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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. [ABSTRACT FROM AUTHOR]

Recently software industry has paid significant attention to customizing software products across distributed boundaries. Communicating the requirements of multiple clients across distributed borders is a crucial challenge for the software customization process. Local decision-making and local development at the client site are considered methods for reducing difficulties in communicating the requirements of multiple clients across distributed boundaries. This paper introduces a new model called the onshore development model (ODM) for accomplishing the customization requests in the distributed development process of software. This model presents a scenario for enhancing the onsite development of specific requirements to reduce delays andmisunderstandings between the clients and the team involved. This model depends on moving the development process to the client's location. Three empirical studies were conducted to evaluate the proposed model to measure its productivity, time performance, and cost reduction. The proposed model has been compared with two other models: the basic model (BM), which allocates the decision-making process and the development process for teams at the vendor's location, and the local decision-making model (LDec), which assigns the decision-making process for team at the client's location. The results of the empirical studies showed significant outperforming of the proposed model over the basic model and local decision-making model in productivity, time performance, and cost reduction. The productivity of the proposed model improved by 39% and 10% more than the basic model and the local decision-making model, respectively. In addition, the time performance of the proposed model became faster by 49% and 20.8% than the basic model and the local decision-making model, respectively. Also, it reduced the total cost of the development process by 31% in terms of the salaries of all persons involved in requirements collecting, decision-making, and development. [ABSTRACT FROM AUTHOR]

In recent years, the number of Gun-related incidents has crossed over 250,000 per year and over 85% of the existing 1 billion firearms are in civilian hands, manual monitoring has not proven effective in detecting firearms. which is why an automated weapon detection system is needed. Various automated convolutional neural networks (CNN) weapon detection systems have been proposed in the past to generate good results. However, These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection system. These models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance videos. This research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key parameter. The proposed framework is based on You Only Look Once (YOLO) and Area of Interest (AOI). Initially, themodels take pre-processed frames where the background is removed by the use of the Gaussian blur algorithm. The proposed architecture will be assessed through various performance parameters such as False Negative, False Positive, precision, recall rate, and F1 score. The results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are achieved. Speed reached 0.010 s per frame compared to the 0.17 s of the Faster R-CNN. It is promising to be used in the field of security and weapon detection. [ABSTRACT FROM AUTHOR]

In recent years, video surveillance application played a significant role in our daily lives. Images taken during foggy and haze weather conditions for video surveillance application lose their authenticity and hence reduces the visibility. The reason behind visibility enhancement of foggy and haze images is to help numerous computer and machine vision applications such as satellite imagery, object detection, target killing, and surveillance. To remove fog and enhance visibility, a number of visibility enhancement algorithms and methods have been proposed in the past. However, these techniques suffer from several limitations that place strong obstacles to the real world outdoor computer vision applications. The existing techniques do not perform well when images contain heavy fog, large white region and strong atmospheric light. This research work proposed a new framework to defog and dehaze the image in order to enhance the visibility of foggy and haze images. The proposed framework is based on a Conditional generative adversarial network (CGAN) with two networks; generator and discriminator, each having distinct properties. The generator network generates fog-free images fromfoggy images and discriminator network distinguishes between the restored image and the original fog-free image. Experiments are conducted on FRIDA dataset and haze images. To assess the performance of the proposed method on fog dataset, we use PSNR and SSIM, and for Haze dataset use e, r-, and s as performance metrics. Experimental results shows that the proposed method achieved higher values of PSNRand SSIM which is 18.23, 0.823 and lower values produced by the compared method which are 13.94, 0.791 and so on. Experimental results demonstrated that the proposed framework Has removed fog and enhanced the visibility of foggy and hazy images. [ABSTRACT FROM AUTHOR]