Pedro Javier Herrera

A featured-based strategy for stereovision matching in sensors with fish-eye lenses for forest environments.

This paper describes a novel feature-based stereovision matching process based on a pair of omnidirectional images in forest stands acquired with a stereovision sensor equipped with fish-eye lenses. The stereo analysis problem consists of the following steps: image acquisition, camera modelling, feature extraction, image matching and depth determination. Once the depths of significant points on the trees are obtained, the growing stock volume can be estimated by considering the geometrical camera modelling, which is the final goal. The key steps are feature extraction and image matching. This paper is devoted solely to these two steps. At a first stage a segmentation process extracts the trunks, which are the regions used as features, where each feature is identified through a set of attributes of properties useful for matching. In the second step the features are matched based on the application of the following four well known matching constraints, epipolar, similarity, ordering and uniqueness. The combination of the segmentation and matching processes for this specific kind of sensors make the main contribution of the paper. The method is tested with satisfactory results and compared against the human expert criterion.

A stereovision matching strategy for images captured with fish-eye lenses in forest environments.

We present a novel strategy for computing disparity maps from hemispherical stereo images obtained with fish-eye lenses in forest environments. At a first segmentation stage, the method identifies textures of interest to be either matched or discarded. This is achieved by applying a pattern recognition strategy based on the combination of two classifiers: Fuzzy Clustering and Bayesian. At a second stage, a stereovision matching process is performed based on the application of four stereovision matching constraints: epipolar, similarity, uniqueness and smoothness. The epipolar constraint guides the process. The similarity and uniqueness are mapped through a decision making strategy based on a weighted fuzzy similarity approach, obtaining a disparity map. This map is later filtered through the Hopfield Neural Network framework by considering the smoothness constraint. The combination of the segmentation and stereovision matching approaches makes the main contribution. The method is compared against the usage of simple features and combined similarity matching strategies.

Fuzzy multi-criteria decision making in stereovision matching for fish-eye lenses in forest analysis

This paper describes a novel stereovision matching approach based on omni-directional images obtained with fish-eye lenses in forest environments. The goal is to obtain a disparity map as a previous step for determining the volume of wood in the imaged area. The interest is focused on the trunks of the trees. Due to the irregular distribution of the trunks, the most suitable features are the pixels. A set of six attributes is used for establishing the matching between the pixels in both images of each stereo pair analysed. The final decision about the matched pixels is taken based on a well tested Fuzzy Multi-Criteria Decision Making approach, where the attributes determine the criteria and the potential matches in one image of the stereo pair for a given pixel in the other one determine the alternatives. The application of this decision making approach makes the main finding of the paper. The full procedure is based on the application of three well known matching constraints. The proposed approach is compared favourably against the usage of simple features.

Automatic image segmentation of greenness in crop fields

This paper describes a new automatic image segmentation strategy for segmenting green plants. The final goal is its application in Precision Agriculture. The goal is to identify several classes of greenness coming from the plants. We exploit the performance of several existing approaches so that conveniently combined allow us to design the automatic approach based on non automatic methods. First we apply a well known index-based strategy that accentuates the green spectral band from the remainder, giving a gray image. From the resulting image we apply the well-known thresholding Otsus method obtaining a binary image, where the green part appears separated from the soil. Taking as input the green pixels we apply an unsupervised method and they are partitioned in a fixed number of classes. The performance of the method is tested against a set of available images and acquired in several crop fields of cereal and maize.

A Stereovision Sensor for Forest Inventories

This paper describes a device, based on stereovision, which is designed for forest inventories purposes. It captures pairs of omni-directional stereoscopic images through a fish-eye lens, from which different tri-dimensional measures can be obtained by applying a stereovision process, including image acquisition, feature and attribute extraction, feature matching and depth determination. This paper explains and summarizes two specific methods for solving the matching problem, as the key step in stereovision. They are pixel-based and region-based, applied in two kinds of forests environments, the first one highly illuminated and the second one with poor illumination. The performance achieved validates both designs.

Image-Based Airborne Sensors: A Combined Approach for Spectral Signatures Classification through Deterministic Simulated Annealing

The increasing technology of high-resolution image airborne sensors, including those on board Unmanned Aerial Vehicles, demands automatic solutions for processing, either on-line or off-line, the huge amountds of image data sensed during the flights. The classification of natural spectral signatures in images is one potential application. The actual tendency in classification is oriented towards the combination of simple classifiers. In this paper we propose a combined strategy based on the Deterministic Simulated Annealing (DSA) framework. The simple classifiers used are the well tested supervised parametric Bayesian estimator and the Fuzzy Clustering. The DSA is an optimization approach, which minimizes an energy function. The main contribution of DSA is its ability to avoid local minima during the optimization process thanks to the annealing scheme. It outperforms simple classifiers used for the combination and some combined strategies, including a scheme based on the fuzzy cognitive maps and an optimization approach based on the Hopfield neural network paradigm.

Shrub architecture and occupation of space in a Mediterranean sclerophyllous shrubland

. Shrubland communities in Central Spain were studied through classifying growth forms of woody species and determining the shared use of the ground in progressively smaller spaces. 516 plants belonging to the six most abundant species and taken from different sites were included in biometric measurements. Principal Component Analysis (PCA) was used to detect the trends of variation in the architecture of plants. The individuals were classified on the basis of the results of the PCA and different morphological types were detected, mainly ‘elliptical’, ‘spherical’or ‘variable’according to their shape. These morphological types were adopted by most plants depending on their location and community. The horizontal occupation of space seems to be determined by whether or not the species rooted close to each other are able to occupy different strata. The co-occurrence of two species in a reduced space is not facilitated when the two species have the same architecture. Then a spatial segregation tends to occur at a fine scale. The results can be interpreted as an optimization strategy of the shrubland ‘biomass/horizontal occupied area ratio’, which can be maximized in different environmental situations. It can also help to explain the ‘grain’ size of the pattern of horizontal spatial organization of the shrubland.

Deciphering Egyptian Hieroglyphs: Towards a New Strategy for Navigation in Museums

This work presents a novel strategy to decipher fragments of Egyptian cartouches identifying the hieroglyphs of which they are composed. A cartouche is a drawing, usually inside an oval, that encloses a group of hieroglyphs representing the name of a monarch. Aiming to identify these drawings, the proposed method is based on several techniques frequently used in computer vision and consists of three main stages: first, a picture of the cartouche is taken as input and its contour is localized. In the second stage, each hieroglyph is individually extracted and identified. Finally, the cartouche is interpreted: the sequence of the hieroglyphs is established according to a previously generated benchmark. This sequence corresponds to the name of the king. Although this method was initially conceived to deal with both high and low relief writing in stone, it can be also applied to painted hieroglyphs. This approach is not affected by variable lighting conditions, or the intensity and the completeness of the objects. This proposal has been tested on images obtained from the Abydos King List and other Egyptian monuments and archaeological excavations. The promising results give new possibilities to recognize hieroglyphs, opening a new way to decipher longer texts and inscriptions, being particularly useful in museums and Egyptian environments. Additionally, devices used for acquiring visual information from cartouches (i.e., smartphones), can be part of a navigation system for museums where users are located in indoor environments by means of the combination of WiFi Positioning Systems (WPS) and depth cameras, as unveiled at the end of the document.

Segmentation and Stereoscopic Correspondence in Images Obtained with Omnidirectional Projection for Forest Environments

Stereoscopic vision systems have been used manually for decades to capture threedimensional information of the environment in different applications. With the growth experienced in recent years by the techniques of computer image processing, stereoscopic vision has been increasingly incorporating automated systems of different nature. The central problem in the automation of a stereoscopic vision system is the determination of the correspondence between pixels of the pair of stereoscopic images that come from the same point in three-dimensional scene. The research undertaken in this work comprises the design of a global strategy to solve the stereoscopic correspondence problem for a specific kind of hemispherical image from forest environments. The images are obtained through an optical system based on the lens known as fisheye because this optic system can recover 3D information in a large field-of-view around the camera; in our system it is 183o×360o. This is an important advantage because it allows one to image the trees in the 3D scene close to the system from the base to the top, unlike in systems equipped with conventional lenses where close objects are partially mapped (Abraham & Forstner, 2005). The focus is on obtaining this information from tree trunks using stereoscopic images. The technicians carry out forest inventories which include studies on wood volume and tree density as well as the evolution and growth of the trees with the measurements obtained. Because the trees appear completely imaged, the stereoscopic system allows the calculation of distances from the device to significant points into the trees in the 3D scene, including diameters along the stem, heights and crown dimensions to be measured, as well as determining the position of the trees. These data may be used to obtain precise taper equations, leaf area or volume estimations (Montes et al., 2009). As the distance from the device to each tree can be calculated, the density of trees within a determined area can be also surveyed and growing stock; tree density, basal area (the section of stems at 1.30 m height in a hectare) and other interesting variables may be estimated at forest stand level using statistical inference (Gregoire, 1998).