scholarly journals Automatic Extraction of Color Features from Landscape Images Based on Image Processing

2021 ◽  
Vol 38 (3) ◽  
pp. 747-755
Author(s):  
Cong Tan ◽  
Shaoyu Yang
Keyword(s):  
Image Processing ◽  
Color Space ◽  
Color Constancy ◽  
Support Vector ◽  
Automatic Extraction ◽  
Color Features ◽  
Image Library ◽  
Presentation Effect ◽  
High Level

The dominant color features determine the presentation effect and visual experience of landscapes. The existing studies rarely quantify the application effect of landscape colors through image colorization. Besides, it is unreasonable to analyze landscape images with multiple standard colors with a single color space. To solve the problem, this paper proposes an automatic extraction method for color features from landscape images based on image processing. Firstly, a landscape lighting model was constructed based on color constancy theories, and the quality of landscape images was improved with color constant image enhancement technology. In this way, the low-level color features were extracted from the landscape image library. Next, support vector machine (SVM) and fuzzy c-means (FCM) were innovatively integrated to extract high-level color features from landscape images. The proposed method was proved effective through experiments.

scholarly journals Vision Based Algorithm for People Counting Using Deep Learning

2018 ◽  
Vol 7 (3.6) ◽  
pp. 74
Author(s):  
M Padmashini ◽  
R Manjusha ◽  
Latha Parameswaran
Keyword(s):  
Image Processing ◽  
Deep Neural Networks ◽  
Local Binary Pattern ◽  
Support Vector ◽  
Shopping Malls ◽  
Energy Usage ◽  
People Detection ◽  
False Negatives ◽  
People Counting

Estimating the number of people in a particular scene has always been an important topic of research in computer vision and digital image processing. People counting has wide applications in scenario ranging from analyzing the customer's choice and improving the quality of service in retail stores, supermarkets and shopping malls to managing human resources and optimizing the energy usage in office buildings. While there exists algorithms for counting people in a scene, some algorithm have set their benchmark in performance with respect to efficiency, flexibility and accuracy. In this paper, an attempt has been made to perform people counting using Deep Neural Networks (DNN) on comparison with existing image processing based algorithms like Histogram of Oriented Gradients with Support Vector Machine (HoG with SVM), Local Binary Pattern (LBP) based Adaboost classifier and contour based people detection. The proposed DNN based approach has higher accuracy at 90% and less false negatives.  

scholarly journals Predicting sensory evaluation of spinach freshness using machine learning model and digital images

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248769
Author(s):  
Kento Koyama ◽  
Marin Tanaka ◽  
Byeong-Hyo Cho ◽  
Yusaku Yoshikawa ◽  
Shige Koseki
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Sensory Evaluation ◽  
Color Space ◽  
Local Features ◽  
Support Vector ◽  
Nondestructive Technique ◽  
Color Features ◽  
Artificial Neural ◽  
Four Levels

The visual perception of freshness is an important factor considered by consumers in the purchase of fruits and vegetables. However, panel testing when evaluating food products is time consuming and expensive. Herein, the ability of an image processing-based, nondestructive technique to classify spinach freshness was evaluated. Images of spinach leaves were taken using a smartphone camera after different storage periods. Twelve sensory panels ranked spinach freshness into one of four levels using these images. The rounded value of the average from all twelve panel evaluations was set as the true label. The spinach image was removed from the background, and then converted into a gray scale and CIE-Lab color space (L*a*b*) and Hue, Saturation and Value (HSV). The mean value, minimum value, and standard deviation of each component of color in spinach leaf were extracted as color features. Local features were extracted using the bag-of-words of key points from Oriented FAST (Features from Accelerated Segment Test) and Rotated BRIEF (Binary Robust Independent Elementary Features). The feature combinations selected from the spinach images were used to train machine learning models to recognize freshness levels. Correlation analysis between the extracted features and the sensory evaluation score showed a positive correlation (0.5 < r < 0.6) for four color features, and a negative correlation (‒0.6 < r < ‒0.5) for six clusters in the local features. The support vector machine classifier and artificial neural network algorithm successfully classified spinach samples with overall accuracy 70% in four-class, 77% in three-class and 84% in two-class, which was similar to that of the individual panel evaluations. Our findings indicate that a model using support vector machine classifiers and artificial neural networks has the potential to replace freshness evaluations currently performed by non-trained panels.

scholarly journals Lemon Leaf Disease Detection and Classification using SVM and CNN

2019 ◽  
Vol 8 (4) ◽  
pp. 11485-11488
Keyword(s):  
Image Processing ◽  
Plant Disease ◽  
Color Space ◽  
Input Image ◽  
Plant Diseases ◽  
Image Processing Technique ◽  
Support Vector ◽  
Training Time ◽  
Leaf Disease

India is a developing country and agriculture has always played a major role in bolstering the country’s economic growth. Due to various factors like industrialization, mechanization and globalization, the green fields are facing complications. So, identifying the plant disease incorrectly will lead to a huge loss of both quantity and quality of the product and it will also incur loss in time and money. Hence, identifying the condition of the plant plays a major role for successful cultivation. Now a day’s image processing technique is being employed as a focal technique for diagnosing the various features of the crop. The image processing techniques can be used for identification of the plant disease and hence classify the plant disease. Generally, the symptoms of the disease are observed on leaves, stems, flowers etc. Here, the leaves of the affected plant are used for the identification and classification of the disease. Leaf image is captured using a smart phone as the first step and then they are processed to determine the condition of the plant. Identification of plant disease follows the steps like loading the image of the plant leaf, histogram equalization for enhancing contrast of the image, segmentation process by using Lab color space model, extracting features of the segmented image using GLCM (Grey Level Cooccurrence Matrix) and finally classification of leaf disease by using MCSVM (Multi Class Support Vector Machine).This procedure obtained an accuracy percentage of 83.6%.Also, it takes long training time for large datasets. To improve the accuracy of the detection and the classification of the plants, Convolutional Neural Network (CNN) is used. The main advantage of CNN is that it automatically detects the main features of the input without any supervision of human. In CNN identification of disease follow the steps like loading the image as the input image, convolution of the feature map and finally max pooling the layers to calculate the features of the image in detail. The plant diseases are classified with an accuracy of 93.8 %.

scholarly journals Assessment the quality of apricots in the process of drying with neural networks and support vector machines

2019 ◽  
Vol 292 ◽  
pp. 03019
Author(s):  
Mаrtin Dejanov ◽  
Darinka Ilieva-Stefanova ◽  
Iva Chelik
Keyword(s):  
Neural Networks ◽  
Spectral Characteristics ◽  
Support Vector ◽  
Carotene Content ◽  
Color Features ◽  
Rbf Kernel ◽  
Linear Svm ◽  
Lab Color Model ◽  
Cie Lab

The paper presents an analysis of the assessment the quality of apricots during the drying process using two types of classifires: ANNs and SVMs. The quality of apricots is categorized in three classes according to the color and b-carotene content through the process of drying. The classification is made by using ‘CIE Lab’ color model and spectral characteristics in the VIS range. Neural networks are BPN and PNN, and classifiers are kernel and linear SVM. The spectral characteristics are pre-processed with SNV, MSC, First derivative and PCA. According to the results for color features, BPN and SVM with “rbf” kernel have the best performance while PNN has the worst performance. When using spectral characteristics the BPN network performs well: eavg = 4.1% and emax = 12.1% but the SVM linear (eavg = 3.4%, emax =5.3%) and SVM with “rbf” kernel (eavg = 2.4%, emax =5.2%) classifiers have better results. As a conclusion, it could be said that classifiers using spectral features perform well with errors at about 2-5%. Classification with color features is an alternative method, which is less complex, cheaper and with acceptable errors.

scholarly journals A Multi-Sensor System for Silkworm Cocoon Gender Classification via Image Processing and Support Vector Machine

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2656 ◽  
Author(s):  
Alex Noel Joseph Raj ◽  
Rahul Sundaram ◽  
Vijayalakshmi G.V. Mahesh ◽  
Zhemin Zhuang ◽  
Alessandro Simeone
Keyword(s):  
Image Processing ◽  
Support Vector Machine ◽  
Digital Camera ◽  
Computation Time ◽  
Sensor System ◽  
Support Vector ◽  
Gender Classification ◽  
Load Sensor ◽  
Silkworm Cocoon

Sericulture is traditionally a labor-intensive rural-based industry. In modern contexts, the development of process automation faces new challenges related to quality and efficiency. During the silkworm farming life cycle, a common issue is represented by the gender classification of the cocoons. Improper cocoon separation negatively affects quantity and quality of the yield resulting in disruptive bottlenecks for the productivity. To tackle this issue, this paper proposes a multi sensor system for silkworm cocoons gender classification and separation. Utilizing a load sensor and a digital camera, the system acquires weight and digital images from individual silkworm cocoons. An image processing procedure is then applied to extract significant shape-related features from each image instance, which, combined with the weight data, are provided as inputs to train a Support Vector Machine-based pattern classifier for gender classification. Subsequently, an air blower mechanism and a conveyor system sort the cocoons into their respective bins. The developed system was trained and tested on two different types of silkworm cocoons breeds, respectively CSR2 and Pure Mysore. The system performances are finally discussed in terms of accuracy, robustness and computation time.

Mathematical models for some problems in textile engineering and its corresponding discussions

2014 ◽  
Vol 05 (02) ◽  
pp. 1350030
Author(s):  
Li Zhang ◽  
Xinhua You ◽  
Jun Chen ◽  
Liang Zhang
Keyword(s):  
Image Processing ◽  
Cluster Analysis ◽  
Textile Industry ◽  
Programming Algorithm ◽  
Support Vector ◽  
Support Vector Machine Algorithm ◽  
Image Processing Techniques ◽  
Chinese Industry ◽  
Processing Techniques

Textile industry is very important to the development of Chinese industry and economy. Image processing techniques are beneficial to improve the quality of cotton goods. Suitable blending ratio of yarn is good for it. It is significant to measure the blending ratio of yarn in the practice of textile engineering. Combined with results done by other scholars, this paper uses the concepts of acreage index, abnormity index and fluctuation index. Based on these morphologic indices, it is convenient to construct corresponding eigenvector and to discuss useful mathematical method for the cluster analysis during the measurement of the blending ratio. This paper also sets up some kinds of nonlinear optimization model for the problem. Using classical integer programming algorithm, support vector machine algorithm and genetic algorithm to the problem, we get fine results for cluster analysis. Finally, we give out another problem of image processing and have some discussions about it.

scholarly journals Recognition of Teaching Features and Behaviors in Online Open Courses Based on Image Processing

2021 ◽  
Vol 38 (1) ◽  
pp. 155-164
Author(s):  
Sheliang Li ◽  
Huaqi Chai
Keyword(s):  
Image Processing ◽  
Support Vector ◽  
Teaching Behaviors ◽  
High Quality ◽  
Behavior Recognition ◽  
Wide Audience ◽  
Teaching Behavior ◽  
And Behaviors ◽  
Open Courses

High-quality online open courses have a wide audience. To further improve the quality of these courses, it is critical to analyze the teaching behaviors in class, which are the manifestation of the overall quality of the teacher. Considering the popularity of image processing-based behavior recognition in many disciplines, this paper explores deep into the teaching features and behaviors in online open courses based on image processing. Firstly, a coding scale was designed for teaching behaviors in online open courses. Next, the principle of optical flow solving was explained for teaching video images. Then, a teaching behavior feature extraction model was established based on dual-flow deep CNN, and used to extract the key points of teacher body and the behavior features of the teacher. After that, a teaching behavior recognition method was developed combining histogram of oriented gradients (HOG) and support vector machine (SVM) to accurately allocate the teaching features and behaviors to the corresponding teaching links. Finally, the proposed model was proved effective through experiments. Based on the recognized teaching behaviors, the frequency and duration of such behaviors were subject to comparative analysis, revealing the teaching features in high-quality online open courses.

Research on the Feature Parameter Extraction of Wheat Seeds’ Bad Point Based on Image Processing

2014 ◽  
Vol 644-650 ◽  
pp. 4140-4143
Author(s):  
Zhe Wen ◽  
Qian Dong ◽  
Jie Zhu ◽  
Ya Bin Fan
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Parameter Extraction ◽  
Extraction Methods ◽  
Automatic Extraction ◽  
Wheat Seed ◽  
Manual Operation ◽  
Feature Parameter ◽  
Wheat Seeds

It is very important that study the feature parameter extraction of bad point of wheat seeds based on image processing for judging the quality of wheat. Using image processing extract and analyze the collected images information, and based on the collected information analyze the bad point information of wheat seed, then extract the feature parameters. Traditional bad point’s feature extraction methods are completed by the manual operation, and the efficient is lower. Currently, by means of image processing technology can extract the bad point’s feature of wheat seed automatically. To this end, the research status of seed feature extraction based on image processing are reviewed and prospected. Experiments show that the method can better complete the bad point’s feature automatic extraction and recognition of wheat seeds.

scholarly journals Image Analysis Techniques for Ripeness Detection of Palm Oil Fresh Fruit Bunches

2019 ◽  
Vol 18 (3) ◽  
pp. 57-62
Author(s):  
Shuwaibatul Aslamiah Ghazali ◽  
Hazlina Selamat ◽  
Zaid Omar ◽  
Rubiyah Yusof
Keyword(s):  
Palm Oil ◽  
Visual Word ◽  
Support Vector ◽  
Palm Fruit ◽  
Analysis Techniques ◽  
Color Features ◽  
Average Accuracy ◽  
Fresh Fruit Bunches ◽  
Image Analysis Techniques

Being one of the biggest producers and exporters of palm oil and palm oil products, Malaysia has an important role to play in fulfilling the growing global need for oils and fats sustainably. Quality is an important factor that ensuring palm oil industries fulfill the demands of palm oil product. There has significant relationship between the quality of the palm oil fruits and the content of its oil. Ripe FFB gives more oil content, while unripe FFB give the least content. Overripe FFB shows that the content of oil is deteriorates. There have 4 classes of ripeness stages involves in this paper which are ripe, unripe, underipe and overripe. The proposes approach in this paper uses color features and bag of visual word  for classifying oil palm fruit ripeness stages. Experiments conducted in this paper consisted of smartphone camera for image acquisition, python and matlab software for image pre processing and Support Vector Machine for classification. A total of 400 images is taken in a few plant in north Malaysia. Experiments involved on a dataset of 360 images for training for four classes and 40 images for testing. The average accuracy for the 4 classes of the FFB by color features is 57% while the accuracy for ripeness classification by using bag of visual word is 70%.

scholarly journals KLARIFIKASI KEMATANGAN BUAH NANAS DENGAN RUANG WARNA HUE SATURATION INTENSITY (HSI)

2021 ◽  
Vol 6 (1) ◽  
pp. 135
Author(s):  
Budi Yanto ◽  
Jufri Jufri ◽  
Adyanata Lubis ◽  
B.Herawan Hayadi ◽  
Erna Armita, NST
Keyword(s):  
Image Processing ◽  
Selection Process ◽  
Color Space ◽  
Accuracy Rate ◽  
Tropical Fruit ◽  
Saturation Intensity ◽  
Image Test ◽  
Pineapple Fruit ◽  
Processed Products

Pineapple fruit is included in the type of tropical fruit, which is quite popular because it contains a lot of Vitamin C, which is quite high. Pineapple is a local fruit in the Kampar area, this fruit can be consumed directly and become other local processed products. Therefore, the quality of pineapple ripeness must be maintained. The problem that occurs at this time is that the pineapple fruit selection process is still done manually, by looking at it visually, so mistakes can occur in the process of clarifying pineapple fruit identification according to standards. Therefore, it is necessary to research the ripeness of pineapples using the Color Space Algorithm Hue Saturation Intensity (HIS). The variables to be input are based on photos of ripe, half ripe, and raw pineapples using a smartphone camera or DSLR camera with a minimum resolution of 8 MP. Clarifying the results with image processing and Hue Saturation Intensity (HIS) transformation has an accuracy rate of 80% for the 20 image test data. So that the expected results can help pineapple farmers in detecting the level of maturity of pineapple fruit, which is difficult, can minimize errors in determining the ripeness of pineapple fruit

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