Valoración de la calidad y fragilidad visual del paisaje

The physical characteristics of landscape can be identified by their visual attributes, since the planning, thedecisions of handling, the natural interaction of the culture and processes take to physical changes that will be seen inthe future in the landscape. The scenic landscapes are one of the majors sources for the human enjoyment and in somecases it has been the object of direct criminal action to conserve his quality. In addition, the necessity to count with validresources to quantify the scenic characters of the landscapes has increased substantially with the development of theplanning of the use of the earth and its requirements of environmental data in which to base the decisions of earth use.A methodology for the valuation of the Visual Quality and its Fragility appears, as much of the rural landscapes as of theurban ones, with the purpose of having a valuation of the landscape that allows to order of suitable form the implantationof certain uses and activities in a territoryLas características físicas del paisaje pueden ser identificadas por sus atributos visuales, yaque el planeamiento, las decisiones de manejo, la interacción de la cultura y los procesos naturales llevan a cambios físicos que se verán en el futuro en el paisaje. Los paisajes escénicos son una de las mayores fuentes para el goce humano y en algunos casos ha sido el objeto de acción pública directa paraconservar su calidad. Además, la necesidad de contar con medios válidos por cuantificar los caracteresescénicos de los paisajes ha aumentado substancialmente con el desarrollo de la planificación del usode la tierra y sus requisitos de datos medioambientales en que basar las decisiones de uso de tierra. Sepresenta una metodología para la valoración de la Calidad Visual y su Fragilidad, tanto de los paisajesrurales como de los urbanos, con el fin de disponer de una valoración del paisaje que permita ordenarde forma adecuada la implantación de determinados usos y actividades en un territorio.As características físicas da paisagem podem ser identificadas por seus atributos visuais, já que o planejamento,as decisões de manejo, a interação da cultura e os processos naturais levam a mudanças físicas que se verão no futuro napaisagem. As paisagens cênicas são uma das maiores fontes para o goze humano e em alguns casos foi o objeto de açãopública direta para conservar sua qualidade. Alem disso, a necessidade de contar com meios válidos para quantificar oscarateres cênicos das paisagens aumentou substancialmente com o desenvolvimento do planejamento do uso da terra eseus requisitos de dados meio ambientais em que basear as decisões de uso da terra. Apresenta-se uma metodologia paraa valoração da Qualidade Visual e sua Fragilidade, tanto das paisagens rurais como dos urbanos, com o fim de dispor deuma valoração da paisagem que permita ordenar de maneira adequada a implantação de determinados usos e atividadesnum território

Improving Depth Perception in Immersive Media Devices by Addressing Vergence-Accommodation Conflict

: Recently, immersive media devices have seen a boost in popularity. However, many problems still remain. Depth perception is a crucial part of how humans behave and interact with their environment. Convergence and accommodation are two physiological mechanisms that provide important depth cues. However, when humans are immersed in virtual environments, they experience a mismatch between these cues. This mismatch causes users to feel discomfort while also hindering their ability to fully perceive object distances. To address the conflict, we have developed a technique that encompasses inverse blurring into immersive media devices. For the inverse blurring, we utilize the classical Wiener deconvolution approach by proposing a novel technique that is applied without the need for an eye-tracker and implemented in a commercial immersive media device. The technique's ability to compensate for the vergence-accommodation conflict was verified through two user studies aimed at reaching and spatial awareness, respectively. The two studies yielded a statistically significant 36% and 48% error reduction in user performance to estimate distances, respectively. Overall, the work done demonstrates how visual stimuli can be modified to allow users to achieve a more natural perception and interaction with the virtual environment

Virtual Reality to Simulate Visual Tasks for Robotic Systems

Virtual reality (VR) can be used as a tool to analyze the interactions between the visual system of a robotic agent and the environment, with the aim of designing the algorithms to solve the visual tasks necessary to properly behave into the 3D world. The novelty of our approach lies in the use of the VR as a tool to simulate the behavior of vision systems. The visual system of a robot (e.g., an autonomous vehicle, an active vision system, or a driving assistance system) and its interplay with the environment can be modeled through the geometrical relationships between the virtual stereo cameras and the virtual 3D world. Differently from conventional applications, where VR is used for the perceptual rendering of the visual information to a human observer, in the proposed approach, a virtual world is rendered to simulate the actual projections on the cameras of a robotic system. In this way, machine vision algorithms can be quantitatively validated by using the ground truth data provided by the knowledge of both the structure of the environment and the vision system

Design strategies for direct multi-scale and multi-orientation feature extraction in the log-polar domain

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La enseñanza de los conocimientos procedimentales en las carreras agro-ambientales

Agronomic and environmental studies have many disciplines in their syllabus with a high percentage of procedural knowledge. This knowledge consists of a specific and organized set of actions that leads to achieving an aim, which includes the use of rules, techniques, methods, skills, and strategies. However, traditional universities of agronomic studies have based their classes on “master classes” to teach cognitive or discursive content, but also to teach procedures and attitude. Through this work, it will be showed and explained how important it is to define, recognize, and strongly incorporate procedural knowledge cross-sectionally during the teaching and evaluation processes in all the subject-matters of these high studies. Agro-environmental university studies are mainly composed of disciplines with a preponderance of procedural knowledge. These are the set of ordered actions aimed at achieving an end, including the use of rules, techniques, methods, skills and strategies. However, traditional university agronomic education has had a “master class” teaching model for the dictation of cognitive or discursive knowledge and unfortunately applied to the teaching of procedural and even attitudinal knowledge. It exposes the need to define, recognize and strongly incorporate the teaching and evaluation of procedural knowledge in a transversal way to all the subjects of the agronomic and environmental university studies.Las carreras agro-ambientales están integradas mayoritariamente por disciplinas con preponderancia de conocimientos procedimentales. Éstos son el conjunto de acciones ordenadas dirigidas a la consecución de un fin, comprende el uso de reglas, técnicas, métodos, destrezas y estrategias. Sin embargo, la enseñanza agronómica universitaria tradicional ha tenido un modelo didáctico de tipo “clase magistral” para el dictado de conocimientos cognoscitivos o discursivos y desgraciadamente aplicada a la enseñanza de conocimientos procedimentales e incluso actitudinales. En este trabajo, se expone sobre la necesidad de definir, reconocer e incorporar fuertemente la enseñanza y evaluación de conocimientos procedimentales en forma transversal a todas las asignaturas de las carreras universitarias agronómicas y ambientales

Advances in human-computer interactions: methods, algorithms, and applications

Editoria

Can Neuromorphic Computer Vision Inform Vision Science? Disparity Estimation as a Case Study

The primate visual system efficiently and effectively solves a multitude of tasks from orientation detection to motion detection. The Computer Vision community is therefore beginning to implement algorithms that mimic the processing hierarchies present in the primate visual system in the hope of achieving flexible and robust artificial vision systems. Here, we reappropriate the neuroscience “borrowed” by the Computer Vision community and ask whether neuromorphic computer vision solutions may give us insight into the functioning of the primate visual system. Specifically, we implement a neuromorphic algorithm for disparity estimation and compare its performance against that of human observers. The algorithm greatly outperforms human subjects when tuned with parameters to compete with non-neural approaches to disparity estimation on benchmarking stereo image datasets. Conversely, when the algorithm is implemented with biologically plausible receptive field sizes, spatial selectivity, phase tuning, and neural noise, its performance is directly relatable to that of human observers. The receptive field size and the number of spatial scales sensibly determine the range of spatial frequencies in which the algorithm successfully operates. The algorithm’s phase tuning and neural noise in turn determine the algorithm’s peak disparity sensitivity. When included, retino-cortical mapping strongly degrades disparity estimation in the model’s periphery, further closening human and algorithm performance. Hence, a neuromorphic computer vision algorithm can be reappropriated to model human behavior, and can provide interesting insights into which aspects of human visual perception have been or are yet to be explained by vision science

A Space-Variant Model for Motion Interpretation across the Visual Field

We implement a neural model for the estimation of the focus of radial motion (FRM) at different retinal locations and we assess the model by comparing its results with respect to the precision with which human observers can estimate the FRM in naturalistic, moving dead leaves stimuli. The proposed neural model describes the deep hierarchy of the first stages of the dorsal visual pathway [Solari et al., 2014]. Such a model is space-variant, since it takes into account the retino-cortical transformation of the primate visual system through log-polar mapping that produces a cortical representation of the visual signal to the retina. The log-polar transform of the retinal image is the input to the cortical motion estimation stage where optic flow is computed by a three-layer population of cells. A population of spatio-temporal oriented Gabor filters approximates the simple cells of area V1 (first layer), which are combined into complex cells as motion energy units (second layer). The responses of the complex cells are pooled (third layer) to encode the magnitude and direction of velocities as in the extrastriate motion pathway between area MT and MST. The sensitivity to complex motion patterns that has been found in area MST is modeled through a population of adaptive templates, and from the responses of such a population the first order description of optic flow is derived. Information about self-motion (e.g. direction of heading) is estimated by combining such first-order descriptors computed in the cortical domain

Phase-Based Binocular Perception of Motion in Depth: Cortical-Like Operators and Analog VLSI Architectures

We present a cortical-like strategy to obtain reliable estimates of the motions of objects in a scene toward/away from the observer (motion in depth), from local measurements of binocular parameters derived from direct comparison of the results of monocular spatiotemporal filtering operations performed on stereo image pairs. This approach is suitable for a hardware implementation, in which such parameters can be gained via a feedforward computation (i.e., collection, comparison, and punctual operations) on the outputs of the nodes of recurrent VLSI lattice networks, performing local computations. These networks act as efficient computational structures for embedded analog filtering operations in smart vision sensors. Extensive simulations on both synthetic and real-world image sequences prove the validity of the approach that allows to gain high-level information about the 3D structure of the scene, directly from sensorial data, without resorting to explicit scene reconstruction