Antón García-Díaz

Saliency from hierarchical adaptation through decorrelation and variance normalization

This paper presents a novel approach to visual saliency that relies on a contextually adapted representation produced through adaptive whitening of color and scale features. Unlike previous models, the proposal is grounded on the specific adaptation of the basis of low level features to the statistical structure of the image. Adaptation is achieved through decorrelation and contrast normalization in several steps in a hierarchical approach, in compliance with coarse features described in biological visual systems. Saliency is simply computed as the square of the vector norm in the resulting representation. The performance of the model is compared with several state-of-the-art approaches, in predicting human fixations using three different eye-tracking datasets. Referring this measure to the performance of human priority maps, the model proves to be the only one able to keep the same behavior through different datasets, showing free of biases. Moreover, it is able to predict a wide set of relevant psychophysical observations, to our knowledge, not reproduced together by any other model before.

On the relationship between optical variability, visual saliency, and eye fixations: A computational approach

A hierarchical definition of optical variability is proposed that links physical magnitudes to visual saliency and yields a more reductionist interpretation than previous approaches. This definition is shown to be grounded on the classical efficient coding hypothesis. Moreover, we propose that a major goal of contextual adaptation mechanisms is to ensure the invariance of the behavior that the contribution of an image point to optical variability elicits in the visual system. This hypothesis and the necessary assumptions are tested through the comparison with human fixations and state-of-the-art approaches to saliency in three open access eye-tracking datasets, including one devoted to images with faces, as well as in a novel experiment using hyperspectral representations of surface reflectance. The results on faces yield a significant reduction of the potential strength of semantic influences compared to previous works. The results on hyperspectral images support the assumptions to estimate optical variability. As well, the proposed approach explains quantitative results related to a visual illusion observed for images of corners, which does not involve eye movements.

Decorrelation and Distinctiveness Provide with Human-Like Saliency

In this work, we show the capability of a new model of saliency, of reproducing remarkable psychophysical results. The model presents low computational complexity compared to other models of the state of the art. It is based in biologically plausible mechanisms: the decorrelation and the distinctiveness of local responses. Decorrelation of scales is obtained from principal component analysis of multiscale low level features. Distinctiveness is measured through the Hotelling’s T2 statistic. The model is conceived to be used in a machine vision system, in which attention would contribute to enhance performance together with other visual functions. Experiments demonstrate the consistency with a wide variety of psychophysical phenomena, that are referenced in the visual attention modeling literature, with results that outperform other state of the art models.

Dynamic Whitening Saliency

General dynamic scenes involve multiple rigid and flexible objects, with relative and common motion, camera induced or not. The complexity of the motion events together with their strong spatio-temporal correlations make the estimation of dynamic visual saliency a big computational challenge. In this work, we propose a computational model of saliency based on the assumption that perceptual relevant information is carried by high-order statistical structures. Through whitening, we completely remove the second-order information (correlations and variances) of the data, gaining access to the relevant information. The proposed approach is an analytically tractable and computationally simple framework which we call Dynamic Adaptive Whitening Saliency (AWS-D). For model assessment, the provided saliency maps were used to predict the fixations of human observers over six public video datasets, and also to reproduce the human behavior under certain psychophysical experiments (dynamic pop-out). The results demonstrate that AWS-D beats state-of-the-art dynamic saliency models, and suggest that the model might contain the basis to understand the key mechanisms of visual saliency. Experimental evaluation was performed using an extension to video of the well-known methodology for static images, together with a bootstrap permutation test (random label hypothesis) which yields additional information about temporal evolution of the metrics statistical significance.

A new radial symmetry measure applied to photogrammetry

This work presents a new measure for radial symmetry and an algorithm for its computation. This measure identifies radially symmetric blobs as locations with contributions from all orientations at some scale. Hence, at a given scale, radial symmetry is computed as the product of the responses of a set of even symmetric feature detectors, with different orientations. This operator presents low sensitivity to shapes lacking radial symmetry, is robust to noise, contrast changes and strong perspective distortions, and shows a narrow point spread function. A multi-resolution measure is provided, computed as the maximum of the symmetry measure evaluated over a set of scales. We have applied this measure in the field of photogrammetry for the detection of circular coded fiducial targets. The detection of local maxima of multi-resolution radial symmetry is combined with a step of false-positive rejection, based on elliptical model fitting. In our experiments, the efficiency of target detection with this method is improved regarding a well-known commercial system, which is expected to improve the performance of bundle adjustment techniques. In order to fulfill all steps previous to bundle adjustment, we have also developed our own method for recognition of coded targets. This is accomplished by a standard procedure of segmentation and decoding of the ring sequence. Nevertheless, we have included a step for the verification of false positives of decoding based on correlation with reference targets. As far as we know, this approach cannot be found in literature.

Saliency Based on Decorrelation and Distinctiveness of Local Responses

In this paper we validate a new model of bottom-up saliency based in the decorrelation and the distinctiveness of local responses. The model is simple and light, and is based on biologically plausible mechanisms. Decorrelation is achieved by applying principal components analysis over a set of multiscale low level features. Distinctiveness is measured using the Hotellings T2 statistic. The presented approach provides a suitable framework for the incorporation of top-down processes like contextual priors, but also learning and recognition. We show its capability of reproducing human fixations on an open access image dataset and we compare it with other recently proposed models of the state of the art.

OpenLMD, multimodal monitoring and control of LMD processing

This paper presents OpenLMD, a novel open-source solution for on-line multimodal monitoring of Laser Metal Deposition (LMD). The solution is also applicable to a wider range of laser-based applications that require on-line control (e.g. laser welding). OpenLMD is a middleware that enables the orchestration and virtualization of a LMD robot cell, using several open-source frameworks (e.g. ROS, OpenCV, PCL). The solution also allows reconfiguration by easy integration of multiple sensors and processing equipment. As a result, OpenLMD delivers significant advantages over existing monitoring and control approaches, such as improved scalability, and multimodal monitoring and data sharing capabilities.

A high-speed MWIR uncooled multi-aperture snapshot spectral imager for IR surveillance and monitoring

This work describes the development of a modular, compact and cost effective snapshot multi-spectral imaging camera in the short and mid-wavelength infrared (S/MWIR) range. The solution combines an array of six uncooled PbSe focal plane arrays (FPAs) with a multi-aperture optical arrangement, and it is endowed with embedded processing capabilities to perform computational imaging. The spectral imager performance allows high-speed multi-spectral video acquisition at a maximum rate of 1000 frames per second. With this approach, the aim is to provide a versatile and easily customizable device for different applications. To demonstrate the performance of the camera, high temperature measurements of a blackbody were carried out. Taking advantage of the spectrally resolved measurements, a procedure for increasing the dynamic range and sensitivity of the sensor is proposed. Combining the response from 4 detectors in different MWIR narrow bands, an increase of 60% in the dynamic range is obtained.

Dynamic Saliency from Adaptative Whitening

This paper describes a unified framework for the static and dynamic saliency detection by whitening both the chromatic characteristics and the spatio-temporal frequencies. This approach is grounded in the statistical adaptation to the input data, resembling the human visual system’s early codification. Our approach, AWS-D, outperforms state-of-the-art models in the ability to predict human eye fixations while freely viewing a set of videos from three open access datasets (task free). We used as assessment measure an adaptation of the shuffling-AUC metric to spatio-temporal stimulus, together with a permutation test. Under this criterion, AWS-D not only reaches the highest AUC values, but also holds significant AUC figures for longer periods of time (more frames), over all the videos used in the test. The model also reproduces psychophysical results obtained in pop-out experiments in agreement with human behavior.

Goal-Driven Phenotyping Through Spectral Imaging for Grape Aromatic Ripeness Assessment

In this paper, we describe a systematic approach to the design of an active spectral imaging system for in vivo phenotyping. Our approach takes into account two major factors: spectral sensitivity of the sensor and spectral composition of the illuminant. Similarly to previous works, we adopt a scheme consisting on dimensionality reduction and SVR regression of target chemical parameters from spectral datacubes. We find that high prediction accuracies may be achieved for different sets of parameters depending on the illuminant. Furthermore, in most cases the combination of a single monochromatic illuminant with a dichromatic image sensor (passband and stopband) suffices, which paves the way for the design of tailored low cost imagers. Besides, we demonstrate in vivo estimation of aromatically relevant compounds of white and red grape varieties, not addressed before to our knowledge.