Hue Component Research Articles

Intuitionistic fuzzy set and fuzzy mathematical morphology applied to color leukocytes segmentation

This work presents a new algorithm based on Atanassov’s intuitionistic fuzzy sets and fuzzy mathematical morphology to leukocytes segmentation in color images. The main idea is based on modeling a color image as an Atanassov’s intuitionistic fuzzy set using the hue component in the HSV color space. Then, a pixel labeled as leukocyte is selected and compared to the whole image with a similarity measure. Thus, the leukocyte is segmented and separated from the rest of the image. The experimental results show that the algorithm has a good performance, reaching a value of 99.41% for the correct classification of leukocytes and a 99.23% for the correct classification of the background. Other metrics such as accuracy, precision and recall have been calculated obtaining 99.32%, 99.41% and 99.24%, respectively. The algorithm presents two important characteristics: It works directly over the color images without the need of converting the image in gray scale, and it does not produce false colors because fuzzy morphological operators guarantee it.

A real-time approach for automatic defect detection from PCBs based on SURF features and morphological operations

This paper presents an automatic inspection approach for Printed Circuit Boards (PCBs) with accurate determination of the fault location and identification of the fault type. This approach depends on several digital image processing techniques including registration, filtering, foreground segmentation, mathematical morphological operations, subtraction, feature extraction, and component matching. The Speeded Up Robust Feature extraction (SURF) technique is used for two purposes: registration of the PCB to be checked to a reference PCB and detection of feature points of each missing component on the PCB that is localized from the subtraction process from the reference PCB. Operation is performed on the hue component of the color PCB images. A dictionary is first built for all possible components on the available PCBs with SURF feature descriptors, and hence if a missing item is detected on a PCB during the inspection process, the SURF feature descriptors for features extracted from the difference between the tested and reference PCBs at the position of the lost component are matched with those in the built dictionary or database. A distance metric is used in the matching process. The importance of the proposed approach lies in its ability to build a dictionary of feature descriptors for all possible components in a diversity of PCBs and its ability to localize and identify the missing components regardless of the PCB position, rotation, or type. All operations are formulated in a Graphical User Interface (GUI) using MATLAB environment.

Computer vision-based localisation of picking points for automatic litchi harvesting applications towards natural scenarios

Locating picking points plays an important role in robotic litchi harvesting in orchards. However, the localisation accuracy can easily be affected by unstructured growing environments with variable illumination conditions and the unpredictable growing shapes of weight-bearing stems carrying litchi clusters. A computer-vision-based method is proposed to locate acceptable picking points for litchi clusters. To improve the illumination distributions of weakly illuminated images while leaving those of well-illuminated images unchanged, an iterative retinex algorithm was developed. Then, the litchi regions are segmented by combining red/green chromatic mapping and Otsu thresholding in the RGB colour space. By incorporating both local spatial and local hue-level intensity relationships, the hue component in the HSV colour space is re-constructed to filter the noise, and the stem regions are segmented in accordance with the distributed intervals of hue-level intensity. Finally, based on the connection and position relationship among the segmented litchi and stem regions, the structural distribution of the corners generated via Harris corner detection is analysed to locate the picking points. More than 97% of the pixels within litchi regions are correctly segmented after illumination compensation, and an improvement of 7% in the number of correctly segmented pixels within stem regions is achieved with the re-construction of the hue-level intensity. The overall performance of the proposed method was evaluated on test images with high illumination diversity, and accuracy greater than 83% in locating litchi picking points was achieved, showing better performance than three similar frameworks that do not incorporate our proposed procedures.

Renkli Resimlerin Yüksek Boyutlu Kaotik Lineer Dönüşümlerle Şifrelenmesi

In this study, a well-known chaotic transformation namely Arnold’s CAT map is used to extend 2-dimensional mapping to a higher dimension. This extension is achieved by means of the planar extension and Multinacci series. The demonstration of a 3-dimensional Arnold’s CAT map is performed by RGB component substitution of a colored image. For this purpose, the colored image is converted from a standard RGB space into an intensity-hue-saturation (IHS) space. Consequently, both Chebyshev and Hadamard map is employed for encryption of the intensity component. Besides, CAT map is engaged to encrypt the hue and saturation components. According to the results, the proposed method has a great potential to be an efficient tool for data encryption.

Image Segmentation Approach for Acute and Chronic Leukaemia Based on Blood Sample Images

The uncontrolled development of abnormal white blood cells (blast) in bone marrow is the starting point where leukaemia cancer begins. It does not die like it should. Instead, it goes on dividing and crowding out the healthy blood cells, making it difficult for these healthy bloods to function normally. The diagnosis process from haematologist consumes a lot of time. Therefore, a good segmentation procedure is necessary in order to successfully identify and diagnose leukaemia cells automatically from blood sample images. This paper proposes segmentation procedures which consist of contrast enhancement, extraction of hue component information, as well as segmentation based on Otsu’s thresholding and watershed technique. The experimental results shows that the proposed segmentation procedure has successfully segmented 200 images consisting of acute and chronic leukaemia with average accuracy, sensitivity and specificity obtained of 98.90%, 82.14% and 99.49%, respectively. The result of segmentation performance achieved shows the significant of this approach. Based on results obtained, this segmentation technique is applicable to segment both acute and chronic leukaemia images with good segmentation performance.

Active phase error suppression for color phase-shifting fringe projection based on hue pre-correction

Color phase-shifting fringe projection is one of the single-shot three-dimensional shape measurement techniques based on fringe projection profilometry. In practice, the nonlinearity and color crosstalk of the projector-camera system yield undesired phase errors when using phase-shifting algorithm. In this paper, an active phase error suppression method based on hue pre-correction is proposed. Theoretical analysis of phase error is performed taking account of the gamma nonlinearity of the camera. Based on the analysis, the distortion of hue component of the acquired color fringe pattern is employed to represent the overall phase error induced by the combined effect of nonlinearity and color crosstalk. In this method, the hue is pre-corrected to be linear by measuring the function of the combined effect and modifying the computer-generated hue according to the source-distorted relationship. Then, the new modified color fringe pattern can be generated and saved for the further measurement of objects. Experimental results demonstrate the feasibility of the proposed method and it can effectively suppress the induced overall phase error caused by the nonlinearity and color crosstalk of the projector-camera system.

Study Fire Detection Based On Color Spaces

In this paper, the fire color feature is analysis and test on a set of color spaces (RGB, HSV, YCbCr, Lab, Yiq) to account the hue component and determination the best color space to represent the properties of fire and used in fire detection to increase accuracy and reduce the detection time and false alarms,four common types of fire (wood, cork, cloth, paper or cardboard ) were used to compare them with an image containing all the basic colors and analyze them and calculating Scale factor that depending on the histogram for hues value

Automatic Recognition of Ripening Tomatoes by Combining Multi-Feature Fusion with a Bi-Layer Classification Strategy for Harvesting Robots

Automatic recognition of ripening tomatoes is a main hurdle precluding the replacement of manual labour by robotic harvesting. In this paper, we present a novel automatic algorithm for recognition of ripening tomatoes using an improved method that combines multiple features, feature analysis and selection, a weighted relevance vector machine (RVM) classifier, and a bi-layer classification strategy. The algorithm operates using a two-layer strategy. The first-layer classification strategy aims to identify tomato-containing regions in images using the colour difference information. The second classification strategy is based on a classifier that is trained on multi-medium features. In our proposed algorithm, to simplify the calculation and to improve the recognition efficiency, the processed images are divided into 9 × 9 pixel blocks, and these blocks, rather than single pixels, are considered as the basic units in the classification task. Six colour-related features, namely the Red (R), Green (G), Blue (B), Hue (H), Saturation (S) and Intensity (I) components, respectively, colour components, and five textural features (entropy, energy, correlation, inertial moment and local smoothing) were extracted from pixel blocks. Relevant features and their weights were analysed using the iterative RELIEF (I-RELIEF) algorithm. The image blocks were classified into different categories using a weighted RVM classifier based on the selected relevant features. The final results of tomato recognition were determined by combining the block classification results and the bi-layer classification strategy. The algorithm demonstrated the detection accuracy of 94.90% on 120 images, this suggests that the proposed algorithm is effective and suitable for tomato detection

Degradation kinetics of anthocyanin and physicochemical changes in fermented turnip juice exposed to pulsed UV light

In this study, the effects of pulsed UV (PUV) light on the degradation kinetics of anthocyanins and physicochemical properties of turnip juice were investigated. PUV light was applied to turnip juice at 3 different distances (5, 8, 13cm) from the quartz window of the xenon lamp for 5 different times (5, 15, 30, 45, 60s). The pH, total acidity (% lactic acid), monomeric anthocyanin content, color density, hue, brightness, and percent color components (yellow, red, and blue) of turnip juice changed significantly after PUV-light treatments at each level. The maximum degradation of anthocyanin after PUV-light treatments was found to be about 63%. The anthocyanin degradation, brightness, yellow and blue color (%) increased, while red color (%) decreased with longer treatment time and shorter distance. The degradation of monomeric anthocyanins in turnip juice exposed to PUV light was described by the Weibull model (R2 0.982-0.998, RMSE 0.087-0.133) more accurately than the first-order kinetics (R2 0.906-0.992, RMSE 0.071-0.192).

A Mixed Property-Based Automatic Shadow Detection Approach for VHR Multispectral Remote Sensing Images

Shadows in very high-resolution multispectral remote sensing images hinder many applications, such as change detection, target recognition, and image classification. Though a wide variety of significant research has explored shadow detection, shadow pixels are still more or less omitted and are wrongly confused with vegetation pixels in some cases. In this study, to further manage the problems of shadow omission and vegetation misclassification, a mixed property-based shadow index is developed for detecting shadows in very high-resolution multispectral remote sensing images based on the difference of the hue component and the intensity component between shadows and nonshadows, and the difference of the reflectivity of the red band and the near infrared band between shadows and vegetation cover in nonshadows. Then, the final shadow mask is achieved, with an optimal threshold automatically obtained from the index image histogram. To validate the effectiveness of our approach for shadow detection, three test images are selected from the multispectral WorldView-3 images of Rio de Janeiro, Brazil, and are tested with our method. When compared with other investigated standard shadow detection methods, the resulting images produced by our method deliver a higher average overall accuracy (95.02%) and a better visual sense. The highly accurate data show the efficacy and stability of the proposed approach in appropriately detecting shadows and correctly classifying shadow pixels against the vegetation pixels for very high-resolution multispectral remote sensing images.

Tongue image segmentation via color decomposition and thresholding

SummaryTongue image segmentation is a crucial step in developing an automatic tongue diagnosis system. After exploring characteristics of image thresholding in different color spaces, we propose a simple and effective tongue image segmentation method. Specifically, the proposed method first extracts an initial tongue body region by performing image thresholding on the transformed hue component in HSI (Hue, Saturation, and Intensity) color space. Then, one of two image thresholding results on the red component of an original tongue image is adaptively selected to find the gap region between the tongue body root and the upper lip. Finally, the initial tongue body region is refined by removing fake object regions such as the upper lip to obtain final tongue image segmentation result. Experimental results on a tongue image dataset containing 200 subjects demonstrate the effectiveness of the proposed method.

Synthesis and Characterization of Yellow Pigments of Bi1.7RE0.3W0.7Mo0.3O6 (RE = Y, Yb, Gd, Lu) with High NIR Reflectance

Brilliant yellow-orange pigments based on the formula Bi1.7RE0.3W0.7Mo0.3O6 (RE = Y, Yb, Gd, Lu) are developed by the traditional solid-state reaction method. Relevant techniques are used for characterization of the crystal structure, morphology feature, reflectance property, and color performance of the prepared pigment. The doping of RE/Mo in Bi2WO6 results in the colors of pigments varying from faint yellow to brilliant yellow-orange. Compared with Bi2WO6 pigment, the typical pigment of Bi1.7Lu0.3W0.7Mo0.3O6 greatly improves the yellow hue component (b*) value to be 72.03. Moreover, the NIR reflectances of all of the samples are higher than 95% , and their NIR solar reflectances are above 94%. The typical pigment of Bi1.7Lu0.3W0.7Mo0.3O6 coated on galvanized sheet exhibits excellent coloring performance with a high NIR solar reflectance (83.32%). The acid/alkali resistance studies reveal that the pigments are chemically stable under the atrocious weather conditions. Thus, the superior performances of s...

MULTIMODAL MEDICAL IMAGE FUSION USING IHS-DTCWT-PCA INTEGRATED APPROACH FOR EXTRACTING TUMOR FEATURES

Medical image fusion is a technique where multiple imaging modalities are merged together to obtain a single image that contains maximum information. Spatial domain approach such as IHS-PCA fusion provides best visual quality but required large storage space and also lags directional information. A novel technique is proposed here that integrates Dual Tree Complex Wavelet Transform (DTCWT) with PCA using histogram matching and IHS space that improves the fusion of MRI with PET/SPECT images. The multispectral image PET/SPECT with RGB channels are converted to intensity hue and saturation components by IHS transform. Pathological information in the images can be highlighted in multiscale and multidirection by using DTCWT. PCA with weighted average fusion rule is used for extracting tumor features by using principal components. Visual and quantitative analysis show that our algorithm provides high structural information content, high mutual information with high spatial and spectral resolution thereby enhancing the tumor region compared to other methods.

Plasticine modeling of material mixing in friction stir welding

Plasticine of primary colours was utilized and the hue attribute of generated secondary colour was tracked. The degree of mixing was indicated by the intensity of secondary colour. Hue component was obtained from digital images of joined plasticine cross-section by converting RGB colour-maps to HSV colour-maps. Effect of tool pin profile that included circular, hexagonal, square and triangular was examined in this context. As the number of faces on the tool pin reduces, up-till pin with four faces (square) the pin induced mixing increases and results in the elimination of joint line remnant. Material adjacent to the face was transported in lumps and experienced spinning/whirling movement. During mixing phenomenon flow velocities of constituents undergoing it tends to fluctuate. The fluctuation is significantly large for higher levels of mixing and the same was noticed for a square profile from the results of finite element simulations. The results obtained from plasticine and finite element simulations are validated using aluminium weld.

Hue preserving‐based approach for underwater colour image enhancement

In this study, a novel underwater colour image enhancement approach based on hue preserving is presented by combining hue-saturation-intensity (HSI) and HS-value (HSV) colour models. In this study, the proposed wavelet-domain filtering (WDF) and constrained histogram stretching (CHS) algorithms are operated on HSI and HSV colour models, respectively. The degraded image is first converted from red-green-blue colour model into the HSI colour model, wherein the hue component H is preserved and WDF algorithm is executed on the S and I components. Similarly, the image is further converted into the HSV colour model, wherein H component is kept invariant as well and CHS algorithm is applied on the S and V components. The authors' key contribution is that the H preserving method can improve image quality in terms of contrast, colour rendition, non-uniform illumination, and denoising. In addition, experimental results show that the proposed approach outperforms several other state-of-the-art algorithms.

Fourier transform near infrared diffuse reflectance spectroscopy and two spectral acquisition modes for evaluation of external and internal quality of intact pomegranate fruit

Fourier transform near infrared (FT-NIR) diffuse reflectance spectroscopy as a non-destructive method for the development of calibration models has been investigated as a means of assessing the quality of whole pomegranate fruit. FT-NIR diffuse reflectance spectrometers with different spectral acquisition modes were evaluated using direct contact between the sample and the integrating sphere (IS), or by a contact-less measurement using an optic fibre coupled emission head (EH) to scan fruit over a distance of 170 mm. Fruit weight, firmness and colour components (a*, Chroma, hue angle), total soluble solids (TSS), pH, titratable acidity (TA), sugar to acid ratio (TSS:TA), BrimA, total phenolics, total anthocyanin and vitamin C. The best prediction statistics obtained from calibration models of the EH were firmness (R2 = 83.0, residual predictive deviation (RPD) = 2.43), fruit colour components (a*: R2 = 90.9, RPD = 3.34); Chroma: R2 = 83.0, RPD = 2.43, TSS (R2 = 78.1, RPD = 2.17), TA (R2 = 76.8, RPD = 2.12), BrimA (R2 = 76.2, RPD = 2.08), total phenolics (R2 = 88.0, RPD = 2.91) and vitamin C (R2 = 76.2, RPD = 2.06). The best prediction obtained from calibration models of IS were colour component Hue (R2 = 83.9, RPD = 2.50), TSS:TA (R2 = 86.8, RPD = 2.72) and total anthocyanin (R2 = 62.6, RPD = 1.64). Overall, good prediction was observed for both the EH and IS; however, better prediction performance was obtained with the EH which gave the best prediction for 9 of the 13 quality parameters evaluated. These findings have demonstrated that the EH (a contactless option of the Matrix-F) can be implemented as an online tool for the analysis of pomegranate fruit quality.

Weighted intensity hue saturation transform for image enhancement and pansharpening

In this paper, a weighted intensity hue saturation transform (WIHS) is proposed for image enhancement and pansharpening. The proposed transform obtains the intensity component of the classical intensity hue saturation (IHS) transform, by weighting the bands (red, green, and blue) of the image, whereas the hue and saturation components remain unchanged. The weight for each band is determined according to the brightness interval of the corresponding band and its standard deviation. The purpose of obtaining the intensity component by weighting is to keep more information from the bands of the input image. The classical intensity component used in image processing algorithms is replaced with the proposed weighted intensity component. The visual and quantitative comparisons show that the proposed transform is superior to the classical IHS transform.

Hue preserving colour image enhancement models in RGB colour space without gamut problem

All hue preserving colour image enhancement techniques are associated with the change of colour space, such as RGB to hue saturation intensity (HSI), hue saturation value (HSV), lightness-hue-saturation (LHS), and YUV etc. In these colour spaces intensity or saturation or both have been processed then recombined with unchanged hue component to build a hue preserving method. But all the above techniques are time-consuming for colour space conversion and also introduce gamut problem for which the values of enhanced pixels may not lie within their respective range. In this study, three hue preserving models corresponding to three colour spaces viz. HSI, HSV and YUV, have been proposed by considering only RGB and CMY colour spaces. Any grey level contrast enhancement method can be successfully employed for a colour image through those models. In this paper, a novel variant of Histogram equalisation (HE) has been proposed based on entropy based segmentation to enhance the contrast. The proposed variant called entropy based brightness preserved dynamic histogram equalisation (EBBPDHE) is a modification of brightness preserved dynamic histogram equalisation (BPDHE).

Taking advantage of multi-regions-based diagonal texture structure descriptor for image retrieval

With the rapid development of information technology, the capacity of Web images database becomes larger and larger. How to quickly and effectively find the desired images in Web image databases becomes the challenge needed to resolve with high priority. In this paper a novel diagonal texture structure descriptor (DTSD) is proposed, and a new framework considering hue, saturation and value components is utilized for image retrieval. In specific, we firstly use Otsu algorithm to segment image into foreground and background, and the features of multi-regions are respectively considered. That is, we present the contents of these multi-regions distinctively to reduce the influence of each other, which would perform hierarchical feature description and realize more accurate content match for image retrieval. In this study, to simulate the characteristic of human eyes for perceiving colors, hue and saturation components are quantized into various bins which can obtain more detailed description for color difference. Meanwhile, DTSD is extracted based on value component to represent the edge information as the feature of receptive field. Such a method can improve the spatial resolution ability of the descriptor, and identify finer structure of an image. Moreover, histogram with respect to these three components, i.e., hue, saturation and value, is utilized to generate the feature vector of an image. We carry out the experiments on benchmark Corel and UCID image datasets, and the extensive experimental results demonstrate that our method achieves better performance in comparison with state of the art image retrieval algorithms. The proposed method is very promising, which can provide more accurate retrieved results on the basis of color & texture descriptions in multi-regions, and further enhances the performance of the intelligent image retrieval system.

AUTOMATIC SHADOW DETECTION IN AERIAL AND TERRESTRIAL IMAGES

Abstract: Shadows exist in almost all aerial and outdoor images, and they can be useful for estimating Sun position estimation or measuring object size. On the other hand, they represent a problem in processes such as object detection/recognition, image matching, etc., because they may be confused with dark objects and change the image radiometric properties. We address this problem on aerial and outdoor color images in this work. We use a filter to find low intensities as a first step. For outdoor color images, we analyze spectrum ratio properties to refine the detection, and the results are assessed with a dataset containing ground truth. For the aerial case we validate the detections depending of the hue component of pixels. This stage takes into account that, in deep shadows, most pixels have blue or violet wavelengths because of an atmospheric scattering effect.