Over the years significant research has been performed for machine vision based fabric inspection systems in order to replace manual inspection, which is time consuming and not accurate enough. Automated fabric inspection systems mainly involve two challenging problems: one is defect detection and another is classification, which remains elusive despite considerable research effort in automated fabric inspection. The research reported to date to solve the defect classification problem appears to be insufficient, particularly in selecting appropriate set of features. Scene analysis and feature selection play a very important role in the classification process. Insufficient scene analysis results in an inappropriate set of features. Selection of an inappropriate feature set increases complexities of subsequent steps and makes the classification task harder. Considering this observation, we present a possibly appropriate feature set in order to address the problem of fabric defect classification using neural network (NN). We justify the features from the point of view of distinguishing quality and feature extraction difficulty. We performed some experiments in order to show the utility of proposed features and compare performances with recently reported relevant works. More than 98% classification accuracy has been found, which appears to be very promising
Bangladesh has its own abundance of water resources which helps to identify its customs that are related to freshwater fish. Due to environmental issues along with some other reasons, the amount of water resources of Bangladesh is reducing day-by-day. Consequently, many of our territorial freshwater fishes are getting abolished. Thus, the new generation people of Bangladesh lacks the knowledge of local freshwater fish. For this problem, a solution has been found with the collaboration of vision-based technology. As a solution, a machine-vision based local freshwater fish recognition system is presented that can be proceed with an image of fish captured with a mobile or handheld device and recognize the fish in order to introduce the fish. To demonstrate the utility of the proposed expert system, several experiments are performed. At first, a set of fourteen features, which consists of four types of features, are presented. Then the color image has been converted into gray-scale image and the gray-scale histogram is formed. Image segmentation takes place using histogram-based method and then the features are extracted. PCA is used for decreasing the feature numbers. Three classifiers are used for recognizing fish, where SVM gives the highest accuracy showing a value of 94.2%.Keywords Bangladeshi local fish · Machine vision · Thresholding · Principle component analysis · Support vector machine · Classifiers · k-nearest neighbor * Israt Sharmin, ima.sharmin23@gmail.com; Nuzhat Farzana Islam, nuzhat15-5316@diu.edu.bd; Israt Jahan, israt15-5461@diu.edu.bd; Tasnem Ahmed Joye, ahmed15-
A significant attention of researchers has been drawn by automated textile inspection systems in order to replace manual inspection, which is time consuming and not accurate enough. Automated textile inspection systems mainly involve two challenging problems, one of which is defect classification. The amount of research done to solve the defect classification problem is inadequate. Scene analysis and feature selection play a very important role in the classification process. Inadequate scene analysis results in an inappropriate set of features. Selection of an inappropriate feature set increases the complexities of the subsequent steps and makes the classification task harder. By taking into account this observation, we present a possibly appropriate set of geometric features in order to address the problem of neural network-based textile defect classification. We justify the features from the point of view of discriminatory quality and feature extraction difficulty. We conduct some experiments in order to show the utility of the features. Our proposed feature set has obtained classification accuracy of more than 98%, which appears to be better than reported results to date.
Bangladesh is an agricultural country having a tropical monsoon climate. A large variety of tropical and sub-tropical fruits abound in Bangladesh. People of Bangladesh are fruit-lovers too. Currently, most of the people of this country are failing to recognize many of the rare local fruits and the number of this portion of people is increasing day by day. Thus, not only the natural heritage but also good sources of food are being diminished. Performing a machine vision based recognition of these fruits can help people recognize them. In this paper, we perform an in-depth exploration of a computer vision approach for recognizing rare local fruits of Bangladesh. A number of rare local fruits are classified based on the features extracted from their images. For our experiment, we have used a total of 480 images of 6 rare local fruits. We perform some preprocessing on the captured image and then expected features are extracted using image segmentation. Classification of the fruits is accomplished using support vector machines (SVMs). We have achieved 94.79% classification accuracy, which is not only good but also promising for future research.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.