The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
The objective of this research is to develop algorithm to recognize black germ wheat based on image processing. The sample used for this study involved wheat from major producing areas of China. Images of wheat were acquired with a color linear CCD machine vision system. Each image was pre-processed to correct color offset. Then double threshold method was used to segment black germ from background...
We propose an automatic color segmentation system that (1) incorporates domain knowledge to guide histological image segmentation and (2) normalizes images to reduce sensitivity to batch effects. Color segmentation is an important, yet difficult, component of image-based diagnostic systems. User-interactive guidance by domain experts-i.e., pathologists-often leads to the best color segmentation or...
The objective of this research is to develop algorithm to recognize black germ wheat based on image processing. The sample used for this study involved wheat from major producing areas of China. Images of wheat were acquired with a color linear CCD machine vision system. Each image was pre-processed to correct color offset. Then double-threshold method was used to segment black germ from background...
In this work, a combination of artificial neural network (ANN), Fourier descriptors (FD) and spatial domain analysis (SDA) has been proposed for the development of an automatic fruits identification and sorting system. Fruits images are captured using digital camera inclined at different angles to the horizontal. Segmentation is used for the classification of the preprocessed images into two non-overlapping...
In this paper, we apply the principal component analysis (PCA) to extract significant image features and then incorporated them with the proposed two-phase fuzzy adaptive resonance theory neural network (Fuzzy-ART) for image content classification to overcome the gap between the low level features and high level semantic concepts. In general, Fuzzy-ART is an unsupervised clustering. Meanwhile, the...
The work presented in this article consists in evaluating the contribution of the use of a texture-color combination in forest mapping. We transform a Spot image into different color spaces, then we extract nine parameters using Laws filter. The resulting set of parameters is reduced using MIFS algorithm based on mutual information. The results are promising and give satisfying classification results.
Color and color difference are important information for a lesion in dermatological diagnosis. This paper presents various supervised ANN models for plaque classification using RGB indices. Images are taken from selected skin at the dermatological clinic which the images are captured using digital camera with controlled environment. The analysis of dermatological digital images is performed by measurements...
Color variation in medical images degrades the classification performance of computer aided diagnosis systems. Traditionally, color segmentation algorithms mitigate this variability and improve performance. However, consistent and robust segmentation remains an open research problem. In this study, we avoid the tenuous phase of color segmentation by adapting a bag-of-features approach using scale...
We propose a report on automatic classification of three common types of malignant lymphoma: chronic lymphocytic leukemia, follicular lymphoma, and mantle cell lymphoma. The goal was to find patterns indicative of lymphoma malignancies and allowing classifying these malignancies by type. We used a computer vision approach for quantitative characterization of image content. A unique two-stage approach...
Recent years have seen a rapid increase in the size of digital image collections. Any advance in our ability to organize unlabelled images according to their semantic content is a very useful step in managing these collections. In this paper we present a content based model for image categorization. In this model images in the database are grouped into classes of images with similar visual patterns...
Ear biometric is considered as one of the most reliable and invariant biometrics characteristics in line with iris and fingerprint characteristics. In many cases, ear biometrics can be compared with face biometrics regarding many physiological and texture characteristics. In this paper, a robust and efficient ear recognition system is presented, which uses Scale Invariant Feature Transform (SIFT)...
Nowadays, with the development of high quality graphical softwares, almost every presentation, in addition to text, contains some kind of images too. According to the presentation needs, different kinds of images are used by the presenters but different kinds of images needs different type of treatments which evolve the image categorization research. In our work we try to categorize images into two...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.