Plan de negocio para la implementaci?n de un Planner Virtual para eventos infantiles en las Zonas 6 y 7 del departamento de Lima

Las fiestas infantiles son celebraciones muy esperadas; sin embargo, demandan mucho tiempo y esfuerzo de los padres. Fiesta al Toque ofrece un servicio que alivia el stress de los padres antes, durante y despu?s de la fiesta, un servicio tipo ?llave en mano? con supervisi?n incluida, la cual, garantiza que la fiesta se implemente seg?n lo acordado en el contrato. Adem?s, ofrece un adecuado balance entre costo y beneficio, pues la plataforma virtual, ofrece variedad de paquetes de calidad, seg?n la econom?a de cada usuario. Asimismo, genera beneficios a los proveedores de servicios, puesto que les asegura una agenda de trabajo ocupada, d?ndoles as? oportunidades de desarrollo. El modelo de negocio estar? soportado por herramientas tecnol?gicas que centralizar?n la informaci?n de todos los proveedores a tiempo real, para que el padre de familia pueda ingresar sus requerimientos y obtener s?lo aquellas opciones que est?n disponibles para la fecha en que necesitan realizar el evento. As?, se facilita no solo la venta, sino que permite que el negocio pueda crecer en forma m?s sencilla y sostenible

Mitigation of motion-induced artifacts in cone beam computed tomography using deep convolutional neural networks

Background: Cone beam computed tomography (CBCT) is often employed on radiation therapy treatment devices (linear accelerators) used in image-guided radiation therapy (IGRT). For each treatment session, it is necessary to obtain the image of the day in order to accurately position the patient, and to enable adaptive treatment capabilities including auto-segmentation and dose calculation. Reconstructed CBCT images often suffer from artifacts, in particular those induced by patient motion. Deep-learning based approaches promise ways to mitigate such artifacts. Purpose: We propose a novel deep-learning based approach with the goal to reduce motion induced artifacts in CBCT images and improve image quality. It is based on supervised learning and includes neural network architectures employed as pre- and/or post-processing steps during CBCT reconstruction. Methods: Our approach is based on deep convolutional neural networks which complement the standard CBCT reconstruction, which is performed either with the analytical Feldkamp-Davis-Kress (FDK) method, or with an iterative algebraic reconstruction technique (SART-TV). The neural networks, which are based on refined U-net architectures, are trained end-to-end in a supervised learning setup. Labeled training data are obtained by means of a motion simulation, which uses the two extreme phases of 4D CT scans, their deformation vector fields, as well as time-dependent amplitude signals as input. The trained networks are validated against ground truth using quantitative metrics, as well as by using real patient CBCT scans for a qualitative evaluation by clinical experts. Results: The presented novel approach is able to generalize to unseen data and yields significant reductions in motion induced artifacts as well as improvements in image quality compared with existing state-of-the-art CBCT reconstruction algorithms (up to +6.3 dB and +0.19 improvements in peak signal-to-noise ratio, PSNR, and structural similarity index measure, SSIM, respectively), as evidenced by validation with an unseen test dataset, and confirmed by a clincal evaluation on real patient scans (up to 74% preference for motion artifact reduction over standard reconstruction). Conclusions: For the first time, it is demonstrated, also by means of clinical evaluation, that inserting deep neural networks as pre- and post-processing plugins in the existing 3D CBCT reconstruction and trained end-to-end yield significant improvements in image quality and reduction of motion artifacts

Rotation-Invariant and scale-invariant steerable pyramid decomposition for texture image retrieval

This paper proposes a new rotation-invariant and scaleinvariant representation for texture image retrieval based on Steerable Pyramid Decomposition. By calculating the mean and standard deviation of decomposed image subbands, the texture feature vectors are extracted. To obtain rotation or scale invariance, the feature elements are aligned by considering either the dominant orientation or dominant scale of the input textures. Experiments were conducted on the Brodatz database aiming to compare our approach to the conventional Steerable Pyramid Decomposition, and a recent proposal for texture characteriztion based on Gabor Wavelets with regard to their retrieval effectiveness. Results demonstrate the superiority of the proposed method in rotated and scaled image datasets.

Semantic segmentation of aerial images in urban areas with class-specific higher-order cliques

In this paper we propose an approach to multi-class semantic segmentation of urban areas in high-resolution aerial images with classspecific object priors for buildings and roads. What makes model design challenging are highly heterogeneous object appearances and shapes that call for priors beyond standard smoothness or co-occurrence assumptions. The data term of our energy function consists of a pixel-wise classifier that learns local co-occurrence patterns in urban environments. To specifically model the structure of roads and buildings, we add high-level shape representations for both classes by sampling large sets of putative object candidates. Buildings are represented by sets of compact polygons, while roads are modeled as a collection of long, narrow segments. To obtain the final pixel-wise labeling, we use a CRF with higher-order potentials that balances the data term with the object candidates. We achieve overall labeling accuracies of > 80%.ISSN:2194-9042ISSN:2194-905

Wavelet-based Feature Extraction for Fingerprint Image Retrieval

This paper presents a novel approach to fingerprint retrieval for personal identification by joining three image retrieval tasks, namely, feature extraction, similarity measurement, and feature indexing, into a wavelet-based fingerprint retrieval system. We propose the use of different types of Wavelets for representing and describing the textural information present in fingerprint images. For that purposes, the feature vectors used to characterize the fingerprints are obtained by computing the mean and the standard deviation of the decomposed images in the Wavelet domain. These feature vectors are used to retrieve the most similar fingerprints given a query image, while their indexation is used to reduce the search spaces of image candidates. The different types of Wavelets used in our study include: Gabor Wavelets (GWs), Tree-Structured Wavelet Decomposition using both Orthogonal Filter Banks (TOWT) and Bi-orthogonal Filter Banks (TBOWT), as well as the Steerable Wavelets. To evaluate the retrieval accuracy of the proposed approach, a total number of eight different data sets were used. Experiments also evaluated different combinations of Wavelets with six similarity measures. The results show that the Gabor Wavelets combined with the Square Chord similarity measure achieves the best retrieval effectiveness.