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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]

This paper proposes a new edge based stereo matching approach for road applications. The new approach consists in matching the edge points extracted from the input stereo images using temporal constraints. At the current frame, we propose to estimate a disparity range for each image line based on the disparity map of its preceding one. The stereo images are divided into multiple parts according to the estimated disparity ranges. The optimal solution of each part is independently approximated via the state-of-the-art energy minimization approach Graph cuts. The disparity search space at each image part is very small compared to the global one, which improves the results and reduces the execution time. Furthermore, as a similarity criterion between corresponding edge points, we propose a new cost function based on the intensity, the gradient magnitude and gradient orientation. The proposed method has been tested on virtual stereo images, and it has been compared to a recently proposed method and the results are satisfactory.