Designing of rule base for a TSK- fuzzy system using bacterial foraging optimization algorithm (BFOA)

AbstractManual construction of a rule base for a fuzzy system is a hard and time-consuming task that requires expert knowledge. To ameliorate that, researchers have developed some methods that are more based on training data than on expert knowledge to gradually identify the structure of rule bases. In this paper we propose a method based on bacterial foraging optimization algorithm (BFOA), which simulates the foraging behavior of “E.coli” bacterium, to tune Gaussian membership functions parameters of a TSK-fuzzy system rule base. The effectiveness of modified BFOA in such identifications is then revealed for designing a fuzzy control system, via a comparison with available methods

Deep-MDS Framework for Recovering the 3D Shape of 2D Landmarks from a Single Image

In this paper, a low parameter deep learning framework utilizing the Non-metric Multi-Dimensional scaling (NMDS) method, is proposed to recover the 3D shape of 2D landmarks on a human face, in a single input image. Hence, NMDS approach is used for the first time to establish a mapping from a 2D landmark space to the corresponding 3D shape space. A deep neural network learns the pairwise dissimilarity among 2D landmarks, used by NMDS approach, whose objective is to learn the pairwise 3D Euclidean distance of the corresponding 2D landmarks on the input image. This scheme results in a symmetric dissimilarity matrix, with the rank larger than 2, leading the NMDS approach toward appropriately recovering the 3D shape of corresponding 2D landmarks. In the case of posed images and complex image formation processes like perspective projection which causes occlusion in the input image, we consider an autoencoder component in the proposed framework, as an occlusion removal part, which turns different input views of the human face into a profile view. The results of a performance evaluation using different synthetic and real-world human face datasets, including Besel Face Model (BFM), CelebA, CoMA - FLAME, and CASIA-3D, indicates the comparable performance of the proposed framework, despite its small number of training parameters, with the related state-of-the-art and powerful 3D reconstruction methods from the literature, in terms of efficiency and accuracy

Human Perception-based Image Enhancement Using a Deep Generative Model

In this paper we propose a deep model for perceptual image en-hancement based on generative modeling. The proposed frame-work is inspired by the Conditional Variational AutoEncoder (CVAE)which is a well-known deep generative structure. In generativemodels, there are efficient regularizers for controlling the outputdistributions using information from input data which lead to accu-rate and visually plausible results with few parameters. Additionally,we propose to use an image quality assessment network to deter-mine the best result among those obtained by the implementedCVAEs. The proposed CVAE structure models the histogram vec-tors of different color channels and parameters of image data (i.e.,the networks do not work directly on pixel values). This configu-ration makes the proposed framework capable of using images ofdifferent sizes. Qualitative and numerical evaluations on a relateddataset compared to state-of-the-art indicate superiority of the pro-posed framework in improving image quality and content