color histogram
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2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ali Shams Nateri ◽  
Laleh Asadi

Purpose The purpose of this study is evaluate the optical properties of polyacrylonitrile (PAN) nanofibers containing fluorescent agents such as fluorescent dye and carbon quantum dots (CQDs) by using image-processing technique of Fluorescence microscope image. Design/methodology/approach The fluorescence microscope image of the pure PAN, PAN/CQDs and PAN/fluorescent dye nanofibers composite was analyzed using several image-processing techniques such as color histogram, lookup table (LUT), Fourier transform, RGB profile and surface plot analysis. Findings The fluorescence microscope image indicates that the fluorescence emission of nanocomposites depends on the type of fluorescent agent. The fluorescence intensity of nanofiber containing CQDs is more than nanofiber containing fluorescent dye. Various image-processing methods provide similar results for optical property of nanocomposites. Analyzing the LUT, the blue value of CQDs/PAN nanocomposite image was significantly higher than other nanocomposites. This was confirmed by other methods such as Fourier transform, color histogram and 3D topography of the electrospun nanofibers. According to analysis of colorimetric parameters, higher negative value of b* indicates bluer color for CQDs/PAN nanofibers than other nanocomposites. The obtained results indicate that the image-processing technique can be used to evaluate the optical property of fluorescent nanocomposite. Originality/value This study evaluates the optical properties of fluorescent nanocomposites by using image-processing techniques such as Fourier transform, color histogram, RGB profiles, LUT, surface plot and histogram analysis.


2022 ◽  
Vol 2146 (1) ◽  
pp. 012040
Author(s):  
Huaben Wang

Abstract With the rapid development of Internet technology, using images to express the characteristics of things more direct, compared with text, audio, image expression content is more ambiguous, which makes the rapid increase of digital images on the Internet. Nowadays one of the hot directions of computer vision research is how to accurately and quickly retrieve the target image from a large amount of image data. This paper summarizes the development of image retrieval technology at home and abroad, and proposes an image search method based on color histogram and Chi-square distance. This paper discusses how to construct an image search system, which can search the image quickly, describe the color distribution of the photo with color histogram, divide the image into five regions, extract image features from the color histogram of each region, and then get the data set of multi-dimensional image features. Then the chi-square distance is used to calculate the similarity of color histogram, and the closest image is selected as the first similar image, which realizes the necessary logic of receiving query image and returning related results.


2021 ◽  
Vol 11 (24) ◽  
pp. 11997
Author(s):  
Hye-Jin Park ◽  
Jung-In Jang ◽  
Byung-Gyu Kim

A web-based search system recommends and gives results such as customized image or video contents using information such as user interests, search time, and place. Time information extracted from images can be used as a important metadata in the web search system. We present an efficient algorithm to classify time period into day, dawn, and night when the input is a single image with a sky region. We employ the Mask R-CNN to extract a sky region. Based on the extracted sky region, reference color histograms are generated, which can be considered as the ground-truth. To compare the histograms effectively, we design the windowed-color histograms (for RGB bands) to compare each time period from the sky region of the reference data with one of the input images. Also, we use a weighting approach to reflect a more separable feature on the windowed-color histogram. With the proposed windowed-color histogram, we verify about 91% of the recognition accuracy in the test data. Compared with the existing deep neural network models, we verify that the proposed algorithm achieves better performance in the test dataset.


2021 ◽  
Vol 7 (9) ◽  
pp. 187
Author(s):  
Seena Joseph ◽  
Oludayo O. Olugbara

Salient object detection represents a novel preprocessing stage of many practical image applications in the discipline of computer vision. Saliency detection is generally a complex process to copycat the human vision system in the processing of color images. It is a convoluted process because of the existence of countless properties inherent in color images that can hamper performance. Due to diversified color image properties, a method that is appropriate for one category of images may not necessarily be suitable for others. The selection of image abstraction is a decisive preprocessing step in saliency computation and region-based image abstraction has become popular because of its computational efficiency and robustness. However, the performances of the existing region-based salient object detection methods are extremely hooked on the selection of an optimal region granularity. The incorrect selection of region granularity is potentially prone to under- or over-segmentation of color images, which can lead to a non-uniform highlighting of salient objects. In this study, the method of color histogram clustering was utilized to automatically determine suitable homogenous regions in an image. Region saliency score was computed as a function of color contrast, contrast ratio, spatial feature, and center prior. Morphological operations were ultimately performed to eliminate the undesirable artifacts that may be present at the saliency detection stage. Thus, we have introduced a novel, simple, robust, and computationally efficient color histogram clustering method that agglutinates color contrast, contrast ratio, spatial feature, and center prior for detecting salient objects in color images. Experimental validation with different categories of images selected from eight benchmarked corpora has indicated that the proposed method outperforms 30 bottom-up non-deep learning and seven top-down deep learning salient object detection methods based on the standard performance metrics.


2021 ◽  
Vol 7 (2) ◽  
pp. 187-193
Author(s):  
Nanik Wuryani ◽  
Sarifah Agustiani

Covid-19 merupakan virus yang menyebar dan meluas sehingga berubah menjadi suatu pandemi. Virus Covid-19 menyerang melalui organ vital manusia yaitu paru-patu, oleh karena itu peneliti lebih berfokus untuk mengidentifikasi Covid-19 pada paru-paru. Penelitian ini dilakukan dengan menggunakan citra CT Scan paru-paru dan bertujuan untuk mendeteksi ada tidaknya virus dengan cara mengklasifikasikan citra Covid-19 ke dalam tiga kelas menggunakan algoritma Random Forest serta mengkombinasikannya dengan menyertakan beberapa ekstraksi fitur yaitu Haralick, Color Histogram, dan Hu-Moments. Penelitian dimulai dengan hanya memasukkan satu fitur ke dalam percobaan, lalu mengkombinasikan dengan fitur yang lain, kemudian membandingkannya menggunakan klasifikasi oleh algoritma lain seperti K-Nearest Neighbor (KNN), Decision Tree, Linear Discriminant Analysis (LDA), Logistic Regression, Support Vector Machine (SVM), dan Naive Bayes. Hasil penelitian menunjukkan bahwa akurasi tertinggi dihasilkan oleh algoritma Random Forest dengan memasukkan fitur Haralick dan Color Histogram ke dalam proses yaitu sebesar 96,9%, diikuti oleh KNN sebesar 96,5%, Decision Tree sebesar 95,5%, dan yang paling rendah yaitu Naive Bayes sebesar 42,4%


Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yu Liu ◽  
Xiaoyan Wang

We analyze and study the tracking of nonrigid complex targets of sports video based on mean shift fusion color histogram algorithm. A simple and controllable 3D template generation method based on monocular video sequences is constructed, which is used as a preprocessing stage of dynamic target 3D reconstruction algorithm to achieve the construction of templates for a variety of complex objects, such as human faces and human hands, broadening the use of the reconstruction method. This stage requires video sequences of rigid moving target objects or sets of target images taken from different angles as input. First, the standard rigid body method of Visuals is used to obtain the external camera parameters of the sequence frames as well as the sparse feature point reconstruction data, and the algorithm has high accuracy and robustness. Then, a dense depth map is computed for each input image frame by the Multi-View Stereo algorithm. The depth reconstruction with a too high resolution not only increases the processing time significantly but also generates more noise, so the resolution of the depth map is controlled by parameters. The multiple hypothesis target tracking algorithms are used to track multiple targets, while the chunking feature is used to solve the problem of mutual occlusion and adhesion between targets. After finishing the matching, the target and background models are updated online separately to ensure the validity of the target and background models. Our results of nonrigid complex target tracking by mean shift fusion color histogram algorithm for sports video improve the accuracy by about 8% compared to other studies. The proposed tracking method based on the mean shift algorithm and color histogram algorithm can not only estimate the position of the target effectively but also depict the shape of the target well, which solves the problem that the nonrigid targets in sports video have complicated shapes and are not easy to track. An example is given to demonstrate the effectiveness and adaptiveness of the applied method.


Electronics ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 915
Author(s):  
Eissa Alreshidi ◽  
Rabie A. Ramadan ◽  
Md. Haidar Sharif ◽  
Omer Faruk Ince ◽  
Ibrahim Furkan Ince

Face recognition is one of the emergent technologies that has been used in many applications. It is a process of labeling pictures, especially those with human faces. One of the critical applications of face recognition is security monitoring, where captured images are compared to thousands, or even millions, of stored images. The problem occurs when different types of noise manipulate the captured images. This paper contributes to the body of knowledge by proposing an innovative framework for face recognition based on various descriptors, including the following: Color and Edge Directivity Descriptor (CEDD), Fuzzy Color and Texture Histogram Descriptor (FCTH), Color Histogram, Color Layout, Edge Histogram, Gabor, Hashing CEDD, Joint Composite Descriptor (JCD), Joint Histogram, Luminance Layout, Opponent Histogram, Pyramid of Gradient Histograms Descriptor (PHOG), Tamura. The proposed framework considers image set indexing and retrieval phases with multi-feature descriptors. The examined dataset contains 23,707 images of different genders and ages, ranging from 1 to 116 years old. The framework is extensively examined with different image filters such as random noise, rotation, cropping, glow, inversion, and grayscale. The indexer’s performance is measured based on a distributed environment based on sample size and multiprocessors as well as multithreads. Moreover, image retrieval performance is measured using three criteria: rank, score, and accuracy. The implemented framework was able to recognize the manipulated images using different descriptors with a high accuracy rate. The proposed innovative framework proves that image descriptors could be efficient in face recognition even with noise added to the images based on the outcomes. The concluded results are as follows: (a) the Edge Histogram could be best used with glow, gray, and inverted images; (b) the FCTH, Color Histogram, Color Layout, and Joint Histogram could be best used with cropped images; and (c) the CEDD could be best used with random noise and rotated images.


2021 ◽  
Vol 39 (1B) ◽  
pp. 11-20
Author(s):  
Hanaa M. Ahmad ◽  
Shrooq R. Hameed

A human eye is a vital organ responsible for a person's vision. So, the early detection of eye diseases is essential. The objective of this paper deals with diagnosing of seven different external eye diseases that can be recognized by a human eye. These diseases cause problems either in eye pupil, in sclera of eye or in both or in eyelid. Color histogram and texture features extraction techniques with classification technique are used to achieve the goal of diagnosing external eye diseases.  Hue Min Max Diff (HMMD) color space is used to extract color histogram and texture features which were fed to Back Propagation Artificial Neural Network (BPANN) for classification. The comparative study states that the features extracted from HMMD color space is better than other features like Histogram of Oriented Gradient (HOG) features and give the same accuracy as features extracted directly from medical expert recorded symptoms. The proposed method is applied on external eye diseases data set consisting of 416 images with an accuracy rate of 85.26315%, which is the major result that was achieved in this study.


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