Traffic sign detection and recognition systems are essential components of Advanced Driver Assistance Systems and self-driving vehicles. In this contribution we present a vision-based framework which detects and recognizes traffic signs inside the attentional visual field of drivers. This technique takes advantage of the driver's 3D absolute gaze point obtained through the combined use of a front-view stereo imaging system and a non-contact 3D gaze tracker. We used a linear Support Vector Machine as a classifier and a Histogram of Oriented Gradient as features for detection. Recognition is performed by using Scale Invariant Feature Transforms and color information. Our technique detects and recognizes signs which are in the field of view of the driver and also provides indication when one or more signs have been missed by the driver.

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Osmotic dehydration, as a minimal processing method, has found increasingly wide prospects during the past few decades. This process involves mass transfer which is commonly modeled by applications of different procedures, mostly based on Fick's law. In this research, we approach the modeling process by first obtaining experimental measurement of carrot’s solid gain and water loss under different conditions of solution concentrations ( 20, 40 and 60% w/w), temperatures ( 40, 60 and 80°C) as well as time intervals (1-6h). Then two paradigms of artificial neural networks (ANN), feed forward neural networks (FFNN) and radial basis function neural networks (RBFNN) are applied and compared for modeling this process. Additionally, genetic algorithm is used to determine optimal conditions for osmotic dehydration.

Clustering involves grouping data points together according to some measure of similarity. Clustering is one of the most significant unsupervised learning problems and do not need any labeled data. There are many clustering algorithms, among which fuzzy c-means (FCM) is one of the most popular approaches. FCM has an objective function based on Euclidean distance. Some improved versions of FCM with rather different objective functions are proposed in recent years. Generalized Improved fuzzy partitions FCM (GIFP-FCM) is one of them, which uses norm distance measure and competitive learning and outperforms the previous algorithms in this field. In this paper, we present a novel FCM clustering method with improved fuzzy partitions that utilizes shadowed sets and try to improve GIFP-FCM in noisy data sets. It enhances the efficiency of GIFP-FCM and improves the clustering results by correctly eliminating most outliers during steps of clustering. We name the novel fuzzy clustering method shadowed set-based GIFP-FCM (SGIFP-FCM). Several experiments on vessel segmentation in retinal images of DRIVE database illustrate the efficiency of the proposed method.

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The main goal of medical imaging applications is to diagnose some diseases, try to prevent the progression of them, and actually cure the patients. The number of people that suffer from diabetes is growing very fast these recent years in many countries and it is needed to diagnose this disease in the beginning to prevent the subsequent side effects like blindness and so on. One of the first ways to detect this disease is analysis of vessels in some parts of the eye such as retina and conjunctiva. Some studies have been done on effects of vessel changes of conjunctiva in diabetes diagnosis and it is proved that conjunctival vessel extraction and analysis is a good way for this purpose. In this paper, we proposed a method to detect and extract the vessels of conjunctiva automatically. It is the first stage of the process of diabetes diagnosis. We first extract some textural features from each pixel of the conjunctiva image using LBP and then classify each pixel to vessels or nonvessels according to the features vector based on a supervised classifier, ANFIS. We tested the proposed algorithm on 40 conjunctival images to show the performance and efficiency of our method.