Finite-time complete periodic synchronization of memristive neural networks with mixed delays.

In this paper we study the oscillatory behavior of a new class of memristor based neural networks with mixed delays and we prove the existence and uniqueness of the periodic solution of the system based on the concept of Filippov solutions of the differential equation with discontinuous right-hand side. In addition, some assumptions are determined to guarantee the globally exponentially stability of the solution. Then, we study the adaptive finite-time complete periodic synchronization problem and by applying Lyapunov-Krasovskii functional approach, a new adaptive controller and adaptive update rule have been developed. A useful finite-time complete synchronization condition is established in terms of linear matrix inequalities. Finally, an illustrative simulation is given to substantiate the main results.

68 landmarks are efficient for 3D face alignment: what about more?: 3D face alignment method applied to face recognition.

This paper proposes a 3D face alignment of 2D face images in the wild with noisy landmarks. The objective is to recognize individuals from their single profile image. We first proceed by extracting more than 68 landmarks using a bag of features. This allows us to obtain a bag of visible and invisible facial keypoints. Then, we reconstruct a 3D face model and get a triangular mesh by meshing the obtained keypoints. For each face, the number of keypoints is not the same, which makes this step very challenging. Later, we process the 3D face using butterfly and BPA algorithms to make correlation and regularity between 3D face regions. Indeed, 2D-to-3D annotations give much higher quality to the 3D reconstructed face model without the need for any additional 3D Morphable models. Finally, we carry out alignment and pose correction steps to get frontal pose by fitting the rendered 3D reconstructed face to 2D face and performing pose normalization to achieve good rates in face recognition. The recognition step is based on deep learning and it is performed using DCNNs, which are very powerful and modern, for feature learning and face identification. To verify the proposed method, three popular benchmarks, YTF, LFW, and BIWI databases, are tested. Compared to the best recognition results reported on these benchmarks, our proposed method achieves comparable or even better recognition performances.

Dress-up: deep neural framework for image-based human appearance transfer.

The fashion industry is at the brink of radical transformation. The emergence of Artificial Intelligence (AI) in fashion applications creates many opportunities for this industry and make fashion a better space for everyone. Interesting to this matter, we proposed a virtual try-on interface to stimulate consumers purchase intentions and facilitate their online buying decision process. Thus, we present, in this paper, our flexible person generation system for virtual try-on that aiming to treat the task of human appearance transfer across images while preserving texture details and structural coherence of the generated outfit. This challenging task has drawn increasing attention and made huge development of intelligent fashion applications. However, it requires different challenges, especially in the case of a wide divergences between the source and target images. To solve this problem, we proposed a flexible person generation framework called Dress-up to treat the 2D virtual try-on task. Dress-up is an end-to-end generation pipeline with three modules based on the task of image-to-image translation aiming to sequentially interchange garments between images, and produce dressing effects not achievable by existing works. The core idea of our solution is to explicitly encode the body pose and the target clothes by a pre-processing module based on the semantic segmentation process. Then, a conditional adversarial network is implemented to generate target segmentation feeding respectively, to the alignment and translation networks to generate the final output results. The novelty of this work lies in realizing the appearance transfer across images with high quality by reconstructing garments on a person in different orders and looks from simlpy semantic maps and 2D images without using 3D modeling. Our system can produce dressing effects and provide significant results over the state-of-the-art methods on the widely used DeepFashion dataset. Extensive evaluations show that Dress-up outperforms other recent methods in terms of output quality, and handles a wide range of editing functions for which there is no direct supervision. Different types of results were computed to verify the performance of our proposed framework and show that the robustness and effectiveness are high by utilizing our method.

You can try without visiting: a comprehensive survey on virtually try-on outfits.

Since the last years and until now, technology has made fast progress for many industries, in particularly, garment industry which aims to follow consumer desires and demands. One of these demands is to fit clothes before purchasing them on-line. Therefore, many research works have been focused on how to develop an intelligent apparel industry to ensure the online shopping experience. Image-based virtual try-on is among the most potential approach of virtual fitting that tries on target clothes into customer's image, therefore, it has received considerable research efforts in the recent years. However, there are several challenges involved in development of virtual try-on that make it difficult to achieve naturally looking virtual outfit such as shape, pose, occlusion, illumination cloth texture, logo and text etc. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of virtual try-on. This review first introduces virtual try-on and its challenges followed by its demand in fashion industry. We summarize state-of-the-art image based virtual try-on for both fashion detection and fashion synthesis as well as their respective advantages, drawbacks, and guidelines for selection of specific try-on model followed by its recent development and successful application. Finally, we conclude the paper with promising directions for future research.

Do individual characteristics influence the beta-elliptic modeling errors during ellipse drawing movements?

This paper investigates whether age, gender, and degree of familiarity with writing have an influence on the Beta-elliptic model errors during hand-drawing on a graphical tablet. A database of elliptical hand drawing movements was built within a sample of 99 participants aged between 19 and 85 years. Using the Beta-elliptic model, the velocity profile was modeled by overlapped Beta functions and the drawing trajectory was segmented between velocity extrema and each segment geometry was modeled by elliptic arcs. Average absolute and relative geometric, curvature and curvilinear velocity errors were 0.27 mm, 0.68%, 4.54 mm, 0.48%, 4.68 mm/s, and 8.79% respectively. Statistical analyses revealed not significant or low correlation between modeling errors and age and movement velocity, and no significant or low error differences according to gender or degree of familiarity with writing.

SRD5A3-CDG: 3D structure modeling, clinical spectrum, and computer-based dysmorphic facial recognition.

Pathogenic variants in Steroid 5 alpha reductase type 3 (SRD5A3) cause rare inherited congenital disorder of glycosylation known as SRD5A3-CDG (MIM# 612379). To date, 43 affected individuals have been reported. Despite the development of various dysmorphic features in significant number of patients, facial recognition entity has not yet been established for SRD5A3-CDG. Herein, we reported a novel SRD5A3 missense pathogenic variant c.460 T > C p.(Ser154Pro). The 3D structural modeling of the SRD5A3 protein revealed additional transmembrane α-helices and predicted that the p.(Ser154Pro) variant is located in a potential active site and is capable of reducing its catalytic efficiency. Based on phenotypes of our patients and all published SRD5A3-CDG cases, we identified the most common clinical features as well as some recurrent dysmorphic features such as arched eyebrows, wide eyes, shallow nasal bridge, short nose, and large mouth. Based on facial digital 2D images, we successfully designed and validated a SRD5A3-CDG computer based dysmorphic facial analysis, which achieved 92.5% accuracy. The current work integrates genotypic, 3D structural modeling and phenotypic characteristics of CDG-SRD5A3 cases with the successful development of computer tool for accurate facial recognition of CDG-SRD5A3 complex cases to assist in the diagnosis of this particular disorder globally.

Morphological Convolutional Neural Network Architecture for Digit Recognition.

Deep neural networks have proved promising results in many applications and fields, but they are still assimilated to a black box. Thus, it is very useful to introduce interpretability aspects to prevent the blind application of deep networks. This paper proposed an interpretable morphological convolutional neural network called Morph-CNN for pattern recognition, where morphological operations were incorporated using counter-harmonic mean into the convolutional layer in order to generate enhanced feature maps. Morph-CNN was extensively evaluated on MNIST and SVHN benchmarks for digit recognition. The different tested configurations showed that Morph-CNN outperforms the existing methods.

When mental fatigue maybe characterized by Event Related Potential (P300) during virtual wheelchair navigation.

The goal of this study is to investigate the influence of mental fatigue on the event related potential P300 features (maximum pick, minimum amplitude, latency and period) during virtual wheelchair navigation. For this purpose, an experimental environment was set up based on customizable environmental parameters (luminosity, number of obstacles and obstacles velocities). A correlation study between P300 and fatigue ratings was conducted. Finally, the best correlated features supplied three classification algorithms which are MLP (Multi Layer Perceptron), Linear Discriminate Analysis and Support Vector Machine. The results showed that the maximum feature over visual and temporal regions as well as period feature over frontal, fronto-central and visual regions were correlated with mental fatigue levels. In the other hand, minimum amplitude and latency features didn't show any correlation. Among classification techniques, MLP showed the best performance although the differences between classification techniques are minimal. Those findings can help us in order to design suitable mental fatigue based wheelchair control.

Spatiotemporal representation of 3D hand trajectory based on beta-elliptic models.

The aim of this paper was to model the hand trajectory during grasping by an extension in 3D of the 2D written language beta-elliptic model. The interest of this model is that it takes into account both geometric and velocity information. The method relies on the decomposition of the task space trajectories in elementary bricks. The latter is characterized by a velocity profile modelled with beta functions and a geometry modelled with elliptic shapes. A data base of grasping movements has been constructed and the errors of reconstruction were assessed (distance and curvature) considering two variations of the beta-elliptic model ('quarter ellipse' and 'two tangents points' method). The results showed that the method based on two tangent points outperforms the quarter ellipse method with average and maximum relative errors of 2.73% and 8.62%, respectively, and a maximum curvature error of 9.26% for the former. This modelling approach can find interesting application to characterize the improvement due to a rehabilitation or teaching process by a quantitative measurement of hand trajectory parameters.

Influence of mental fatigue on P300 and SSVEP during virtual wheelchair navigation.

The aim of this paper is to investigate the influence of mental fatigue on Positive 300 (P300) and Steady State Visual Evoked Potentials (SSVEP) during virtual wheelchair navigation. For this purpose, experimental protocols were setup in order to induce mental fatigue, P300 and SSVEP. Next, the correlation between mental fatigue and P300/SSVEP parameters were investigated. At the end, the best correlated features from both modalities were used as inputs for three classification techniques. Depending on the subject samples (healthy vs palsy), The best overall classification rate reached 80% for P300 modality. The results of this investigation constitute the first steps towards an anticipatory system that can assist the wheelchair driver during navigation, depending on his mental fatigue level.

Existence and uniqueness of pseudo almost-periodic solutions of recurrent neural networks with time-varying coefficients and mixed delays.

This paper is concerned with the existence and uniqueness of pseudo almost-periodic solutions to recurrent delayed neural networks. Several conditions guaranteeing the existence and uniqueness of such solutions are obtained in a suitable convex domain. Furthermore, several methods are applied to establish sufficient criteria for the globally exponential stability of this system. The approaches are based on constructing suitable Lyapunov functionals and the well-known Banach contraction mapping principle. Moreover, the attractivity and exponential stability of the pseudo almost-periodic solution are also considered for the system. A numerical example is given to illustrate the effectiveness of our results.

Natural language morphology integration in off-line Arabic optical text recognition.

In this paper, we propose a new linguistic-based approach called the affixal approach for Arabic word and text image recognition. Most of the existing works in the field integrate the knowledge of the Arabic language in the recognition process in two ways: either in post-recognition using the language of dictionary (dictionary of words) to validate the word hypotheses suggested by the OCR or in the course of the recognition process (recognition directed by a lexicon) using a statistical model of the language (Hidden Markov Model or N-gram). The proposed approach uses the linguistic concepts of the vocabulary to direct and simplify the recognition process. The principal contribution of the proposed approach is to be able to categorize the word hypotheses in words that are either derived or not derived from roots and to characterize morphologically each word hypothesis in order to prepare the text hypotheses for later analyses (for example, syntactic analysis; to filter the sentence hypotheses).