Search results for: color texture classification

Authors: Seynabou Toure, Oumar Diop, Kidiyo Kpalma, Amadou S. Maiga

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Color and texture are the two most determinant elements for perception and recognition of the objects in an image. For this reason, color and texture analysis find a large field of application, for example in image classification and segmentation. But, the pioneering work in texture analysis was conducted on grayscale images, thus discarding color information. Many grey-level texture descriptors have been proposed and successfully used in numerous domains for image classification: face recognition, industrial inspections, food science medical imaging among others. Taking into account color in the definition of these descriptors makes it possible to better characterize images. Color texture is thus the subject of recent work, and the analysis of color texture images is increasingly attracting interest in the scientific community. In optical remote sensing systems, sensors measure separately different parts of the electromagnetic spectrum; the visible ones and even those that are invisible to the human eye. The amounts of light reflected by the earth in spectral bands are then transformed into grayscale images. The primary natural colors Red (R) Green (G) and Blue (B) are then used in mixtures of different spectral bands in order to produce RGB images. Thus, good color texture discrimination can be achieved using RGB under controlled illumination conditions. Some previous works investigate the effect of using different color space for color texture classification. However, the selection of the best performing color space in land-sea segmentation is an open question. Its resolution may bring considerable improvements in certain applications like coastline detection, where the detection result is strongly dependent on the performance of the land-sea segmentation. The aim of this paper is to present the results of a study conducted on different color spaces in order to show the best-performing color space for land-sea segmentation. In this sense, an experimental analysis is carried out using five different color spaces (RGB, XYZ, Lab, HSV, YCbCr). For each color space, the Haar wavelet decomposition is used to extract different color texture features. These color texture features are then used for Fusion of Over Segmentation (FOOS) based classification; this allows segmentation of the land part from the sea one. By analyzing the different results of this study, the HSV color space is found as the best classification performance while using color and texture features; which is perfectly coherent with the results presented in the literature.

Keywords: classification, coastline, color, sea-land segmentation

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Authors: Khalid Salhi, El Miloud Jaara, Mohammed Talibi Alaoui

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In this paper, we present a neural approach for unsupervised natural color-texture image segmentation, which is based on both Kohonen maps and mathematical morphology, using a combination of the texture and the image color information of the image, namely, the fractal features based on fractal dimension are selected to present the information texture, and the color features presented in RGB color space. These features are then used to train the network Kohonen, which will be represented by the underlying probability density function, the segmentation of this map is made by morphological watershed transformation. The performance of our color-texture segmentation approach is compared first, to color-based methods or texture-based methods only, and then to k-means method.

Keywords: segmentation, color-texture, neural networks, fractal, watershed

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Authors: Sujata S. Kulkarni

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Effective and efficient texture feature extraction and classification is an important problem in image understanding and recognition. This paper gives a review on effective texture classification method. The objective of the problem of texture representation is to reduce the amount of raw data presented by the image, while preserving the information needed for the task. Texture analysis is important in many applications of computer image analysis for classification include industrial and biomedical surface inspection, for example for defects and disease, ground classification of satellite or aerial imagery and content-based access to image databases.

Keywords: compressed sensing, feature extraction, image classification, texture analysis

Procedia PDF Downloads 397

Authors: Mohamed Alimoussa, Alice Porebski, Nicolas Vandenbroucke, Rachid Oulad Haj Thami, Sana El Fkihi

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Color and texture are highly discriminant visual cues that provide an essential information in many types of images. Color texture representation and classification is therefore one of the most challenging problems in computer vision and image processing applications. Color textures can be represented in different color spaces by using multiple image descriptors which generate a high dimensional set of texture features. In order to reduce the dimensionality of the feature set, feature selection techniques can be used. The goal of feature selection is to find a relevant subset from an original feature space that can improve the accuracy and efficiency of a classification algorithm. Traditionally, feature selection is focused on removing irrelevant features, neglecting the possible redundancy between relevant ones. This is why some feature selection approaches prefer to use feature clustering analysis to aid and guide the search. These techniques can be divided into two categories. i) Feature clustering-based ranking algorithm uses feature clustering as an analysis that comes before feature ranking. Indeed, after dividing the feature set into groups, these approaches perform a feature ranking in order to select the most discriminant feature of each group. ii) Feature clustering-based subset search algorithms can use feature clustering following one of three strategies; as an initial step that comes before the search, binded and combined with the search or as the search alternative and replacement. In this paper, we propose a new feature clustering-based sequential selection approach for the purpose of color texture representation and classification. Our approach is a three step algorithm. First, irrelevant features are removed from the feature set thanks to a class-correlation measure. Then, introducing a new automatic feature clustering algorithm, the feature set is divided into several feature clusters. Finally, a sequential search algorithm, based on a filter model and a separability measure, builds a relevant and non redundant feature subset: at each step, a feature is selected and features of the same cluster are removed and thus not considered thereafter. This allows to significantly speed up the selection process since large number of redundant features are eliminated at each step. The proposed algorithm uses the clustering algorithm binded and combined with the search. Experiments using a combination of two well known texture descriptors, namely Haralick features extracted from Reduced Size Chromatic Co-occurence Matrices (RSCCMs) and features extracted from Local Binary patterns (LBP) image histograms, on five color texture data sets, Outex, NewBarktex, Parquet, Stex and USPtex demonstrate the efficiency of our method compared to seven of the state of the art methods in terms of accuracy and computation time.

Keywords: feature selection, color texture classification, feature clustering, color LBP, chromatic cooccurrence matrix

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Authors: Sidra Naeem, Ayesha Naeem, Sahar Rahim, Nadia Nawaz Qadri

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Citrus greening is a bacterial disease that causes considerable damage to citrus fruits worldwide. Efficient method for this disease detection must be carried out to minimize the production loss. This paper presents a pattern recognition system that comprises three stages for the detection of citrus greening from Orange leaves: segmentation, feature extraction and classification. Image segmentation is accomplished by adaptive thresholding. The feature extraction stage comprises of three visual descriptors i.e. shape, color and texture. From shape feature we have used asymmetry index, from color feature we have used histogram of Cb component from YCbCr domain and from texture feature we have used local binary pattern. Classification was done using support vector machines and k nearest neighbors. The best performances of the system is Accuracy = 88.02% and AUROC = 90.1% was achieved by automatic segmented images. Our experiments validate that: (1). Segmentation is an imperative preprocessing step for computer assisted diagnosis of citrus greening, and (2). The combination of shape, color and texture features form a complementary set towards the identification of citrus greening disease.

Keywords: citrus greening, pattern recognition, feature extraction, classification

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Authors: Jia-Hong Lee, Mei-Yi Wu, Hsien-Tsung Kuo

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Texture is an important characteristic in real and synthetic scenes. Texture analysis plays a critical role in inspecting surfaces and provides important techniques in a variety of applications. Although several descriptors have been presented to extract texture features, the development of object recognition is still a difficult task due to the complex aspects of texture. Recently, many robust and scaling-invariant image features such as SIFT, SURF and ORB have been successfully used in image retrieval and object recognition. In this paper, we have tried to compare the performance for texture classification using these feature descriptors with k-means clustering. Different classifiers including K-NN, Naive Bayes, Back Propagation Neural Network , Decision Tree and Kstar were applied in three texture image sets － UIUCTex, KTH-TIPS and Brodatz, respectively. Experimental results reveal SIFTS as the best average accuracy rate holder in UIUCTex, KTH-TIPS and SURF is advantaged in Brodatz texture set. BP neuro network works best in the test set classification among all used classifiers.

Keywords: texture classification, texture descriptor, SIFT, SURF, ORB

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Authors: Tajinder Kaur, Anu Bala

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An image retrieval system is a computer system for browsing, searching, and retrieving images from a large database of digital images a new algorithm meant for content-based image retrieval (CBIR) is presented in this paper. The proposed method combines the color and texture features which are extracted the global and local information of the image. The local texture feature is extracted by using local binary patterns (LBP), which are evaluated by taking into consideration of local difference between the center pixel and its neighbors. For the global color feature, the color histogram (CH) is used which is calculated by RGB (red, green, and blue) spaces separately. In this paper, the combination of color and texture features are proposed for content-based image retrieval. The performance of the proposed method is tested on Corel 1000 database which is the natural database. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to LBP and CH.

Keywords: color, texture, feature extraction, local binary patterns, image retrieval

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Authors: Tudor Barbu

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An automatic moment-based texture segmentation approach is proposed in this paper. First, we describe the related work in this computer vision domain. Our texture feature extraction, the first part of the texture recognition process, produces a set of moment-based feature vectors. For each image pixel, a texture feature vector is computed as a sequence of area moments. Second, an automatic pixel classification approach is proposed. The feature vectors are clustered using some unsupervised classification algorithm, the optimal number of clusters being determined using a measure based on validation indexes. From the resulted pixel classes one determines easily the desired texture regions of the image.

Keywords: image segmentation, moment-based, texture analysis, automatic classification, validation indexes

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Authors: Mohd Zamri Osman, Mohd Aizaini Maarof, Mohd Foad Rohani

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Human skin detection recognized as the primary step in most of the applications such as face detection, illicit image filtering, hand recognition and video surveillance. The performance of any skin detection applications greatly relies on the two components: feature extraction and classification method. Skin color is the most vital information used for skin detection purpose. However, color feature alone sometimes could not handle images with having same color distribution with skin color. A color feature of pixel-based does not eliminate the skin-like color due to the intensity of skin and skin-like color fall under the same distribution. Hence, the statistical color analysis will be exploited such mean and standard deviation as an additional feature to increase the reliability of skin detector. In this paper, we studied the effectiveness of statistical color feature for human skin detection. Furthermore, the paper analyzed the integrated color and texture using eight classifiers with three color spaces of RGB, YCbCr, and HSV. The experimental results show that the integrating statistical feature using Random Forest classifier achieved a significant performance with an F1-score 0.969.

Keywords: color space, neural network, random forest, skin detection, statistical feature

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Authors: Insaf Bellamine, Hamid Tairi

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Detecting moving objects in sequences is an essential step for video analysis. This paper mainly contributes to the Color Space-Time Interest Points (CSTIP) extraction and detection. We propose a new method for detection of moving objects. Two main steps compose the proposed method. First, we suggest to apply the algorithm of the detection of Color Space-Time Interest Points (CSTIP) on both components of the Color Structure-Texture Image Decomposition which is based on a Partial Differential Equation (PDE): a color geometric structure component and a color texture component. A descriptor is associated to each of these points. In a second stage, we address the problem of grouping the points (CSTIP) into clusters. Experiments and comparison to other motion detection methods on challenging sequences show the performance of the proposed method and its utility for video analysis. Experimental results are obtained from very different types of videos, namely sport videos and animation movies.

Keywords: Color Space-Time Interest Points (CSTIP), Color Structure-Texture Image Decomposition, Motion Detection, clustering

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Authors: Hasan Eskin, Gizem Özmen, A. Nazmi Çeler

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When we examine the home textile development process, it follows a parallel line with the other textile products especially in the garment fabrics in terms of raw materials, production technologies and pattern characteristics. As a result, the expectations of people regarding textile, comfort, pattern (texture) and color properties are increasing. One of the places where comfort is most sought after is bath, pool, sea and baths. In addition to the material and technique that make up the physical structure in woven fabrics, color has an impressive importance with its strong effects. Color is the most prominent element in the fabric, and the color and texture are visually reinforcing. Evaluation of color in fabric is a personal phenomenon. Factors that determine color determination in fabric are the amount of color used, color ratio and its relationship with other colors. In this project; Considering the effect of color dimensions on human, we are talking about the crystallized colored towel that we developed in terms of comfort and texture properties. The basis of the effect created in the towel; It is formed by bending the yarn from its own special blend and the harmonious appearance of the natural crystallized rainbow colors with the pattern effect it determines on the warp yarns by using the weft yarns in the weaving. In addition, by using different weaving techniques and colors, alternatives can be created and personalized patterns can be created. One side of the towel determines the properties related to color, while the pile part determines the comfort characteristics with its soft touch and water absorbency.

Keywords: color effect, comfort, towel, weaving technique

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Authors: Ghazala Hcini, Imen Jdey, Hela Ltifi

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Malaria disease should be considered and handled as a potential restorative catastrophe. One of the most challenging tasks in the field of microscopy image processing is due to differences in test design and vulnerability of cell classifications. In this article, we focused on applying deep learning to classify patients by identifying images of infected and uninfected cells. We performed multiple forms, counting a classification approach using the Hue, Saturation, Value (HSV) color space. HSV is used since of its superior ability to speak to image brightness; at long last, for classification, a convolutional neural network (CNN) architecture is created. Clusters of focus were used to deliver the classification. The highlights got to be forbidden, and a few more clamor sorts are included in the information. The suggested method has a precision of 99.79%, a recall value of 99.55%, and provides 99.96% accuracy.

Keywords: deep learning, convolutional neural network, image classification, color transformation, HSV color, malaria diagnosis, malaria cells images

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Authors: Babar Khan, Wang Zhijie

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Machine Vision has been playing a significant role in Industrial Automation, to imitate the wide variety of human functions, providing improved safety, reduced labour cost, the elimination of human error and/or subjective judgments, and the creation of timely statistical product data. Despite the intensive research, there have not been any attempts to classify fabric prints based on printed texture and colour, most of the researches so far encompasses only black and white or grey scale images. We proposed a biologically inspired processing architecture to classify fabrics w.r.t. the fabric print texture and colour. We created a texture descriptor based on the HMAX model for machine vision, and incorporated colour descriptor based on opponent colour channels simulating the single opponent and double opponent neuronal function of the brain. We found that our algorithm not only outperformed the original HMAX algorithm on classification of fabric print texture and colour, but we also achieved a recognition accuracy of 85-100% on different colour and different texture fabric.

Keywords: automatic classification, texture descriptor, colour descriptor, opponent colour channel

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Authors: A. J. S. P. Nileema, S. Kulatunga , S. H. Palihawadana

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Computer aided detection (CAD) system was developed for the diagnosis of pulmonary tuberculosis using digital chest X-rays with MATLAB image processing techniques using a statistical approach. The study comprised of 200 digital chest radiographs collected from the National Hospital for Respiratory Diseases - Welisara, Sri Lanka. Pre-processing was done to remove identification details. Lung fields were segmented and then divided into four quadrants; right upper quadrant, left upper quadrant, right lower quadrant, and left lower quadrant using the image processing techniques in MATLAB. Contrast, correlation, homogeneity, energy, entropy, and maximum probability texture features were extracted using the gray level co-occurrence matrix method. Descriptive statistics and normal distribution analysis were performed using SPSS. Depending on the radiologists’ interpretation, chest radiographs were classified manually into PTB - positive (PTBP) and PTB - negative (PTBN) classes. Features with standard normal distribution were analyzed using an independent sample T-test for PTBP and PTBN chest radiographs. Among the six features tested, contrast, correlation, energy, entropy, and maximum probability features showed a statistically significant difference between the two classes at 95% confidence interval; therefore, could be used in the classification of chest radiograph for PTB diagnosis. With the resulting value ranges of the five texture features with normal distribution, a classification algorithm was then defined to recognize and classify the quadrant images; if the texture feature values of the quadrant image being tested falls within the defined region, it will be identified as a PTBP – abnormal quadrant and will be labeled as ‘Abnormal’ in red color with its border being highlighted in red color whereas if the texture feature values of the quadrant image being tested falls outside of the defined value range, it will be identified as PTBN–normal and labeled as ‘Normal’ in blue color but there will be no changes to the image outline. The developed classification algorithm has shown a high sensitivity of 92% which makes it an efficient CAD system and with a modest specificity of 70%.

Keywords: chest radiographs, computer aided detection, image processing, pulmonary tuberculosis

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Authors: Zulkiflee Abd Latif, Sitinor Atikah Nordin, Alawi Sulaiman

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Different tree species possess different and various benefits. Arenga Pinnata tree species own several potential uses that is valuable for the economy and the country. Mapping vegetation using remote sensing technique involves various process, techniques and consideration. Using satellite imagery, this method enables the access of inaccessible area and with the availability of near infra-red band; it is useful in vegetation analysis, especially in identifying tree species. Pixel-based and object-based classification technique is used as a method in this study. Pixel-based classification technique used in this study divided into unsupervised and supervised classification. Object based classification technique becomes more popular another alternative method in classification process. Using spectral, texture, color and other information, to classify the target make object-based classification is a promising technique for classification. Classification of Arenga Pinnata trees is overlaid with elevation, slope and aspect, soil and river data and several other data to give information regarding the tree character and living environment. This paper will present the utilization of remote sensing technique in order to map Arenga Pinnata tree species

Keywords: Arenga Pinnata, pixel-based classification, object-based classification, remote sensing

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Authors: Andre Peres Ramos, Franklin Cesar Flores

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Colorization is the process of add colors to a monochromatic image or video. Usually, the process involves to segment the image in regions of interest and then apply colors to each one, for videos, this process is repeated for each frame, which makes it a tedious and time-consuming job. We propose a new assisted method for video colorization; the user only has to colorize one frame, and then the colors are propagated to following frames. The user can intervene at any time to correct eventual errors in color assignment. The method consists of to extract intensity and texture descriptors from the frames and then perform a feature matching to determine the best color for each segment. To reduce computation time and give a better spatial coherence we narrow the area of search and give weights for each feature to emphasize texture descriptors. To give a more natural result, we use an optimization algorithm to make the color propagation. Experimental results in several image sequences, compared to others existing methods, demonstrates that the proposed method perform a better colorization with less time and user interference.

Keywords: colorization, feature matching, texture descriptors, video segmentation

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Authors: Seyhan Karaçavuş, Bülent Yılmaz, Ömer Kayaaltı, Semra İçer, Arzu Taşdemir, Oğuzhan Ayyıldız, Kübra Eset, Eser Kaya

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In this study, our goal was to perform tumor staging and subtype determination automatically using different texture analysis approaches for a very common cancer type, i.e., non-small cell lung carcinoma (NSCLC). Especially, we introduced a texture analysis approach, called Law’s texture filter, to be used in this context for the first time. The 18F-FDG PET images of 42 patients with NSCLC were evaluated. The number of patients for each tumor stage, i.e., I-II, III or IV, was 14. The patients had ~45% adenocarcinoma (ADC) and ~55% squamous cell carcinoma (SqCCs). MATLAB technical computing language was employed in the extraction of 51 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and Laws’ texture filters. The feature selection method employed was the sequential forward selection (SFS). Selected textural features were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). In the automatic classification of tumor stage, the accuracy was approximately 59.5% with k-NN classifier (k=3) and 69% with SVM (with one versus one paradigm), using 5 features. In the automatic classification of tumor subtype, the accuracy was around 92.7% with SVM one vs. one. Texture analysis of FDG-PET images might be used, in addition to metabolic parameters as an objective tool to assess tumor histopathological characteristics and in automatic classification of tumor stage and subtype.

Keywords: cancer stage, cancer cell type, non-small cell lung carcinoma, PET, texture analysis

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Authors: Birmohan Singh, V.K.Jain

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Computer aided diagnosis systems provide vital opinion to radiologists in the detection of early signs of breast cancer from mammogram images. Masses and microcalcifications, architectural distortions are the major abnormalities. In this paper, a computer aided diagnosis system has been proposed for distinguishing abnormal mammograms with architectural distortion from normal mammogram. Four types of texture features GLCM texture, GLRLM texture, fractal texture and spectral texture features for the regions of suspicion are extracted. Support Vector Machine has been used as classifier in this study. The proposed system yielded an overall sensitivity of 96.47% and accuracy of 96% for the detection of abnormalities with mammogram images collected from Digital Database for Screening Mammography (DDSM) database.

Keywords: architecture distortion, mammograms, GLCM texture features, GLRLM texture features, support vector machine classifier

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Authors: El Asnaoui Khalid, Aksasse Brahim, Ouanan Mohammed

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This paper presents an unsupervised color image segmentation method. It is based on a hierarchical analysis of 2-D histogram in RGB color space. This histogram minimizes storage space of images and thus facilitates the operations between them. The improved segmentation approach shows a better identification of objects in a color image and, at the same time, the system is fast.

Keywords: image segmentation, hierarchical analysis, 2-D histogram, classification

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Authors: L. S. Godlevsky, N. V. Kresyun, V. P. Martsenyuk, K. S. Shakun, T. V. Tatarchuk, K. O. Prybolovets, L. F. Kalinichenko, M. Karpinski, T. Gancarczyk

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Structures of eye bottom were extracted using multiscale texture gradient method and color characteristics of macular zone and vessels were verified in CIELAB scale. The difference of average values of L*, a* and b* coordinates of CIE (International Commision of Illumination) scale in patients with diabetes and healthy volunteers was compared. The average value of L* in diabetic patients exceeded such one in the group of practically healthy persons by 2.71 times (P < 0.05), while the value of a* index was reduced by 3.8 times when compared with control one (P < 0.05). b* index exceeded such one in the control group by 12.4 times (P < 0.05). The integrated index on color difference (ΔE) exceeded control value by 2.87 times (P < 0.05). More pronounced differences with ΔE were followed by a shorter period of MA appearance with a correlation level at -0.56 (P < 0.05). The specificity of diagnostics raised by 2.17 times (P < 0.05) and negative prognostic index exceeded such one determined with the expert method by 2.26 times (P < 0.05).

Keywords: diabetic retinopathy, multiscale texture gradient, color spectrum analysis, medical diagnostics

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Authors: Fei Gao, Xinyang Dong, Yisu Ge, Shufang Lu, Libo Weng

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Vehicle color recognition plays an important role in traffic accident investigation. However, due to the influence of illumination, weather, and noise, vehicle color recognition still faces challenges. In this paper, a vehicle color classification network based on spatial cluster loss and channel attention mechanism (SCNet) is proposed for vehicle color recognition. A channel attention module is applied to extract the features of vehicle color representative regions and reduce the weight of nonrepresentative color regions in the channel. The proposed loss function, called spatial clustering loss (SC-loss), consists of two channel-specific components, such as a concentration component and a diversity component. The concentration component forces all feature channels belonging to the same class to be concentrated through the channel cluster. The diversity components impose additional constraints on the channels through the mean distance coefficient, making them mutually exclusive in spatial dimensions. In the comparison experiments, the proposed method can achieve state-of-the-art performance on the public datasets, VCD, and VeRi, which are 96.1% and 96.2%, respectively. In addition, the ablation experiment further proves that SC-loss can effectively improve the accuracy of vehicle color recognition.

Keywords: feature extraction, convolutional neural networks, intelligent transportation, vehicle color recognition

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Authors: Supatchalee Sirichokworrakit, Pannin Intasen, Chansuda Angkawut

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Hom Nin rice (Oryza Sativa L.) was processed into flour and used to substitute wheat flour in donuts. The donuts were prepared with 0, 20, 40, 60, and 80% Hom Nin rice flour (HNF). The donuts were subjected to proximate, texture, color and sensory evaluations. The results of the study revealed that the ash, moisture, crude fiber contents increased while crude fat and protein contents decreased as the level of HNF increased. The hardness and chewiness of donut increased as the HNF increased but the cohesiveness, springiness, and specific volume decreased. Color of donut (L*, a*, and b* values) decreased with the addition of HNF. Overall acceptability for the 20-40% HNF additions did not differ significantly from the score of the 100% wheat flour.

Keywords: Hom Nin rice, donut, texture evaluation, sensory evaluation

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Authors: Hassan Masoumi, Ahad Salimi, Nazanin Barhemmat, Babak Gholami

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Artificial neural networks have gained a lot of interest as empirical models for their powerful representational capacity, multi input and output mapping characteristics. In fact, most feed-forward networks with nonlinear nodal functions have been proved to be universal approximates. In this paper, we propose a new supervised method for color image classification based on self organizing feature maps (SOFM). This algorithm is based on competitive learning. The method partitions the input space using self-organizing feature maps to introduce the concept of local neighborhoods. Our image classification system entered into RGB image. Experiments with simulated data showed that separability of classes increased when increasing training time. In additional, the result shows proposed algorithms are effective for color image classification.

Keywords: classification, SOFM algorithm, neural network, neighborhood, RGB image

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Authors: N. Kazantseva, P. Krakhmalev, I. Yadroitsev, A. Fefelov, N. Vinogradova, I. Ezhov, T. Kurennykh

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Martensitic texture-phase transition in Selective Laser Melting (SLM) Ti-6Al-4V (ELI) alloys was found. Electron Backscatter Diffraction (EBSD) analysis showed the initial cubic beta < 100 > (001) BCC texture. Such kind of texture is observed in BCC metals with flat rolling texture when axis is in the direction of rolling and the texture plane coincides with the plane of rolling. It was found that the texture of the parent BCC beta-phase determined the texture of low-temperature HCP alpha-phase limited the choice of its orientation variants. The {10-12} < -1011 > twinning system in titanium alloys after SLM was determined. Analysis of the oxygen contamination in SLM alloys was done. Comparison of the obtained results with the conventional titanium alloys is also provided.

Keywords: additive technology, texture, twins, Ti-6Al-4V, oxygen content

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Authors: Damanjit Kaur, Avani Bhatia

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Color constancy is significant for human vision since color is a pictorial cue that helps in solving different visions tasks such as tracking, object recognition, or categorization. Therefore, several computational methods have tried to simulate human color constancy abilities to stabilize machine color representations. Two different kinds of methods have been used, i.e., normalization and constancy. While color normalization creates a new representation of the image by canceling illuminant effects, color constancy directly estimates the color of the illuminant in order to map the image colors to a canonical version. Color constancy is the capability to determine colors of objects independent of the color of the light source. This research work studies the most of the well-known color constancy algorithms like white point and gray world.

Keywords: color constancy, gray world, white patch, modified white patch

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Authors: Annalakshmi G., Sakthivel Murugan S.

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This paper presents a local directional encoded derivative binary pattern (LDEDBP) feature extraction method that can be applied for the classification of submarine coral reef images. The classification of coral reef images using texture features is difficult due to the dissimilarities in class samples. In coral reef image classification, texture features are extracted using the proposed method called local directional encoded derivative binary pattern (LDEDBP). The proposed approach extracts the complete structural arrangement of the local region using local binary batten (LBP) and also extracts the edge information using local directional pattern (LDP) from the edge response available in a particular region, thereby achieving extra discriminative feature value. Typically the LDP extracts the edge details in all eight directions. The process of integrating edge responses along with the local binary pattern achieves a more robust texture descriptor than the other descriptors used in texture feature extraction methods. Finally, the proposed technique is applied to an extreme learning machine (ELM) method with a meta-heuristic algorithm known as weighted distance grey wolf optimizer (GWO) to optimize the input weight and biases of single-hidden-layer feed-forward neural networks (SLFN). In the empirical results, ELM-WDGWO demonstrated their better performance in terms of accuracy on all coral datasets, namely RSMAS, EILAT, EILAT2, and MLC, compared with other state-of-the-art algorithms. The proposed method achieves the highest overall classification accuracy of 94% compared to the other state of art methods.

Keywords: feature extraction, local directional pattern, ELM classifier, GWO optimization

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Authors: Katsuhiro Narikiyo, Shota Hashikawa

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This paper proposes a way of removing noises and reducing the number of colors contained in a JPEG image. Main purpose of this project is to convert color images to monochrome images for the color-blind. We treat the crispy color images like the Tokyo subway map. Each color in the image has an important information. But for the color blinds, similar colors cannot be distinguished. If we can convert those colors to different gray values, they can distinguish them. Therefore we try to convert color images to monochrome images.

Keywords: color-blind, JPEG, monochrome image, denoise

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Authors: Saeedeh Barzegar Khalilsaraei, Manoocheher Kelarestaghi, Farshad Eshghi

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The vehicle color, as a prominent and stable feature, helps to identify a vehicle more accurately. As a result, vehicle color recognition is of great importance in intelligent transportation systems. Unlike conventional methods which use only a single Convolutional Neural Network (CNN) for feature extraction or classification, in this paper, four CNNs, with different architectures well-performing in different classes, are trained to extract various features from the input image. To take advantage of the distinct capability of each network, the multiple outputs are combined using a stack generalization algorithm as an ensemble technique. As a result, the final model performs better than each CNN individually in vehicle color identification. The evaluation results in terms of overall average accuracy and accuracy variance show the proposed method’s outperformance compared to the state-of-the-art rivals.

Keywords: Vehicle Color Recognition, Ensemble Algorithm, Stack Generalization, Convolutional Neural Network

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Authors: Wei-Jong Yang, Wei-Hau Du, Pau-Choo Chang, Jar-Ferr Yang, Pi-Hsia Hung

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The demands of smart visual thing recognition in various devices have been increased rapidly for daily smart production, living and learning systems in recent years. This paper proposed a visual thing recognition system, which combines binary scale-invariant feature transform (SIFT), bag of words model (BoW), and support vector machine (SVM) by using color information. Since the traditional SIFT features and SVM classifiers only use the gray information, color information is still an important feature for visual thing recognition. With color-based SIFT features and SVM, we can discard unreliable matching pairs and increase the robustness of matching tasks. The experimental results show that the proposed object recognition system with color-assistant SIFT SVM classifier achieves higher recognition rate than that with the traditional gray SIFT and SVM classification in various situations.

Keywords: color moments, visual thing recognition system, SIFT, color SIFT

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Authors: Roza Dzierzak, Waldemar Wojcik, Piotr Kacejko

Abstract:

Analysis of the texture of medical images consists of determining the parameters and characteristics of the examined tissue. The main goal is to assign the analyzed area to one of two basic groups: as a healthy tissue or a tissue with pathological changes. The CT images of the thoracic lumbar spine from 15 healthy patients and 15 with confirmed osteoporosis were used for the analysis. As a result, 120 samples with dimensions of 50x50 pixels were obtained. The set of features has been obtained based on the histogram, gradient, run-length matrix, co-occurrence matrix, autoregressive model, and Haar wavelet. As a result of the image analysis, 290 descriptors of textural features were obtained. The dimension of the space of features was reduced by the use of three selection methods: Fisher coefficient (FC), mutual information (MI), minimization of the classification error probability and average correlation coefficients between the chosen features minimization of classification error probability (POE) and average correlation coefficients (ACC). Each of them returned ten features occupying the initial place in the ranking devised according to its own coefficient. As a result of the Fisher coefficient and mutual information selections, the same features arranged in a different order were obtained. In both rankings, the 50% percentile (Perc.50%) was found in the first place. The next selected features come from the co-occurrence matrix. The sets of features selected in the selection process were evaluated using six classification tree methods. These were: decision stump (DS), Hoeffding tree (HT), logistic model trees (LMT), random forest (RF), random tree (RT) and reduced error pruning tree (REPT). In order to assess the accuracy of classifiers, the following parameters were used: overall classification accuracy (ACC), true positive rate (TPR, classification sensitivity), true negative rate (TNR, classification specificity), positive predictive value (PPV) and negative predictive value (NPV). Taking into account the classification results, it should be stated that the best results were obtained for the Hoeffding tree and logistic model trees classifiers, using the set of features selected by the POE + ACC method. In the case of the Hoeffding tree classifier, the highest values of three parameters were obtained: ACC = 90%, TPR = 93.3% and PPV = 93.3%. Additionally, the values of the other two parameters, i.e., TNR = 86.7% and NPV = 86.6% were close to the maximum values obtained for the LMT classifier. In the case of logistic model trees classifier, the same ACC value was obtained ACC=90% and the highest values for TNR=88.3% and NPV= 88.3%. The values of the other two parameters remained at a level close to the highest TPR = 91.7% and PPV = 91.6%. The results obtained in the experiment show that the use of classification trees is an effective method of classification of texture features. This allows identifying the conditions of the spongy tissue for healthy cases and those with the porosis.

Keywords: classification, feature selection, texture analysis, tree algorithms

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