Astronomical images are characterized by having a high component of noise, a non-homogeneous background, and a great number of sources (objects) difficult to identify even by experts. All these factors are especially remarkable in high wavelength images such as infrared and radio. Hence, great efforts have been done to solve the automatic detection of sources in this type of images. In this paper, we propose a new approach based on multiscale decomposition and the recently developed Distilled Sensing method. Their combined use allows the minimization of the complex background effects as well as the highlighting of the sources. The experimental results obtained using public infrared and radio images demonstrate the validity of the approach, detecting a greater number of true sources than the original Distilled Sensing and the well-known SExtractor algorithm.

In this paper we point out four key factors that influence on a research lab performance: 1) the political context, 2) the organization in which the research takes place, 3) the internal organization of the lab, and 4) the individuals who compose its structure. The research team ViCOROB (Computer Vision and Robotics Research Lab) of the University of Girona (Spain) suffered a change in its way of working in July 2008. The team received an excellence recognition from Government, awarded to be one of the most productive research teams of the region in terms of technology transfer performance. In order to maintain the level of competence, the team decided to change its internal organization, developing a high level change in terms of technology transfer organization as well as research skills and personnel growth. The present paper conducts a diagnosis on how this structural change influenced the team using a case study methodology. After two years from recognition, where the research team is continuously growing, we are able to analyse and highlight the key actors in the evolution of the research group's performance.

A hybrid framework of probabilistic atlas and statistical shape and appearance model (SSAM) is proposed to achieve 3D prostate segmentation. An initial 3D segmentation of the prostate is obtained by registering the probabilistic atlas to the test dataset with deformable Demons registration. The initial results obtained are used to initialize multiple SSAMs corresponding to the apex, central and base regions of the prostate gland to incorporate local variabilities. Multiple mean parametric models of shape and appearance are derived from principal component analysis of prior shape and intensity information of the prostate from the training data. The parameters are then modified with the prior knowledge of the optimization space to achieve 2D segmentation. The 2D labels are registered to the 3D labels generated using probabilistic atlas to constrain the pose variation and generate valid 3D shapes. The proposed method achieves a mean Dice similarity coefficient value of 0.89±0.11 and mean Hausdorff distance of 3.05±2.25 mm when validated with 15 prostate volumes of a public dataset in a leave-one-out validation framework.

Having large databases of annotated images is important for many applications in computer vision and computer graphics. Some of the largest databases of annotated images rely on user participation (as in Flickr, LabelMe or Peekaboom). In this paper we address the problem of performing semiautomatic object labeling as a way of providing new annotated images. Some current efforts in this direction provide bounding boxes as the annotations (i.e. OPTIMOL and Seville Systems). However, in this work we present an approach that relies on a boosting process to automatically create polygonal annotations for objects similar to those entered by users in tools such as LabelMe. In particular, we train single class boosting classifiers using local image features to perform the simultaneous object detection and segmentation. We validate our approach using different object classes from the LabelMe, the TUD and the Weizmann databases. Moreover, our experiments show that we are able to correctly annotate new data returned by internet search engines.

Prostate volume estimation from segmented prostate contours in Trans Rectal Ultrasound (TRUS) images aids in diagnosis and treatment of prostate diseases, including prostate cancer. However, accurate, computationally efficient and automatic segmentation of the prostate in TRUS images is a challenging task owing to low Signal-To-Noise-Ratio (SNR), speckle noise, micro-calcifications and heterogeneous intensity distribution inside the prostate region. In this paper, we propose a probabilistic framework for propagation of a parametric model derived from Principal Component Analysis (PCA) of prior shape and posterior probability values to achieve the prostate segmentation. The proposed method achieves a mean Dice similarity coefficient value of 0.96±0.01, and a mean absolute distance value of 0.80±0.24 mm when validated with 24 images from 6 datasets in a leave-one-patient-out validation framework. Our proposed model is automatic, and performs accurate prostate segmentation in presence of intensity heterogeneity and imaging artifacts.

The automatic segmentation of extended real structures (such as SNRs, HII regions, bow shocks, etc) in large surveys is a difficult task due to their morphological complexity and their wide variety in scale and surface brightness. With the aim of dealing with these issues we propose in this paper an automatic segmentation method based on applying wavelet decomposition in the residual thresholded images. The use of this strategy, instead of a wavelet decomposition on the original image, allows to avoid the artifacts produced by strong sources. Experimental results with radio images demonstrate that the proposed method successfully segments extended structures at different scales and therefore is suitable for further morphological analysis and object recognition processes.

Numerous approaches to object detection and segmentation have been proposed so far. However, these methods are prone to fail in some general situations due to the proper object nature. For instance, classical approaches of object detection and segmentation obtain good results for some specific object classes (i.e. detection of pedestrians or segmentation of cars). However, these methods have troubles when detecting or segmenting object classes with different distinctive characteristics (i.e. cars and horses versus sky and road). In this paper, we propose a general framework to simultaneously perform object detection and segmentation on objects of different nature. Our approach is based on a boosting procedure which automatically decides according to the object properties whether is better to give more weight to the detection or segmentation process to improve both results. We validate our approach using different object classes from La belMe, TUD and Weizmann databases, obtaining competitive detection and segmentation results.

The detection of architectural distortions and abnormal structures in mammographic images can be based on the analysis of bilateral and temporal cases using image registration. This paper presents a quantitative evaluation of state-of-the art intensity based image registration methods applied to mammographic images. These methods range from a global and rigid transformation to local deformable paradigms using various metrics and multiresolution approaches. The aim of this study is to assess the suitability of these methods for mammographic image analysis. Evaluation using temporal cases based on quantitative analysis and a multiobserver study is presented which gives an indication of the accuracy and robustness of the different algorithms. Although previous studies suggested that local deformable methods were not suitable due to the generation of unrealistic distortions, in this work we show that local deformable paradigms (multiresolution B-Spline deformations) obtain the most accurate registration results.

We present in this paper a supervised approach for automatic detection of micro-calcifications. The system is based on learning the different morphology of the micro-calcifications using local features, which are extracted using a bank of filters. Afterwards, this set of features is used to train a pixel-based boosting classifier which at each round automatically selects the most salient one. Therefore, when a new mammogram is tested only the salient features are computed and used to classify each pixel of the mammogram as being part of a micro-calcification or actually being normal tissue. The experimental results shows the validity of our approach. Moreover, the robustness of our method is also demonstrated using a digitised database for the learning process and a different one for the testing, providing satisfactory results.

Several techniques have been proposed so far in order to perform faint compact source detection in wide field interferometric radio images. However, all these methods can easily miss some detections or obtain a high number of false positive detections due to the low intensity of the sources, the noise ratio, and the interferometric patterns present in the images. In this paper we present a novel strategy to tackle this problem. Our approach is based on using local features extracted from a bank of filters in order to provide a description of different types of faint source structures. We then perform a training step in order to automatically learn and select the most salient features, which are used in a Boosting classifier to perform the detection. The validity of our method is demonstrated using 19 real images that compose a radio mosaic. The comparison with two well-known state of the art methods shows that our approach is able to obtain more source detections, reducing also the number of false positives.

It has been shown that the accuracy of mammographic abnormality detection methods is strongly dependent on the breast tissue characteristics, where a dense breast drastically reduces detection sensitivity. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: 1) the segmentation of the breast area into fatty versus dense mammographic tissue; 2) the extraction of morphological and texture features from the segmented breast areas; and 3) the use of a Bayesian combination of a number of classifiers. The evaluation, based on a large kappa = 0.81 and 0.67 for the two data sets) between automatic and expert-based Breast Imaging Reporting and Data System mammographic density assessment.

A recent trend in digital mammography is computer-aided diagnosis systems, which are computerised tools designed to assist radiologists. Most of these systems are used for the automatic detection of abnormalities. However, recent studies have shown that their sensitivity is significantly decreased as the density of the breast increases. This dependence is method specific. In this paper we propose a new approach to the classification of mammographic images according to their breast parenchymal density. Our classification uses information extracted from segmentation results and is based on the underlying breast tissue texture. Classification performance was based on a large set of digitised mammograms. Evaluation involves different classifiers and uses a leave-one-out methodology. Results demonstrate the feasibility of estimating breast density using image processing and analysis techniques.

In this paper we investigate the effect of the illuminant tilt rotation over surface textures by analysing a set of image texture features extracted from the co-occurrence matrix. From the behaviour of each texture feature, a simple method able to predict the illuminant tilt angle of test images is developed. Moreover, the method is also used to perform a texture classification invariant to the illuminant tilt angle rotation. This study and experimental results over different real textures show that the illumination tilt angle can be accurately predicted, as part of the texture classification process. 1

In this paper a colour texture segmentation method, which unifies region and boundary information, is proposed. The algorithm uses a coarse detection of the perceptual (colour and texture) edges of the image to adequately place and initialise a set of active regions. Colour texture of regions is modelled by the conjunction of non-parametric techniques of kernel density estimation (which allow to estimate the colour behaviour) and classical co-occurrence matrix based texture features. Therefore, region information is defined and accurate boundary information can be extracted to guide the segmentation process. Regions concurrently compete for the image pixels in order to segment the whole image taking both information sources into account. Furthermore, experimental results are shown which prove the performance of the proposed method.

A new method for the automated selection of colour features is described. The algorithm consists of two stages of processing. In the first, a complete set of colour features is calculated for every object of interest in an image. In the second stage, each object is mapped into several n-dimensional feature spaces in order to select the feature set with the smallest variables able to discriminate the remaining objects. The evaluation of the discrimination power for each concrete subset of features is performed by means of decision trees composed of linear discrimination functions. This method can provide valuable help in outdoor scene analysis where no colour space has been demonstrated as being the most suitable. Experiment results recognizing objects in outdoor scenes are reported.

One of the major problems in machine vision is the segmentation of images of natural scenes. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. The main contours of the scene are detected and used to guide the posterior region growing process. The algorithm places a number of seeds at both sides of a contour allowing stating a set of concurrent growing processes. A previous analysis of the seeds permits to adjust the homogeneity criterion to the regions's characteristics. A new homogeneity criterion based on clustering analysis and convex hull construction is proposed.

Image segmentation of natural scenes constitutes a major problem in machine vision. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. This approach begins by detecting the main contours of the scene which are later used to guide a concurrent set of growing processes. A previous analysis of the seed pixels permits adjustment of the homogeneity criterion to the region's characteristics during the growing process. Since the high variability of regions representing outdoor scenes makes the classical homogeneity criteria useless, a new homogeneity criterion based on clustering analysis and convex hull construction is proposed. Experimental results have proven the reliability of the proposed approach.

This paper presents an interface for the interaction between a human and an adapted kitchen where different elements, including a robot, have to be controlled. After the global structure of the interface is described a more detailed explanation is presented on the way the interface offers the user an easy and friendly way to select the desired objects and to provide their position to the robot for their manipulation.