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As an emerging technology that uses a pill‐sized camera to visualize images of the digestive tract. Wireless capsule endoscopy (WCE) presents several advantages, since it is far less invasive, does not need sedation and has less possible complications compared to standard endoscopy. Hence, it might be exploited as alternative to the standard procedure. WCE is used to diagnosis a variety of gastro‐intestinal diseases such as polyps, ulcers, crohns disease, and hemorrhages. Nevertheless, WCE videos produced after a test may consist of thousands of frames per patient that must be viewed by medical specialists, besides, the capsule free mobility and technological limits cause production of a low quality images. Hence, development of an automatic tool based on artificial intelligence might be very helpful. Moreover, most state‐of‐the‐art works aim at images classification (normal/abnormal) while ignoring diseases segmentation. Therefore, in this work a novel method based on Feature Pyramid Network model is presented. This approach aims at diseases segmentation from WCE images. In this model, modules to optimize and combine features were employed. Specifically, semantic and spatial features were mutually compensated by spatial attention and cross‐level global feature fusion modules. The proposed method testing F1‐score and mean intersection over union are 94.149% and 89.414%, respectively, in the MICCAI 2017 dataset. In the KID Atlas dataset, the method achieved a testing F1‐score and mean intersection over union of 94.557% and 90.416%, respectively. Through the performance analysis, the mean intersection over union in the MICCAI 2017 dataset is 20.414%, 18.484%, 11.444%, 8.794% more than existing approaches. Moreover, the proposed scheme surpassed the methods used for comparison by 29.986% and 9.416% in terms of mean intersection over union in KID Atlas dataset. These results indicate that the proposed approach is promising in diseases segmentation from WCE images. [ABSTRACT FROM AUTHOR]

Wireless capsule endoscopy (WCE) is a novel imaging technique that can view the entire small bowel in human body. Thus, it is presented as an excellent diagnostic tool for evaluation of gastrointestinal diseases compared with traditional endoscopies. However, the diagnosis by the physicians is tedious since it requires reviewing the video extracted from the capsule and analysing all of its frames. This tedious task has encouraged the researchers to provide automated diagnostic technics for WCE frameworks to detect symptoms of gastrointestinal illness. In this paper, we present the prelimenary results of red lesion detection in WCE images using Dense-Unet deep learning segmentation model. To this end, we have used a dataset containing two subsets of anonymized video capsule endoscopy images with annotated red lesions. The first set, used in this work, has 3,295 non-sequential frames and their corresponding annotated masks. The results obtained by the proposed scheme are promising.

Recently, intelligent transportation systems (ITS) attracts more and more attention for its wide applications. Traffic sign detection and recognition (TSDR) system is an essential task of ITS. It enhances the safety by informing the drivers about the current state of traffic signs and offering valuable information about precautions. This paper reviews the popular traffic sign detection methods (TSD) prevalent in recent literature. The methods are divided into color-based, shape-based, and machine learning based ones. Color space, segmentation method, features, and shape detection method are the terms considered in the review of the detection module. The paper presents a comparison between these methods. Furthermore, a list of publicly available data sets and a discussion on possible future works are provided.

Road sign detection is an important function for driver assistance systems. Although it has been studied for many years, it still has some performance limitations. This paper proposes a new method for road sign detection by employing both color and shape cues. The proposed method consists of three steps. First, the initial image is pre-processed using mean shift clustering algorithm. The clustering is carried out based on color information. Second, a random forest classifier is used to segment the clustered image. In the final step, a shape based classification is performed using a log-polar transform and cross correlation technique. The proposed detection method has been tested on both the German traffic sign detection benchmark (GTSDB) and the Swedish traffic signs (STS) datasets, and yields to 93.50 % and 94.22 % on the GTSDB dataset in terms of F-measure and area under curve(AUC), respectively. These results are satisfactory when compared to recent state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Traffic sign recognition is one of the main components of intelligent transportation systems (ITS). It improves safety by informing the driver of the current state of the road, e.g., warnings, prohibitions, restrictions, and other information useful for driving. This paper presents a new road sign recognition method that is achieved in three main steps. The first step maps the input image from the Cartesian coordinate system to the log-polar one. The second step computes the histogram of oriented gradients, local binary pattern, and local self-similarity characteristics from the image represented in the log-polar coordinate system. The third step performs classification on the basis of the random forest classifier and the features computed in the second step. The proposed method has been tested on the German Traffic Sign Recognition Benchmark dataset, and the results obtained are satisfactory when compared to the state-of-the-art approaches.