Color Segmentation Method Research Articles

Unmixing-Based Soft Color Segmentation for Image Manipulation

We present a new method for decomposing an image into a set of soft color segments that are analogous to color layers with alpha channels that have been commonly utilized in modern image manipulation software. We show that the resulting decomposition serves as an effective intermediate image representation, which can be utilized for performing various, seemingly unrelated, image manipulation tasks. We identify a set of requirements that soft color segmentation methods have to fulfill, and present an in-depth theoretical analysis of prior work. We propose an energy formulation for producing compact layers of homogeneous colors and a color refinement procedure, as well as a method for automatically estimating a statistical color model from an image. This results in a novel framework for automatic and high-quality soft color segmentation that is efficient, parallelizable, and scalable. We show that our technique is superior in quality compared to previous methods through quantitative analysis as well as visually through an extensive set of examples. We demonstrate that our soft color segments can easily be exported to familiar image manipulation software packages and used to produce compelling results for numerous image manipulation applications without forcing the user to learn new tools and workflows.

Unmixing-Based Soft Color Segmentation for Image Manipulation

We present a new method for decomposing an image into a set of soft color segments that are analogous to color layers with alpha channels that have been commonly utilized in modern image manipulation software. We show that the resulting decomposition serves as an effective intermediate image representation, which can be utilized for performing various, seemingly unrelated, image manipulation tasks. We identify a set of requirements that soft color segmentation methods have to fulfill, and present an in-depth theoretical analysis of prior work. We propose an energy formulation for producing compact layers of homogeneous colors and a color refinement procedure, as well as a method for automatically estimating a statistical color model from an image. This results in a novel framework for automatic and high-quality soft color segmentation that is efficient, parallelizable, and scalable. We show that our technique is superior in quality compared to previous methods through quantitative analysis as well as visually through an extensive set of examples. We demonstrate that our soft color segments can easily be exported to familiar image manipulation software packages and used to produce compelling results for numerous image manipulation applications without forcing the user to learn new tools and workflows.

3D Visual Tracking of an Articulated Robot in Precision Automated Tasks.

The most compelling requirements for visual tracking systems are a high detection accuracy and an adequate processing speed. However, the combination between the two requirements in real world applications is very challenging due to the fact that more accurate tracking tasks often require longer processing times, while quicker responses for the tracking system are more prone to errors, therefore a trade-off between accuracy and speed, and vice versa is required. This paper aims to achieve the two requirements together by implementing an accurate and time efficient tracking system. In this paper, an eye-to-hand visual system that has the ability to automatically track a moving target is introduced. An enhanced Circular Hough Transform (CHT) is employed for estimating the trajectory of a spherical target in three dimensions, the colour feature of the target was carefully selected by using a new colour selection process, the process relies on the use of a colour segmentation method (Delta E) with the CHT algorithm for finding the proper colour of the tracked target, the target was attached to the six degree of freedom (DOF) robot end-effector that performs a pick-and-place task. A cooperation of two Eye-to Hand cameras with their image Averaging filters are used for obtaining clear and steady images. This paper also examines a new technique for generating and controlling the observation search window in order to increase the computational speed of the tracking system, the techniques is named Controllable Region of interest based on Circular Hough Transform (CRCHT). Moreover, a new mathematical formula is introduced for updating the depth information of the vision system during the object tracking process. For more reliable and accurate tracking, a simplex optimization technique was employed for the calculation of the parameters for camera to robotic transformation matrix. The results obtained show the applicability of the proposed approach to track the moving robot with an overall tracking error of 0.25 mm. Also, the effectiveness of CRCHT technique in saving up to 60% of the overall time required for image processing.

The study of blood smear as the analysis of images of various objects

Processing of microscope images in medicine is one of the priority research areas. Among the many medical imaging follows allocate the image of blood preparations. This is due to the fact that study of the image of blood preparations allows to conduct a comprehensive diagnosis of human health state. However, the specific complexity of visualization process of blood preparations and their subsequent processing with the use of automated processing determine the necessity to study different possibilities to use any approaches for image processing. We consider the image of blood preparations is a complex image. For image analysis of blood preparations’ structure, we use the method of color segmentation. For image analysis, we also use the methodology of the human-machine. This allows you to clarify the structure of the image of blood preparations. We give some of examples that show the effectiveness of the proposed image processing of blood preparations.

POLLEN BEARING HONEY BEE DETECTION IN HIVE ENTRANCE VIDEO RECORDED BY REMOTE EMBEDDED SYSTEM FOR POLLINATION MONITORING

Abstract. Honey bees have crucial role in pollination across the world. This paper presents a simple, non-invasive, system for pollen bearing honey bee detection in surveillance video obtained at the entrance of a hive. The proposed system can be used as a part of a more complex system for tracking and counting of honey bees with remote pollination monitoring as a final goal. The proposed method is executed in real time on embedded systems co-located with a hive. Background subtraction, color segmentation and morphology methods are used for segmentation of honey bees. Classification in two classes, pollen bearing honey bees and honey bees that do not have pollen load, is performed using nearest mean classifier, with a simple descriptor consisting of color variance and eccentricity features. On in-house data set we achieved correct classification rate of 88.7% with 50 training images per class. We show that the obtained classification results are not far behind from the results of state-of-the-art image classification methods. That favors the proposed method, particularly having in mind that real time video transmission to remote high performance computing workstation is still an issue, and transfer of obtained parameters of pollination process is much easier.

POLLEN BEARING HONEY BEE DETECTION IN HIVE ENTRANCE VIDEO RECORDED BY REMOTE EMBEDDED SYSTEM FOR POLLINATION MONITORING

Honey bees have crucial role in pollination across the world. This paper presents a simple, non-invasive, system for pollen bearing honey bee detection in surveillance video obtained at the entrance of a hive. The proposed system can be used as a part of a more complex system for tracking and counting of honey bees with remote pollination monitoring as a final goal. The proposed method is executed in real time on embedded systems co-located with a hive. Background subtraction, color segmentation and morphology methods are used for segmentation of honey bees. Classification in two classes, pollen bearing honey bees and honey bees that do not have pollen load, is performed using nearest mean classifier, with a simple descriptor consisting of color variance and eccentricity features. On in-house data set we achieved correct classification rate of 88.7% with 50 training images per class. We show that the obtained classification results are not far behind from the results of state-of-the-art image classification methods. That favors the proposed method, particularly having in mind that real time video transmission to remote high performance computing workstation is still an issue, and transfer of obtained parameters of pollination process is much easier.

An adaptive color similarity function suitable for image segmentation and its numerical evaluation

AbstractIn this article, we present an adaptive color similarity function defined in a modified hue‐saturation‐intensity color space, which can be used directly as a metric to obtain pixel‐wise segmentation of color images among other applications. The color information of every pixel is integrated as a unit by an adaptive similarity function thus avoiding color information scattering. As a direct application we present an efficient interactive, supervised color segmentation method with linear complexity respect to the number of pixels of the input image. The process has three steps: (1) Manual selection of few pixels in a sample of the color to be segmented. (2) Automatic generation of the so called color similarity image (CSI), which is a gray level image with all the gray level tonalities associated with the selected color. (3) Automatic threshold of the CSI to obtain the final segmentation. The proposed technique is direct, simple and computationally inexpensive. The evaluation of the efficiency of the color segmentation method is presented showing good performance in all cases of study. A comparative study is made between the behavior of the proposed method and two comparable segmentation techniques in color images using (1) the Euclidean metric of the a* and b* color channels rejecting L* and (2) a probabilistic approach on a* and b* in the CIE L*a*b* color space. Our testing system can be used either to explore the behavior of a similarity function (or metric) in different color spaces or to explore different metrics (or similarity functions) in the same color space. It was obtained from the results that the color parameters a* and b* are not independent of the luminance parameter L* as one might initially assume in the CIE L*a*b* color space. We show that our solution improves the quality of the proposed color segmentation technique and its quick result is significant with respect to other solutions found in the literature. The method also gives a good performance in low chromaticity, gray level and low contrast images. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 156–172, 2017

Hand Gesture Recognition and Its Application in Robot Control

In view of the problem that the accuracy and robustness of hand gesture recognition technology based on vision in the process of using gestures to interact with robots were unstable due to its background, illumination and other factors, this paper presented a gesture segmentation and recognition method Combining depth information and color images. First, it used Kinect sensor to obtain depth information and color images, then used depth information to pick the hand part from color images, and then got gesture images through color segmentation method. Second, it calculated HU invariant moments and shape features of the gesture images as feature information. Finally, it used the feature information to train the support vector machine, then implemented hand gesture recognition for static hand gestures. Experimental results show that the method has strong robustness to the influence of background interference, illumination variation, translation, rotation and zoom, and can be applied to control intelligent robot.

An automated sign retroreflectivity condition evaluation methodology using mobile LIDAR and computer vision

Traffic sign retroreflectivity condition is one of the most critical factors impacting nighttime driving safety. The federal rule on minimum traffic sign retroreflectivity incorporated in the 2009 Manual on Uniform Traffic Control Devices (MUTCD) issued by the Federal Highway Administration (FHWA) is compelling transportation agencies to evaluate how to comply. As the traditional manual methods have become financially and/or practically infeasible, there is an urgent need for an effective and efficient retroreflectivity evaluation method. This paper investigates the possibility and proposes a methodology for automatically evaluating traffic sign retroreflectivity condition using mobile light detection and ranging (LIDAR) and computer vision. The proposed methodology uses (a) the traffic sign detection and color segmentation methods that are introduced for the first time to evaluate the retroreflectivity of different traffic sign colors separately in an automated manner; (b) the proposed theoretical-empirical LIDAR retro-intensity normalization scheme to more reliably model the radiometric responses of traffic sign captured in the mobile LIDAR data; (c) the population-based condition assessment method for the first time to statistically quantify the retroreflectivity condition of traffic signs rather than an four-point average. An experimental test was conducted on 35 Type I Engineer Grade stop signs. The result shows that the proposed methodology can produce a promising outcome with consistent retroreflectivity evaluations based on handheld retroreflectometer measurements; the proposed methodology can, also, better identify traffic signs with non-homogeneous deteriorated retroreflectivity. The reliable retroreflectivity evaluation results make the proposed methodology an appealing alternative for transportation agencies to use to comply with the FHWA’s requirement.

TRAFFIC SIGN DETECTION AND RECOGNITION: REVIEW AND ANALYSIS

Over the years, traffic sign detection and recognition systems gives extra value to driver assistance when driving, leading to more user-friendly driving experience and much improved safety for passengers. As part of Advanced Driving Systems (ADAS) one can be benefitted by using this system especially with driving incapacities by alerting and aid them about the existence of traffic signs to minimized unwanted circumstances during driving such as fatigue, poor sight and adverse weather conditions. Though a various number of traffic sign detection systems have been revised in literature; the need of design with a robust algorithm still remains open for further research. This paper purposes to design a system capable of performing traffic sign detection while considering variations of challenges such as color illumination, computational difficulty and functional constraints existed. Traffic sign detection is divided into three main parts namely; Pre-processing, Color segmentation and Thresholding. The color segmentation method is vital as it presents a detailed investigation of vision based color spaces in this case RGB, HSV and CMYK considering varying illumination conditions under different environments. This paper further highlights possible improvements to the proposed approaches for traffic sign detection.

Predictive efficiency of distinct color image segmentation methods for measuring intramuscular fat in beef

Intramuscular fat (IMF) influences important quality characteristics of meat, such as flavor, juiciness, palatability, odor and tenderness. Thus, the objective of this study was to apply the following image processing techniques to quantify the IMF in beef: palette; sampling, interval of coordinates; black and white threshold; and discriminant function of colors. Thirty-five samples of beef, with a wide range of IMF, were used. Color images were taken of the meat samples from different muscles, with variability in the IMF content. The IMF of a thin cross-section meat was determined by chemical lipid extraction and was predicted by image analysis. The chemical method was compared with the image analysis. The segmentation procedures were validated by the adjustment of a linear regression equation to the series of values that were observed and predicted, as well as the regression parameters evaluated by the F-test. The predictive power of these approaches was also compared by residual analysis and by the decomposition of the mean square deviations. The results showed that the discriminant function was the best color segmentation method to measure intramuscular fat via digital images, but required adjustments in the prediction pattern.

Decision support of farmland intelligent image processing based on multi-inference trees

In order to meet the basic need of farmland image processing for non-professionals, to realize intelligent selection of image processing steps and common methods which were classified and analyzed based on knowledge description and inference of intelligent decision in this paper. According to the image features and requirements of users, the related rules were designed and then the color spaces, graying methods, de-noising methods, segmentation methods and morphology post-processing methods multi-inference trees were built. A manual evaluation method was adopted to evaluate the artificial and intelligent processing results. Therefore, the selection of image processing steps and methods can be made through these trees, and decision support of intelligent image processing can be realized. The experimental results show that the evaluation ratio of intelligent processing that more than 80 points is up to 83.0%, the average ratio is 75.9%, and the average processing time is about 0.23s per image, which can greatly reduce the workload and blindness of methods selection and achieve higher level of comprehensiveness and operational efficiency.

Real-time segmentation methods for monocular soccer videos

The analysis of video records of soccer games is a topic of extensive and challenging research in image sequence processing, particularly that of player and field tracking. In this paper, we propose a new approach for detecting players and field lines in monocular TV video data that involves determining the convex field area and grass colors. This is carried out by considering contextual knowledge, together with a new method for color segmentation that selects polyhedrons in a frame-wise manner within the RGB cube. In summary, for every input image, a binary mask of the field area and a background mask are determined. Both mask creation algorithms achieve per pixel F1 scores of about 98% within our representative test set and are real-time capable. Applications like line detection and player tracking are presented.

License Plate Recognition System Design

The license plate recognition system is an important part of modern traffic management. application which is very extensive. In this paper, a method to achieve three main modules split from the image pre-processing, license plate location and character. Image pre-processing module of this article is to image gray and step by Roberts operator edge detection. License plate positioning and segmentation using mathematical morphology is used to determine the license plate location method, and then use the license plate color information of color segmentation method to complete the license plate parts division. This article is to research its main part and use MATLAB to do the image processing simulation.

The Role of Improved K-Means Clustering Algorithm in the Motion Parameters Determination of Breaker's Moving Contact

High voltage circuit breaker's motion parameters of the moving contact is one of the key performance indicators in the evaluated of breaker. By means of extracting color features of LW10B-252 circuit breaker by image segmentation, then determining its center point of the minimum circumscribed rectangle, getting the movement distance of center point and the use of time in adjacent images, realization motion parameter's measurement of the moving contact. This paper presents a algorithm based on improved k-means clustering algorithm to extract the color characteristics of the mechanical linkage of breaker, at the same time contrast with classical OTSU method and RGB color segmentation method. Experimental results show that the k-means clustering algorithm have a better segmentation result.

PD23-08 COMBINATION OF TAMSULIN (0.4 MG) AND DUTASTERIDE (5 MG) FOR BENIGN PROSTATIC HYPERPLASIA. OBJECTIVE ANALYSIS OF STROMAL COMPONENTS OF THE TRANSITION ZONE

You have accessJournal of UrologyBenign Prostatic Hyperplasia: Medical & Hormonal Therapy1 Apr 2014PD23-08 COMBINATION OF TAMSULIN (0.4 MG) AND DUTASTERIDE (5 MG) FOR BENIGN PROSTATIC HYPERPLASIA. OBJECTIVE ANALYSIS OF STROMAL COMPONENTS OF THE TRANSITION ZONE Andre Cavalcanti, Felipe Restrepo, Waldemar Costa, Carla Gallo, Bruna Pereira, Nathalia Machado, and Francisco Sampaio Andre CavalcantiAndre Cavalcanti More articles by this author , Felipe RestrepoFelipe Restrepo More articles by this author , Waldemar CostaWaldemar Costa More articles by this author , Carla GalloCarla Gallo More articles by this author , Bruna PereiraBruna Pereira More articles by this author , Nathalia MachadoNathalia Machado More articles by this author , and Francisco SampaioFrancisco Sampaio More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2014.02.1845AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES We conducted an objective (stereological) histological analysis of the stromal components of the transition zone of benign prostatic hyperplasia (BPH) patients using or not using a combination of tamsulin (0.4-mg) and dutasteride (5-mg). METHODS BPH samples were analyzed in the transition zone of 5 patients submitted to open surgery without previous treatment (control group) and 5 patients with BPH, submitted to open surgery, which underwent treatment with tamsulosin + dutasteride for 3 months prior to surgery. The samples were removed after open surgery, and fixed in 4% buffered formalin for a maximum period of 24 hours. Fragments from the 2 groups were subjected to the following histochemical techniques; hematoxylin-eosin, Masson's trichrome and Picrosirius red for analysis of connective tissue, and immunohistochemical staining by using the anti-alpha-actin for the analysis of smooth muscle fibers and anti-elastin for analysis elastic system. Quantitative analysis was performed using Image J software with the differentiation method of color segmentation. RESULTS The analysis of sections stained by Picrosirius observed under polarized light showed no difference in collagen arrangement and distribution between treated and untreated patients. Both groups presented reddish homogeneous fibers in all analyzed fields, characterizing the presence of thicker fibers, possibly collagen type I. In the sections stained by Masson's trichrome, the quantitative analysis showed a significant decrease of approximately 34% of the collagen in individuals that used combo dart, when compared to controls. The immunohistochemical analysis showed an increase of 42% in the elastic system fibers in patients using combodart, when compared to controls. Regarding smooth muscle fibers, the patients in combodart group presented an increase of 81%. CONCLUSIONS Although these results are preliminary, with a small number of patients, to our knowledge, this is the first report of a comparative objective of BPH stromal components of controls and patients using combodart. The study would continue increasing the number of patients and an analysis of other components of the stroma and parenchyma. © 2014FiguresReferencesRelatedDetails Volume 191Issue 4SApril 2014Page: e669 Advertisement Copyright & Permissions© 2014MetricsAuthor Information Andre Cavalcanti More articles by this author Felipe Restrepo More articles by this author Waldemar Costa More articles by this author Carla Gallo More articles by this author Bruna Pereira More articles by this author Nathalia Machado More articles by this author Francisco Sampaio More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

An algorithm to estimate speed by colour-based object detection

This paper describes a method to determine the travelling speed of a moving object from a sequence of captured video image frames. Using a new method for the detection of objects in colour images, the target moving object is identified and located in each video frame. The object detection process is performed by a colour segmentation method and a labelling algorithm. By defining a rectangular bounding box around it in every frame, the detected object image is isolated from other moving objects in the video, and tracked. The velocity can then be determined by calculating the distance through which the bounding box centroid moves over a sequence of frames with respect to the frame rate of the video recording.

Analisis Segmentasi Warna pada Citra Dua Dimensi Dengan Pendekatan Komputasi Fotografi

Color segmentation techniques on two-dimensional image was originally used in digital image processing applications in engineering sciences. Recent years, these techniques have been widely used in other disciplines. Photography in the digital era use this technique in a variety of needs, as an example for selecting objects based on color pixel elements. This research was done with computational photography approach to perform selection of object area based on color elements in each pixel. Observations and analysis done by comparing three common color segmentation method, namely k-means clustering method, index coloring, and Giannakopoulos HSV color detection. This research is a preliminary project of automation process for body segmentation based on colors. After testing, it turns out segmentation based on Giannakopoulos HSV color detection has added value. This is because the ease of data training process to determined the classification of color values and tolerance segmentation based on intensity variation from selected color.

Depth map misalignment correction and dilation for DIBR view synthesis

The quality of the synthesized views by Depth Image Based Rendering (DIBR) highly depends on the accuracy of the depth map, especially the alignment of object boundaries of texture image. In practice, the misalignment of sharp depth map edges is the major cause of the annoying artifacts at the disoccluded regions of the synthesized views. Conventional smooth filter approach blurs the depth map to reduce the disoccluded regions. The drawbacks are the degradation of 3D perception of the reconstructed 3D videos and the destruction of the texture in background regions. Conventional edge preserving filter utilizes the color image information in order to align the depth edges with color edges. Unfortunately, the characteristics of color edges and depth edges are very different which causes annoying boundaries artifacts in the synthesized virtual views. Recent solution of reliability-based approach uses reliable warping information from other views to fill the holes. However, it is not suitable for the view synthesis in video-plus-depth based DIBR applications. In this paper, a new depth map preprocessing approach is proposed. It utilizes Watershed color segmentation method to correct the depth map misalignment and then the depth map object boundaries are extended to cover the transitional edge regions of color image. This approach can handle the sharp depth map edges lying inside or outside the object boundaries in 2D sense. The quality of the disoccluded regions of the synthesized views can be significantly improved and unknown depth values can also be estimated. Experimental results show that the proposed method achieves superior performance for view synthesis by DIBR especially for generating large baseline virtual views.

Color segmentation by fuzzy co-clustering of chrominance color features

This paper presents a novel color segmentation technique using fuzzy co-clustering approach in which both the objects and the features are assigned membership functions. An objective function which includes a multi-dimensional distance function as the dissimilarity measure and entropy as the regularization term is formulated in the proposed fuzzy co-clustering for images (FCCI) algorithm. The chrominance color cues a⁎ and b⁎ of CIELAB color space are used as the feature variables for co-clustering. The experiments are conducted on 100 natural images obtained from the Berkeley segmentation database. It is observed from the experimental results that the proposed FCCI yields well formed, valid and high quality clusters, as verified from Liu’s F-measure and Normalized Probabilistic RAND index. The proposed color segmentation method is also compared with other segmentation methods namely Mean-Shift, NCUT, GMM, FCM and is found to outperform all the methods. The bacterial foraging global optimization algorithm gives image specific values to the parameters involved in the algorithm.