus carota) was grown on loess soil at five Ntot application rates. Ntot weight (kg/ha) was obtained by standard laboratory analysis and by image processing. Total yield of the treatments was the sum of canopy and roots dry matter. A new logistic decay curve Nc = a/[1 + (W/W0) b ] agreed with experimental data. Nc (g/kg) is the critical nitrogen level, a and b are coefficients, and W/W0 is the relative dry matter biomass. Values of Nitrogen Nutrition Index (NNI) ≥ 1.0 indicated that there was no nitrogen deficiency in treatment larger than 100% application of Ntot. The weight of nitrogen in form of multiplication of the dry leaves weight (W) and N percent is suitable for the determination of nitrogen status. The availability of image-based data for N percent is faster, timely and less expensive than that of laboratory test. Applicability of digital color camera to monitor Ntot in crops instead of laboratory test was successfully demonstrated.

Image quality degradation of object-color metamer mismatching in digital camera color reproduction.

Metamer mismatching is a phenomenon where two objects that are colorimetrically indistinguishable under one lighting condition become distinguishable under another one. Due to the unavailability of spectral information, metamer mismatching introduces an inherent uncertainty into cameras' color reproduction. To investigate the degree of image quality degradation by the metamer mismatching, a large spectral reflectance database was compiled in this study to search the object-color metamers sets of the spectra in hyperspectral images. Then, metamer-degraded images were constructed and compared with the ground truth images by directional statistics-based color similarity index image quality assessment metrics to evaluate the perceptual image degradation. The results indicate that the object-color metamer mismatching has only little impact on the image quality degradation, whereas the inappropriate selection of color correction matrices involved with the illumination metamerism is the primary factor for the accuracy decrease in the digital camera color reproduction.

Machine vision smart sprayer for spot-application of agrochemical in wild blueberry fields

An essential part of the wild blueberry cropping system is the proper management of agrochemical inputs including herbicides, fungicides and insecticides. A machine vision system was developed and mounted on the rear sprayer boom 0.18 m in front of the sprayer nozzles capable of targeting the agrochemical application on an as-needed basis. The three-point hitch mounted sprayer featured 27 nozzles over a 13.7 m boom width and a storage tank capacity of 1135 l. Nine digital color cameras continually take images in real-time while computer software processes the images in 0.15 s to determine the target locations where the nozzles open and spray at speeds up to 1.77 m s−1. Two wild blueberry fields in central Nova Scotia were selected for smart sprayer performance testing with spot-application (SA) of agrochemical as compared to control and uniform application techniques. Chateau® herbicide was applied in a field with an infestation of hair cap moss. Spray droplet comparison showed moss patches were properly targeted using the smart sprayer. SA provided the same coverage performance as compared to uniform on the moss targets with herbicide application savings of 78.5% using the smart sprayer. Harvestable yield results were similar for all application tracks. TruPhos Magnesium and ZincMax foliar fertilizers were tank mixed with Bravo® and Proline® fungicides and applied to compare the difference of SA, control and uniform application. Results showed SA of foliar fertilizer and fungicide led to less premature leaf drop and increased the blueberry stem height, number of branches, stem diameter and fruit buds. SA of foliar fertilizer and fungicide also increased the percent of healthy wild blueberry plants by 57.8% and the harvestable yield by 137.8%. Fungicide application savings using the smart sprayer for SA were 11.6%.

Effects of pressure and fuel dilution on coflow laminar methane–air diffusion flames: A computational and experimental study

In this study, the influence of pressure and fuel dilution on the structure and geometry of coflow laminar methane–air diffusion flames is examined. A series of methane-fuelled, nitrogen-diluted flames has been investigated both computationally and experimentally, with pressure ranging from 1.0 to 2.7 atm and CH4 mole fraction ranging from 0.50 to 0.65. Computationally, the MC-Smooth vorticity–velocity formulation was employed to describe the reactive gaseous mixture, and soot evolution was modelled by sectional aerosol equations. The governing equations and boundary conditions were discretised on a two-dimensional computational domain by finite differences, and the resulting set of fully coupled, strongly nonlinear equations was solved simultaneously at all points using a damped, modified Newton's method. Experimentally, chemiluminescence measurements of CH* were taken to determine its relative concentration profile and the structure of the flame front. A thin-filament ratio pyrometry method using a colour digital camera was employed to determine the temperature profiles of the non-sooty, atmospheric pressure flames, while soot volume fraction was quantified, after evaluation of soot temperature, through an absolute light calibration using a thermocouple. For a broad spectrum of flames in atmospheric and elevated pressures, the computed and measured flame quantities were examined to characterise the influence of pressure and fuel dilution, and the major conclusions were as follows: (1) maximum temperature increases with increasing pressure or CH4 concentration; (2) lift-off height decreases significantly with increasing pressure, modified flame length is roughly independent of pressure, and flame radius decreases with pressure approximately as P−1/2; and (3) pressure and fuel stream dilution significantly affect the spatial distribution and the peak value of the soot volume fraction.

Optimized Color Filter Arrays for Sparse Representation-Based Demosaicking

Demosaicking is the problem of reconstructing a color image from the raw image captured by a digital color camera that covers its only imaging sensor with a color filter array (CFA). Sparse representation-based demosaicking has been shown to produce superior reconstruction quality. However, almost all existing algorithms in this category use the CFAs, which are not specifically optimized for the algorithms. In this paper, we consider optimally designing CFAs for sparse representation-based demosaicking, where the dictionary is well-chosen. The fact that CFAs correspond to the projection matrices used in compressed sensing inspires us to optimize CFAs via minimizing the mutual coherence. This is more challenging than that for traditional projection matrices because CFAs have physical realizability constraints. However, most of the existing methods for minimizing the mutual coherence require that the projection matrices should be unconstrained, making them inapplicable for designing CFAs. We consider directly minimizing the mutual coherence with the CFA's physical realizability constraints as a generalized fractional programming problem, which needs to find sufficiently accurate solutions to a sequence of nonconvex nonsmooth minimization problems. We adapt the redistributed proximal bundle method to address this issue. Experiments on benchmark images testify to the superiority of the proposed method. In particular, we show that a simple sparse representation-based demosaicking algorithm with our specifically optimized CFA can outperform LSSC [1]. To the best of our knowledge, it is the first sparse representation-based demosaicking algorithm that beats LSSC in terms of CPSNR.

Supersensitive single-particle plasmonic scattering-based cancer antigen 125 nanoimmunosensor by enhanced dark-field microscopy with dual-detection mode

A supersensitive nanoimmunosensor of cancer antigen 125 (CA125), which is a stage marker of several types of cancers, was developed based on characterization of single-particle plasmonic scattering by wavelength-dependent enhanced dark-field microscopy with dual-detection mode (EDFM-DM). Considering the optical properties of gold and silver nanoparticles (AuNPs/AgNPs), 40-nm AgNP was selected as an optimal fluorescence-free probe. CA125 was analyzed using the wavelength-dependent plasmonic scattering intensity of 40-nm AgNP-labeled CA125 immunoreacted on gold nanodots under the dual-detection mode. A color digital camera and an electron multiplying charge-coupled device camera were used for qualitative and quantitative analysis, respectively, based on the dark-field scattering images. Under optimal conditions, the developed immunosensor exhibited a lower detection limit of 4μU/mL (S/N=3) with a wide dynamic detection range of 4μU/mL-80U/mL (R=0.9935), which was a 100–375,000-fold lower detection limit with a 100–100,000-fold wider dynamic range than previous methods. In addition, recovery was greater than 98% with the spiking of standard CA125 in human serum samples. This method may be an excellent nanoimmunoassay platform for supersensitive detection at a wide dynamic detection range, supporting the earliest-stage detection of various disease-related protein molecules at the single-molecule level.

Wide range instantaneous temperature measurements of convective fluid flows by using a schlieren system based in color images

In the schlieren method, the deflection of light by the presence of an inhomogeneous medium is proportional to the gradient of its refractive index. Such deflection, in a schlieren system, is represented by light intensity variations on the observation plane. Then, for a digital camera, the intensity level registered by each pixel depends mainly on the variation of the medium refractive index and the status of the digital camera settings. Therefore, in this study, we regulate the intensity value of each pixel by controlling the camera settings such as exposure time, gamma and gain values in order to calibrate the image obtained to the actual temperature values of a particular medium. In our approach, we use a color digital camera. The images obtained with a color digital camera can be separated on three different color-channels. Each channel corresponds to red, green, and blue color, moreover, each one has its own sensitivity. The differences in sensitivity allow us to obtain a range of temperature values for each color channel. Thus, high, medium and low sensitivity correspond to green, blue, and red color channel respectively. Therefore, by adding up the temperature contribution of each color channel we obtain a wide range of temperature values. Hence, the basic idea in our approach to measure temperature, using a schlieren system, is to relate the intensity level of each pixel in a schlieren image to the corresponding knife-edge position measured at the exit focal plane of the system. Our approach was applied to the measurement of instantaneous temperature fields of the air convection caused by a heated rectangular metal plate and a candle flame. We found that for the metal plate temperature measurements only the green and blue color-channels were required to sense the entire phenomena. On the other hand, for the candle case, the three color-channels were needed to obtain a complete measurement of temperature. In our study, the candle temperature was took as reference and it was found that the maximum temperature value obtained for green, blue and red color-channel was ∼275.6, ∼412.9, and ∼501.3°C, respectively.

A decision-based multi-sensor classification system using thermal hyperspectral and visible data in urban area

ABSTRACTMulti-sensor data fusion has become more and more popular for classification applications. The fusion of multisource remote-sensing data can provide more information about the same observed site results in a superior comprehension of the scene. In this field of study, a combination of very high-resolution data collected by a digital color camera and a new coarse resolution hyperspectral data in the long-wave infrared range for urban land-cover classification has been extensively enticed much consideration and turned into a research hot spot in image analysis and data fusion research community. In this paper, a decision-based multi-sensor classification system is proposed to completely use the advantages of both sensors to attain enhanced land-cover classification results. In this context, spectral, textural and spatial features are extracted for the proposed multilevel classification. Then, a land-cover separability preprocessing is employed to identify how the proposed method can fully utilize the sensor advantages. Next, a support vector machine is applied to classify road classes by using thermal hyperspectral image data; plants, roofs and bare soils are classified by the joint use of sensors via Dempster–Shafer classifier fusion. Finally, an object-based post-processing is employed to improve the classification results. Experiments carried out on the dataset of 2014 IEEE GRSS data fusion contest indicate the superiority of the proposed methodology for the potentialities and possibilities of the joint utilization of sensors and refine the classification outcomes when evaluated against single sensor data. Meanwhile, the obtained classification accuracy can be a competitor against the results issued by the 2014 IEEE GRSS data fusion contest.

Prediction of Pork Fatty Acid Content Using Image Texture Features

ObjectivesThe aim of this study was to investigate the analytical potential of computer image processing and texture analysis techniques to quantify fatty acids associated with soft pork (specifically oleic, linoleic, linolenic, and IV).Materials and MethodsTexture features could be related to the softness of fat that would be related to level of unsaturation (Marshall, 1990). Plus, subtle changes in fatty acids profile could influence L*a*b* value or other color features not detectable by human eye. Fresh pork loins were obtained from 9 pork sides used in a separate research project that compared feeding obese swine a diet containing 16% corn oil (VEG) or 36% beef tallow (ANIM). The 3 mo feeding strategy resulted in significant differences in fatty acid profiles of the subcutaneous fat. The iodine value (IV) and concentration of oleic, linoleic, and linolenic acid expressed as percentage of total fat in a sample of subcutaneous fat collected adjacent the 10th thoracic vertebra were 85.8 (± 5.1), 36.2 (± 2.8), 26.9 (± 3.6), and 2.4 (± 0.4)% for VEG and were 60.2 (± 3.5), 44.5 (± 1.7), 9.8 (± 1.2), and 0.6 (± 0.2)% for ANIM, respectively. After a 24 h chill (2°C), each pork carcass was fully quartered at the 10th/11th rib interface and the blade and picnic shoulder were removed from the anterior section. The remaining rib section was placed against a black backdrop with the posterior (10th rib) facing the high-resolution camera (Fig. 1). After image acquisition, a 40-g sample of subcutaneous fat was collected from the 10thrib section, frozen, and sent to the University of Missouri Experiment Station Chemical Laboratories (Columbia, MO) for fatty acid profile (FAP) analysis. Pork loin images were acquired in the processing plant using a color digital camera (Model EOS 60D, Canon Corporation, Japan) with contrasting (black) background. Images were obtained in the plant to simulate the real-time industrial environment. Subsequent image analysis was conducted separately using image analyzing software (Matlab Version 7, The Math-works, Natick, MA). Five texture features (roughness, contrast, directionality, line-likeness, and heterogeneity) were extracted from the images based on previous work by Tamura, Mori, & Yamawaki (1978).Figure 1Segmentation of subcutaneous fat from a representative pork loin sample.ResultsThe image texture features of contrast, roughness, heterogeneity, line-likeness, and directionality were successfully extracted from images. The image texture features were utilized in linear and stepwise regression models to predict percentages of total fat for oleic, linoleic, and linolenic fatty acids and IV.For all predictions, linear regression models had a higher R2 than stepwise regression models. For oleic acid, the linear regression model had an R2 of 0.86 while the stepwise regression model had an R2 of 0.65. For linoleic and linolenic, both linear regression models resulted in an R2 value of 0.95 while the stepwise regression R2 values for linoleic and linolenic were 0.80 and 0.81, respectively. For IV, the linear regression model reached an R2 of 0.95 while the stepwise regression only had an R2 of 0.82. Due to the small sample size, no validation data was generated for the linear regression model.ConclusionThis research shows the potential for the extraction of image texture features from pork loin images obtained in a processing plant environment as a means for quantifying fatty acids known to influence processing attributes of pork fat.

Research on the Color Temperature & White Balance for Multimedia Sensor

With Internet of Things development, multimedia sensors have been employed in lots of applications, such as in smart grid, intelligent transportation etc. How to improve the function of camera becomes an important issue, especially on the color temperature and white balance. White balance is a very important concept in image processing. The so-called white balance, white object is to restore. Since humans from birth after the growth process, the human brain has been the reduction of the adaptability of colored objects under different light. However, as the imaging devices such as digital cameras, cannot adaptability in a different light, since the Charge-coupled Device (CCD) output imbalance, resulting in a digital camera color reproduction distortion. This paper analyzes the principles and characteristics of white balance and color temperature to study the environmental impact of shooting on the captured image and find the most appropriate white balance method.

Prediction of Pork Fatty Acid Content using Image Texture Features

ObjectivesThe aim of this study was to investigate the analytical potential of computer image processing and texture analysis techniques to quantify fatty acids associated with soft pork (specifically oleic, linoleic, linolenic, and IV). Materials and MethodsTexture features could be related to the softness of fat that would be related to level of unsaturation (Marshall, 1990). Plus, subtle changes in fatty acids profile could influence L*a*b* value or other color features not detectable by human eye. Fresh pork loins were obtained from 9 pork sides used in a separate research project that compared feeding obese swine a diet containing 16% corn oil (VEG) or 36% beef tallow (ANIM). The 3 mo feeding strategy resulted in significant differences in fatty acid profiles of the subcutaneous fat. The iodine value (IV) and concentration of oleic, linoleic, and linolenic acid expressed as percentage of total fat in a sample of subcutaneous fat collected adjacent the 10th thoracic vertebra were 85.8 (± 5.1), 36.2 (± 2.8), 26.9 (± 3.6), and 2.4 (± 0.4)% for VEG and were 60.2 (± 3.5), 44.5 (± 1.7), 9.8 (± 1.2), and 0.6 (± 0.2)% for ANIM, respectively. After a 24 h chill (2°C), each pork carcass was fully quartered at the 10th/11th rib interface and the blade and picnic shoulder were removed from the anterior section. The remaining rib section was placed against a black backdrop with the posterior (10th rib) facing the high-resolution camera (Fig. 1). After image acquisition, a 40-g sample of subcutaneous fat was collected from the 10thrib section, frozen, and sent to the University of Missouri Experiment Station Chemical Laboratories (Columbia, MO) for fatty acid profile (FAP) analysis. Pork loin images were acquired in the processing plant using a color digital camera (Model EOS 60D, Canon Corporation, Japan) with contrasting (black) background. Images were obtained in the plant to simulate the real-time industrial environment. Subsequent image analysis was conducted separately using image analyzing software (Matlab Version 7, The Math-works, Natick, MA). Five texture features (roughness, contrast, directionality, line-likeness, and heterogeneity) were extracted from the images based on previous work by Tamura, Mori, & Yamawaki (1978). Figure 1Segmentation of subcutaneous fat from a representative pork loin sample. ResultsThe image texture features of contrast, roughness, heterogeneity, line-likeness, and directionality were successfully extracted from images. The image texture features were utilized in linear and stepwise regression models to predict percentages of total fat for oleic, linoleic, and linolenic fatty acids and IV. For all predictions, linear regression models had a higher R² than stepwise regression models. For oleic acid, the linear regression model had an R² of 0.86 while the stepwise regression model had an R² of 0.65. For linoleic and linolenic, both linear regression models resulted in an R² value of 0.95 while the stepwise regression R² values for linoleic and linolenic were 0.80 and 0.81, respectively. For IV, the linear regression model reached an R2 of 0.95 while the stepwise regression only had an R² of 0.82. Due to the small sample size, no validation data was generated for the linear regression model. ConclusionThis research shows the potential for the extraction of image texture features from pork loin images obtained in a processing plant environment as a means for quantifying fatty acids known to influence processing attributes of pork fat.

Robust Grape Cluster Detection in a Vineyard by Combining the AdaBoost Framework and Multiple Color Components.

The automatic fruit detection and precision picking in unstructured environments was always a difficult and frontline problem in the harvesting robots field. To realize the accurate identification of grape clusters in a vineyard, an approach for the automatic detection of ripe grape by combining the AdaBoost framework and multiple color components was developed by using a simple vision sensor. This approach mainly included three steps: (1) the dataset of classifier training samples was obtained by capturing the images from grape planting scenes using a color digital camera, extracting the effective color components for grape clusters, and then constructing the corresponding linear classification models using the threshold method; (2) based on these linear models and the dataset, a strong classifier was constructed by using the AdaBoost framework; and (3) all the pixels of the captured images were classified by the strong classifier, the noise was eliminated by the region threshold method and morphological filtering, and the grape clusters were finally marked using the enclosing rectangle method. Nine hundred testing samples were used to verify the constructed strong classifier, and the classification accuracy reached up to 96.56%, higher than other linear classification models. Moreover, 200 images captured under three different illuminations in the vineyard were selected as the testing images on which the proposed approach was applied, and the average detection rate was as high as 93.74%. The experimental results show that the approach can partly restrain the influence of the complex background such as the weather condition, leaves and changing illumination.

Spatiotemporal Distribution of Soot Temperature for Flames Using Optical Pyrometry Under Unsteady Inlet Airflow Conditions

Two pyrometric tools for measuring soot temperature response in fuel-rich flames under unsteady inlet airflow conditions are developed. High-speed pyrometry using a high-speed color camera is used in producing soot temperature distributions, with its results compared with those of global soot temperature response measured using a multiwavelength pyrometer. For the former, the pixel red, green, and blue (RGB) values pertaining to respective bandwidths of red, green, and blue filters are used to calculate temperature and for the latter, the emission from whole flame at 660 nm, 730 nm, and 800 nm is used to measure temperature. The combustor, running on jet-A fuel, achieves unsteady inlet airflow using a siren running at frequencies of 150 and 250 Hz and with modulation levels (root mean square (RMS)) 20–50% of mean velocity. Spatiotemporal response of flame temperature measured by the high-speed camera is presented by phase-averaged with average subtracted images and by fast Fourier transform (FFT) at the modulation frequencies of inlet velocity. Simultaneous measurement of combustor inlet air velocity and flame soot temperature using the multiwavelength pyrometer is used in calculating the flame transfer function (FTF) of flame temperature response to unsteady inlet airflow. The results of global temperature and temperature fluctuation from the three-color pyrometer show qualitative agreement with the local temperature response measured by the high-speed camera. Over the range of operating conditions employed, the overall flame temperature fluctuation increases linearly with respect to the inlet velocity fluctuation. The two-dimensional map of flame temperature under unsteady combustion determined using a high-speed digital color camera shows that the local temperature fluctuation during unsteady combustion occurs over relatively small region of flame and its level is greater (∼10% to 20%) than that of overall temperature fluctuation (∼1%).

Novel Chroma Subsampling Strategy Based on Mathematical Optimization for Compressing Mosaic Videos With Arbitrary RGB Color Filter Arrays in H.264/AVC and HEVC

To save manufacturing cost, most color digital video cameras employ a single-sensor technology with a red–green–blue (RGB) color filter array (CFA) to capture real-world scenes. Due to only one primary color measured at each pixel location, the captured videos are usually referred to as the mosaic videos. For the purposes of economical storage and transmission, it is very important to achieve a good tradeoff between the quality and bitrate when compressing mosaic videos with different RGB-CFA structures. In this paper, based on mathematical optimization technique, a novel chroma subsampling strategy is presented for compressing mosaic videos with arbitrary RGB-CFA structures in H.264/AVC and High Efficiency Video Coding (HEVC). For each $2 \times 2$ YUV block to be subsampled with 4:2:0 format, the proposed strategy determines the proper sampled $U$ and $V$ components by minimizing, prior to compression, the quality distortion between the original colocated mosaic block and the mosaic block conversed from the current subsampled YUV block. Through the mathematical optimization formulated in the proposed strategy, the significance of the sampled $U$ and $V$ components for reconstructing R, G, and B pixels can be simultaneously taken into consideration. The experimental results demonstrate that the proposed chroma subsampling strategy has the best quality and bitrate tradeoff at a similar execution time requirement for compressing mosaic videos with arbitrary RGB-CFA structures in H.264/AVC and HEVC compared with the state-of-the-art ones by Chen et al. and Yang et al. as well as the three commonly used ones.

Accumulation and Toxicity of Superparamagnetic Iron Oxide Nanoparticles in Cells and Experimental Animals.

The uptake and distribution of negatively charged superparamagnetic iron oxide (Fe3O4) nanoparticles (SPIONs) in mouse embryonic fibroblasts NIH3T3, and magnetic resonance imaging (MRI) signal influenced by SPIONs injected into experimental animals, were visualized and investigated. Cellular uptake and distribution of the SPIONs in NIH3T3 after staining with Prussian Blue were investigated by a bright-field microscope equipped with digital color camera. SPIONs were localized in vesicles, mostly placed near the nucleus. Toxicity of SPION nanoparticles tested with cell viability assay (XTT) was estimated. The viability of NIH3T3 cells remains approximately 95% within 3–24 h of incubation, and only a slight decrease of viability was observed after 48 h of incubation. MRI studies on Wistar rats using a clinical 1.5 T MRI scanner were showing that SPIONs give a negative contrast in the MRI. The dynamic MRI measurements of the SPION clearance from the injection site shows that SPIONs slowly disappear from injection sites and only a low concentration of nanoparticles was completely eliminated within three weeks. No functionalized SPIONs accumulate in cells by endocytic mechanism, none accumulate in the nucleus, and none are toxic at a desirable concentration. Therefore, they could be used as a dual imaging agent: as contrast agents for MRI and for traditional optical biopsy by using Prussian Blue staining.

Detecting tomatoes in greenhouse scenes by combining AdaBoost classifier and colour analysis

Despite the rapid development of agricultural robotics, a lack of access to automatic fruit detection and precision picking is limiting the commercial application of harvesting robots. An algorithm for the automatic detection of ripe tomatoes in greenhouse was developed for a simple machine vision system. The images of tomato planting scenes were captured by a colour digital camera, and most of the ripe tomatoes were correctly recognised using the proposed algorithm. The proposed tomato detection approach worked in two steps: (1) by extracting the Haar-like features of grey scale image and classifying with the AdaBoost classifier, the possible tomato objects were identified; (2) the false negatives in the results of classification were eliminated using average pixel value (APV) based colour analysis approach. Comparative test results showed that the C style of Haar-like features and I component image were optimum in the proposed algorithm. The results of validation experiments show that combination of AdaBoost classification and colour analysis can correctly detect over 96% of ripe tomatoes in the real-world environment. However, the false negative rate was about 10% and 3.5% of the tomatoes were not detected.

Multispectral and colour imaging systems for the detection of small vertebrate fossils: A preliminary study

The process of separating small vertebrate fossils from sediment is a monotonous exercise, usually carried out manually and mainly based on morphological features. The large amount of time and personnel required results in the high economic costs associated with this antiquated technique. In this study we test the potential of colour and multispectral imaging for the detection of small vertebrate fossils to facilitate their separation from sediment. This technique is based on the analysis of the spectral and fluorescence characteristics of fossils. Specifically, a multispectral system with a charge-coupled device (CCD) camera attached to a liquid crystal tunable filter and a digital colour camera was used in combination with daylight and ultraviolet light sources. The results show that the reflectance of bones, teeth and sediment are usually different at longer wavelengths, and that fluorescence rates also differ in the bluegreen region. Unfortunately, samples from different paleontological sites present different patterns, hindering the standardization of a method that discriminates microfossils from sediments. However, the combination of colour and multispectral imaging systems with recent advances in laser-stimulated fluorescence in fossils could constitute an optimal solution for an efficient sorting process of small vertebrate remains within geological samples.

Therapeutic effects of dihydroartemisinin and transferrin against glioblastoma.

BACKGROUND/OBJECTIVESArtemisinin, a natural product isolated from Gaeddongssuk (artemisia annua L.) and its main active derivative, dihydroartemisinin (DHA), have long been used as antimalarial drugs. Recent studies reported that artemisinin is efficacious for curing diseases, including cancers, and for improving the immune system. Many researchers have shown the therapeutic effects of artemisinin on tumors such as breast cancer, liver cancer and kidney cancer, but there is still insufficient data regarding glioblastoma (GBM). Glioblastoma accounts for 12-15% of brain cancer, and the median survival is less than a year, despite medical treatments such as surgery, radiation therapy, and chemotherapy. In this study, we investigated the anti-cancer effects of DHA and transferrin against glioblastoma (glioblastoma multiforme, GBM).MATERIALS/METHODSThis study was performed through in vitro experiments using C6 cells. The toxicity dependence of DHA and transferrin (TF) on time and concentration was analyzed by MTT assay and cell cycle assay. Observations of cellular morphology were recorded with an optical microscope and color digital camera. The anti-cancer mechanism of DHA and TF against GBM were studied by flow cytometry with Annexin V and caspase 3/7.RESULTSMTT assay revealed that TF enhanced the cytotoxicity of DHA against C6 cells. An Annexin V immune-precipitation assay showed that the percentages of apoptosis of cells treated with TF, DHA alone, DHA in combination with TF, and the control group were 7.15 ± 4.15%, 34.3 ± 5.15%, 66.42 ± 5.98%, and 1.2 ± 0.15%, respectively. The results of the Annexin V assay were consistent with those of the MTT assay. DHA induced apoptosis in C6 cells through DNA damage, and TF enhanced the effects of DHA.CONCLUSIONThe results of this study demonstrated that DHA, the derivative of the active ingredient in Gaeddongssuk, is effective against GBM, apparently via inhibition of cancer cell proliferation by a pharmacological effect. The role of transferrin as an allosteric activator in the GBM therapeutic efficacy of DHA was also confirmed.

Measurement of food colour in L*a*b* units from RGB digital image using least squares support vector machine regression

The aim of this work is to evaluate the potential of least squares support vector machine (LS-SVM) regression to develop an efficient method to measure the colour of food materials in L*a*b* units by means of a computer vision systems (CVS). A laboratory CVS, based on colour digital camera (CDC), was implemented and three LS-SVM models were trained and validated, one for each output variables (L*, a*, and b*) required by this problem, using the RGB signals generated by the CDC as input variables to these models. The colour target-based approach was used to camera characterization and a standard reference target of 242 colour samples was acquired using the CVS and a colorimeter. This data set was split in two sets of equal sizes, for training and validating the LS-SVM models. An effective two-stage grid search process on the parameters space was performed in MATLAB to tune the regularization parameters γ and the kernel parameters σ<sup>2</sup> of the three LS-SVM models. A 3-8-3 multilayer feed-forward neural network (MFNN), according to the research conducted by León <em>et al.</em> (2006), was also trained in order to compare its performance with those of LS-SVM models. The LS-SVM models developed in this research have been shown better generalization capability then the MFNN, allowed to obtain high correlations between L*a*b* data acquired using the colorimeter and the corresponding data obtained by transformation of the RGB data acquired by the CVS. In particular, for the validation set, R<sup>2</sup> values equal to 0.9989, 0.9987, and 0.9994 for L*, a* and b* parameters were obtained. The root mean square error values were 0.6443, 0.3226, and 0.2702 for L*, a*, and b* respectively, and the average of colour differences ΔE<sub>ab</sub> was 0.8232±0.5033 units. Thus, LS-SVM regression seems to be a useful tool to measurement of food colour using a low cost CVS.