Pablo García Rodríguez

Short Note: Analyzing magnetic resonance images of Iberian pork loin to predict its sensorial characteristics

Iberian pork comes from genuinely bred Southwest Iberian Peninsula pigs traditionally fattened with acorns and pasture in an extensive production system. Dry-cured loins and hams constitute the main uncooked pork products with high sensorial quality and a first rate consumer acceptance, leading to high prices in the market. Several aspects related to quality in Iberian products have been examined by using chemical and sensorial procedures to provide quality. However, all these approaches are tedious and destroy the item. In addition, food science has shown little interest in MRI to explore meat products in a non-invasive way. Therefore, this paper introduce an objective and non-destructive methodology to classify Iberian loins consistently. It is based on texture analysis of MRI images displaying dry-cured pork loins. A statistical evaluation is provided for a set of 47 loins to predict three levels of different sensorial characteristics.

Monitoring the ripening process of Iberian ham by computer vision on magnetic resonance imaging

This paper explores the use of MRI (Magnetic Resonance Imaging) in combination with a fully automated Image Analysis method for the recognition of Biceps Femoris and Semimembranosus muscles in Iberian ham. A quantitative description of volume and a study of moisture and weight relationships during the products ripening process are included. Three Active Contour methods (Variational Calculus, Dynamic Programming, and Greedy Algorithms) are used to recognize the Biceps Femoris and Semimembranosus muscles by means of Computer Vision techniques. The recognition of both muscles via MRI entails a low error rate (3-10%). A loss of weight in hams during the ripening process is related to a decrease in size (r(2)=0.992). The high correlation implies that the information obtained by means of Computer Vision techniques can be used as a non-invasive complement to the traditional processes of ham weighing and moisture estimation.

FPGA Design of an Automatic Target Generation Process for Hyperspectral Image Analysis

Onboard processing of remotely sensed hyper spectral data is a highly desirable goal in many applications. For this purpose, compact reconfigurable hardware modules such as field programmable gate arrays (FPGAs) are widely used. In this paper, we develop a new implementation of an automatic target generation process (ATGP) for hyper spectral images. Our implementation is based on a design methodology that starts from a high-level description in Matlab (or alternative C/C++) and obtains a register transfer level (RTL) description that can be ported to FPGAs. In order to validate our new implementation, we develop a quantitative and comparative study using two different FPGA architectures: Xilinx Virtex-5 and Altera Stratix-III Altera. Experimental results have been obtained in the context of a real application focused on the detection of mineral components over the Cup rite mining district (Nevada), using hyper spectral data collected by NASAs Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS). Our experimental results indicate that the proposed implementation can achieve peak frequency designs above 200MHz in the considered FPGAs, in addition to satisfactory results in terms of target detection accuracy and parallel performance. This represents a step forward towards the design of real-time onboard implementations of hyper spectral image analysis algorithms.

Finding the largest area rectangle of arbitrary orientation in a closed contour

Abstract For many software applications, it is sometimes necessary to find the rectangle of largest area inscribed in a polygon, in any possible direction. Thus, given a closed contour C, we consider approximation algorithms for the problem of finding the largest area rectangle of arbitrary orientation that is fully contained in C. Furthermore, we compute the largest area rectangle of arbitrary orientation in a quasi-lattice polygon, which models the C contour. In this paper, we propose an approximation algorithm that solves this problem with an O ( n 3 ) computational cost, where n is the number of vertices of the polygon. There is no other algorithm having lower computational complexity regardless of any constraints. In addition, we have developed a web application that uses the proposed algorithm.

Automatic decision support system based on SAR data for oil spill detection

Global trade is mainly supported by maritime transport, which generates important pollution problems. Thus, effective surveillance and intervention means are necessary to ensure proper response to environmental emergencies. Synthetic Aperture Radar (SAR) has been established as a useful tool for detecting hydrocarbon spillages on the oceans surface. Several decision support systems have been based on this technology. This paper presents an automatic oil spill detection system based on SAR data which was developed on the basis of confirmed spillages and it was adapted to an important international shipping route off the Galician coast (northwest Iberian Peninsula). The system was supported by an adaptive segmentation process based on wind data as well as a shape oriented characterization algorithm. Moreover, two classifiers were developed and compared. Thus, image testing revealed up to 95.1% candidate labeling accuracy. Shared-memory parallel programming techniques were used to develop algorithms in order to improve above 25% of the system processing time. HighlightsAn automatic oil spill detection system based on SAR images was developed.A database with confirmed oil spills was used to develop the system.Image testing revealed up to 95.1% candidate labeling accuracy.Two classifiers were compared from the labeling accuracy viewpoint.The processing time was optimized via shared memory parallelization techniques.

Positional Accuracy Analysis of Satellite Imagery by Circular Statistics

The proposed method in this paper uses circular statistics for the analysis of errors in the positional accuracy of geometric corrections satellite images using Independent Check Lines (ICL) instead of Independent Check Points (ICP). Circular statistics has been preferred because of the vectorial nature of the spatial error. A study case has been presented and discussed in detail. From the TERRA-ASTER images of Extremadura area (Spain), the Ground Control Point (GCP), ICP, and ICL data were acquired using differential GPS through field survey, and the planimetric positional accuracy was analyzed by both the conventional method (using ICP) and the proposed method (using 1CL). Comparing conventional and proposed methods, the results indicated that modulus statistics are similar (e.g., RMSE of Geometric Correction 1 were 17.5 for the conventional method and 17.2 m for proposed method). But as additional results, azimuthal component statistics was calculated (e.g., mean direction: 247.2° in Geometric Correction 1), and several tests were made which showed the error distribution are not uniform and normal.

A perceptual similarity method by pairwise comparison in a medical image case

The evolution of image techniques in medicine has improved decision making based on physicians’ experience by means of computer-aided diagnosis (CAD). This paper focuses on the development of content-based image retrieval (CBIR) and CAD techniques applied to bronchoscopies and according to different pathologies. A novel pairwise comparison method based on binary logistic regression is developed to determine those images must alike to a new image from incomplete property information, after accounting for the physicians’ appreciation of the image similarity. This method is particularly useful when problems with both a large number of features and few images are involved.

A Web-Based System for Classification of Remote Sensing Data

The availability of satellite imagery has expanded over the past few years, and the possibility to perform fast processing of massive databases comprising this kind of imagery data has opened ground-breaking perspectives in many different fields. This paper describes a web-based system (available online: http://hypergim.ceta-ciemat.es), which allows an inexperienced user to perform unsupervised classification of satellite/airborne images. The processing chain adopted in this work has been implemented in C language and integrated in our proposed tool, developed with HTML5, JavaScript, Php, AJAX and other web programming languages. Image acquisition with the applications programmer interface (API) is fast and efficient. An important added functionality of the developed tool is its capacity to exploit a remote server to speed up the processing of large satellite/airborne images at different zoom levels. The ability to process images at different zoom levels allows the tool an improved interaction with the user, who is able to supervise the final result. The previous functionalities are necessary to use efficient techniques for the classification of images and the incorporation of content-based image retrieval (CBIR). Several experimental validation types of the classification results with the proposed system are performed by comparing the classification accuracy of the proposed chain by means of techniques available in the well-known Environment for Visualizing Images (ENVI) software package.

Behaviour of Texture Features in a CBIR System

Searching and processing in databases of general and non-specific images are highly subjective. The process of texture feature extraction from images produces results of highly theoretical and mathematical character that have little to do with human perception. We present a method to select from low-level texture features, statistics and numerical groupings and to transform them into other high-level features, with visual meaning. We also aim to facilitate their use within CBIR systems. The detailed study of the composition and behaviour of the texture characteristics has enabled us to abstract and use them in an automated manner, similarly to how an observer would do.

Mathematical Morphology on MRI for the Determination of Iberian Ham Fat Content

Intermuscular Fat Content and its distribution during the ripening process of the Iberian ham is a relevant task from the point of view of technological interest. This paper attempts to study the Iberian ham during the ripening process with images obtained from a MRI (Magnetic Resonance Imaging) device using Pattern Recognition and Image Analysis algorithms, in particular Mathematical Morphology techniques. The main advantage of this method is the non-destructive nature. A concrete algorithm is proposed, which is based on the Watershed transformation. In addition, the results are compared with the Otsu thresholding algorithm. The decreases of the total volume in the ripening process are shown. Also the decrease of the meat percentage and intermuscular fat content are calculated. As a conclusion, the viability of these techniques is proved for the possible future utilization in the meat industries to discover new characteristics in the ripening process.